# MarketSizer - Full Site Content > Complete text of all marketing pages, features, solutions, blog posts, and FAQ. > Generated: 2026-10-26 15:14 UTC --- # Features ## Feature: acv estimates ACV Estimates Reps spend equal time on a 5K deal and a 200K deal Deal size surprises derail forecasting every quarter No visibility into what the prospect is currently spending ACV projections on every account before the first touchpoint Sort your entire pipeline by revenue opportunity Earlier revenue visibility for more accurate forecasts Opportunity Discovery Win Likelihood ICP Scoring Know what every deal is worth before you dial ACV Estimates make the Opportunity Score actionable ACV Estimates gives your team account-level contract value projections before the first call - so reps prioritise by revenue opportunity, not just ICP fit. Without revenue estimates attached to accounts, reps allocate effort based on ICP fit alone. A great-fit 5K account gets the same attention as a great-fit 200K account - and pipeline value suffers. When revenue estimates only become available after the first call, your forecast is based on gut feel until late in the cycle. Surprises in deal size compound into forecast errors that cause replanning and missed targets. Knowing a company is a good ICP fit is one thing. Knowing they are currently paying a competitor 80K per year - and you can capture that budget - is the intelligence that drives real pipeline value. ACV Estimates combines current vendor contract data, company size, and historical deal benchmarks to generate a contract value projection for every account in your addressable market - before your reps ever reach out. Filter and sort your addressable market by estimated ACV. Identify your highest-revenue-potential accounts in any segment or territory, and ensure your best reps are focused on the accounts that move the number. When ACV estimates are attached to accounts before the pipeline stage, forecast modelling becomes possible earlier. Revenue operations can build realistic projections from the top of funnel - not just from stage 3 onwards. A daily ranked feed of accounts that fit your ICP, are evaluating, and are winnable. Scenario-specific win probability from 70M+ historical outcomes. Every account matched against your ideal customer profile in real time. Revenue potential calculated before outreach begins Contract data, deal benchmarks, and company size signals combined into a revenue estimate for every account in your market. Key capabilities: - Estimated current vendor spend and potential replacement ACV - Historical deal size benchmarking by company size, industry, and region - Confidence ranges so reps know how firm the estimate is - Pipeline sorted by potential ACV with segment and territory filters - Revenue tier groupings to help managers assign the right reps - Opportunity value ranking integrated into the Opportunity Discovery feed - Top-of-funnel revenue estimates for earlier forecast modelling - ACV trend data to spot shifts in deal size by segment over time - CRM sync to attach estimates to account records from day one Metrics: - Deal benchmarks in the dataset - Vendors with contract data - Countries covered - Contract data refresh ## Feature: ai agents Your AI tools are flying blind on your accounts Every useful AI query costs context you have to find yourself Manual qualification can't reach most of your market One setup. Every AI tool your team uses. Ask Claude to qualify an account - it does the work Opportunity Discovery ICP Scoring Market Intelligence Your account intelligence, available to any AI agent in one command. AI agents return the same Opportunity Score your team already trusts Connect once, and every member of your team can query live account intelligence from inside Claude, Cursor, or Claude Desktop. No exports, no tab-switching, no stale data. Without a live data connection, Claude has no idea whether an account fits your ICP, where they are in the buying cycle, or whether you have deal history with them. It can only work with what you paste in. Before your team can ask Claude anything meaningful about an account, someone has to open MarketSizer, find the account, and copy the relevant signals across. That context cost adds up across every rep, every session, every day. Reps qualify the accounts they already know. The rest of your addressable market - thousands of high-fit accounts they have never heard of - goes untouched because there are not enough hours to research them all. MarketSizer exposes your account intelligence as a set of live tools any AI agent can call. Once connected, team members can ask Claude or Cursor about any account and get ICP scores, intent signals, and Opportunity Scores returned instantly. Instead of opening MarketSizer, searching for the account, and summarising what you find, you just ask. The AI agent queries the live data, applies your ICP criteria, and returns a plain-English qualification summary in seconds. The ranked daily feed, now queryable by any AI agent in your team. AI agents check ICP fit for any account using your own scoring model. Pull live market context and renewal cycle data into any AI conversation. Use MarketSizer account intelligence directly in Claude, Cursor, and other AI tools. Ask any AI agent to qualify an account, surface intent signals, or check ICP fit - live data, no exports. From question to qualified account in seconds MarketSizer uses MCP - a standard that lets AI agents call external tools - to make your account intelligence available to any compatible AI. Connect once and the data is live wherever your team works. Key capabilities: - Works with Claude, Cursor, and Claude Desktop - Set up once - works for every team member - No exports or scheduled syncs - data is live on every query - Full account qualification from inside your AI tool - ICP fit, intent signals, and Opportunity Score in one answer - Works in the same Claude or Cursor session your team already uses Metrics: - Works with Claude, Cursor, and Claude Desktop - Every AI tool your team already uses. - Live data on every query - No exports, no scheduled syncs, no stale context. - One connection for the whole team - Set up once and every team member can query MarketSizer. ## Feature: chrome extension You switch tools every time you need account context Your CRM shows contacts, not whether they are worth calling Intent data tells you who read an article, not who is buying Know instantly whether an account is worth your time Go deeper without leaving the page Prioritise and start credible conversations, in context Opportunity Discovery ICP Scoring Qualified opportunities, everywhere you browse. Every account Copilot surfaces carries the same Opportunity Score your team already acts on Copilot is a Chrome extension that brings MarketSizer's subscription intelligence into LinkedIn, your CRM, and any website - as a sidebar, without switching tabs. Not intent noise, no data dump. Prospects live on LinkedIn, in your CRM, and across a dozen outreach surfaces. The intelligence that helps you qualify them is locked inside MarketSizer, waiting for you to go and find it. HubSpot and Salesforce tell you who is in your pipeline. They do not tell you whether an account is actively evaluating, has a renewal coming up, or fits your ICP - so reps qualify by gut, not by data. Generic intent platforms fire signals when a company browses a category page. MarketSizer detects real evaluation behaviour - active trials, subscription renewals, and buying-cycle signals that tell you when to move. Open any website or LinkedIn profile and Copilot surfaces the account's Opportunity Score, ICP fit, and subscription status as a sidebar - no searches, no tab-switching, no manual lookups. One click connects you to the full MarketSizer account view - historical subscription data, ACV estimates, and win likelihood - so you build context before you call, not after. Copilot gives your reps the signal they need to decide who to contact first and what to say - informed by real subscription and evaluation data, not category intent noise. Full account intelligence, now accessible from inside Claude, Cursor, and any AI tool. The daily ranked feed of accounts Copilot shows you the scores for. The ICP model powering the fit scores Copilot displays on every account you visit. Copilot is a Chrome extension that surfaces MarketSizer account intelligence as a sidebar on LinkedIn, your CRM, and any website - Opportunity Score, ICP fit, and subscription signals without switching tabs. From any page to qualified account in seconds Copilot overlays MarketSizer's account intelligence directly onto the pages your team already works on - no context-switching, no manual lookups, no stale exports. Key capabilities: - Works on any website, LinkedIn, HubSpot, Salesforce, Zoho CRM, Freshworks, Apollo, Lemlist, and Clay - Opportunity Score and ICP fit surfaced at a glance - Subscription status, active trial detection, and renewal signals in one view - Linked directly to the MarketSizer web app for deeper discovery - ACV estimates and win likelihood without opening another tab - Historical subscription data and deal context surfaced automatically - Prioritise outreach based on live Opportunity Score and subscription signals - Save opportunity to your CRM without leaving the page - Start credible first conversations with context your prospects will not expect Metrics: - historical subscriptions in the dataset - active subscriptions monitored - detection methods across several sources - data refresh lag, updated daily ## Feature: icp scoring ICP Scoring Reps waste hours researching accounts before the first call No consistent definition of what a good account looks like Great-fit accounts go cold while reps work poor ones Every account in your market scored against your ICP Built from your best customers, not generic benchmarks Route high-fit accounts to the right rep automatically Win Likelihood Purchase Intent Opportunity Discovery Stop guessing which accounts fit - know it instantly ICP Scoring anchors the Opportunity Score ICP Scoring matches every account in your addressable market against your ideal customer profile in real time - so reps only work accounts worth working. Without automated ICP matching, every rep runs their own qualification process. You get inconsistent results, wasted research time, and pipeline filled with accounts nobody can close. Marketing has one ICP. Sales has another. RevOps has a third. Without a single scored model, everyone is optimising for different things - and your best-fit accounts fall through the cracks. When reps manually decide who to call, they default to the accounts they recognise. High-fit accounts you have never heard of - the ones you consistently close when you find them - go untouched. ICP Scoring applies your ideal customer profile criteria - firmographics, technographics, industry, growth signals - to every account in your addressable market. Every account gets a score before a rep sees it. Your ICP model is trained on the characteristics of your actual closed-won customers - not industry averages. The more you win, the sharper the model gets. And you can override any dimension manually. ICP scores sync into your CRM so the routing rules your team already runs can act on them. High-fit accounts get routed to your best enterprise reps, mid-fit accounts get held for nurture in your existing tool, and low-fit accounts are suppressed before they enter any sequence. Scenario-specific win probability from 70M+ historical outcomes. Daily-refreshed evaluation signals and switching propensity data. A daily ranked feed of accounts that fit your ICP, are evaluating, and are winnable. Your ICP, applied at market scale A scoring model built from your best customers, applied to every account in your market before your reps touch them. Key capabilities: - Real-time ICP match score on every account in your TAM - Firmographic, technographic, and behavioural signal weighting - Scores refreshed continuously as company data changes - Model trained on your closed-won data for segment-specific accuracy - Manual weight overrides for criteria your data does not yet reflect - Version history so you can see how your ICP has evolved over time - ICP scores sync to CRM account records so existing routing rules can act on them - Suppression flags keep low-fit accounts out of the sequences you already run - Configurable score thresholds per territory and segment Metrics: - Score refresh cadence - ICP dimensions scored - Countries covered - Historical benchmarks ## Feature: lead qualification Lead Qualification Reps spend more time qualifying leads than closing them High-intent leads get the same response as low-intent leads Good inbound leads rejected because they do not look like past wins Every lead enriched with ICP, intent, and win likelihood on arrival A composite qualification score your whole team agrees on High-quality leads reach the right rep in under a minute ICP Scoring Purchase Intent Win Likelihood Qualify every inbound lead against market data - not gut feel Lead Qualification uses every signal in the platform Lead Qualification overlays real-time ICP fit, purchase intent, and win likelihood onto every inbound lead the moment it arrives - so reps only work leads worth working. Manual qualification - researching firmographics, checking fit, reading LinkedIn - takes 20 minutes per lead. With 50 inbound leads a week, that is 17 hours of research before a single meaningful conversation. When every lead looks the same in the CRM, reps respond in order of arrival - not in order of buying urgency. The lead that is actively evaluating your category right now waits while a passive enquiry gets called first. Pattern matching on previous closed-won deals misses emerging segments. A new vertical that fits your ICP perfectly gets disqualified because it does not match the historical template. Market data catches what gut feel misses. The moment an inbound lead enters your CRM, Lead Qualification overlays it with ICP fit score, active intent signals, win likelihood, and ACV estimate. Reps see a complete qualification picture before the first touch. Lead Qualification produces a single composite score combining ICP fit, purchase intent, win likelihood, and ACV potential. No more debates about whether a lead is worth calling - the score reflects market reality, not individual judgement. Qualification scores trigger automated routing rules. High-score leads go directly to your best closers with full context. Mid-score leads enter nurture tracks. Low-score leads are suppressed - protecting rep time without human review. Every account matched against your ideal customer profile in real time. Daily-refreshed evaluation signals and switching propensity data. Scenario-specific win probability from 70M+ historical outcomes. Every lead enriched and scored before the first reply From form fill to fully qualified in seconds - using market data your competitors do not have. Key capabilities: - Automatic enrichment with ICP score, intent flags, and win probability - ACV estimate attached to every lead on arrival - Enrichment latency under 30 seconds from form submission - Composite lead score weighted across ICP fit, intent, win probability, and ACV - Configurable thresholds to define SQL criteria for your team - Score history so you can track how leads develop over time - Score-based routing rules that fire automatically on lead creation - Slack and email alerts for leads crossing your SQL threshold - CRM assignment with full qualification context attached Metrics: - Enrichment latency from form fill - Qualification dimensions scored - Historical deal benchmarks - Intent signal matching ## Feature: market intelligence Market Intelligence Market strategy based on analyst reports that are 18 months old No visibility into which competitors are winning right now Territory planning disconnected from where the market is actually moving Daily-refreshed evaluation volume and velocity across your market Vendor share movements updated daily, not annually Territory and coverage decisions based on live market data Purchase Intent Opportunity Discovery Vendor Intelligence See what is happening in your market before it shows up in the news Market Intelligence sharpens every score in the platform Market Intelligence gives you a live view of evaluation activity, vendor movements, and buying patterns across your total addressable market - before it shows up in analyst reports. Gartner and Forrester are excellent for understanding the past. They tell you almost nothing about what is happening in your market this quarter - where evaluations are clustering, which vendors are gaining traction, and where the buying is shifting. You know your win-loss rate from your own deals. You do not know whether a competitor has doubled their win rate in a specific segment over the last 90 days - until they have already taken the accounts you were planning to target. Territories are designed once per year, based on last year's data. When evaluation volume shifts to a new segment or geography, you find out at QBR - after you have already missed the window. Market Intelligence surfaces evaluation activity trends across your total addressable market - by industry, company size, region, and vendor category. See where buying is accelerating before your competitors notice the same pattern. Market Intelligence tracks evaluation outcomes across your competitive landscape - which vendors are winning more evaluations, in which segments, and against which competitors. Updated from live signal data, not survey-based research. Market Intelligence feeds into territory planning, headcount modelling, and capacity decisions. Know where evaluation density is highest before you allocate resources - so your coverage matches the market, not last year's assumptions. Daily-refreshed evaluation signals and switching propensity data. A daily ranked feed of accounts that fit your ICP, are evaluating, and are winnable. Live win/loss data on every major vendor - win rates, churn destinations, in-market customers. A live market view no analyst report can give you Daily-refreshed evaluation data, vendor share movements, and buying pattern shifts - updated daily, not annually. Key capabilities: - Live evaluation volume trends by segment, geography, and vendor category - Velocity indicators showing where activity is accelerating or contracting - Historical trend comparison to identify seasonal and structural shifts - Vendor evaluation win share tracked by segment and deal size - Competitive displacement signals showing who is replacing whom - Your relative win rate versus competitors in each market segment - Evaluation density maps for territory planning by region and segment - Market size estimates based on current evaluation activity, not census data - Coverage gap analysis showing where you have capacity versus opportunity Metrics: - Active evaluations tracked - Countries covered - Vendors monitored - Market signal refresh ## Feature: opportunity discovery Opportunity Discovery Hours wasted on accounts that are not buying Finding out too late a deal was winnable No way to rank which accounts to call first Every account scored before a rep sees it See who is evaluating right now - and when the window closes Know what every opportunity is worth before you pick up the phone Win Likelihood ICP Scoring Purchase Intent Stop chasing accounts that already chose someone else Every signal feeds the Opportunity Score Opportunity Discovery gives your team a daily ranked feed of accounts that fit your ICP, are actively evaluating, and are historically winnable. You are prospecting into companies with no active evaluation, no budget cycle, no switching intent. Every cold call to a dead account is time stolen from a live one. By the time you see the signal, the competitor already had three calls. The information existed - it just was not in front of you when it mattered. Your list is sorted by company size, not by who is actually ready to buy right now. Without a priority signal, every account looks the same. ICP scoring, purchase intent signals, and scenario-specific win likelihood combined into a single Opportunity Score. Accounts are ranked by composite fit - not just firmographic match. Daily detection of active software evaluations across your addressable market. Know which vendor is being trialled, how far into the process they are, and when the decision window closes. ACV estimates for every account based on their current vendor contracts and historical deal data. Prioritise by revenue potential, not just fit. Scenario-specific win probability from 70M+ historical outcomes. Every account matched against your ideal customer profile in real time. Daily-refreshed evaluation signals and switching propensity data. Every signal, one ranked feed From raw evaluation data to a daily list of accounts your team can actually win. Key capabilities: - Opportunity Score ranking across every account in your market - Composite of ICP match (40%), purchase intent (35%), and win likelihood (25%) - Refreshed daily as new evaluation signals emerge - 2.5M+ active trials monitored, refreshed daily - Timing windows with expected decision dates - Signal validation and confidence scoring - Estimated current and potential annual contract value - Historical deal size benchmarking by segment and vendor - Filter and sort your entire pipeline by revenue opportunity Metrics: - Active evaluations tracked - Historical outcomes - Countries covered - Market refresh cycle ## Feature: purchase intent Evaluation Signals Cold outreach to accounts with no buying intent Arriving too late to active evaluations already in progress No signal for which accounts are likely to switch vendors Daily detection of active evaluations across your market Switching propensity scores that predict who is ready to move Decision windows so you know exactly when to reach out Win Likelihood ICP Scoring Vendor Intelligence See who is actively buying - before your competitors do Purchase Intent drives 35% of the Opportunity Score Evaluation Signals detect active software evaluations and switching propensity across your addressable market, refreshed daily - so you reach buyers while they are still looking. Most of your market is not looking right now. Outreaching into dormant accounts produces low reply rates, burnt contacts, and demoralised reps - when the same effort on an active buyer would close. By the time intent shows up in your CRM enrichment or a marketing touchpoint, the shortlist is already set. You need to know about evaluations at day one - not day thirty. An account that fits your ICP but loves their current vendor is a multi-year project. An account that fits your ICP and is quietly evaluating alternatives is a 90-day opportunity. You need to know the difference. Evaluation Signals track over 2.5M active software evaluations, refreshed daily. When an account in your addressable market starts evaluating, it shows up in your next daily refresh - with context on which vendor is being trialled and how far the process has progressed. Not every company in an evaluation is equally ready to switch. Switching propensity scores combine contract age, vendor satisfaction signals, and company growth indicators to tell you which accounts are most likely to change - before the evaluation starts. Evaluation Signals track the timeline of every active evaluation - expected decision date, stage velocity, and how that compares to historical patterns. Your reps know when to reach out for maximum impact. Scenario-specific win probability from 70M+ historical outcomes. Every account matched against your ideal customer profile in real time. See which vendors are winning, losing, and being trialled right now - live, per vendor. Signals that tell you who is ready to buy right now From evaluation detection to propensity modelling - every signal that tells you a company is in-market. Key capabilities: - 2.5M+ active trials monitored, refreshed daily - Vendor identification and evaluation stage tracking - Signal validation and confidence scoring to filter false positives - Propensity score combining contract age, satisfaction, and growth signals - High-propensity flags that surface before formal evaluations begin - Trend tracking so you can see propensity rising over time - Decision date estimates based on stage and historical evaluation velocity - Stage progression alerts when an evaluation accelerates - Urgency scores to prioritise outreach across your active intent list Metrics: - Active evaluations tracked - Signal refresh cadence - Countries covered - Historical evaluation outcomes ## Feature: retention and expansion Retention & Expansion CS teams find out a customer is at risk on the churn call Expansion conversations happen too late in the renewal cycle No way to identify which customers are ready to expand Early churn risk detection from external market signals Identify expansion-ready accounts before they ask Prioritise your renewal pipeline by risk and revenue impact Winback Opportunities Win Likelihood ICP Scoring Spot at-risk accounts before they start evaluating alternatives The same signals that find new business protect existing revenue Retention and Expansion monitors your customer base for early churn risk and expansion readiness - so your CS team acts before the situation becomes irreversible. Health scores built from internal product usage data miss the external signals - the competitor evaluation that started six weeks ago, the job postings for a new platform team, the procurement activity that signals a formal review. Expansion opportunities identified only at renewal are already half-lost. The best expansion conversations happen six months before renewal - when the customer has budget, appetite, and time to evaluate options. Growth signals - new office locations, headcount increases, expanded use cases - exist in public data. Without a system to surface them, your CS team misses expansion conversations until the customer brings it up first. Retention and Expansion monitors your customer accounts for external signals of churn risk - active competitor evaluations, procurement team activity, and switching propensity increases. You see the warning before internal health scores register it. Expansion signals - hiring for roles that indicate new use cases, opening new offices, acquiring companies in adjacent segments - surface automatically. Your CS team knows who to call for an expansion conversation before the customer even knows they need one. Renewal prioritisation that combines churn risk score, expansion potential, and ACV - so your CS team spends the most time on the accounts where intervention has the highest revenue impact. Former customers who are now back in an active evaluation cycle - ranked by win likelihood. Scenario-specific win probability from 70M+ historical outcomes. Every account matched against your ideal customer profile in real time. Market signals applied to your existing customer base The same evaluation intelligence that powers new business acquisition, pointed inward at your customer portfolio. Key capabilities: - Competitor evaluation detection within your existing customer base - Switching propensity monitoring triggered by external company signals - Risk score combined with internal health data for a complete picture - Growth signal monitoring across hiring, geography, and product signals - Expansion score that ranks your book of business by expansion readiness - Playbook triggers that route accounts to the right CSM at the right time - Renewal pipeline sorted by combined risk score and revenue impact - ACV-weighted prioritisation so high-value at-risk accounts get first attention - Timeline alerts as renewal dates approach with recommended actions Metrics: - External signal monitoring - Historical outcome benchmarks - Vendors tracked - Countries covered ## Feature: win likelihood Win Likelihood Reps can not tell a winnable deal from a waste of time Win-loss reviews happen months after the damage is done Competing blind against vendors you do not recognise A win probability for every account in your market Not just overall odds - win rates by specific scenario Prioritise your pipeline by probability, not just size ICP Scoring Purchase Intent Opportunity Discovery Know if you can win before you waste a call Win Likelihood feeds the Opportunity Score Win Likelihood scores every account against 70M+ historical deal outcomes to give your team a real probability of winning - by scenario, competitor, and deal size. Without historical benchmarking, every deal looks the same. Reps optimise for pipeline volume instead of win probability - and quota attainment suffers. Post-mortem analysis tells you why you lost last quarter. It does not tell you which active deals have a 20% chance of closing - before you invest three more months of effort. You discover who the incumbent is on the first call. By then, the evaluation is half over. Knowing the competitive landscape before outreach changes everything. Win Likelihood uses 70M+ historical deal outcomes to calculate the probability of winning each account - before your first touchpoint. Scores factor in vendor context, deal size, company stage, and your historical performance in similar scenarios. Win Likelihood breaks down probability by competitive scenario. Your odds when displacing Salesforce are different from displacing HubSpot. Your odds in enterprise differ from mid-market. Scores reflect reality, not averages. Sort and filter your entire pipeline by Win Likelihood score. Identify which deals deserve investment and which need a different approach - so your best reps spend time where the probability is highest. Every account matched against your ideal customer profile in real time. Daily-refreshed evaluation signals and switching propensity data. A daily ranked feed of accounts that fit your ICP, are evaluating, and are winnable. Probability built from real outcomes Not a model trained on your own win-loss data - a score built from every deal the market has ever seen. Key capabilities: - Win probability score on every account in your addressable market - Benchmarked against 70M+ historical deal outcomes across all verticals - Updated daily as evaluation signals and competitive context change - Scenario-specific win rates by incumbent vendor and deal type - Segment-level benchmarking across company size, industry, and region - Confidence banding so reps know how much to rely on each score - Pipeline view sorted by composite Win Likelihood score - Deal-level flags for low-probability accounts currently in late-stage - CRM sync so probability data lives where your reps already work Metrics: - Historical deal outcomes - Countries covered - Vendors benchmarked - Score refresh cycle ## Feature: winback opportunities Winback Opportunities Former customers return to the market and you never find out Winback outreach happens months after re-engagement starts No way to rank which former customers are worth chasing Former customers identified the moment they start evaluating again Ranked by win likelihood and time to decision Personalised context for every winback conversation Win Likelihood Purchase Intent Retention & Expansion Your churned customers are evaluating again - be first in the door Win Likelihood recalibrates for winback scenarios Winback Opportunities identifies former customers who are back in an active evaluation cycle - ranked by win likelihood and time to decision, so you get there before a competitor does. A company that churned 18 months ago for price reasons is now evaluating again - with a larger budget and a more mature use case. Without a detection system, they make their decision before you even realise they were looking. Relying on former customers to reach out, or waiting for a marketing touchpoint to trigger a re-engagement flag, means you are always late. By the time you call, they are already in late-stage with someone else. Not every churned customer is worth winback effort. Some left for reasons that have not changed. Others churned at a bad moment and have since grown into your ICP sweet spot. Without scoring, you treat them all the same. Winback Opportunities cross-references your churn history against active evaluation signals in real time. The moment a former customer re-enters the market, they appear in your winback feed - with the context of your previous relationship attached. Not all winbacks are equal. Winback Opportunities ranks former customers by composite win likelihood - factoring in why they churned, how their company has changed, and how far into the evaluation they are - so your best reps focus on the most recoverable accounts. Winback conversations are different from cold outreach. You have history, context, and often a genuine reason to reconnect. Winback Opportunities surfaces the right talking points - what changed, what improved, what the account needs now versus then. Scenario-specific win probability from 70M+ historical outcomes. Daily-refreshed evaluation signals and switching propensity data. Monitor your customer base for churn risk and expansion readiness before it is too late. Former customers detected the moment they re-enter the market Evaluation signals, matched against your churn history, surfaced as a ranked winback list before your competitors notice. Key capabilities: - Automatic cross-referencing of churn records against active evaluation signals - Previous relationship context - why they left, deal size, product tier - Detection within 24 hours of an evaluation signal appearing - Composite winback score combining churn reason, growth, and win probability - Decision timeline estimates to sequence outreach for maximum impact - ACV estimates reflecting current company size versus previous contract - Previous contract and product context attached to every winback account - Signal-based talking points based on why the evaluation is happening now - CRM integration to alert the original account owner first where appropriate Metrics: - Average detection latency - Historical outcome benchmarks - Countries covered - Signal refresh cycle # Solutions ## Solution: crm Salesforce renewals are invisible HubSpot's freemium funnel masks displacement intent CRM trials are ops-led and invisible Track who is trialling your competitors right now A daily feed of CRM buyers ranked by your chance of winning Marketing Automation Sales Enablement CRM deals are decided in procurement rooms your reps never enter MarketSizer tracks every active evaluation, upcoming renewal, and competitive displacement across our CRM coverage - so your team reaches buyers before the shortlist is finalised. Salesforce renewal evaluations happen inside procurement with no external signal until a contract is signed with a challenger. By the time a competitor hears about it, the evaluation is already closed. Accounts upgrading from free HubSpot or evaluating Pipedrive as an alternative show no distinguishable intent in standard tools. The evaluation is invisible until it surfaces as a lost deal. CRM pilots start in the ops or admin team, not the VP of Sales. These leads never appear in ABM lists or intent platforms because the people running the trial are not the people who publish RFPs. 5-year contract history across our CRM coverage. Know when every Salesforce, HubSpot, or Dynamics renewal window opens and which accounts have the highest switching propensity. Vendor-specific intent signals across G2, Capterra, TrustRadius, and 40+ B2B review platforms. Know when a prospect is researching Pipedrive vs Salesforce - not just generic CRM intent. See every active evaluation across the CRM vendors we cover, including Salesforce, HubSpot and Pipedrive. Know which vendor is being tested, how far along the process is, and when the window closes. Every account in the CRM market scored by ICP fit, purchase intent, and win likelihood. Your reps start each day knowing exactly which accounts to pursue and which are historically winnable. IT service management platform vendors competing for mid-market and enterprise accounts. Marketing automation vendors targeting in-market accounts with timing intelligence. Sales enablement platform vendors tracking consolidation and displacement opportunities. MarketSizer tracks every active trial, renewal, and competitive displacement across our CRM coverage so your team reaches buyers before a decision is made. Selling CRM software has never been harder Salesforce has entrenched most enterprise accounts. HubSpot owns mid-market through freemium. And CRM evaluations happen in ops teams and procurement, invisible to outbound sellers until a decision is nearly made. Tracking our CRM coverage across SMB, mid-market, and enterprise segments ## Solution: customer success teams Client Success & AMs Churn risk surfaces in QBRs and EBRs - by then it is too late Expansion opportunities go unspotted without market context No early warning when a customer starts evaluating a replacement Ranked list of at-risk accounts with renewal date and competitor interest Accounts showing intent signals for adjacent products you sell SDRs & AEs Revenue Operations Marketing & Demand Gen Spot churn risk and expansion signals before it's too late MarketSizer monitors renewal cycles, switching signals, and competitor activity across your entire book of business so CS teams act before accounts start evaluating alternatives. Customers do not announce they are evaluating alternatives. By the time dissatisfaction appears in a business review, they are already three weeks into a competitive trial. AMs lack visibility into adjacent products a customer might need. Without market intelligence, expansion conversations are guesswork rather than timed to real purchase intent signals. The first sign an account is leaving is often an email saying they have already decided. Switching signals appear weeks before that - but only if you are monitoring the right data sources. Flag renewal risk 90 days early by tracking competitor evaluation signals, switching propensity scores, and contract proximity across every account in your book. Act before the conversation turns adversarial. Detect the moment a customer starts comparing alternatives. See which competitor is being evaluated, how actively, and when the decision window closes - so your CS team can intervene at the right moment. Understand switching propensity by segment so AMs can spot which accounts are most likely to expand into adjacent products - and time those conversations to real buying signals, not calendar cadence. Every account in your book ranked by churn risk - weighted by renewal proximity, competitor evaluation signals, and historical switching patterns. Prioritise your outreach on the accounts most likely to leave, not the loudest ones. MarketSizer surfaces accounts in your book that are actively researching adjacent products in your portfolio. Give AMs a daily list of expansion conversations worth having - backed by purchase intent data, not instinct. Stop prospecting blind with a daily ranked feed of accounts actively evaluating right now. Give every GTM team a single source of account truth fed directly into your CRM. Run campaigns timed to active evaluation windows rather than firmographic filters. You find out about churn risk when it is already too late to act By the time a customer raises concerns in a QBR, they have already shortlisted your competitors. The signals were there weeks earlier - you just had no way to see them. Built for CS and AM teams managing accounts across CRM, CX, ITSM, and enterprise SaaS ## Solution: cx live chat CX & Live Chat Buyers shortlist before you know they exist Renewals and switches happen on a schedule you cannot see Competitive displacement is invisible until it is too late Track who is trialling your competitors right now A daily feed of CX buyers ranked by your chance of winning Marketing Automation Your buyers are already evaluating. Are you in the conversation? MarketSizer tracks every active trial, upcoming renewal, and competitive displacement across 140+ customer support and live chat products - so your team reaches prospects while they are still deciding. By the time a CX vendor hears about an opportunity through intent data or inbound, the prospect has already narrowed to 2-3 vendors. You are pitching into a decision that is nearly made. Every CX platform contract has a renewal window. If you do not know when that window opens, you are relying on the prospect to come to you - which they will not. When a Zendesk customer starts trialling Freshdesk, nobody sends you an email. Without visibility into active evaluations, you are always reacting, never anticipating. Every CX buyer in your addressable market scored by ICP fit, purchase intent, and win likelihood. See who is evaluating before they publish an RFP or post on G2. 5-year contract history across 140+ CX products. Know when every renewal window opens and which accounts have the highest switching propensity. Daily detection of active trials across Zendesk, Freshdesk, Intercom, LiveChat, Crisp, Gorgias, and 130+ others. See which vendor is being evaluated and when the window closes. See every active evaluation across Zendesk, Freshdesk, Intercom, and 130+ other CX platforms. Know which vendor is being tested, how far along the process is, and when the decision window closes. Every account in the CX market scored by ICP fit, purchase intent, and scenario-specific win likelihood. Your reps start each day knowing exactly which accounts to pursue. IT service management platform vendors competing for mid-market and enterprise accounts. CRM platform vendors tracking competitive displacement and expansion opportunities. Marketing automation vendors targeting in-market accounts with timing intelligence. MarketSizer tracks every active trial, renewal, and competitive displacement across 140+ CX and live chat products so your team reaches prospects while they are still deciding. Selling CX software has never been harder Enterprise deals take 12-18 months. Buyers shortlist before you know they exist. And Zendesk, Freshdesk, and Intercom have brand gravity that pulls every evaluation their way. Tracking 140+ products across customer support, live chat, helpdesk, and conversational AI ## Solution: enterprise Enterprise Territory planning without ground truth Market sizing that does not hold up No single picture of competitive activity Market Coverage Map Competitive Signal Dashboard Revenue Operations SaaS Vendors SDRs & AEs Market-wide intelligence for enterprise GTM teams. MarketSizer maps your full addressable market across every territory - renewal cycles, vendor distribution, active evaluations, and switching propensity - so large GTM teams plan and execute from real market data, not historical estimates. CRM history tells you where you have won, not what the market looks like today. Territory sizing built on past deals rather than live subscription data leads to uneven coverage and structural blind spots across regions. Investor and board-level market sizing built on analyst models is hard to defend and harder to act on. Without live subscription data across 70M+ historical outcomes, TAM estimates remain a hypothesis rather than a plan. Multiple teams, territories, and product lines each build their own view of who is evaluating, renewing, or switching. Without a shared source of market truth, GTM coordination breaks down at the territory level. Map your total addressable market across 140+ countries - renewal cycles, vendor distribution, and switching propensity - so territory allocations and board-level market sizing are grounded in live subscription data rather than estimates. Surface every active evaluation across your target territories, ranked by Opportunity Score, so enterprise GTM teams prioritise the accounts that carry the highest win probability right now. Apply consistent ICP scoring across all territories and product lines so every team - from SDR to CRO - is qualifying from the same account universe with the same criteria. A live view of your addressable market across every territory - vendor distribution, renewal timing, and switching propensity - so territory allocations reflect actual market opportunity rather than historical deal patterns. Every active evaluation, trial, and renewal signal across your competitive landscape consolidated into a single enterprise-wide view - updated daily so the picture your teams act on is always current. Quantify your addressable market and calibrate territory sizing across multiple product lines and geographies. The focused view - single-category competitive displacement and renewal intelligence for high-velocity outbound teams. Give every rep on every team a daily ranked feed of in-market accounts to work, regardless of territory. MarketSizer gives enterprise B2B software vendors the market coverage data, territory intelligence, and competitive signals to plan, prioritise, and execute across every market they sell into. Scale amplifies the cost of Signal Blindness When 86% of CRM records are out-of-market at any given moment, that waste compounds across every territory, product line, and team. At enterprise scale, poor timing and invisible signals cost more budget and more pipeline moments than any team can recover. Used by enterprise GTM teams managing multi-territory markets and growth-stage expansion ## Solution: implementation partners Implementation Partners New software purchases are invisible until announced No signal on unhappy existing deployments Business development is reactive, not proactive New Deal Radar Unhappy Deployment Alerts Revenue Operations SDRs & AEs SaaS Vendors Find clients before they shortlist an implementation partner. MarketSizer identifies companies that just purchased or are actively evaluating software platforms you implement - so you reach them during the partner selection window. By the time a vendor announces a new customer, implementation partner selection is already complete. You missed the window before you even knew it existed. Some of your best opportunities are re-implementations - existing deployments that are underperforming or switching platforms. These accounts are invisible without market signals. Partner pipelines are built on referrals and vendor relationships - both of which are slow and unpredictable. There is no systematic way to find clients before they engage someone else. Identify accounts in active research mode for platforms you implement - including comparison and review site activity that signals an imminent decision. Map which platforms are being adopted by segment and region - so you know where to build capacity and where to run proactive outreach. A daily feed of companies entering evaluation mode for platforms you implement, sorted by estimated deal size, ICP fit, and geographic region. Existing deployments showing dissatisfaction signals - support volume spikes, competitor research, contract end proximity - ranked by re-implementation likelihood. Align your BD and delivery teams around a single pipeline intelligence layer. Reach new deployment prospects with the right message at the right time. Understand adoption trends and renewal cycles across your platform ecosystem. MarketSizer identifies companies actively evaluating software platforms you implement - so you reach them during the partner selection window, not after it closes. By the time you hear about a new deployment, the partner is already chosen Implementation partner selection happens fast, often in parallel with vendor selection. Without early signals, you're always chasing announcements, not opportunities. Used by implementation and consultancy teams across major SaaS platforms ## Solution: itsm IT procurement cycles are invisible ServiceNow dominance hides secondary evaluations Pilots start in IT ops, not with the buyer Track who is trialling your competitors right now A daily feed of ITSM buyers ranked by your chance of winning CX & Live Chat Marketing Automation Your buyers are shortlisting before your BDRs make first contact MarketSizer tracks every active evaluation, upcoming renewal, and competitive displacement across our ITSM and ESM coverage - so your team reaches IT buyers while they are still in the decision process. ITSM contracts renew on 3-year cycles locked inside IT procurement. No signal crosses to sales. By the time a vendor hears about an opportunity, a shortlist is already in place. Every mid-market account also trialling Freshservice or Jira Service Management is invisible until a decision is nearly made. Enterprise bias in intent data misses the challenger evaluations that are actually winnable. Early-stage ITSM evaluations are run by IT administrators and operations leads who never appear in ABM lists or intent platforms. By the time procurement gets involved, the decision is nearly made. 5-year contract history across our ITSM coverage. Know when every renewal window opens and which accounts have the highest switching propensity away from their current platform. Every ITSM buyer in your addressable market scored by ICP fit, purchase intent, and win likelihood. See who is evaluating before they publish an RFP or contact your sales team. See every active evaluation across ServiceNow, Freshservice, Jira Service Management, and other tracked ITSM platforms. Know which vendor is being tested, how far along the process is, and when the decision window closes. Every account in the ITSM market scored by ICP fit, purchase intent, and win likelihood. Your reps start each day knowing exactly which accounts are in-market and which are historically winnable. Customer support and live chat platform vendors competing for mid-market and enterprise accounts. CRM platform vendors tracking competitive displacement and expansion opportunities. Marketing automation vendors targeting in-market accounts with timing intelligence. MarketSizer tracks every active trial, renewal, and competitive displacement across ITSM and ESM coverage - 13 vendors tracked today, expanding through 2026 - so your team reaches IT buyers while they are still deciding. Selling ITSM software has never been harder Enterprise IT contracts run 3-5 years and renew inside procurement. Buyers shortlist before vendor outreach. And ServiceNow has brand gravity that pulls every evaluation in the enterprise segment. 13 ITSM vendors tracked today across ITSM, ESM, and IT helpdesk - expanding through 2026 ## Solution: lead gen agencies Lead Gen Agencies Lead lists are built from firmographics, not buying signals Intent data is expensive and siloed Reporting is activity-based, not outcome-based Signal-Enriched Lists Client Signal History SDRs & AEs Marketing & Demand Gen Revenue Operations Deliver clients pipeline that's already in-market. MarketSizer gives lead gen agencies a proprietary signal layer - ICP match, purchase intent, and evaluation signals - so every lead list you deliver is full of accounts actively looking to buy. Filtering by company size, industry, and headcount gives you a universe of accounts - not a set of buyers. Clients get contacts, not conversations. One intent provider covers one vertical. Stitching together a multi-market view is expensive, inconsistent, and still doesn't show who's actively comparing vendors right now. Open rates, call connects, and meetings booked tell clients nothing about revenue attribution. You can't connect your leads to closed pipeline without better signal data. Build precise segments from 200+ firmographic and behavioural signals - so every list you deliver is pre-qualified against your client's ideal customer profile. Layer daily-refreshed buying activity on top of any target list - so clients know not just who matches their ICP, but who's actively in-market right now. Upload a client's ICP criteria and get back a ranked list with intent scores, evaluation status, and renewal proximity - so every outreach sequence starts from the best possible account set. Account-level signal history showing which contacts engaged at which buying stage - so you can demonstrate the connection between your leads and your client's closed revenue. Show clients how their reps can work signal-enriched lists more effectively. Power client campaigns with intent-driven audience segments. Help clients build the RevOps infrastructure to act on the signals you deliver. Your clients want pipeline. You're delivering contacts. Firmographic lists and intent subscriptions get you names. What clients need is accounts that are actively in-market - and the signals to prove it. Used by lead generation and demand generation agencies across B2B SaaS markets ## Solution: marketing automation Marketing Automation Multi-year contracts hide the evaluation window HubSpot's freemium flywheel masks upgrade signals Intent signals cannot distinguish research from buying Track who is trialling your competitors right now A daily feed of MAP buyers ranked by your chance of winning Sales Enablement CX & Live Chat Your biggest displacement opportunities start 6 months before you hear about them Marketing Automation vendor coverage is coming Q3 2026. Join the waitlist to track every active evaluation, upcoming renewal, and competitive displacement across the MAP and email platform market the moment it ships. Marketing automation contracts lock buyers in for 2-3 years. Evaluations begin 6 months before renewal with no public signal. By the time standard intent tools detect activity, the shortlist is already set. Free-tier HubSpot users upgrading or evaluating Marketo and Pardot as alternatives show no distinguishable intent in standard tools. The displacement opportunity is invisible until it becomes a lost deal. A prospect reading about marketing automation and a prospect actively comparing Marketo and Pardot look identical in most intent platforms. Without vendor-specific evaluation signals, you are chasing noise. 5-year contract history across MAP coverage launching Q3 2026. Know when every renewal window opens and which accounts are historically likely to switch away from their current platform. Every MAP buyer in your addressable market scored by ICP fit, purchase intent, and win likelihood. Vendor-specific signals separate genuine evaluations from category-level research. From Q3 2026, see every active evaluation across HubSpot, Marketo, Pardot, ActiveCampaign, and the wider marketing automation platform landscape. Know which vendor is being tested, how far along the process is, and when the decision window closes. Every account in the marketing automation market scored by ICP fit, purchase intent, and win likelihood. Your reps start each day knowing exactly which accounts are in-market and historically winnable. CRM platform vendors tracking competitive displacement and expansion opportunities. Sales enablement platform vendors tracking consolidation and displacement opportunities. Customer support and live chat platform vendors competing for mid-market and enterprise accounts. MarketSizer's Marketing Automation coverage launches Q3 2026. The platform already tracks every active trial, renewal, and competitive displacement across the wider B2B software market - so your team reaches buyers 6 months before they announce an evaluation. Selling marketing automation has never been harder Multi-year contracts mean evaluations start 6-12 months before renewals and happen quietly. HubSpot's freemium flywheel obscures upgrade and displacement signals. And generic intent data cannot distinguish a buyer from a researcher. Marketing Automation coverage - coming Q3 2026 ## Solution: marketing teams Marketing & Demand Gen Campaigns hit the full TAM regardless of buying readiness Lead scoring is based on engagement, not true purchase intent No visibility into renewal cycles means campaigns miss the buying window Build precise campaign segments from ICP, intent, and renewal data Launch sequences triggered by evaluation signals, not arbitrary schedules SDRs & AEs Revenue Operations Client Success & AMs Turn market signals into pipeline before your competitors do MarketSizer gives marketing teams live intent data, ICP scoring, and renewal cycle intelligence to run campaigns that reach buyers at the exact moment they're ready. Without renewal cycle data, every account looks the same. You are spending ad budget on accounts that renewed three months ago and will not evaluate again for two years. A contact downloading a whitepaper is not the same as an account actively trialling a competitor. Engagement-based MQL scores send reps after contacts that will never convert. The highest-intent moment for any account is the 90-day window before contract renewal. Without renewal cycle data, your campaigns land at the wrong time and your competitors arrive first. Narrow campaign audiences to accounts that match your best customers across 200+ firmographic and behavioural signals. Stop spending budget on accounts outside your real ICP. Identify accounts actively researching right now - not just those who clicked an ad last quarter. Layer daily-refreshed buying activity on top of any segment to surface the accounts worth targeting today. Catch prospects mid-comparison and launch competitor displacement campaigns before they make a decision. Know which vendor is being evaluated and how far along the process has gone. Combine ICP score, purchase intent stage, and renewal proximity into a single ranked segment. Export to your MAP or ad platform and run campaigns against accounts that are actually ready to buy - not just firmographically similar. Stop running always-on campaigns to cold audiences. MarketSizer triggers campaign sequences the moment an account enters an active evaluation - so your message arrives when it has the highest chance of landing. Give reps a daily ranked feed of accounts that are actively evaluating right now. Feed a single source of account truth into your CRM so every GTM team works from the same data. Spot churn risk and expansion signals across your book of business before accounts start evaluating alternatives. Your campaigns reach everyone except the accounts ready to buy You are spending budget on the full TAM while a fraction of those accounts are actually in-market. The rest will not buy this quarter regardless of how good your creative is. Built for demand gen teams targeting accounts across CRM, CX, ITSM, and SaaS categories ## Solution: revenue operations Revenue Operations Each GTM team runs its own scoring model CRM data goes stale within weeks No unified pipeline health view Pipeline Health Dashboard Signal Routing SDRs & AEs Marketing & Demand Gen Client Success & AMs Give every GTM team a single source of buying truth. MarketSizer feeds ICP scores, intent signals, and renewal intelligence into your CRM and MAP so reps, marketers, and CS teams all work from the same prioritised pipeline. Reps, marketing, and CS disagree on which accounts matter. Leads get routed badly, pipeline reviews become arguments, and revenue leaks through the gaps. Firmographic and intent fields are outdated before reps use them. Manual enrichment is slow and inconsistent. The data you're making decisions on is already wrong. Activity metrics - calls made, emails sent, meetings booked - tell you nothing about actual buying intent. Pipeline looks healthy until it collapses at quarter end. A single ranked account list fed into every CRM seat - so reps, marketing, and CS all work the same prioritised pipeline. A data-backed ideal customer profile that standardises qualification across all teams - replacing gut feel with a shared definition of a good account. A real-time view of pipeline quality weighted by ICP match, intent stage, and win likelihood - not just deal count and ARR. Spot coverage gaps before they become miss quarters. Automatically assign territory, sequence, and owner when accounts hit signal thresholds - so the best account always reaches the right rep without manual triage. Give reps a daily ranked feed of accounts that are in-market and ready to engage. Run campaigns timed to buying signals, not arbitrary schedules. Flag at-risk accounts and surface expansion signals before renewal conversations. Your GTM teams are flying blind - and disagreeing about why Three teams, three scoring models, three definitions of a good account. The result is a pipeline that looks healthy until it isn't. Trusted by RevOps teams at SaaS vendors across CX, ITSM, CRM, and marketing automation ## Solution: revops consultancies RevOps Consultancies Clients think their pipeline problem is a process problem TAM and market sizing rely on stale reports GTM audits surface what happened, not what's happening Market Audit View Pipeline Gap Analysis Revenue Operations SaaS Vendors SDRs & AEs Bring clients data they can't buy anywhere else. MarketSizer gives RevOps consultancies a market intelligence layer to embed in client engagements - showing the full buying landscape, signal quality gaps, and where the pipeline is leaking. It's often a signal problem - reps are working the wrong accounts because the intelligence layer is missing. Without market data, you can't prove it or fix it. Third-party market reports are 12 - 18 months old by the time they're used in an engagement. Clients are making territory and headcount decisions on data that doesn't reflect the current market. Win/loss analysis, stage conversion rates, and CRM hygiene reviews tell you where the process broke down. They can't tell you what's happening in the market right now or where the opportunity is. Live category-level data for TAM analysis and pipeline benchmarking - showing the full buying landscape, renewal cycle distribution, and competitive dynamics across your client's market. Show clients where qualified pipeline exists that their current process isn't capturing - backed by ICP scoring, intent data, and evaluation signals. Establish a data-backed ideal customer profile baseline for the engagement - replacing subjective qualification criteria with a consistent, market-verified scoring model. Category-level intelligence showing TAM, renewal cycle distribution, and competitive landscape for a client's target market - delivered in the first week of an engagement. A comparison of your client's current pipeline against all in-market accounts that match their ICP - showing exactly where coverage is missing and why. Give in-house RevOps teams the intelligence layer to sustain your recommendations. Understand the full competitive landscape of the categories your clients compete in. Show sales teams how to operationalise the signals you surface in your engagements. MarketSizer gives RevOps consultancies a market intelligence layer to embed in client engagements - showing the full buying landscape, signal quality gaps, and where pipeline is leaking. Your audits show what happened. Clients need to know what's happening now. Process reviews and CRM audits surface historical patterns. The market intelligence layer - what's actually happening with buyers right now - is missing from most engagements. Used by RevOps consultancies delivering GTM audits and pipeline strategy engagements ## Solution: saas vendors SaaS Vendors Category-wide TAM visibility is impossible Competitor renewal cycles are invisible Displacement campaigns are broad and untargeted Category Map Displacement Pipeline SDRs & AEs Revenue Operations Marketing & Demand Gen Know every active evaluation, renewal, and displacement opportunity in your category. MarketSizer maps the entire buying landscape for your software category - who's in-market, who's renewing, and who's unhappy with the incumbent. You can only see accounts that engage with your content - not the full market. Most in-market buyers never visit your site before choosing a vendor. You have no signal when a competitor's customer enters renewal mode. By the time they reach out to you, they've already renewed - or they're about to. Without signal on which competitor accounts are actually ready to move, displacement outreach hits the full installed base - and conversion rates show it. Map renewal cycles, switching propensity, and vendor distribution across 2.5M+ active evaluations and 17M+ active subscriptions - not just the accounts that engage with you. Detect competitor customers showing research and comparison signals before they formally enter an evaluation - so you reach them first. Re-engage former customers the moment they return to the market - ranked by ACV potential and win likelihood. A full vendor landscape view showing which accounts use which products, when contracts likely renew, and which segments are showing switching propensity. Competitor accounts ranked by switching propensity, renewal proximity, and live intent signals - so displacement campaigns reach the right accounts at exactly the right moment. Give reps a daily ranked feed of in-market accounts to work. Align every GTM team around a single source of market truth. Time campaigns to renewal cycles and buying signals, not guesswork. MarketSizer maps every active evaluation, renewal, and displacement opportunity across your software category - so your team reaches in-market accounts before the shortlist is set. You can only see the accounts that come to you Your TAM is visible to you only when accounts engage with your content. The rest - the majority - are invisible until it's too late. Used by GTM teams at SaaS vendors across 200+ tracked vendors and 250+ products ## Solution: sales enablement Sales Enablement Tech stack consolidations absorb enablement tools silently Champion-led evaluations bypass procurement Category overlap makes intent data noisy Track who is evaluating your competitors right now A daily feed of enablement buyers ranked by your chance of winning Marketing Automation Enablement consolidations happen during budget reviews your team never sees MarketSizer tracks every active evaluation, tech stack rationalisation, and competitive displacement across our sales enablement coverage (launching Q3 2026) - so your team reaches buyers while they are still in the decision process. When finance-led rationalisation projects review the sales tech stack, enablement tools are first to be cut or replaced. No external signal is visible. Vendors only hear about it when the contract is already cancelled. Sales leadership evaluating Seismic vs Highspot rarely surfaces in ABM or intent tools until a shortlist is set. By the time procurement issues an RFP, the champion has already made the decision. Companies researching Gong, Chorus, and Mindtickle appear as generic 'sales tool intent' in most platforms. Without vendor-specific evaluation signals, you cannot tell who is replacing what. 5-year contract history across our sales enablement coverage (launching Q3 2026). Know when every renewal window opens and which accounts are historically likely to switch or consolidate their enablement stack. Vendor-specific win probability based on 70M+ historical outcomes. Know which sales enablement accounts you are historically most likely to win before committing outreach resources. From Q3 2026, see every active evaluation across Seismic, Highspot, Showpad, Outreach, and the wider sales enablement landscape. Know which vendor is being assessed, how far along the process is, and when the decision window closes. Every account in the sales enablement market scored by ICP fit, purchase intent, and win likelihood. Your reps start each day knowing exactly which accounts are consolidating their stack and which are historically winnable. CRM platform vendors tracking competitive displacement and expansion opportunities. Marketing automation vendors targeting in-market accounts with timing intelligence. IT service management platform vendors competing for mid-market and enterprise accounts. MarketSizer tracks every active evaluation, tech stack consolidation, and competitive displacement across our sales enablement coverage (launching Q3 2026) so your team reaches buyers before a decision is made. Selling sales enablement software has never been harder Budget rationalisation projects absorb enablement tools silently. Champion-led evaluations bypass procurement until a shortlist is set. And category overlap makes it impossible to distinguish genuine buying signals from background noise. Sales enablement, content, and readiness coverage - launching Q3 2026 ## Solution: sdrs and aes SDRs & AEs You are calling accounts that are not in-market Your best opportunities go to the fastest competitor Every account on your list looks the same Open MarketSizer and see exactly who to call today Open every call with the exact signal that triggered it Marketing & Demand Gen Client Success & AMs Revenue Operations Stop prospecting blind. Start every day knowing who to call. MarketSizer gives you a daily ranked feed of accounts that match your ICP, are actively evaluating right now, and have the highest probability of becoming your next closed deal. Without timely evaluation signals, you are working a static list. Some accounts renewed three months ago. Others have no budget cycle until next year. You have no way to know. When an account starts evaluating, the first vendor with relevant context wins the first meeting. If you find out a week later through intent data, you are already behind. Company size and industry are not priority signals. An account with a renewal next month and an active competitive trial should be at the top of your list - but nothing tells you that. Every account in your market scored by ICP fit, live purchase intent, and scenario-specific win probability. Your list refreshes daily based on real evaluation signals, not stale firmographic data. Know the moment an account starts trialling a competitor. See which vendor, how far into the process, and when the decision window closes - so you reach them while they are still open. Every account ranked by a composite score that weights ICP match (40%), purchase intent (35%), and win likelihood (25%). ACV estimates show what each deal is worth. Every morning, your opportunity feed is refreshed with accounts ranked by Opportunity Score. The accounts at the top have the highest combination of ICP fit, active evaluation signals, and historical win probability. MarketSizer surfaces the vendor being evaluated, the renewal timeline, and the switching history alongside each account. Push the account and its context into the CRM or sequencing tool your team already uses, or pull it into the Chrome Extension on LinkedIn. Run campaigns timed to active evaluation windows, not just firmographic fit. Catch churn risk at the signal stage and spot expansion windows across your base. Replace gut-feel territory planning with market signal data. MarketSizer gives SDRs and AEs a daily ranked feed of accounts that match your ICP, are actively evaluating, and have the highest win probability. You already know this is broken Your outreach list is built on firmographics and gut feel. Half the accounts you call are not buying. The other half already picked someone else. Built for the daily workflow of SDR and AE teams across CX, ITSM, CRM, and SaaS vendors # Pages ## Page: Platform (/platform) The Opportunity Score | MarketSizer Platform Opportunity intelligence for B2B software vendors. Every account scored by ICP fit, purchase intent, and win likelihood, delivered daily. Free trial, no card required. Credit-based pricing beyond that with half of the activation fee deferred until conversion. Meet the Opportunity Score: one score per account that tells you which companies are in-market, in fit, and historically winnable - so your GTM team focuses effort where it matters most. ## Page: Integrations (/integrations) Integrations | MarketSizer MarketSizer works inside the CRMs, prospecting tools, and workflow automations your GTM team already runs. No connection required to get started. ## Page: Our Data (/our-data) Our Data | MarketSizer Prioritise Traceable to the source Fresh enough to act on Historical, not isolated Reasoning you can inspect Coverage & freshness Scoring & methodology Contact data Privacy & compliance The evidence behind every MarketSizer opportunity: what we observed, what we inferred and what we predicted. 50M+ companies, 200+ vendors across 250+ products, 70M+ subscription outcomes, refreshed daily and verified on lookup. ## Page: Book a Demo (/book-a-demo) Book a Demo | MarketSizer See MarketSizer in action with your own accounts. We pull live Opportunity Scores during a 30-minute call so you leave knowing exactly which accounts to pursue. See MarketSizer in action with your own accounts. Live Opportunity Scores in 30 minutes. # Case studies ## Case study: cloudmotion (/customers/cloudmotion) How cloudmotion finds companies evaluating Zendesk The challenge How cloudmotion uses MarketSizer What cloudmotion can do now Make it part of a weekly sales routine Building around how cloudmotion sells What cloudmotion is doing next See companies evaluating Zendesk Find the people behind the opportunity Move prospects into Pipedrive cloudmotion case study | MarketSizer Ralph can identify businesses showing the specific market activity cloudmotion wants to reach, rather than starting from a general company list. Once he has identified an account, he can surface and enrich relevant contacts without researching each company separately. The company and contact information can move into Pipedrive as structured data, so Ralph can continue working the prospect where cloudmotion already manages its outbound activity. cloudmotion, Byteleaders Customer experience and technology consulting, including Zendesk and Pipedrive implementation Netherlands Ralph Heeneman, Commercial Director & Co-Owner Finding companies using competing CX and CRM products, identifying and enriching relevant contacts, and moving prospects into Pipedrive How a Dutch CX and CRM reseller finds companies evaluating Zendesk, and moves them into Pipedrive. Ralph Heeneman, Commercial Director and Co-Owner, operates across cloudmotion and Byteleaders, two businesses that together cover customer service operations, CX consulting, Zendesk expertise, automation and sales technology. For cloudmotion’s new-business motion, Ralph wants to know which companies are evaluating Zendesk, find the relevant people inside those businesses, and turn that activity into prospects he can work. MarketSizer helps cloudmotion move from that market signal to a contact and into Pipedrive, where Ralph already runs his outbound workflow. Ralph was clear about the market he wanted to reach: companies actively evaluating Zendesk. Finding the company is only part of the job. cloudmotion runs its outbound activity through Pipedrive, so Ralph also needs to identify the right person, get the contact information he needs and bring the prospect into the CRM before he can act on it. Without that connection, prospecting becomes a series of separate steps across different tools before an account is ready to work. Ralph has made MarketSizer part of a fixed weekly prospecting routine. Every Tuesday morning, he uses it to identify relevant companies, including businesses evaluating Zendesk and accounts using competing products. From there, he can find and enrich the people he wants to approach with details such as job title, work email, phone number and LinkedIn profile. The selected prospect can then move into Pipedrive, where cloudmotion already manages its outbound activity. Rather than stopping at a company signal, Ralph can move from identifying an opportunity to having a named prospect ready inside the system his team already uses. MarketSizer is now part of Ralph\u2019s regular prospecting process. Ralph has also been actively shaping the workflow around how cloudmotion works. His feedback has included how prospects should move into Pipedrive, how different outreach scenarios should work and how the most promising opportunities should be surfaced. For Ralph, the responsiveness to that feedback has become part of the experience of working with MarketSizer. cloudmotion works across both Zendesk and Pipedrive. The next step is extending the same approach across both parts of its portfolio, giving Ralph a repeatable way to identify relevant market activity, find the right people and move those opportunities into the sales workflow cloudmotion already uses. Quotes: - "I want to see the customers which are evaluating Zendesk." - Ralph Heeneman, Commercial Director & Co-Owner, cloudmotion - "I’m very happy with the Pipedrive connection. It’s just great. It makes my life easier." - Ralph Heeneman, Commercial Director & Co-Owner, cloudmotion - "I’m doing a fixed block every week, every Tuesday morning, ten or twelve outreaches." - Ralph Heeneman, Commercial Director & Co-Owner, cloudmotion ## Case study: Gallabox (/customers/gallabox) How Gallabox prioritises a 10,000+ account target market The challenge How Gallabox uses MarketSizer A second use: giving unreachable inbound leads another look What Gallabox is doing next Find the accounts that actually fit See competitor presence before outreach Find the people behind the accounts Turn the research into assigned sales work Gallabox case study | MarketSizer Gallabox can narrow large target lists around the criteria that matter to its sales team, helping Sam identify the companies worth taking forward. Before a rep approaches an account, the team can see whether named competitors are already present and use that context to shape the conversation. For markets where identifying the right company is easier than finding the right person, Gallabox can surface relevant contacts and bring them into the same workflow. Selected accounts do not stop at a spreadsheet. Sam can assign them to individual reps in Zoho based on territory, language and sales ownership. He has already assigned 50 leads from one MarketSizer output to Gallabox’s outbound team. AI-powered platform unifying WhatsApp, Instagram, web chat and voice to turn conversations into revenue Headquartered in the US, with offices in the UAE and India, serving customers across India, the GCC and beyond Gajendra “Sam” Boopathi, AVP Global Sales; Yogesh Narayanan, Co-founder and Head of Technology Target account prioritisation, competitor intelligence, contact discovery and sales handoff How a business messaging platform turns a 10,000+ account target list into prioritised, assigned sales work. Gallabox uses AI to help businesses convert WhatsApp, Instagram, web chat and voice conversations into bookings, appointments and revenue, with enterprise-grade security built in. As the company grows across India and the GCC, AVP Global Sales Gajendra Boopathi needs to decide which companies are worth pursuing, what competitors they already use, and which accounts his team should work first. MarketSizer helps Gallabox turn large target lists into prioritised sales opportunities, with account fit, competitor intelligence and relevant contacts ready for the outbound team. Gallabox already had large lists of companies it wanted to target. The harder part was knowing which ones deserved sales attention and getting the right people inside those companies. Sam was doing much of that validation himself. He would open company websites to check whether each business was legitimate, cross-check company information on LinkedIn and then decide how strongly the account fit Gallabox. From there, he grouped accounts into different tiers. The strongest opportunities went to Sam and the co-founders, the next tier to the field sales team, and lower-priority accounts into email and WhatsApp outreach. s // quote. Inline, one directly under the other, the second is just the // first again in quote marks, so only the narration is kept. Publishing // less of what a customer approved is always safe. work through a // prioritised set and validate which ones are genuinely worth pursuing Gallabox gives MarketSizer its target accounts, the criteria that define a good-fit company and the competitors its sales team cares about. MarketSizer then helps Sam prioritise those companies around Gallabox’s own sales criteria, while showing relevant competitor signals and surfacing the people his team should reach. A recent Gallabox list contained 10,466 domains. Instead of researching every company from scratch, Sam can work through a prioritised set of accounts and validate which ones are genuinely worth pursuing. s draft. The quote it introduces is // gated until Sam approves it, and a sentence ending in a colon with // nothing after it reads as a page that has broken. A full stop is // correct in both states. The result then moves into the sales workflow Gallabox already uses. Sam assigns selected accounts to individual reps in Zoho, including routing prospects based on territory and language. Sam has also started using MarketSizer when an inbound lead would otherwise be difficult to qualify. When his team cannot reach a prospect, he can look at the software already present at that company and use that context to decide whether the account is still worth pursuing. Gallabox initially brought MarketSizer into its new-business sales process. Sam’s next use case is retention. The aim is to bring the same competitive intelligence into the customer-facing team, so Gallabox can identify when an existing customer begins exploring alternative providers and give the team an earlier opportunity to respond. That extends the same idea across the customer lifecycle: knowing where to focus attention before the team spends time acting. Quotes: - "In the UAE we know whom we should pitch, but we do not have the contact information. In India we can get the contact information, but it is hard to know whom we should pitch to." - Yogesh Narayanan, Co-founder and Head of Technology, Gallabox - "When my inbound team says a prospect is unreachable, I go to MarketSizer, see what software they are already using, and qualify them from there." - Gajendra Boopathi, AVP Global Sales, Gallabox ## Case study: Moonside (/customers/moonside) How Moonside builds a focused account list without starting from scratch The challenge How Moonside uses MarketSizer What Moonside can do now Asking Iggy instead of building a query Build a focused account list Find the people behind the accounts Continue the work inside the CRM Return without rebuilding Moonside Customer Story | MarketSizer See companies matching Moonside’s criteria without researching the market one company at a time. Relevant contacts are surfaced alongside each company, so Rens can start with someone to approach. Move accounts and contacts into Moonside’s existing CRM, where the team already manages its sales activity. Once the campaign criteria are configured, Rens can return to the same market view without starting again. Moonside Consulting & Services Technology consulting and Odoo implementation Netherlands Rens van Gool, Business Development & AE Manager Finding relevant accounts, surfacing contacts and moving them into the team’s CRM How a European technology consultancy builds a focused account list and finds the people behind it. Moonside Consulting & Services is a European technology consultancy with more than 140 professionals supporting clients across Europe. Rens van Gool runs business development for the Odoo side of the company. Finding relevant accounts previously meant piecing together information one company at a time. MarketSizer gives him a focused account list, surfaces the people behind each company and connects the work to the CRM his team already uses. Rens does not need a general list of businesses. Before MarketSizer, getting to a usable account list meant researching the market himself. General-purpose tools could surface individual companies, but building a complete view still required repeated searches and manual checking. He needed a way to identify relevant accounts without rebuilding the research for every campaign. Rens can start with the market he wants to explore rather than a blank page. MarketSizer shows companies matching Moonside’s criteria, ranked so the most relevant accounts surface first. The people worth approaching are shown alongside the companies, so an account does not arrive as another name to research. Once Rens has configured the criteria, he can return to the same campaign without rebuilding it. From there, the accounts and contacts can move into Moonside’s existing CRM. Ask Iggy is the MarketSizer assistant. Rens can ask questions in plain language and work from the same live data behind the rest of the platform. It gives him another way to explore accounts and campaigns without building every query manually. Rens has been trying it alongside his campaign work. Quotes: - "MarketSizer gives me the companies I want to reach and the people behind them, all in one place. It makes my life easier." - Rens van Gool, Business Development & AE Manager, Moonside - "I didn’t have to set it up again. It’s already done." - Rens van Gool, Business Development & AE Manager, Moonside # Blog ## Blog: How SaaS Teams Turn Competitive Intelligence Into Product Decisions Win/loss patterns and switching behaviour can shape the product roadmap, not just battlecards. How to separate real product gaps from sales and onboarding problems, and validate them before you build. In most SaaS companies, competitive intelligence belongs to sales. It feeds competitor monitoring, battlecards, pricing comparisons and objection handling. The same data can also shape product decisions. When prospects keep choosing another platform, or existing customers leave for the same reasons, the pattern may point to unmet needs, missing capabilities or weaknesses in the product experience. Not every competitive loss is a product problem, though. Sometimes the cause is pricing, onboarding, positioning, implementation or how the sale was run. The hard part is working out which signals reflect real product weaknesses, and confirming them before they reach the roadmap. Start With Buyer Decisions, Not Competitor Features A competitor's new dashboard, AI assistant or integration does not, on its own, create a product need. What matters is buyer behaviour: the capabilities customers actually base their decisions on. Product teams should look for behavioural patterns. Do prospects stall at the same stage of the buying process? Do customers give the same reason for cancelling? These patterns answer a more useful question than "What features does the competitor have? " They show what customers need to get done but cannot do well with your product. Take reporting. If ten lost deals cite a competitor's "better reports", copying that competitor's reporting is not necessarily the answer. Each buyer may have had a different reason for preferring it. The competitor's feature is only the visible part of the need. Define the job to be done before deciding what to build. Combine Win/Loss Data With Switching Evidence Win/loss analysis and churn analysis usually live in separate places. Sales operations reviews lost opportunities, customer success examines churn, marketing tracks competitors, and product teams look at usage and feature requests. Bringing that information together produces much stronger conclusions. For each competitive win, loss or switch, record: the market segment and use case whether a competitor was considered or selected the main reason for the decision the product capabilities mentioned the revenue won, lost or at stake related requests from current customers The aim is to find clusters of events rather than react to individual stories. Say 18 companies report that a product lacks a key CRM integration. At first glance, that looks like a simple feature request. A closer look might show that 15 of them are enterprise accounts that run core operations through the CRM, and that several chose a competitor specifically because it offered two-way sync. The finding changes from "customers want this integration" to something far more precise: a specific segment, workflow and revenue opportunity. Customers who have already switched away are a valuable source here. Interviews with them should cover what triggered the search for a replacement, which alternatives they considered, what they chose, and why. For more on reading competitive losses, see what win data actually shows about losing deals to competitors . Separate Product Gaps From Non-Product Problems Not every competitive loss is an engineering problem, and this distinction matters more than any other when applying competitive intelligence to product strategy. A prospect may say a competitor has a "better feature" when the real problem is that the sales team could not show what the product already does. Customers may also switch because implementation took longer than they expected, or because a competitor offered better service. Competitive feedback needs interpreting before it goes into a plan. A useful set of categories: capability, usability, integrations, performance, reliability, onboarding, pricing and packaging, implementation, positioning, and sales execution. For example, if customers keep saying a competitor was "simpler to implement", the instinct may be to invest in automating setup. Interviews might instead show that both platforms can do the same things, and the competitor simply provides better templates. In that case, the better response is a stronger activation process, not new functionality. Validate the Gap Before Changing the Roadmap Competitive intelligence should produce hypotheses, not automatic roadmap commitments. Before moving engineering resources, test each gap against five criteria. Frequency. Does the issue recur across win/loss interviews, churn feedback, support chats, sales notes and customer research? Segment fit. Does it affect your ideal customer profile, or only customers you are not targeting? Economic impact. How much potential ARR, retention and expansion revenue is tied to it? Behavioural evidence. Are people already building workarounds, abandoning workflows, resubmitting requests or using another tool for the job? Strategic fit. Would solving it strengthen the core product, or pull the product into a different market? The last question prevents some of the most expensive roadmap mistakes. A lightweight project management tool might lose many enterprise deals for lack of advanced resource planning. Building it could lift enterprise win rates, but it could also bring heavier implementations, longer sales cycles, more support demand and a more complex product for existing users. The gap is real. Filling it may still not make sense. Real-World Example: Lemon Learning Feeds One Win/Loss Programme Into Sales and Product Lemon Learning, a European digital adoption platform, set three goals for its win/loss programme: lift sales conversion, sharpen positioning against competitors, and align the product roadmap with what buyers actually wanted. According to a case study by Diffly , the company combined surveys sent to prospects after every opportunity, post-mortems from the sales team, and in-depth interviews on its most important deals. The measurable gains landed on the sales side. Diffly reports a 20% increase in conversion on deals in direct competition, which it credits to sharper battlecards, and a 15% increase in overall conversion. Lost deals that were re-engaged using loss-reason analysis converted 30% better. On the product side, Diffly says the interview feedback "informed development priorities" and led to features inspired by prospect and customer needs, though the case study does not say which. That gap is the useful lesson: the sales impact of win/loss research is easy to measure, while the product impact usually goes unrecorded. If you want competitive intelligence to influence the roadmap, log which findings led to which build decisions, so the product impact can be counted as well. Test Demand Before Making a Major Build Even with good evidence of a real product gap, development does not have to start with a full build. Prototypes, beta programmes, limited integrations or a manually delivered version of the feature can all test the hypothesis first. For example, show a prototype to customers who recently switched to a competitor and ask whether it would have changed their decision. That question tells you far more than asking whether they are interested in the feature. Usage data from beta users adds another check: whether the feature correlates with activation or retention, and whether target customers actually adopt it. Together, these steps reduce the risk of building functionality that customers mention but never use. Build a Product Intelligence Loop Competitive intelligence is most useful when it runs continuously rather than appearing in the occasional presentation. Sales contributes objections and competitive losses. Customer success adds churn reasons, recurring pain points and the workarounds customers have built. Marketing tracks competitor positioning and shifting market expectations. Product combines all of this with behavioural analytics, interviews, strategic goals and technical constraints. A regular review of these inputs should produce three outputs: new competitive patterns hypotheses that need validation confirmed gaps that could influence the roadmap Timing matters too. Switching evidence is most useful while a decision is still open, which is why some teams also track when accounts start competitor trials and time winback outreach around competitor renewals . From Market Signal to Product Evidence SaaS companies can collect huge volumes of competitive information, but more information does not automatically produce better products. Some of the strongest signals come from purchase decisions themselves: what buyers chose, rejected and replaced, and why. Win/loss patterns and switching behaviour are especially valuable because they capture the people regular customer surveys miss: those who decided not to buy, and those who decided not to stay. Those signals still need validating. A product team has to establish whether a pattern affects the right segment, whether addressing it could bring meaningful revenue, whether real user behaviour reflects it, and whether the solution fits the company's strategy. Once that process is in place, competitive intelligence stops being material for sales battlecards alone. The goal is not to build what competitors already have, but to understand why customers choose them, find the underlying need, and turn that evidence into better product decisions. Frequently Asked Questions How can competitive intelligence be used beyond sales? Competitive intelligence can inform product decisions as well as sales. Win/loss patterns, switching behaviour and churn reasons show which capabilities buyers base their decisions on. Product teams can use those patterns to find unmet needs, prioritise the roadmap and avoid building features that customers mention but would not actually use. How do you know if a competitive loss is a product problem? Classify the loss before acting on it. Many losses that sound like product gaps are really pricing, onboarding, implementation, positioning or sales execution problems. Interview buyers who chose a competitor, ask what triggered their search and why they chose the alternative, and check whether your product could already do what they needed. What should you check before adding a competitive gap to the roadmap? Test it against five criteria: frequency across interviews, churn feedback and sales notes; segment fit with your ideal customer profile; economic impact on ARR, retention and expansion; behavioural evidence such as workarounds; and strategic fit with the product's direction. A gap that fails on strategic fit may be real but still not worth filling. Should you build a feature because a competitor has it? Not on that basis alone. A competitor's feature shows what one vendor chose to build, not what your buyers need. Define the job the customer is trying to get done, check how often and in which segments the need appears, and test demand with a prototype or beta before committing engineering time. How do you test demand for a feature before building it? Use prototypes, beta programmes, limited integrations or a manually delivered version of the feature. Show the prototype to customers who recently switched to a competitor and ask whether it would have changed their decision. Then check beta usage data for adoption by target customers and any link to activation or retention. Yuliya Melnik is a technical writer at Cleveroad , a software development company. She focuses on turning complex technical topics into clear, practical content that helps readers understand ideas quickly and apply them with confidence. ## Blog: Buying Signals vs Intent Data: What Software Activity Actually Tells You Behavioural intent and software signals both start from observations. What six common signals can and cannot tell you, how freshness and detection confidence change the reading, and why the signal should shape outreach rather than be quoted back. Intent data infers interest from research behaviour, such as content engagement and topic surges. Software signals record detectable changes in an account's technology environment, such as a new vendor footprint or a tool disappearing. Both start from observations. What separates them is how closely the observed activity relates to the buying decision you care about, and how much still has to be inferred. Buying processes can leave observable software evidence: a new vendor footprint, a tool disappearing, or a change in the products an account uses. Those events can give sales teams a more specific starting point than a general increase in research activity. They still do not tell you what the buyer intends to do next. Picture two accounts marked "high intent" in your CRM. People at the first have read three articles about support software. At the second, a competing support tool was first detected nine days ago. Both observations can be dated. Neither proves a purchase is coming. But they support different conversations. One suggests interest in a topic. The other suggests a specific tool may be under evaluation or already being deployed. This guide separates what was observed, what it reasonably suggests, what remains unknown and what to do next. It also explains how freshness and detection confidence change the reading. If the wider idea is new to you, start with Timing Intelligence 101 . Separate what you observed from what you inferred. Every signal has two parts: the observation and the inference built on it. Mixing them up is how a reasonable signal turns into a wrong message. Behavioural intent also starts from observations, such as research activity at an account. The inference is what that activity means for buying. One practical difference is that you can inspect first-party engagement on your own site, while a third-party topic surge is usually built from activity elsewhere that you cannot see. A software signal starts from a different observation, a detectable change in the account's technology environment. The inference there is what that change means for the decision. Behavioural signals show research or engagement. Software signals show detectable changes in an account's technology environment. Neither category has a fixed level of buying relevance. A single article view and repeated pricing-page visits from known stakeholders are different signals. So are a newly detected free widget and a possible evaluation of a competing product. Judge each signal by what was observed, how closely it relates to your offer, and what remains unknown. For the definitions in full, see Purchase Intent vs Intent Data . The practical question here is different: given a signal, what can you responsibly conclude? What each signal can and cannot tell you Every signal supports a limited conclusion. The skill is knowing where it stops. The table below sets out six common signal types. The first column separates what was observed from what was inferred or derived. Signal and basis What it reasonably suggests What it cannot establish Appropriate next action Behavioural intent Observed: topic surge or anonymous visits at the account. Inferred: that the topic relates to a buying decision. Someone at the account is interested in the topic Budget, a buying window, who is researching, or whether it is a buyer, a competitor or a student Do not treat topic interest alone as proof of buying readiness. Assess the activity alongside account context, and use it to build a watchlist or nurture audience. Repeat first-party engagement Observed: known stakeholders returning to your pricing or implementation pages. Inferred: that they are assessing your offer. People at the account are looking closely at how your product would be bought or rolled out Whether they are evaluating, comparing, validating a choice already made or researching for someone else, and how far along the decision is Review promptly. If fit and context support it, reach out about the question they seem to be working on, without quoting the visits. New software installation / possible trial Observed: a new vendor installation on the account's domain in the past 30 days. Inferred: a possible trial. A relevant tool may be under evaluation or being deployed That it is a contractual trial, who owns it, how it is going, or that it is being tested against you Prioritise by ICP fit, then reach out within days with a problem-led message. Software change Observed: a new vendor appears and a previous one is removed. Inferred: a replacement or consolidation. The account may have replaced or consolidated tools Why they switched, whether they are happy, whether it covers the whole company, or their procurement status If they left you, investigate the loss and assess whether winback is viable, using the new vendor's estimated renewal as one planning input. If they left a rival, look for the adjacent need. Renewal timing Derived: an estimated window, worked out from typical contract cycles for the vendor. Not observed directly. A natural decision point may be approaching The real contract end date, the term, the notice period or how satisfied they are Share something useful early. Ask about timing in discovery. Never state the date as fact. Churn signal Observed: a previously stable vendor footprint has recently disappeared. Inferred: the tool may have been removed. A tool may have been switched off, so a need may be open That it was cancelled rather than replaced or rebuilt, or whether a successor exists Confirm with a second signal before outreach. For your own customers, start a check-in, not a pitch. Two wording choices are deliberate. A new installation is not a confirmed trial, and an estimated renewal window is not a contract date. Behavioural intent Behavioural intent describes attention, not action. The reader may be evaluating a purchase, writing a report or learning the topic for a new role. Treat it as a reason to pay attention, not as proof of buying readiness. Repeat first-party engagement Repeated visits to your pricing or implementation pages by people you can identify sit closer to your offer than a single article view, and you can inspect them yourself. They can still mislead: the visitor may be checking a decision already made or researching for someone else. Treat a pattern as a prompt to review the account, not a verdict. A new software installation (possible trial) A new vendor installation suggests that a tool in your category may be under evaluation or being deployed. Be careful with the word "trial". An installation is what was observed. Whether it is a formal trial, a quick test by one team or a rollout already agreed is something only a conversation will tell you. A newly detected free widget is also not the same as a competing product. For how these signals are found, see how to detect when a company starts a competitor trial . A software change A software change combines two events: something new appears and something old goes. That suggests the account may have replaced or consolidated tools, not that anyone made a formal decision. If the account moved away from you, investigate the loss before planning a winback, and treat the new vendor's estimated renewal as one input, as in B2B winback on competitor renewal timing . If it moved away from someone else, an adjacent need may be the better angle. Renewal timing Renewal timing is an estimate worked out from typical contract cycles, so it can be wrong in either direction. An account on a multi-year deal may look due when it is not, and a monthly plan has no meaningful window at all. Used well, it tells you when to start being useful, in the stretch before a renewal when switching costs are likely to be lowest. Used badly, it becomes a rep saying "I know your contract ends in March". The warm account play shows how renewal windows and competitor activity work together. A churn signal A churn signal suggests a tool may have been removed and a need may now be unmet. It is also the signal most likely to mislead. A footprint can disappear because a site was rebuilt, a tool was replaced, or the detection missed it on the day, so treat it as a prompt to look for a second signal, not a trigger. For your own customers, the right response is a check-in from customer success, not a sales message. How freshness changes the reading The same signal means different things depending on how old it is. Three things are easy to mix up: how often detection refreshes, how long a signal takes to reach you, and how old the underlying event is. MarketSizer refreshes detection daily, and company lookups are re-checked live when you ask. New trial signals typically reach your CRM within about 5 days of the real-world event, which is an estimate, not a guarantee. Those are refresh frequency and delivery delay. Event age is the third, and it is the one you have to judge. A fresh lookup confirms the footprint that is detectable today. It does not confirm what the account currently intends to do. MarketSizer uses a 30-day classification window for newly observed installations. This is a signal classification, not confirmation of the vendor's contractual trial period or the buyer's evaluation deadline. A signal that persists beyond 30 days is re-classified as an active subscription. First detected also means first observed by the detection system. It is not the exact installation date. Time since first detected How to read it What to do Early in the window The detection is recent. Evaluation or deployment may be under way. If fit supports it, reach out quickly with a message about the problem, not the product. Mid-window Evaluation is plausible but not confirmed. The installation could be a test or a rollout. Check whether the activity persists and whether other evidence supports outreach. Late in the window The signal is ageing. Reassess rather than assuming urgency. Check for a second signal before investing time. Keep the message light. Past the window A persistent signal is re-classified as an active subscription, so it is no longer flagged as a trial. Read it as a tool now in the account's stack. Look for other evidence before outreach, or move the account back to a watchlist. Put an age next to every signal in your CRM, and write down what age is too old for each type. Without that, a stale signal looks as urgent as a fresh one. How detection confidence changes the reading A signal is only as useful as your confidence that it is real. Observed evidence can be wrong. A footprint can be missed, misread or attributed to the wrong product, and that is true of every detection method. MarketSizer grades each vendor signal for confidence, alongside when it was first detected and when it was last seen. Detection confidence measures how strongly the observed footprint is supported. It does not measure the probability that the account will buy from you. A strong grade can mean you are confident the tool is present. It cannot tell you the company is shopping, dissatisfied or ready for your offer. A stronger grade is a reason to take the signal seriously. A weaker grade is better treated as a prompt to look for a second signal first. Freshness and confidence work together, and a reliable signal still has to earn its place against the account priorities your reps already have. Fresh signal Ageing signal Stronger grade Prioritise for review. Reach out when relevance and account context support it. Review, and check whether a second signal confirms the account is still active. Weaker grade Watch closely. Look for a second signal before outreach. Hold. Revisit only if something new appears. Let the signal shape the outreach. Be careful about quoting it back. The signal should help decide who you contact, when you contact them and which problem you raise. Avoid presenting inferred or uncertain software activity as something you know about the prospect. Reference an event directly when it is public, relevant and accurately described. Quoting an uncertain observation back at a prospect rarely lands the way it feels from the sender's side. "I noticed you started a trial last week" reads as being watched. It also commits the rep to a claim that may be wrong, because an installation is not a confirmed trial, and it hands the prospect a reason to wonder how you know. A public product launch, or an evaluation the prospect has told you about, is different. Those you can reference directly. Compare two openings for the same signal. Quoting it back: "Hi Sam, I saw you started testing a new tool for support last week. Want to see how we compare? " Treating it as a hypothesis: "Hi Sam, a lot of support teams we speak to hit the same snag with reporting once ticket volume grows. I do not know if that applies to you, so I would rather ask: is it something you are dealing with, or is something else on your list this quarter? " The second message makes no claim about what you saw, and it treats the problem as a guess rather than a fact. If you know something specific about the account, from a call, a job post or their own site, use that instead. This is the idea behind From Signal to Trust : a signal is not permission to sell. It is a reason to be useful at a moment when usefulness is more likely to be welcome. It is also why signal volume alone did not fix outreach, as covered in why intent data made B2B outreach worse . Where behavioural intent still earns its place None of this makes behavioural intent useless, or software signals a shortcut. Behavioural signals are a good filter and a good way to build audiences. How much weight they deserve depends on what was observed, and a single article view and repeated pricing-page visits are very different. If a topic surge shows interest from accounts that already fit your ICP, put them in a nurture programme or on a watchlist, and look for account context before sales outreach. When a software signal appears on the same account later, you have both: an account that was warming up and a dated event that says something has changed. What to do this week You do not need a new process to start. Three small changes are enough. Pick one signal you can describe precisely. Write down what was observed, what you infer and what remains unknown, then route it to a named owner. Set a freshness rule. Decide how old a signal can be before it stops triggering outreach, and show its age wherever reps see it. Rewrite one template. Take one outreach message and check each line that mentions an observation. Keep it only if it is public, relevant and accurately described. Otherwise, express it as a hypothesis about the problem. When you are ready to rank accounts across all of this, how to prioritise SDR outreach with purchase intent covers the full priority stack. See what is detectable on a real account Pick one of your target accounts and look it up. Company lookups are re-checked live, so you see what is detectable today before you decide anything. Look up a target account → Frequently Asked Questions What is the difference between buying signals and intent data? Intent data infers interest from research behaviour, such as content engagement and topic surges. A buying signal is any observable event that may raise the likelihood of a purchase, such as a new tool appearing, a renewal approaching or repeat pricing-page visits. Intent data is one type of signal. Both start from observations, and both need interpreting. Is a competitor trial a buying signal or intent data? It can be both. A confirmed competitor trial provides evidence of evaluation. A newly detected installation is an observation that may suggest a trial, but does not confirm one. MarketSizer classifies a new installation as a possible trial for 30 days, and it still needs interpreting before you act. Can a buying signal be wrong? Yes. Detection can miss a tool, misread one or attribute it to the wrong product, and a real event can mean something different from what you assumed. That is why signals carry a date and a confidence grade, and why a weaker signal should be confirmed with a second one before outreach. How fresh does a buying signal need to be to act on? The fresher the better, and the right limit depends on the signal. A recent detection may deserve prompt review, but age alone does not establish whether an evaluation is under way. First detected is when the system first observed it, not the installation date. Set a maximum age for each signal type and show it to reps. Should I mention the signal in my outreach? Be careful. Use the signal to decide who to contact, when and about which problem, and avoid presenting uncertain software activity as something you know. Reference an event directly only when it is public, relevant and accurately described. Otherwise, write about the problem the signal implies, as a hypothesis. Are renewal dates a reliable buying signal? Treat them as estimates. A renewal window is worked out from typical contract cycles, not read from a contract, so it can be wrong. It is useful for deciding when to start being helpful. It is not something to quote to a prospect as fact. Does a churn signal mean the customer cancelled? Not necessarily. A churn signal means a previously stable footprint has recently disappeared. The tool may have been cancelled, replaced, rebuilt or simply missed by detection. Confirm with a second signal before acting, and for your own customers, start with a check-in rather than a pitch. Do I still need intent data if I use buying signals? It depends on the job. Intent data remains useful as a filter and for building audiences. Software signals can give a more specific starting point for outreach, but neither type is automatically more relevant. Many teams use both, and judge each signal by what was observed and how closely it relates to their offer. Recommended reading Timing Intelligence 101 - the pillar guide to acting on buying windows. Purchase Intent vs Intent Data - the definitions and why content engagement is not a buying signal. How to Detect When a Company Starts a Competitor Trial - how trial signals are found and why the first weeks matter. The Winnable Account Is Warm, Not Hot - combining renewal windows and competitor activity. From Signal to Trust - why a signal is not permission to sell. Why Intent Data Made B2B Outreach Worse - what happens when inference drives outreach volume. How to Prioritise SDR Outreach with Purchase Intent - the priority stack that puts these signals to work. Purchase intent - how evaluation signals appear in MarketSizer. Our data - what MarketSizer observes and how it is graded. ## Blog: The Best Subscription Intelligence Tools, Compared Subscription intelligence is an emerging category: platforms that detect observable subscription events on target accounts. Here is what to look for, the tools in the category, and how it differs from aggregated intent. Subscription intelligence is a distinct category of B2B signal data: platforms that detect observable subscription events (trials, switches, renewals, churn) on target accounts' software stacks and attach each signal to the specific event that produced it. It sits alongside - but is different from - aggregated intent (Bombora, ZoomInfo, 6sense, HG Insights) and technographic snapshot tools (BuiltWith, Wappalyzer). Two vendors are core to the category: MarketSizer and Bloomberry. Several adjacent tools (BuiltWith, HG Insights) cover pieces of the picture without being event-based end-to-end. This post explains what to look for and how to choose. What is subscription intelligence? Subscription intelligence is a class of B2B signal platform that treats an account's software subscriptions as a first-class dataset. Where aggregated intent providers score topic-level research behaviour and technographic tools show what an account is running today, subscription intelligence records what happens to those subscriptions over time - and fires on the specific events that matter for GTM outreach. The signal categories a subscription-intelligence tool tracks: Trial detection. An account starts a trial of a specific vendor. The fourteen-to-thirty-day window that follows is where competitive-displacement outreach lands or misses (see how to detect competitor trials for the mechanics). Vendor switch. An account replaces one vendor with another in a specific category. The event includes both sides - what left, what arrived, and when. Churn. An account removes a vendor without a replacement, or removes and does not backfill within a defined window. Renewal window. Contract or subscription patterns that suggest an account is approaching a renewal decision - the time-shifted head-start for a competitive pitch. Expansion. Deeper adoption of a vendor already in place - additional modules, higher tier, more seats. The connecting principle across all five: each signal is an event , not a score . A rep can inspect the underlying evidence and reference it directly in outreach. See the full definition for the category primer. How subscription intelligence differs from aggregated intent Three practical differences shape which category fits which team. Signal shape. Aggregated intent (Bombora, ZoomInfo intent, HG Insights intent, 6sense) produces a topic-level score derived from web-behaviour signals. Subscription intelligence produces a specific event on a specific account. Latency. Aggregated intent typically refreshes weekly on topic-level rescoring plus days-to-weeks on the underlying partner-network signals - end-to-end freshness lags real behaviour by 7 to 21 days per Forrester's intent-provider research . Subscription-intelligence signals fire when the underlying event occurs, closer to real time on well-covered vendors. Workflow surface. Aggregated intent is marketing-owned and typically delivered to marketing automation or an ABM orchestration surface. Subscription intelligence is sales-owned and delivered directly into the rep's workflow (CRM record, Chrome extension inside HubSpot / Salesforce / LinkedIn / Apollo). What to look for in a subscription-intelligence tool Six evaluation criteria worth using as a checklist. Event granularity. Does the tool distinguish trial from install, and churn from switch? Some tools flatten these into a single "tech-stack change" event, which loses the timing precision that makes subscription intelligence valuable in the first place. Historical depth per vendor. A signal is more useful when the tool has enough history on the vendor to distinguish baseline noise from a real event. Look for at least two years of subscription history per tracked vendor. Latency. Trial-window outreach fails if the signal fires after the trial ends. Look for daily-refresh detection cadence at minimum on the vendors that matter most to your motion. Coverage shape. Category depth vs product breadth is the real trade-off. Deep coverage in the categories you sell into beats shallow coverage across thousands of unrelated products. Rep workflow. Signals delivered to the surface reps actually use (CRM record, Chrome extension) matter more than a beautiful standalone dashboard. Adoption collapses in tools that require a new login. Signal explainability. A rep should be able to see what event produced the signal, when it fired, and (where possible) which vendor is involved. Black-box scoring reads well on a slide and burns rep trust in the first month of use. See subscription intelligence live on your ICP Free trial, no card required. We'll pull live trial, switch, renewal and churn signals on 25 to 50 real accounts in your target market. Book a demo → See all comparisons → The subscription-intelligence tools in detail MarketSizer Best known for: the full subscription-lifecycle signal set (trial, switch, renewal, churn, expansion) across 200+ vendors in Customer Support, Live Chat and adjacent SaaS categories, with deep subscription history per account. Signal shape: event-based. Each account signal ties to a specific subscription event with the underlying evidence a rep can reference. Coverage: 70M+ historical subscription records, 17M+ active subscriptions, 2. 5M+ active trials, 140+ countries. Deep per-vendor rather than broad across many unrelated products. Workflow: Chrome extension (SignalOps) opens a side panel alongside HubSpot, Salesforce, Zoho, Pipedrive, LinkedIn Sales Navigator, LinkedIn company pages and Apollo. Native CRM sync across the four major CRMs. Pricing: credit-based, from €480 per month on annual billing (per the pricing page ). No per-seat multiplier. Free trial available. Best for: GTM teams selling into Customer Support, Live Chat, ITSM, and adjacent SaaS categories where the buying signal is what the target account owns and evaluates. Bloomberry Best known for: real-time tech-stack install and removal signals across roughly 1,200 tracked products. The closest direct competitor to MarketSizer in the evidence-based signal category. Signal shape: event-based on installs and removals; less depth on distinguishing trial state from paid installation or on renewal-window timing. Coverage: broad across many product categories, shallower per-account historical depth than MarketSizer. Workflow: Chrome extension and CSV export; CRM integrations for the major stacks. Pricing: starter packages around $3,000 per year (public research). Demo-led onboarding. Best for: teams whose motion is install-triggered outreach - "tell me when this account installs X" - across a wide product surface. Full comparison at MarketSizer vs Bloomberry . BuiltWith and HG Insights - adjacent but different Two tools sit adjacent to the subscription-intelligence category without being event-based end-to-end. BuiltWith is a technographic snapshot tool: it tells you what an account is running right now across roughly 58,000 web technologies, and it retains an install-history archive. Where it stops short of subscription intelligence: it does not distinguish trial state from paid subscription, does not fire event-based alerts on switches or churn, and does not track renewal windows. Useful as a lookup layer, weaker as an ongoing signal feed. Full breakdown at MarketSizer vs BuiltWith . HG Insights covers a wider technographic and spend surface (roughly 22,000 technologies plus IT-spend and contract estimates), delivered as an enterprise data-fabric alongside an aggregated buyer-intent overlay. The intent layer is score-based, not event-based - HG's core signal is not the subscription event itself. Useful as a planning input; less useful as an execution signal. Full breakdown at MarketSizer vs HG Insights . Both are legitimate tools with real value. Neither is a substitute for a subscription-intelligence layer if the deciding signal for your motion is a specific subscription event on a specific account. How to choose Three honest questions to work through. Which specific subscription events matter to your motion? Trial detection unlocks fourteen-to-thirty-day competitive-displacement plays. Renewal-window timing unlocks warm-account outreach. Churn detection unlocks winback. If your motion depends on all of them, category depth (MarketSizer) matters more than breadth (Bloomberry). If your motion is only install-triggered across many product categories, breadth wins. Where do reps actually work? Signals that surface beside the CRM and browsing surface reps already use adopt better than signals that live in a new dashboard. Chrome extension coverage is a genuine differentiator here. What is the vendor coverage against your ICP? Ask each tool for its list of tracked vendors and cross-reference against the accounts on your target list. A tool with 22,000 technologies but weak coverage on the ten vendors your ICP actually runs is a worse fit than a tool with 200+ vendors that covers all ten deeply. Frequently asked questions What is subscription intelligence? Subscription intelligence is a category of B2B signal data that treats software subscriptions as a first-class dataset. The term itself is still settling - we use it because it describes the data, not because an analyst firm has blessed it yet. Tools in this category detect observable subscription events - trials, switches, renewals, churn, expansion - on target accounts' software stacks and deliver each signal as an event a rep can reference. See the full definition for more. How is subscription intelligence different from intent data? Intent data is aggregated, inferred and topic-level - a probability that research is happening. Subscription intelligence is event-based - a record that something specific happened on a specific account. Different mechanisms, different confidence shapes. Who are the best subscription-intelligence tools? The two core vendors are MarketSizer (deep per-category subscription-lifecycle coverage) and Bloomberry (broad tech-install signal coverage). BuiltWith and HG Insights sit adjacent to the category with partial coverage. Can subscription intelligence replace intent data? For teams whose deciding signal is a specific account event (trial, switch, renewal, churn), yes. For teams running structured ABM programmes on aggregated topic scores, no - the two answer different questions and often coexist in a mature stack. What does subscription intelligence cost? MarketSizer starts at €480 per month on annual billing (Growth entry-band). Bloomberry starter packages come in around $3,000 per year per public research. Enterprise tiers of both platforms scale with volume and vendor coverage. Does subscription intelligence work for outbound SDRs? Yes. The whole category is designed around rep-facing execution rather than marketing-owned orchestration. Signals fire into CRM records and Chrome-extension side panels alongside the tools reps already use. Sources MarketSizer - What is subscription intelligence - the category primer. Forrester - How to evaluate intent data providers - background on refresh cadence and false-positive rates for the aggregated-intent category. Bloomberry homepage , BuiltWith homepage , HG Insights homepage - positioning language and public product surfaces. Bloomberry pricing figure sourced from public sales-battlecard research. Neither Bloomberry nor HG Insights publish list pricing publicly. MarketSizer platform capabilities and data-coverage claims - internal Claim Registry , sourced from live production tables and refreshed quarterly. Available on request via a demo . Recommended reading What is subscription intelligence? - category primer. How to detect competitor trials - the four detection methods, ranked by fidelity. What replaces intent data in 2026? - why event-based signals are displacing aggregated intent. All /compare pages - side-by-side breakdowns across the whole cluster. ## Blog: The Best Trial Detection Tools for B2B GTM Teams Trial detection means firing a signal within days of a target account starting a specific competitor trial. Here are the tools that do it, how the detection methods rank, and how to evaluate a trial-detection feed. The best trial-detection tools are those that catch the trial while it is still running, produce inspectable evidence per signal, and deliver the fire directly into the workflow reps use. On those criteria, two tools own the category: MarketSizer (full subscription-lifecycle detection with deep per-vendor history across 200+ vendors) and Bloomberry (broad tech-install and removal detection across ~1,200 products). BuiltWith and Wappalyzer are technographic lookup tools rather than detection tools - useful for verification, weaker as ongoing trial-detection feeds. Review-site signals (G2, TrustRadius) are useful corroborators but too slow to be a primary trigger. Homegrown scripts work at small scale and become the wrong answer once outbound volume grows past a handful of target accounts. What trial detection actually is Trial detection is the specific job of firing a signal when a target account starts using a specific vendor's product on a trial basis - within days of the trial starting, ideally on day one or two. It is the highest-fidelity purchase intent signal available in B2B SaaS, because the account has already committed effort rather than attention. The full mechanics are in how to detect competitor trials ; the short version is that most B2B SaaS products leave the observable footprint a product leaves on the trialist's public web presence, which a well-designed detection tool can monitor at scale. Two distinctions matter for evaluating tools in this category: Trial detection vs installed-customer detection. Some technical signatures appear on day one of a trial; others only appear once installation is complete. Trial detection uses the first class; installed-customer detection uses both. Only the first lands you inside the buying window. Trial detection vs technographic snapshot. A snapshot tool tells you what an account is running right now. A detection tool fires when that state changes. Same underlying data, very different job. The four detection methods, ranked Detailed in how to detect competitor trials . Quick recap in order of fidelity: Technical signature monitoring. Daily-refresh monitoring of target accounts for a vendor's product footprint. Fires early in most trials. The core method behind modern subscription-intelligence platforms. Review-site activity. G2, TrustRadius, Capterra activity from account employees - useful as a corroborator, weak as a primary trigger due to low coverage (most companies do not write reviews) and lagged latency. Job-posting inference. Roles that name a competitor tool as a required skill. Ambiguous signal, long latency, useful for account qualification rather than trial detection. Social mention scanning. LinkedIn and social engagement with a competitor's content. Directionally useful, heavily noise-prone. Nurture-side input, not a prioritisation trigger. Practical stacks combine method 1 as the primary trigger with method 2 as a corroborator. Methods 3 and 4 sit in the nurture layer. The best trial-detection tools Each tool below carries a short "best known for", the method it uses, and the coverage shape. MarketSizer - full subscription-lifecycle detection Method: daily-refresh technical signature monitoring across the full subscription lifecycle - trial, switch, renewal, churn, expansion - not just install and removal. Coverage: 200+ vendors deep across Customer Support, Live Chat, ITSM and adjacent SaaS categories. Deep subscription history per account. 70M+ historical subscription records, 17M+ active subscriptions, 2. 5M+ active trials at any given time. Delivery: Chrome extension side panel alongside HubSpot, Salesforce, Zoho, Pipedrive, LinkedIn Sales Navigator, LinkedIn company pages and Apollo. Native CRM sync across the four major CRMs. Native app integrations with Lemlist, Instantly, Apollo, Slack. Pricing: credit-based from €480 per month on annual billing. Best for: teams whose motion depends on the fourteen-to-thirty-day trial window and on distinguishing trial from paid installation and from churn. Bloomberry - broad install-signal coverage Method: tech-stack install and removal event detection across a broad product surface. Coverage: roughly 1,200 tracked products across many technology categories. Broader than MarketSizer on product breadth; shallower per account on historical depth and subscription-lifecycle distinctions. Delivery: Chrome extension and CSV export; CRM integrations for the major stacks. Pricing: starter packages around $3,000 per year (public research). Best for: teams whose motion is install-triggered across many product categories where the specific distinction between trial and paid install matters less than the raw event. Full comparison at MarketSizer vs Bloomberry . BuiltWith and Wappalyzer - technographic lookup, not detection Method: technographic snapshot - shows what a site is running today, plus historical archive of when a technology first appeared or was removed. Where they stop short as detection tools: neither distinguishes an active trial from a paid subscription, neither fires event-based alerts on the customer's own workflow surface, and neither tracks renewal windows or churn. Useful as verification layers - if MarketSizer says an account started trialling Zendesk, a BuiltWith lookup on the account's public infrastructure will typically confirm the signature is present. Coverage: BuiltWith covers roughly 58,000 web technologies; Wappalyzer covers roughly 7,400. Both broad. Pricing: BuiltWith starts around $295 per month; Wappalyzer has a free Chrome extension plus tiered paid plans on lookup volume. Best for: per-account verification, ad-hoc lookups and list-building by installed-tech filter. Weaker as an ongoing detection feed. Full breakdowns at MarketSizer vs BuiltWith and MarketSizer vs Wappalyzer . G2 and TrustRadius - the review-site corroborator Method: monitoring review activity and comparison behaviour on the review platforms themselves. Where they fit: useful as a corroborating signal (a review posted by a named employee of a target account is genuinely evidence of tool consideration or use), weak as a primary detection trigger because reviews typically lag installation by weeks and coverage across the whole target-account base is thin. Best used alongside: a technical-signature-monitoring tool as the primary trigger, with review-site activity treated as a confidence booster on individual accounts. Homegrown scripts - the option most teams outgrow Method: internal RevOps or engineering team writes a script that scrapes target-account domains for specific technographic signatures. Where it works: at very small scale (fewer than 100 target accounts, a handful of tracked competitors) with a technical team that can maintain the infrastructure. Genuinely a reasonable starting point for engineering-heavy teams testing whether trial detection matters to their motion. Where it breaks: at scale. Vendor signatures change (a new SDK version, a redirect, a subdomain migration) and homegrown scripts break silently. Coverage across dozens of competitors requires ongoing signature maintenance that becomes a distraction from GTM. Most teams that build this end up buying a subscription-intelligence tool after 6-12 months. See trial detection live on your ICP Free trial, no card required. We'll pull live trial signals on 25 to 50 real accounts in your target market and show you how the signal ties to the underlying subscription event. Book a demo → Read the detection guide → How to evaluate a trial-detection tool These are the questions that separate a detection tool from a lookup tool. Ask every vendor all of them. Which specific vendors do you cover, and how deeply? Ask for the tracked-vendor list. Cross-reference against the tools your ICP actually runs. Deep coverage on the ten vendors that matter beats shallow coverage across ten thousand. What is your detection latency? Daily-refresh scanning catches most trials within days. Weekly scanning routinely misses the trial window. Monthly scanning is retrospective, not a buying signal. How do you distinguish trial from paid installation? Some signatures appear on day one of a trial; others only appear once installation is complete. A tool that flattens these into a single "tech-stack change" event loses the timing precision that makes trial detection valuable. Where does the signal fire in the rep's workflow? CRM record, Chrome extension, standalone dashboard, email alert. Signals fired into the tools reps already use adopt better than signals that require a new login. What is the false-positive rate and how is it measured? No tool detects trials at 100% precision, and any vendor that quotes you a single headline accuracy figure is glossing over the fact that it varies by vendor and by coverage depth. A credible answer explains how each signal was detected, lets reps inspect the evidence behind it so they can weight each outreach accordingly, and is specific to the vendors you care about, not a blended number across the whole index. Frequently asked questions What is the best trial-detection tool? Depends on the category and coverage you need. MarketSizer covers Customer Support, Live Chat and adjacent SaaS categories deeply, with the full subscription lifecycle. Bloomberry covers a broader tech-install surface with less per-account depth. BuiltWith and Wappalyzer are technographic lookup tools, not detection tools. Can you detect competitor trials without a subscription-intelligence tool? At small scale, yes - a homegrown script that scrapes target-account domains for specific signatures can work for a handful of tracked vendors. At scale it becomes unmaintainable and most teams outgrow it within a year. How fast can trial detection fire? With daily-refresh technical signature monitoring on well-covered vendors, trials are typically detected within days of installation. See how to detect competitor trials for the mechanics. Do intent-data tools detect trials? Not directly. Aggregated intent providers (Bombora, ZoomInfo, HG Insights, 6sense) score topic-level research behaviour, which correlates with buying interest but does not fire on the specific event of a trial starting. See what replaces intent data in 2026 for the split between event-based and aggregated signals. How is trial detection different from technographic detection? Technographic detection tells you what an account is running today (snapshot). Trial detection fires when the account starts using a new tool (event). BuiltWith and Wappalyzer are technographic; MarketSizer and Bloomberry are trial-detection. Can Apollo detect competitor trials? No. Apollo intent covers hiring, funding, news and aggregated topic scores; none of those fire specifically when an account starts a competitor trial. See the full answer , or MarketSizer vs Apollo for how the two layer. What does trial-detection tooling cost? MarketSizer starts at €480 per month on annual billing. Bloomberry starter packages come in around $3,000 per year per public research. Both are self-serve-adjacent; enterprise tiers scale with volume and vendor coverage. Sources MarketSizer - How to detect competitor trials - detailed mechanics of the four detection methods. Bloomberry homepage , BuiltWith homepage , Wappalyzer homepage - positioning language and public product surfaces. Bloomberry pricing sourced from public sales-battlecard research; BuiltWith and Wappalyzer publish pricing publicly. MarketSizer platform capabilities and data-coverage claims - internal Claim Registry , sourced from live production tables and refreshed quarterly. Available on request via a demo . Recommended reading How to detect competitor trials - the pillar piece with detection-method mechanics. Best subscription-intelligence tools - the wider category context. What is subscription intelligence? - category primer. MarketSizer vs Bloomberry - side-by-side breakdown of the two core evidence-based signal tools. Does Apollo detect competitor trials? - focused Q&A on the specific question. ## Blog: The Best Bloomberry Alternatives & Competitors, Compared Bloomberry alternatives depend on which part of Bloomberry you are replacing - subscription-lifecycle depth, technographic snapshot breadth, enterprise data-fabric coverage, or company-event feeds. Honest comparison of the alternatives. The best Bloomberry alternative depends on which part of Bloomberry you are trying to replace. If it is the subscription-event detection with deeper per-vendor history and a fuller lifecycle (trial, switch, renewal, churn), look at MarketSizer. If it is the tech-install snapshot for ad-hoc lookups, look at BuiltWith or Wappalyzer. If it is enterprise technographic depth plus spend estimates, HG Insights is the fit. If it is broader company-event data via API (hiring, funding, exec change), PredictLeads sits in the adjacent slot. For very small target-account lists, homegrown scraping scripts genuinely work. Bloomberry is not weak on any of these axes - each alternative below is stronger than Bloomberry on one specific job and weaker on the others. What are the best Bloomberry alternatives? The best Bloomberry alternatives, by primary job to be done: Deeper per-vendor subscription lifecycle: MarketSizer. Ad-hoc technographic lookups (any site, any time): BuiltWith , Wappalyzer . Enterprise technographic breadth + spend estimates: HG Insights . Broader company-event feeds (hiring, funding, exec change) via API: PredictLeads . DIY at small scale: homegrown scraping scripts. Bloomberry itself is a real product in the evidence-based signal category - roughly 1,200 tracked products across many technology categories, real-time install and removal detection, well-priced for its coverage. The "alternative" you pick is really the axis where Bloomberry falls short for your specific motion. When you actually need a Bloomberry alternative The most common reason teams outgrow Bloomberry is lifecycle depth. Bloomberry is install-focused - it fires when a technology appears on a target-account site and again when it disappears. The distinction between an active trial and a paid installation is thinner, renewal-window timing is not a first-class signal, and churn detection is limited. If your motion depends on trial-window outreach (the fourteen-to-thirty-day window that decides competitive-displacement), MarketSizer's event granularity is a real upgrade. History cuts the same way: if you need the full sequence of what an account has evaluated, switched from, and renewed over the past several years, the shallower Bloomberry archive stops short. A different kind of team leaves for breadth rather than depth. Bloomberry is entirely about technographic events, so a motion that also triggers on hiring, funding, executive changes or news mentions is better served by PredictLeads or a similar company-events tool - a different signal category altogether. And some teams simply need the signal where their reps already work: Bloomberry has a Chrome extension and CRM integrations, but if your team lives inside HubSpot, Salesforce, LinkedIn Sales Navigator or Apollo, in-workflow depth matters - MarketSizer's SignalOps Chrome extension opens its side panel across all four surfaces. See event-based signals on 25-50 real accounts in your ICP Not looking to rip and replace Bloomberry - just want to see what a fuller subscription-lifecycle signal looks like alongside install detection? Free trial, no card required. Book a demo → MarketSizer vs Bloomberry → The alternatives in detail MarketSizer - deeper per-vendor subscription lifecycle Best known for: the full subscription-lifecycle signal set (trial, switch, renewal, churn, expansion) across 200+ vendors in Customer Support, Live Chat, ITSM and adjacent SaaS categories, with deep subscription history per account. Where it beats Bloomberry: deeper per-vendor coverage, distinction between trial and paid installation as first-class signal types, renewal-window and churn events, longer historical horizon per account, in-workflow Chrome extension across the tools reps actually use. Where Bloomberry still wins: product breadth - Bloomberry covers ~1,200 products across many technology categories where MarketSizer covers 200+ vendors deeply. If your ICP straddles categories MarketSizer does not cover, that breadth is a real reason to keep Bloomberry. Pricing: credit-based from €480 per month on annual billing. Free trial. Full comparison: MarketSizer vs Bloomberry . BuiltWith and Wappalyzer - technographic snapshot Best known for: per-site lookups showing what technologies any website is running today, plus a historical archive of when a technology first appeared or was removed. Where they beat Bloomberry: product breadth (BuiltWith covers ~58,000 web technologies, Wappalyzer ~7,400), free lookup tools for ad-hoc use, lower price for teams that only need occasional snapshot data. Where Bloomberry still wins: event-based alerting - BuiltWith and Wappalyzer are snapshot tools that show current state, not detection tools that fire when state changes. If you need an ongoing signal feed rather than an on-demand lookup, Bloomberry does more. Pricing: BuiltWith from $295 per month; Wappalyzer has a free Chrome extension plus paid tiers priced on lookup volume. Full comparisons: MarketSizer vs BuiltWith , MarketSizer vs Wappalyzer . HG Insights - enterprise technographic + spend Best known for: a broad enterprise data-fabric covering roughly 22,000 technologies plus IT-spend and contract-value estimates, sold to strategy, market-intelligence and enterprise ABM teams. Where it beats Bloomberry: enterprise data depth, spend and contract estimates, analyst-friendly reporting, alignment with enterprise procurement processes. Where Bloomberry still wins: real-time event detection (HG's signal shape is aggregated and planning-oriented rather than event-driven), self-serve entry price (HG's enterprise packages start in the tens of thousands per year). Best for: enterprise teams whose primary use case is territory design and TAM sizing rather than day-to-day rep prioritisation. Full comparison: MarketSizer vs HG Insights . Also see what is HG Insights intent data . PredictLeads - company-events API Best known for: a company-events API that surfaces business events (hiring, funding, product launches, expansions, executive changes) as structured signals for GTM teams. Where it beats Bloomberry: broader event categories (hiring, funding, exec change - not just tech installs), API-first delivery for teams building custom signal workflows, global coverage across companies of all sizes. Where Bloomberry still wins: tech-stack event depth - PredictLeads is not primarily a technographic tool, so its coverage of tech-install and removal events is thinner than a specialist evidence-based signal platform. Best for: data and RevOps teams that want raw event feeds via API to plug into their own scoring or enrichment logic. Complementary to Bloomberry rather than a direct replacement. Full comparison: MarketSizer vs PredictLeads . Homegrown scripts - the DIY option Best known for: an internal RevOps or engineering team writes a small scraper that checks a defined target-account list for specific technographic signatures on a schedule. Where it beats Bloomberry: zero recurring cost, full control over which vendors to track and how signals are shaped, works fine at very small scale. Where Bloomberry still wins: maintenance overhead. Vendor signatures change (new SDK versions, subdomain migrations, redirects) and homegrown scripts break silently. Coverage across dozens of competitors requires ongoing signature maintenance that becomes a distraction. Best for: engineering-heavy teams with fewer than 100 target accounts and a handful of tracked competitors, testing whether the signal category matters to their motion before buying a platform. Most teams that build this outgrow it within 6-12 months. See how to detect competitor trials for the mechanics of doing this well. How to choose Work through these in order. Which specific event granularity matters? If you need to distinguish trial from paid install and to fire on renewal windows and churn, MarketSizer's event depth is a real upgrade over Bloomberry. If you only need "installed X" and "removed X" events across a broad product surface, Bloomberry itself is a good fit and the alternative is really a matter of breadth vs depth. What is your ICP's software category shape? If your ICP concentrates in Customer Support, Live Chat, ITSM or adjacent SaaS, MarketSizer's deep per-vendor coverage matters. If it spans many product categories including web tech, ad tech and e-commerce infrastructure, Bloomberry's breadth or BuiltWith's technographic footprint fits better. Where does the signal need to fire? Chrome extension inside HubSpot, Salesforce, LinkedIn and Apollo - MarketSizer. Standalone dashboard or CSV workflow - Bloomberry. API into a custom pipeline - PredictLeads. Ad-hoc lookup - BuiltWith or Wappalyzer. Frequently asked questions What is the best Bloomberry alternative? Depends on which Bloomberry capability is weak for you. MarketSizer covers the deeper per-vendor subscription lifecycle. BuiltWith and Wappalyzer are the ad-hoc technographic lookup alternatives. HG Insights is the enterprise technographic + spend alternative. PredictLeads covers a broader company-event surface. Is MarketSizer a direct replacement for Bloomberry? For teams selling into the categories MarketSizer covers deeply (Customer Support, Live Chat, ITSM, adjacent SaaS), yes. For teams needing broad tech-install coverage across product categories MarketSizer does not track, Bloomberry stays in the stack. Which Bloomberry alternative is cheapest? Wappalyzer's free Chrome extension is the cheapest entry point in the whole list. For a real ongoing signal feed rather than snapshot lookups, MarketSizer's free trial is the cheapest way to validate signal quality before committing, with paid plans from €480 per month on annual billing. Which Bloomberry alternative is best for enterprise? HG Insights, for strategy and market-intelligence use cases. MarketSizer, for enterprise sales teams whose motion depends on subscription-event timing rather than territory planning. Can I use Bloomberry and MarketSizer together? Yes, though the overlap is meaningful. Some teams keep Bloomberry for broad tech-install coverage across many product categories and use MarketSizer for the deep subscription-lifecycle signals in the categories MarketSizer covers deeply. Do I need to switch off Bloomberry to try an alternative? No. Every alternative in this list runs alongside Bloomberry - MarketSizer's free trial lets you validate the signal quality on your ICP without touching the Bloomberry contract. Sources Bloomberry homepage - positioning language and product surface. BuiltWith homepage , Wappalyzer homepage , HG Insights homepage , PredictLeads homepage - positioning and public product surfaces. Bloomberry pricing figure (~$3,000/year starter) sourced from public sales-battlecard research; BuiltWith and Wappalyzer publish pricing publicly; HG Insights and PredictLeads pricing sourced from industry aggregators. MarketSizer platform capabilities and data-coverage claims - internal Claim Registry , sourced from live production tables and refreshed quarterly. Available on request via a demo . Recommended reading MarketSizer vs Bloomberry - side-by-side breakdown. Best subscription-intelligence tools - the wider category context. Best trial-detection tools - the sub-category. How to detect competitor trials - the detection-method mechanics. All comparisons - side-by-side breakdowns across the whole cluster. ## Blog: RB2B vs Warmly: Features, Pricing and How to Choose RB2B and Warmly both do visitor identification but solve different problems. RB2B is focused person-level ID on US traffic. Warmly wraps visitor-ID inside signal orchestration and automated outbound. Where each wins. RB2B and Warmly both target the visitor-identification job but solve different problems. RB2B focuses tightly on person-level de-anonymisation of US website visitors, using an identity graph it describes as opt-in based. Warmly wraps visitor identification inside a broader signal-orchestration platform that also handles aggregated intent, job-change alerts and automated outbound. If your gap is putting a name on anonymous US site traffic, RB2B is the sharper single-purpose fit. If your gap is composing multiple soft signals into automated outbound, Warmly does more of the workflow inside one platform. Neither answers the timing question of what target accounts are doing on their own software stack - a subscription-intelligence layer sits alongside both. What each tool actually is RB2B positions itself as the tool that shows who visits your website - names, not just companies . Its core capability is person-level visitor identification built on a US-ringfenced identity graph (RB2B describes it as opt-in based; it resolves non-US traffic to company level only). A snippet on the marketing site, a feed of identified visitors pushed to Slack, Teams or the CRM out the other end. Sharp, focused, cheap to start. Warmly positions itself around autonomous, signal-based revenue orchestration - AI agents that turn visitor and intent signals into warm outbound. Its core capability is composing visitor identification with third-party intent (including Bombora), job-change alerts, an AI chat agent and automated outbound orchestration into a single platform for sales-led SMB and mid-market teams. Both do visitor identification. The difference is what happens next. Where RB2B wins Four situations where RB2B is the sharper pick. Person-first identity. RB2B surfaces the specific individuals visiting your site - not just an account-level flag. Warmly identifies some individual visitors too (it cites roughly 15% person-level match on top of company identification), but company-level is its stronger layer; RB2B is built person-first for US traffic. US-only compliance simplicity. RB2B's US-ringfenced identity graph avoids most of the GDPR complications that other visitor-ID tools have to work around in Europe. For a US-only motion, this simplifies the legal review, though US state privacy laws (privacy-policy updates, opt-out handling) still fall on the customer. Sharp, focused product. RB2B does one thing well - visitor identification. No orchestration surface to configure, no aggregated intent scoring to interpret, no automated-sequencing engine to learn. That focus is a genuine advantage for teams whose gap is specifically identity. Genuinely self-serve pricing. RB2B has a free tier (company-level identification only since January 2026) plus monthly paid plans from $79 priced on identified-visitor credits; person-level identification requires a paid plan. Adoption path is a single script on your site, not a platform integration. Where Warmly wins Four situations where Warmly is the sharper pick. Composed signal surface. Warmly bundles visitor identification with third-party intent scores and job-change alerts into one composite priority feed. If your motion needs multiple soft signals blended into a single "call this account now" decision, Warmly does more of that composition inside the platform. Teams that would rather build that blend themselves usually reach for Clay instead - MarketSizer vs Clay covers where that trade lands. Automated outbound orchestration. Warmly can fire an outbound sequence off a signal automatically - no hand-off to a separate sequencing tool. RB2B stops at the identity signal; you take the identified visitor and route them through whatever outreach engine you already run. Sales-facing UX built for reps. Warmly's workflow is designed for a rep working the signal in the CRM, not for a marketing operations user configuring the flow. That fits sales-led mid-market motions where visitor-ID needs to activate outbound quickly. Broader geographic coverage. Warmly identifies companies globally, where RB2B's person-level graph is US-only (RB2B falls back to company-level identification abroad). Person-level match rates are strongest on US traffic for both. For teams selling into Europe or other regions, Warmly is the more natural pick. Signal source, side by side Signal type RB2B Warmly Person-level visitor identification Core capability - specific names, US only Native but partial - roughly 15% person-level match, strongest on US traffic Account-level visitor identification Rolls up from person-level Core capability Aggregated third-party intent Not the primary layer Included as one input signal Job-change alerts Not the primary layer Included Automated outbound orchestration Not included - hand off to another tool Included natively Chrome extension Not offered - Slack, Teams and CRM feeds instead Included for reps Geographic coverage Person-level US only; company-level elsewhere Global (company-level) The blunt version: RB2B does one thing well. Warmly does more things, less deeply on any single one. Visitor-ID answers who is on your site. Subscription intelligence answers who is in market. Whether you pick RB2B or Warmly, they only surface accounts that visited your site. MarketSizer surfaces accounts across the wider market that are trialling, switching, renewing or churning right now - even if they have not visited yet. Free trial. Book a demo → See all comparisons → Pricing and procurement The two tools have diverged here. RB2B stays genuinely self-serve: a free tier (company-level identification only since January 2026) and monthly plans from $79 to $199, priced on identified-visitor credits. Setup is a single script on the marketing site. No procurement cycle needed. Warmly keeps a limited free plan, but paid tiers now run on annual contracts starting around $700 to $900 per month (roughly $10,000 per year), priced on identified-visitor volume plus feature bundles (aggregated intent, job-change alerts, orchestration). Enterprise packages are demo-led and custom-priced. Neither tool sits in the ZoomInfo or 6sense procurement bracket, but only RB2B is still an impulse line item. Pricing on both sides has moved recently - the figures above were checked in August 2026; confirm against each vendor's live pricing page before budgeting. The decision tree Three axes usually decide it. Job to be done. Pure identity job - "put a name on my anonymous US traffic" - points to RB2B. Composite-signal job - "unify visitor-ID, intent, job change into one outbound orchestration" - points to Warmly. Geography. US-only motion, either. Europe or global motion, Warmly. Team shape. Marketing-owned inbound signal that gets handed to sales, either. Sales-owned motion that needs the signal to trigger automated outbound in the same platform, Warmly. Teams that end up with both usually keep RB2B for the deeper person-level identity on US traffic and use Warmly for everything else. That combination is real but only makes budget sense at mid-market or above. Where a subscription-intelligence layer fits alongside Both RB2B and Warmly are fundamentally inbound signal tools. They surface accounts that came to your site, or accounts that showed engagement on partner networks - the visitor-ID layer only fires on accounts that have visited. The gap: what about accounts that are in a buying window but have not visited your site yet? That is an opportunity discovery problem rather than an identification one, and answering it needs ICP scoring over the whole addressable market, not just the slice that turned up. A subscription-intelligence tool surfaces what target accounts are doing on their own software stack - starting trials, switching vendors, approaching renewal - regardless of whether they have engaged with your marketing. The practical pattern: RB2B or Warmly handles inbound-signal identification and activation; MarketSizer handles outbound-signal detection on target accounts across the wider market. The two layer in the rep's workflow via the CRM and Chrome extension. See MarketSizer vs RB2B and MarketSizer vs Warmly for the full breakdowns. Frequently asked questions Are RB2B and Warmly competitors? Partly. Both do visitor identification, but their surface areas diverge from there. RB2B is a focused person-level ID tool for US traffic; Warmly is a composite signal-and-orchestration platform. Teams that need only the identity job often prefer RB2B; teams that want signal composition plus automated outbound often prefer Warmly. Which one is better for GDPR-heavy markets? Warmly. RB2B's person-level graph is US-only by design (non-US traffic falls back to company-level identification). For European visitors, Warmly's company-level identification model is the more compatible option. Does RB2B replace Warmly? Only if the entire job you need done is US person-level visitor identification. If you also need aggregated intent, job-change alerts and automated outbound in the same platform, RB2B stops short. Warmly does more. Does Warmly replace RB2B? Not fully on the person-level identity axis. Warmly does identify some individuals natively, but at lower match rates; RB2B is built person-first for US traffic. Warmly even offers an RB2B integration to boost person-level coverage. Can I use both RB2B and Warmly together? Yes, at mid-market or above. RB2B for deeper person-level ID on US traffic, Warmly for the composed signal-and-orchestration surface. The overlap is meaningful; the combination is only budget-justified once volume scales. What does RB2B cost? A free tier (company-level identification only since January 2026) plus monthly plans from $79 to $199 priced on identified-visitor credits; person-level ID requires a paid plan. Published on the RB2B site. What does Warmly cost? A limited free plan plus annual-contract paid tiers starting around $700 to $900 per month, priced on identified-visitor volume and feature bundles. Published on the Warmly site. How does MarketSizer differ from both? MarketSizer is not a visitor-identification tool. It surfaces subscription events (trials, switches, renewals, churn) on target accounts across the wider market - not just accounts that have visited your site. Different job, complementary layer. Full breakdowns at MarketSizer vs RB2B and MarketSizer vs Warmly . Sources RB2B homepage - positioning language, identity graph, pricing model. RB2B docs - what is RB2B and how does it work - the vendor's own description of its identity graph and US ringfencing. RB2B docs - free plan - the January 2026 change to company-level-only resolution on the free tier. RB2B docs - plans side by side - paid tiers and credit volumes. RB2B docs - company-level identification - company-level fallback for non-US traffic. Warmly homepage - positioning language, platform composition. Warmly - Bombora buyer intent - Warmly's third-party intent sources. Warmly - Warmly vs RB2B - the vendor's own comparison (vendor perspective). ZoomInfo Pipeline - RB2B vs Warmly - third-party feature and pricing comparison. MarketBetter - Warmly pricing 2026 - independent breakdown of Warmly's current tier structure and annual-contract move. MarketSizer platform capabilities and data-coverage claims - internal Claim Registry , sourced from live production tables and refreshed quarterly. Available on request via a demo . Recommended reading MarketSizer vs RB2B - side-by-side breakdown. MarketSizer vs Warmly - side-by-side breakdown. Best subscription-intelligence tools - the outbound signal layer alongside visitor-ID. Best purchase-intent data platforms for 2026 - the wider landscape. ## Blog: Does Apollo Detect Competitor Trials? Short answer: no. Apollo intent covers hiring, funding, news and topic-level scores - none of those fire on the specific event of a competitor trial starting. Here is what actually detects trials and how to add it alongside Apollo. No, Apollo does not detect competitor trials. Apollo intent data covers hiring events, funding events, news mentions and aggregated topic-level buyer-intent scores. None of those fire on the specific event that a target account has started using a competitor's product on a trial basis. Detecting a trial requires monitoring the observable footprint a product leaves once an account starts using it - a footprint that appears early in the trial. That is a different signal category and comes from a subscription-intelligence tool (MarketSizer, Bloomberry). Apollo does its own job well - trial detection is simply a different job. Keep Apollo for contacts and sequencing, and add a trial-detection feed on top. What Apollo intent data actually covers Apollo positions itself as the end-to-end AI sales platform . Its intent product bundles four signal types inside the same interface reps use for contact lookup and sequencing: Hiring signals. Continuous scraping of job boards and company career pages. Fires when a target account posts a role. Latency: days. Funding events. Press-release feeds, business data providers and Apollo's own enrichment layer. Fires on capital raises, growth milestones, exec-team expansions. Latency: days to weeks. News mentions. Media coverage feeds. Fires on leadership changes, product launches, M&A activity. Latency: days. Topic-level intent. Aggregated buyer-intent scores per topic per account, derived from web-behaviour signals across partner networks. Latency: weeks (weekly refresh cadence is typical). All four are legitimate signal types. None of them is trial detection. See what is Apollo intent data for the full breakdown of how each signal type is sourced and delivered. Why those signals are not trial detection Three specific gaps between Apollo's signal shape and what trial detection needs. Hiring, funding and news are indirect signals of buying. A company posting a role is one step removed from the buying decision itself. A funding round signals capacity, not action. A news mention signals attention, not evaluation. All three can correlate with buying activity - they do not fire on buying activity. Trial detection needs the direct event: this account started using this specific competitor at this specific time. Topic-level intent is aggregated and inferred. Apollo's topic-intent scores are derived from IP-level web-behaviour on partner networks. When the score surges, it tells you the account's employees are researching a category. It does not tell you the account has started a specific trial. Same underlying data category as ZoomInfo intent and 6sense intent - aggregated, inferential, weekly-refresh cadence, structural false-positive rate. Not designed for trial detection. Refresh latency does not fit the trial window. Most B2B SaaS trials run 14 or 30 days. A signal that fires 10-21 days into that window catches the trial in its late stages, if at all. Weekly-refresh aggregated intent routinely misses trial windows entirely. Trial detection needs daily-refresh scanning on the vendors that matter, delivering the signal within days of the trial starting. Trial detection is a different signal category Keep Apollo for contact data and sequencing. Add MarketSizer for the specific event of a competitor trial starting on a target account, surfaced on a daily data refresh. Free trial, no card required. Book a demo → MarketSizer vs Apollo → What actually detects competitor trials Trial detection relies on a different data mechanism: continuous monitoring of target accounts for the observable traces a competitor's product leaves once an account starts using it. When a vendor's footprint appears where it was absent before, a trial has started. The signal is tied to a real product deployment on a real account, which is why it can be verified rather than inferred - and why it is a usable purchase intent signal rather than a topic score. More on the category in how to detect competitor trials . Two tools currently detect competitor trials at scale as a first-class product feature: MarketSizer (deep per-vendor coverage across 200+ vendors in Customer Support, Live Chat and adjacent SaaS categories) and Bloomberry (broad tech-install coverage across roughly 1,200 products). Both are subscription-intelligence tools; neither is a substitute for Apollo's contact database or sequencing engine. How to add trial detection alongside Apollo Practical layering pattern used by teams running both. Keep Apollo as the contact-data and sequencing layer. Nothing about adding trial detection replaces the value Apollo delivers on contact enrichment, sequence execution and multi-channel outbound. Add a subscription-intelligence tool as the trial-detection layer. MarketSizer or Bloomberry, depending on which vendors matter most to your motion. The tool fires when target accounts start trials of the specific competitors you care about - if you sell into customer support, that means knowing an account started a Zendesk trial on Tuesday, not that it scored highly on a "customer service software" topic last month. Overlay the trial signal inside Apollo. MarketSizer's SignalOps Chrome extension opens as a side panel while the rep works in Apollo - a rep building a list in Apollo sees MarketSizer trial signals in the side panel alongside Apollo's contact data and topic intent. No tab switching, no separate dashboard. Sequence the flagged accounts. Push MarketSizer-flagged accounts into an Apollo sequence with a personalised opener that references the specific trial event. This is the outreach pattern trial-window timing is designed to support. Measure outcomes. Log which trial-signal-triggered sequences produced meetings vs which category-intent-triggered sequences produced meetings. A well-tuned trial-detection signal tends to convert well above aggregated topic intent - log your own numbers to see the gap on your motion. A worked example - Apollo alone vs Apollo + trial detection Consider a hypothetical SDR team of 8 reps selling into mid-market B2B SaaS. Target accounts: 500. Current stack: Apollo Professional at $99/user/month. Apollo alone. The rep filters Apollo for accounts with recent hiring events matching a sales leadership role, layers on topic intent for "sales enablement" above the surge threshold, and pulls a working list of 30-40 accounts. Reaches out with a category-relevant pitch. Some connect; the SDR cannot tell from the flag alone whether any of these accounts is actually evaluating the SDR's specific product right now. Apollo plus trial detection. The MarketSizer Chrome extension opens alongside Apollo. On the same 500 target accounts, a handful fire trial signals for direct competitors in any given week. The SDR opens those accounts in Apollo, sees the trial event in the sidebar with the specific competitor and start date, pulls contacts from Apollo, and sequences them with an opener that references the actual competitor being trialled ("I saw your team just started evaluating X... "). Outreach lands with substantially higher precision because the message ties to a real, verifiable event. The layering does not increase the SDR's target-account count. It changes which of the 500 accounts get worked in a given week and what the opening message looks like. Both changes matter more than the tools' category positioning would suggest. Frequently asked questions Does Apollo detect competitor trials? No. Apollo intent data covers hiring events, funding events, news mentions and aggregated topic-level scores. None of those fire on the specific event that a target account has started a trial of a specific competitor. What does Apollo intent data actually detect? Hiring events (new role postings), funding events (capital raises), news mentions (leadership, product launches, M&A) and topic-level buyer-intent scores derived from aggregated web-behaviour signals. See what is Apollo intent data for the full breakdown. Can Apollo integrations pick up trial signals from other tools? Apollo integrates with many CRMs and third-party data providers, but the intent product itself does not consume subscription-event signals from external sources. Trial signals live in a different data category and typically flow into the CRM directly from a subscription-intelligence tool rather than through Apollo. What tool detects competitor trials for B2B GTM teams? Subscription-intelligence platforms. MarketSizer (deep per-vendor coverage) and Bloomberry (broad product coverage) are the two vendors most focused on the trial-detection job. See best trial-detection tools for the full comparison. Does MarketSizer replace Apollo? Rarely. For most teams the answer is layering, not replacement. Apollo covers contact data and sequencing; MarketSizer covers trial and subscription-event detection. The two integrate directly - the MarketSizer Chrome extension opens alongside Apollo. Full breakdown at MarketSizer vs Apollo . How fast can trial detection fire? With daily-refresh technical signature monitoring on well-covered vendors, trials are typically detected within days of installation. Weekly-refresh scanning routinely misses the trial window; monthly scanning is retrospective. Is trial detection possible without scraping private data? Yes - detection relies only on publicly observable signals from the trialist's own public web presence. Nothing scans private data or accesses the trialist's internal systems. How is trial detection different from ZoomInfo intent or 6sense intent? Both ZoomInfo and 6sense intent are aggregated topic-level signals derived from web-behaviour partner networks. Neither fires on the specific event of a trial starting on a specific account. See what is ZoomInfo intent data and what is 6sense intent data for the mechanics. Sources Apollo homepage and Apollo pricing page - positioning, published pricing tiers. Apollo sales engagement product page - hiring signals, funding events, topic-intent inputs. MarketSizer - How to detect competitor trials - detailed mechanics of the four detection methods. MarketSizer platform capabilities and data-coverage claims - internal Claim Registry , sourced from live production tables and refreshed quarterly. Available on request via a demo . Recommended reading What is Apollo intent data? - full breakdown of what Apollo intent actually covers. How to detect competitor trials - the four detection methods, ranked. Best trial-detection tools - the tool comparison. Best subscription-intelligence tools - the wider category. MarketSizer vs Apollo - side-by-side. Best Apollo alternatives - the wider "what to layer or replace" set. ## Blog: The Best Apollo Alternatives & Competitors, Compared The best Apollo alternative depends on which part of Apollo you are trying to replace - contact data, sequencing, AI personalisation, or intent signals. Honest comparison of ZoomInfo, Cognism, Lusha, Clay, Amplemarket, MarketSizer and the outreach-only tools. The best Apollo alternatives depend on which part of Apollo you are trying to replace. If it is the contact database, look at ZoomInfo (enterprise), Cognism (European compliance-first), Lusha (direct-dial + job-change) or Seamless. ai / LeadIQ (value tier). If it is the sequencing engine, Lemlist and Instantly do outreach without the bundled data. If it is the AI-drafted personalisation, Amplemarket sits closest. If it is the intent-signal layer, MarketSizer surfaces event-based purchase intent (trials, switches, renewals, churn) that Apollo does not fire on. Most teams keep Apollo and add one of these; the honest answer is that Apollo holds up well at what it was built for, and the "alternative" often ends up being a complementary layer. What are the best Apollo alternatives? The best Apollo alternatives, by primary job to be done: Contact database, enterprise scale: ZoomInfo . Contact database, European compliance: Cognism . Direct-dial contact data + job-change tracking: Lusha . Programmable enrichment workbench: Clay . AI-copilot outbound suite: Amplemarket . Timing-signal layer (event-based buying signals): MarketSizer. Outreach-only alternative (bring your own data): Lemlist , Instantly . Value-tier contact data: Seamless. ai , LeadIQ . Apollo is not weak on any of these axes - it is a real end-to-end platform. Each of the tools above is stronger than Apollo on one specific axis and weaker on the others. Choosing the "best" alternative is really choosing which axis matters most for your team. When you actually need an Apollo alternative Four honest reasons teams look for an alternative to Apollo. The intent signals are not accurate enough. Apollo intent bundles hiring, funding, news and topic-level scores. It is genuinely useful for list building, but reps working the flag at face value learn to distrust it after a few unqualified conversations. If you need event-based signals (trials, switches, renewals, churn) rather than aggregated topic scores, Apollo is not the tool - see What is Apollo intent data for the mechanics. The contact database is not deep enough on your segment. Apollo is broad; enterprise-heavy segments and specific regions get better coverage from ZoomInfo or Cognism. Apollo's own SMB and mid-market coverage is genuinely competitive. The bundle is more than you need. Some teams only want sequencing (Lemlist, Instantly do that with lower per-seat cost) or only want the data layer (Cognism, Lusha, Seamless. ai). The bundle economics of Apollo work best when you use most of it. You need timing signals Apollo does not produce. Apollo tells you which accounts to reach out to; it does not tell you when. If you need to reach an account the day it started trialling a competitor, Apollo's aggregated intent will not fire on that. See How to detect competitor trials for the different signal category. Not looking to replace Apollo, just to add timing signals? Most teams keep Apollo for contact data and sequencing and add MarketSizer for the event-based timing layer Apollo does not cover. The two layer cleanly - MarketSizer signals appear in a side panel beside Apollo via the SignalOps Chrome extension. Free trial, live data on your ICP. Book a demo → MarketSizer vs Apollo → The alternatives in detail Each alternative below carries a short "best known for", who it fits, what it charges, and where it is stronger or weaker than Apollo. ZoomInfo - the enterprise contact-data platform Best known for: the deepest B2B contact and firmographic database in the enterprise market, with org-chart data, hierarchy detail and a full GTM suite bought as one package. Best for: enterprise revenue teams that want a single-vendor stack for contact data, intent, engagement and workflow, and can absorb an annual commitment in the $25K-$58K+ range (per Fifty Five and Five research ). Where it beats Apollo: enterprise contact depth, org-chart data, procurement documentation, GDPR / SOC 2 / ISO posture, single-vendor consolidation. Where Apollo still wins: self-serve pricing, free plan, mid-market unit economics, speed to activate. Full comparison: MarketSizer vs ZoomInfo , Apollo vs ZoomInfo . Cognism - the European contact-data specialist Best known for: European and UK contact-data coverage with a compliance-first posture built around GDPR, DNC and Diamond-verified mobile phones. Best for: outbound teams selling into Europe and the UK where compliance and mobile-phone accuracy matter as much as record count. Where it beats Apollo: European mobile-phone coverage, GDPR-native workflow, compliance documentation for enterprise procurement. Where Apollo still wins: US coverage breadth, free-plan entry, bundled sequencing and dialer. Full comparison: MarketSizer vs Cognism . Lusha - the direct-dial and job-change specialist Best known for: fast Chrome-extension contact lookups plus people-movement tracking - alerts when a named contact changes company. Best for: SDR teams that need direct-dial mobile numbers and job-change alerts on named contacts, layered on top of a wider CRM or prospecting tool. Where it beats Apollo: direct-dial mobile accuracy, job-change alerts as a first-class feature, in-region strength for India and adjacent markets. Where Apollo still wins: account-level intent, sequencing engine, broader firmographic dataset. Full comparison: MarketSizer vs Lusha . Clay - the programmable enrichment workbench Best known for: a spreadsheet-style programmable enrichment tool that composes data from many providers with AI-assisted research steps. Best for: RevOps and BDR teams with the technical resource to build their own enrichment and signal workflows using any data source they can wire up. Where it beats Apollo: flexibility, multi-provider composition, custom signal logic under the team's direct control. Where Apollo still wins: out-of-the-box workflow, single-seat sequencing, lower total cost when Clay build effort is factored in. Full comparison: MarketSizer vs Clay . Amplemarket - the AI-copilot outbound suite Best known for: an AI-native outbound platform bundling contact data, sequencing, calling and AI-drafted personalisation in a single workflow. Best for: outbound teams looking to consolidate contact data, enrichment and multi-channel sequencing into one AI copilot seat, and comfortable with an annual mid-five-figure commitment. Where it beats Apollo: AI-drafted personalisation depth, tighter multi-channel orchestration. Where Apollo still wins: free-plan entry, self-serve pricing, broader third-party integration ecosystem. Full comparison: MarketSizer vs Amplemarket . MarketSizer - the timing-signal layer that sits on top Best known for: subscription intelligence - accounts ranked by observable buying events (trials, switches, renewals, churn) rather than aggregated topic scores. Best for: GTM teams that already have contact data (from Apollo, ZoomInfo, Cognism or elsewhere) and want an event-based timing-signal layer on top. Signals appear in a side panel alongside HubSpot, Salesforce, LinkedIn and Apollo via the SignalOps Chrome extension. Where it beats Apollo: event-based signals, signal-level explainability (every signal tied to a specific subscription event), timing latency short enough to fire inside the fourteen-to-thirty-day trial window, category-deep coverage on 200+ customer-support and adjacent SaaS vendors. Where Apollo still wins: contact data breadth, native sequencing, native dialer, free-plan entry. Full comparison: MarketSizer vs Apollo . Lemlist and Instantly - the outreach-only alternative Best known for: multi-channel outreach without a bundled contact database. Bring your own data (or plug in one of the tools above), Lemlist and Instantly do the sending. Best for: teams that already have contact data and want to save the seat cost on Apollo's bundled sequencing, or teams that need heavier email deliverability tooling than Apollo bundles. Where they beat Apollo: per-seat cost when data is not needed, deliverability-focused feature depth, faster iteration on sequence design. Where Apollo still wins: data + sequencing in one seat if you want the bundle, native contact enrichment inside the same tool. Seamless. ai and LeadIQ - the value-tier contact-data plays Best known for: lower-priced contact-data alternatives to Apollo for teams that only want the data layer. Best for: mid-market teams that already have sequencing (Lemlist, Instantly, Salesloft, Outreach) and want a lower-cost data source than Apollo or ZoomInfo. Where they beat Apollo: per-record and per-seat pricing on data-only workflows, faster procurement. Where Apollo still wins: data quality depth, integrated sequencing that removes tool sprawl, broader ecosystem of integrations. How to choose between them Four honest questions to work through. What is the primary job I am trying to replace? Contact data, sequencing, signals, or all of the above. Apollo bundles all three; almost every "alternative" is stronger on one and weaker on the others. Where does the team already run? If sales owns the buying decision, self-serve tools (Apollo, Lusha, Clay, MarketSizer) fit better than enterprise procurement (ZoomInfo, Amplemarket). If marketing owns the decision, ABM platforms sit above this whole comparison. What is the compliance shape of the target market? Selling into Europe means Cognism gets a real advantage. Selling into US mid-market makes the compliance-first framing less differentiating. Is timing a real gap? If reps need to know when accounts enter a buying window - not just who to reach - the answer is event-based signals from MarketSizer or a subscription-intelligence tool, layered on top of whichever contact-data source you keep. The blunt version: most teams that go through this evaluation keep Apollo and add one specialist tool for the axis where Apollo is weakest for them. Rip-and-replace is rarer than the vendor-vs-vendor framing suggests. Frequently asked questions What is the best Apollo alternative? No single answer - depends on which Apollo capability is weak for you. ZoomInfo is the enterprise contact-data alternative. Cognism is the European compliance alternative. Lusha is the direct-dial alternative. Clay is the programmable-enrichment alternative. Amplemarket is the AI-copilot alternative. MarketSizer is the event-based timing-signal alternative. Lemlist and Instantly are the sequencing-only alternative. Is Apollo worth keeping? For most teams doing mid-market outbound, yes. Apollo's free plan and self-serve pricing make it hard to beat on unit economics for teams under 40 reps. The right move is usually to keep Apollo and add a specialist tool for the axis where Apollo falls short. Which Apollo alternative is best for European sales? Cognism, on both mobile-phone accuracy and GDPR-native compliance workflow. Apollo covers Europe but Cognism's coverage on European mobile direct-dials is a real differentiator. Which Apollo alternative is best for competitive-displacement outbound? A subscription-intelligence tool like MarketSizer. Apollo does not fire on the specific event that an account started trialling a competitor - that is a different signal category. Full breakdown in How to detect competitor trials . What is the cheapest Apollo alternative? Apollo's own free plan is the cheapest entry point in the whole comparison. Seamless. ai and LeadIQ compete on per-record price for data-only workflows. Lemlist and Instantly compete on per-seat price for sequencing-only workflows. Do I need to replace Apollo entirely? Rarely. Most teams that evaluate an "alternative" end up keeping Apollo and layering another tool on top. The exception is teams that never used Apollo's sequencing (in which case a data-only alternative may be cheaper) or teams whose intent needs are timing-based (in which case an event-based signal layer sits on top of Apollo, not in place of it). How does MarketSizer fit alongside Apollo? MarketSizer surfaces which accounts are in a buying window; Apollo supplies the contact data and runs the sequencing. MarketSizer signals appear in the SignalOps Chrome extension side panel while reps work in Apollo, so the timing signal sits beside the outreach workflow. See MarketSizer vs Apollo for the full breakdown. What is the best free Apollo alternative? The free tier of Apollo itself is genuinely strong and widely used. For a truly free contact-data alternative, Lusha's free tier is popular. For a free technographic-lookup alternative, Wappalyzer's Chrome extension is free forever. Sources Apollo homepage and Apollo pricing page - positioning, published pricing tiers. ZoomInfo homepage - positioning language. Fifty Five and Five - ZoomInfo pricing guide 2025 - source for the ZoomInfo tier ranges. Cognism homepage , Lusha homepage , Clay homepage , Amplemarket homepage , Lemlist , Instantly , Seamless. ai , LeadIQ , Wappalyzer - positioning language and public product pages. MarketSizer platform capabilities and data-coverage claims - internal Claim Registry , sourced from live production tables. Available on request via a demo . Recommended reading MarketSizer vs Apollo - side-by-side breakdown. What is Apollo intent data? - explainer piece on the intent module. Apollo vs ZoomInfo - head-to-head. Best purchase-intent data platforms for 2026 - the full intent-tool landscape. How to detect competitor trials - the fourteen-to-thirty-day window that decides competitive-displacement outreach. All comparisons - side-by-side breakdowns across the whole cluster. ## Blog: What Is ZoomInfo Intent Data? ZoomInfo's intent add-on layers aggregated topic scores on top of the contact database. Here is how it works, who it is built for, why it attracts false-positive complaints, and how event-based signals compare. ZoomInfo intent data is an intent layer bundled into ZoomInfo's Advanced and Elite tiers that adds aggregated topic-level intent signals to the ZoomInfo contact-and-firmographic database. It sources from IP-to-company web tracking, bidstream advertising data (via ZoomInfo's 2020 Clickagy acquisition) and review-platform signals, and is delivered as topic scores that surface on ZoomInfo account records. The intent layer is well-established in the enterprise ABM stack, but attracts a persistent false-positive complaint - independent research reports 52% of sales-intent users seeing frequent false positives. It is useful for teams that already have the ZoomInfo platform, less useful as a standalone intent source. If event-based signals matter more than aggregated topic scores, see MarketSizer vs ZoomInfo . What is ZoomInfo intent data? ZoomInfo positions itself as the all-in-one AI GTM platform . Intent data comes bundled with the Advanced and Elite tiers of the ZoomInfo contract, with a capped number of intent topics per tier. The intent product overlays topic-level scores on top of ZoomInfo's core contact and firmographic dataset. When an account shows research activity for a topic in ZoomInfo's taxonomy, that topic surges on the account record - visible to reps in the ZoomInfo platform and pushed into the CRM through the ZoomInfo integration. The output is a Signal Score (a 0-100-style surge score measuring a topic's spike against the account's own historical baseline) plus an Audience Strength indicator showing how large the researching group at the account is. Sales, marketing and RevOps teams use it as a filter on top of the contact database - narrowing a large firmographic list to accounts showing topic-intent uplift. How it works ZoomInfo intent originates from a mix of first-party partner data and third-party integrations. The general pattern: IP-level web tracking. Traffic patterns mapped from IP address to company using ZoomInfo's firmographic dataset (ZoomInfo cites 210M+ IP-to-organisation pairings). Bidstream and content-consumption data. Advertising bid requests and page-read activity across the 300,000+ publisher domains of the Clickagy network, which ZoomInfo acquired in 2020, classified into topics by ZoomInfo's NLP (its Intelligent Keyword Extractor). Review-platform signals. Research activity on third-party review platforms such as G2 and TrustRadius feeds the signal. Topic taxonomy. ZoomInfo maps raw behaviour signals to a proprietary topic taxonomy that mirrors the categories customers use for ABM segmentation. Aggregation and delivery. Signals are aggregated at the account level, topic-classified, and delivered via the ZoomInfo platform, CRM sync or API. Delivery is batched - weekly or daily depending on package - with real-time Streaming Intent available at higher tiers or as an add-on. Intent is not included in the base ZoomInfo Professional tier; it comes bundled with Advanced and Elite, and extra topics and Streaming Intent are paid add-ons. Independent pricing research by Fifty Five and Five places the fully-loaded ZoomInfo Advanced tier (with intent) at around $24,995 per year, and Elite tier substantially higher. Who it is built for ZoomInfo intent is built for enterprise revenue teams already on the ZoomInfo platform. The intent add-on is not sold standalone in the way that Bombora or 6sense can be - the value depends on the surrounding ZoomInfo contact and firmographic dataset. Teams that already run ZoomInfo for enrichment and contact data typically add intent as a natural extension rather than as a separate vendor decision. The primary buyer is RevOps and marketing operations at large B2B organisations - functions with the resource to operationalise aggregated topic-intent signals inside a broader ABM workflow, usually alongside whatever market intelligence already informs territory and segment planning. Sales reps consume the intent scores through CRM records rather than working directly in the ZoomInfo platform. The false-positive problem in ZoomInfo intent The single most-cited criticism of ZoomInfo intent is false positives. Independent research by Fifty Five and Five found that 52% of sales-intent users report seeing frequent false positives , with 29% specifically flagging misattributed IP tracking as a key challenge (per Fifty Five and Five's research on ZoomInfo intent). The false-positive rate is not a bug specific to ZoomInfo - it is a structural characteristic of IP-based aggregated intent. Three common failure modes: Coffee-shop IPs. An employee working from a shared office or coffee shop reads a page on the topic. The IP maps to a different company than the person's employer. The wrong account gets flagged. Corporate-network noise. A large employer with thousands of employees has always-on baseline traffic across every topic. Distinguishing "we surged on customer support automation this week" from "we always have some traffic on that topic" requires careful baseline modelling. Cross-account contamination. Consultants, partners and third parties with mapped IPs generate topic activity that gets attributed to the accounts they visit rather than to the party actually researching. None of these fully invalidate the signal - a well-tuned intent programme can extract useful signal from the noise. The point is that the false-positive rate is inherent to the underlying mechanism, and teams should design workflows that account for it (higher thresholds, corroborating signals, first-party filters) rather than expecting the raw score to be actionable at face value. Worked example: a topic surge in the SDR queue To make the mechanics concrete, take a hypothetical scenario in an enterprise SDR queue. The team is targeting mid-market B2B SaaS with the topic "customer data platform. " Here is what a surge event looks like end-to-end, based on the mechanics ZoomInfo publishes for its intent product. Week 1. Baseline. Account X (a 600-employee SaaS company) sits at a low baseline topic score for customer data platform - some background traffic, nothing above the surge threshold. Week 2. Two people at Account X read three articles on customer data platforms across ZoomInfo's publisher network. Bidstream activity ticks up. Score crosses the surge threshold and moves into a higher band. Week 3. The next batched refresh (weekly or daily, depending on package) pushes the surge into ZoomInfo. The following CRM sync delivers a new "In-Market for Customer Data Platform" flag on Account X. Week 4. An SDR working the queue sees the flag on Account X in HubSpot. The rep reads the flag but does not see which two people read what, or which specific competitor they might be comparing. The rep reaches out with a category-relevant message. End-to-end: anywhere from a couple of days on a daily-refresh package to around two weeks on weekly batch plus CRM sync. Either way, what reaches the rep is a topic flag, not the underlying event. Sometimes the research is real and the outreach lands. Sometimes the false-positive pattern kicks in and the account is not actually in market. The SDR cannot tell the difference from the flag alone. Aggregated topic intent, meet event-based signal ZoomInfo intent flags accounts that might be researching a topic. MarketSizer flags accounts that just started trialling a specific competitor - with the event attached. See it live on your ICP - Free trial, no card required. Book a demo → MarketSizer vs ZoomInfo → Where it works well Three motions where ZoomInfo intent lands cleanly. Layered on top of the ZoomInfo contact database. When the team already runs ZoomInfo for enrichment, adding intent gives a light prioritisation layer on top of the existing dataset without introducing a second vendor. Adjacent features like ZoomInfo Workflows can trigger automations on the intent flag. Enterprise topic taxonomy coverage. ZoomInfo's topic library is broad and mature. Teams selling into categories with mainstream buying language get useful topic coverage. Brand-safe procurement. ZoomInfo's compliance posture and enterprise procurement path are well-established. For a regulated buyer, adding the intent module is a low-legal-lift extension rather than a new vendor negotiation. Where it falls short Two limitations show up when reps need signals they can trust and act on individually. False positives are structural. The 52% figure from Fifty Five and Five's research is not an outlier - it reflects the underlying mechanism. Reps who trust the flag at face value learn to distrust it after a few unqualified conversations. Marketing-owned teams with the resource to layer thresholds and corroborating signals get more from it than sales teams working the flag directly. The flag is an aggregate, not an event. Even on a daily refresh - or real-time Streaming Intent at higher tiers - the score is a spike against a baseline rather than a record of a discrete buying event. The rep still cannot see who researched what, or whether a specific competitor is being trialled. Add CRM sync latency and the flag can land well after the behaviour it summarises. The practical version: ZoomInfo intent is useful as a light prioritisation input in a well-tuned marketing-ops workflow. It is not usable as a rep-facing "this account is about to buy" signal without additional filtering. How MarketSizer signals compare MarketSizer's purchase intent signals are event-based rather than inferred. Each signal on an account corresponds to a specific observable event - a competitor trial detected, a vendor switch, a renewal window opening, a churn indicator. The signal is attached to the event, not to a rolled-up topic score. Three practical differences for teams already using ZoomInfo: The signal is inspectable. A rep can see what event triggered the alert and reference it in outreach. That works particularly well for competitive-displacement plays where knowing the specific competitor being trialled changes the whole message. The signal is an event, not a score. Signals fire on the event itself rather than arriving as an aggregated score on a batch cadence. That is what unlocks the fourteen-to-thirty-day trial window. The two tools compose. Most teams keep ZoomInfo for contact data and enrichment and add MarketSizer for the event-based signal layer on top. The SignalOps Chrome extension shows these signals in a side panel while you work in your CRM, beside ZoomInfo's data. Full breakdown at MarketSizer vs ZoomInfo . Frequently asked questions What is ZoomInfo intent data? ZoomInfo intent data is a tier-bundled intent layer that adds aggregated topic-level intent signals to the base ZoomInfo contact and firmographic platform. It sources from IP-to-company tracking, bidstream advertising data and publisher-network content consumption (via the Clickagy network), and is bundled into the Advanced and Elite ZoomInfo tiers. How much does ZoomInfo intent cost? ZoomInfo does not publish pricing publicly. Fifty Five and Five's 2025 research places Advanced (with intent) around $24,995 per year and Elite substantially higher. Intent is bundled with the tier rather than sold standalone. How is ZoomInfo intent different from Bombora? Both are aggregated topic-level intent based on similar mechanics (IP tracking, bidstream, publisher networks). Bombora is a data cooperative sold standalone or resold via many downstream tools; ZoomInfo intent is a module inside the ZoomInfo platform. Similar signal category, different delivery surface. Why does ZoomInfo intent have false positives? The false-positive rate is inherent to IP-based aggregated intent, not specific to ZoomInfo. Fifty Five and Five research puts the "frequent false positives" rate at 52%. Common causes: coffee-shop IPs, corporate-network noise, cross-account contamination. Well-tuned workflows use thresholds and corroborating signals rather than trusting raw scores. Is ZoomInfo intent event-based? No. ZoomInfo intent is aggregated and inferential - a topic-level score derived from web-behaviour signals, not a record of a specific buying event. Event-based signals (competitor trials, vendor switches, subscription renewals) come from a different data category. See What replaces intent data in 2026 . Can ZoomInfo intent and MarketSizer be used together? Yes. Most teams keep ZoomInfo for contact data and enrichment and add MarketSizer as an event-based signal layer. The two workflows layer rather than compete. Full breakdown at MarketSizer vs ZoomInfo . Is ZoomInfo intent usable for outbound SDRs directly? With caveats. The 52% false-positive rate means SDRs working the flag at face value burn through unqualified conversations quickly. It works better as a light prioritisation input inside a marketing-ops-tuned workflow than as a rep-facing "call this account now" signal. How does ZoomInfo intent compare to 6sense intent? 6sense pushes similar underlying signals through a predictive AI model that outputs a buying-stage classification. ZoomInfo intent exposes topic-level scores directly. Different interpretation layer; same underlying signal shape. See What is 6sense intent data . Sources All qualitative and quantitative claims in this piece trace to public sources. ZoomInfo homepage - positioning language. ZoomInfo intent product page - how the intent product describes itself, including Signal Score and real-time claims. ZoomInfo press release - Clickagy acquisition (October 2020) - provenance of the bidstream and publisher-network signal, and the weekly/daily/real-time delivery options. ZoomInfo help centre - using Intent in outreach - official documentation of how intent surfaces on account records. ZoomInfo - streaming vs real-time intent data - ZoomInfo's own explainer on batch vs streaming delivery. Fifty Five and Five - ZoomInfo pricing guide 2025 - independent research on the ZoomInfo tier pricing referenced in this piece. Fifty Five and Five - ZoomInfo intent review - source for the 52% false-positive statistic and misattributed-IP-tracking finding. Forrester - How to evaluate intent data providers - broader context on false-positive rates across the aggregated-intent category. MarketSizer platform capabilities and data-coverage claims (70M+ subscription records, 17M+ active subscriptions, 2. 5M+ active trials, 200+ vendors indexed, 140+ countries) - internal Claim Registry , sourced from live production tables and refreshed quarterly. Available on request via a demo . Recommended reading MarketSizer vs ZoomInfo - side-by-side breakdown. Apollo vs ZoomInfo - head-to-head of the two mid-market and enterprise incumbents. What is Apollo intent data? - explainer on the self-serve alternative. What is 6sense intent data? - explainer on the predictive alternative. Best purchase-intent data platforms for 2026 - the full landscape. What replaces intent data in 2026? - why event-based signals are displacing aggregated intent. ## Blog: What Is 6sense Intent Data? 6sense intent is a predictive buying-stage assignment built on aggregated third-party intent plus 6sense's own AI. Here is how the score is built, who it is built for, and where event-based signals fit. 6sense intent data is a predictive account-level signal that combines anonymous third-party intent, de-anonymised first-party website activity and CRM/MAP engagement with 6sense's own AI models to place accounts on a buying-stage taxonomy - Target, Awareness, Consideration, Decision, Purchase. The signal sits inside the wider 6sense Revenue AI Platform and is activated across advertising, web personalisation and marketing automation. It is designed for enterprise marketing and ABM teams running structured multi-quarter programmes, not for sales reps who need to know which specific accounts to work this week. If timing signals and rep-level actionability matter more than orchestration breadth, an event-based signal source is a better fit; see MarketSizer vs 6sense . What is 6sense intent data? 6sense brands its platform 6sense Revenue AI . Inside that platform, intent data is one of several inputs feeding a predictive account-scoring model. Where a traditional intent provider delivers a raw topic-intent score, 6sense delivers a predictive buying-stage assignment - a claim about where the account is in its evaluation journey, not just what it is researching. The output is an account-level classification. For a given topic or product category, a target account is placed on the buying-stage curve (Target, Awareness, Consideration, Decision, Purchase) alongside an intent score (0-100) and a profile-fit score. Sales and marketing teams pull that stage into their CRM or the 6sense orchestration surface and design plays that fire when accounts transition. Three properties of the score matter more than most buyers realise: It is modelled, not composed. The buying-stage assignment is the output of 6sense's own AI, not a direct aggregation. That means fewer inputs are exposed to the buyer - the model is the interpretation layer. It is stage-shaped. The value is not just "this account is researching" but "this account is at Consideration" - a specific prediction about progress through a funnel. It moves when enough evidence accumulates. Scores rescore daily, but a stage transition only fires once enough activity has built up across the network - so the rep sees the transition after the first underlying behaviour, sometimes well after. How the score is built 6sense sources intent from a combination of anonymous third-party web activity, the customer's own first-party signals, and technographic context. The exact composition varies by product tier, but the general pattern looks like this: Anonymous third-party web signals. IP-level browsing on partner networks and B2B publisher content (6sense names partnerships including Bombora, G2, TrustRadius and TechTarget), mapped from IP to company using firmographic data. De-anonymised first-party activity. 6sense's most-marketed capability - matching anonymous visitors on the customer's own website to accounts (the "Dark Funnel" pitch), combined with CRM and marketing-automation engagement. Ad engagement. Impressions and interactions on 6sense's own advertising network and integrated ad platforms feed back into the model as engagement signals. Firmographic and technographic context. The account's size, industry and existing tech stack - the technographic dataset traces to 6sense's 2021 Slintel acquisition - contextualise raw behaviour signals. Predictive AI model. 6sense's own AI compresses all of the above into a buying-stage assignment, an intent score (0-100) and a profile-fit score. The model is the differentiator. Where Bombora and ZoomInfo intent expose their scores as topic-level intent, 6sense pushes similar third-party inputs - plus the customer's own first-party engagement - through a predictive stage model. The buyer sees the stage prediction, not the underlying signal shape. The buying-stage taxonomy 6sense's account-scoring model is anchored to a five-stage taxonomy that mirrors a typical B2B buying journey: Target. Fit-based account that has not yet shown intent. The rep or marketer has flagged the account but the account has not moved. Awareness. The account has shown some intent activity - a light topic-level signal or engagement uplift - but not enough for a strong stage prediction. Consideration. Activity has crossed a threshold. The account is likely comparing options in the category. Decision. Late-stage signals suggest active vendor evaluation. High-priority moment for outreach. Purchase. Signal pattern consistent with imminent buying decision or contract close. The five-stage framing is what marketers use to design campaign cadences. A campaign might fire different creative to accounts in Awareness vs Consideration; sales notifications fire on Decision-stage transitions. Who it is built for 6sense is built for enterprise marketing and ABM teams running structured multi-quarter programmes. Every part of the product - the buying-stage taxonomy, the orchestration surface, the advertising and web-personalisation modules - assumes marketing is the owner of the buying decision and marketing operations is the deployment surface. Third-party purchasing data (Vendr) places 6sense contracts at roughly $50,000 to $300,000+ per year, with a median in the mid-$50,000s; full multi-module enterprise deployments commonly reach six figures. G2 reviewer data puts average go-live at two to three months, and full enterprise production - data integration, model calibration, orchestration setup and team enablement - typically runs three to six months. That price and timeline make sense for a platform where the return is measured across annual ABM programme outcomes. The typical customer is a large B2B technology company with a dedicated ABM function, a marketing-operations team large enough to run 6sense day-to-day, and a comfortable multi-year commitment structure. Worked example: an account moving from Awareness to Consideration To make the mechanics concrete, take a hypothetical target account - a 1,200-employee B2B SaaS company selling into retail. The marketing team has that account on their ABM watchlist for the topic "customer data platform. " Here is a plausible sequence of what 6sense surfaces, based on the mechanics 6sense publishes about its buying-stage model. Week 1. Baseline. The account is classified as Target - fit-based, no intent activity above the noise floor. Score sits at a low double-digit level in 6sense's scale. Week 2. Two employees at the account read three articles on customer data platforms across 6sense's publisher network. Another employee visits a vendor comparison page. The model moves the account into Awareness. Score ticks up. Week 3. Activity continues. Ad impressions on 6sense's own network land on account IPs. A fourth employee downloads a buyer's guide from a syndicated publisher. The model reclassifies the account as Consideration. Score moves into a higher band. Week 4. A sales notification fires in the rep's CRM: "Account X: stage transition Awareness → Consideration. " The rep opens the account record; the 6sense sidebar shows the stage, the score, and the underlying topics. What the rep does not see: which two employees read what, which comparison page they visited, or whether the buying committee has actually convened. Two things worth noticing in this illustrative scenario. Even though 6sense rescores daily, the stage transition only becomes rep-visible once enough activity has accumulated and the CRM sync has run - days to weeks after the first underlying behaviour. And the rep gets a stage classification plus a delta - not the underlying content, not the specific people, not the specific competitors the account is comparing. Stage prediction is planning. Timing signals are execution. 6sense tells your marketing team an account moved into Consideration. MarketSizer tells your reps the same account started trialling your competitor eleven days ago. Different altitudes; complementary layers. See it live on your ICP - Free trial, no card required. Book a demo → MarketSizer vs 6sense → Where it falls short for sales execution The same properties that make 6sense powerful for marketing planning make it awkward for the rep-facing execution surface where most sales teams actually spend their week. The score is modelled , which means a rep sees a stage classification but not the underlying evidence. There is no specific event to reference in outreach, no timing anchor beyond "the model moved this account this week," no specific behaviour a message can be built around. The score is marketing-shaped . The buying-stage taxonomy maps to marketing campaign design (creative rotation, orchestration cadence), not to the rep's daily prioritisation decision (which specific account to call today). The signal fires on accumulation, not on the event. Scores rescore daily, but a stage transition needs enough accumulated activity to cross a threshold, plus a CRM sync. The transition arrives after the buying activity, not with it. And the tool is enterprise-shaped . Multi-month implementations and annual contracts that regularly run to six figures make 6sense a heavy lift for mid-market teams where sales owns the buying decision and needs signals inside the workflow reps already use. How MarketSizer signals compare MarketSizer sits at a different altitude. Rather than model buying intent into a stage prediction, MarketSizer's purchase intent surfaces observable subscription events - trials, switches, renewals, churn - and attaches each signal to the specific event that produced it. A signal on an account is a record that something happened, not a probability that the account is at a particular funnel stage. Three practical implications: Explainability. Every signal is traceable to the underlying event. A rep can see what triggered it and reference the specific behaviour in outreach. Timing. Signals fire on the event itself, not once modelled activity crosses a stage threshold. That matters when the account's buying window is fourteen to thirty days. Workflow surface. Signals appear in a side panel alongside HubSpot, Salesforce, LinkedIn and Apollo via the SignalOps Chrome extension. Reps do not need to log into a separate marketing-orchestration platform. The comparison is not a rip-and-replace argument. Enterprise teams often keep 6sense at the marketing-planning altitude and add MarketSizer for the sales-execution altitude. The full breakdown lives at MarketSizer vs 6sense . Frequently asked questions What is 6sense intent data? 6sense intent data is a predictive account-level signal that combines anonymous third-party intent (bidstream, ad engagement, content consumption) with 6sense's own AI to classify accounts on a five-stage buying-journey taxonomy: Target, Awareness, Consideration, Decision, Purchase. It sits inside the wider 6sense Revenue AI Platform. How is 6sense intent different from other intent providers? Providers like Bombora and ZoomInfo intent expose a topic-level score derived from web-behaviour signals. 6sense pushes similar third-party signals - plus the customer's own first-party and CRM/MAP engagement - through a predictive AI model that outputs a buying-stage assignment rather than a raw score. Similar third-party inputs; a different interpretation layer with more first-party data in the mix. Who uses 6sense? Enterprise marketing and ABM teams at large B2B technology companies. The pricing (roughly $50,000 to $300,000+ per year per Vendr's purchasing data), implementation arc (two to three months to go live, longer to full production) and product surface all point at structured multi-quarter ABM programmes owned by marketing. Is 6sense intent event-based? No. 6sense intent is predictive - the model outputs a stage classification and an intent score, not a record of a specific buying event. Event-based signals (a trial started, a vendor switched, a subscription approaching renewal) come from a different data category. See What replaces intent data in 2026 for the shift towards event-based signals. How fast does 6sense intent refresh? Intent scores rescore daily and buying-stage predictions update as new signals arrive. The practical lag is elsewhere: a stage transition needs enough accumulated activity to cross a threshold, and reps see it after the CRM sync - so the transition typically lands days to weeks after the first underlying behaviour. Can 6sense and MarketSizer be used together? Yes. 6sense at the marketing-planning altitude, MarketSizer at the sales-execution altitude. Enterprise teams commonly keep both. Full breakdown at MarketSizer vs 6sense . How does 6sense compare with Demandbase? Both are enterprise ABM platforms with predictive intent scoring and orchestration. 6sense leans harder into the AI model as the interpretation layer; Demandbase leans harder into the advertising and account-based-experience surface. Which one fits depends on the ABM programme shape, not on intent quality alone. What does 6sense cost? 6sense does not publish platform pricing publicly. Vendr's transaction data places contracts at roughly $50,000 to $300,000+ per year (median in the mid-$50,000s), with variance based on module bundle and account scope. A free seller tier (50 credits per month) exists for contact-data lookups, but it does not include intent data or predictive scoring - the intent product described in this post is paid-platform only. Sources All qualitative and quantitative claims in this piece trace to public sources. Where a number is a range, we cite the third-party research that produced it. 6sense homepage - positioning line, product architecture. 6sense intent-data product page - the official description of the intent product. 6sense docs - predictive buying stages - the official five-stage taxonomy (Target, Awareness, Consideration, Decision, Purchase). 6sense - breaking down intent and engagement scores - the 0-100 intent score and score-to-stage thresholds. 6sense acquires Slintel (October 2021) - source of the technographic dataset. Vendr - 6sense pricing data - transaction-based contract ranges cited in this piece. G2 - 6sense Revenue Marketing reviews - reviewer data including average implementation time. Forrester - How to evaluate intent data providers - framework for assessing intent-signal scale, accuracy and overlap. MarketSizer platform capabilities and data-coverage claims (70M+ subscription records, 17M+ active subscriptions, 2. 5M+ active trials, 200+ vendors indexed, 140+ countries) - internal Claim Registry , sourced from live production tables and refreshed quarterly. Available on request via a demo . Recommended reading MarketSizer vs 6sense - side-by-side breakdown. HG Insights vs 6sense - head-to-head of the two enterprise ABM incumbents. What is HG Insights intent data? - explainer on the composed-intent alternative. Best purchase-intent data platforms for 2026 - the full landscape. What replaces intent data in 2026? - why event-based signals are displacing aggregated intent. ## Blog: HG Insights vs 6sense: Which One Does Your Team Need? HG Insights and 6sense both target enterprise ABM but optimise for different jobs - HG for planning depth, 6sense for orchestration. Where each wins, how the pricing compares, and how the largest teams end up using both. HG Insights and 6sense are both enterprise-grade B2B intelligence platforms, but they optimise for different jobs. HG's core is depth: technographics, spend, contracts and buyer intent, delivered as a data fabric for enterprise planning teams. 6sense's core is orchestration: anonymous and known intent signals modelled through predictive AI, activated across advertising, marketing automation and, via partner integrations, web personalisation for enterprise ABM programmes. Neither is a wrong answer; the right one depends on whether the buying committee is a data-and-strategy function or a marketing-orchestration function. If timing signals matter as much as planning breadth, an event-based subscription layer sits cleanly alongside either - see MarketSizer vs HG Insights and MarketSizer vs 6sense . What each tool actually is HG Insights positions itself as AI-powered Revenue Growth Intelligence , built on its Revenue Growth Intelligence Fabric data layer. Its core is depth of company-level data: technographic footprint across more than 20,000 products, IT-spend and contract-value estimates, and a buyer-intent overlay layered on top - since June 2025 including verified second-party review intent from its acquisition of TrustRadius. The primary customer is a strategy, marketing or RevOps function at a large B2B technology company using the data fabric to plan territories, size markets and structure ABM programmes. 6sense positions itself as an ABM platform powered by its Revenue AI engine . Its core is orchestration: intent signals from 6sense's proprietary B2B publisher network, third-party providers such as Bombora, review-site intent from G2, TrustRadius and PeerSpot, and first-party website traffic de-anonymised through its company-identification graph (6sense states it does not use bidstream data), modelled through 6sense's own predictive AI and activated across advertising, marketing automation and, via partner integrations, web personalisation as one connected motion. The primary customer is an enterprise marketing or ABM function running a structured multi-quarter programme. Both are enterprise-heavy. Both deliver an account-level score plus supporting data. 6sense typically takes one to three months to implement, HG deployments are usually measured in weeks, and typical contracts on both sides run from the mid tens of thousands into six figures per year. The differences show up in what the platform is doing once configured. Where HG Insights wins Four situations where HG is the sharper pick. Technographic depth as the primary signal. HG's technographic dataset is one of the broadest in the market. If the account-planning question is which technologies does the target run and how much does that stack cost , HG's data is closer to the answer than 6sense's inference layer. Spend and contract-value estimates. HG's spend intelligence is a genuine differentiator for teams building territory or TAM models where IT budget matters. 6sense does not carry the same spend-modelling depth. Market-intelligence and analyst-facing reporting. HG's taxonomy and dataset shape are well-suited to internal reporting where a market-intel team needs to slice adoption curves or category maturity for leadership. 6sense reports on programme performance rather than category structure. Long-horizon programme design. When the deliverable is a 12-month ABM programme design, HG's data-fabric framing feeds planning better than an orchestration platform whose value is realised in live campaigns. Where 6sense wins Four situations where 6sense is the sharper pick. Full-funnel orchestration in one platform. 6sense's value grows with how many channels it activates - display advertising through its native DSP, marketing automation, sales notifications, conversational email and, via partner integrations such as Drift and Folloze, web personalisation - all off the same underlying account model. If the ABM motion depends on that orchestration surface, 6sense is doing something HG does not. Predictive AI account scoring. 6sense's AI models combine intent, firmographics and engagement into predictive account scores tied to a buying-stage taxonomy (Target, Awareness, Consideration, Decision, Purchase). Teams that plan campaigns around buying-stage transitions find that model useful. Anonymous-visitor uplift. 6sense identifies accounts behind anonymous web traffic and pushes them into activation flows. For a marketing team focused on first-party attribution and pipeline sourced from web engagement, that is a real workflow surface HG does not match. Marketing-owned buying decisions. 6sense's DNA is marketing. Where marketing is the owner of ABM budget and platform strategy, 6sense is easier to activate than a data-fabric tool whose value depends on integration into planning workflows. Enterprise ABM is planning. Execution is a different signal. HG plans your programme. 6sense orchestrates it. If your reps need to know which of those accounts is trialling a competitor right now , event-based subscription signals sit at a different altitude. Free trial, live data on your ICP. Book a demo → See all comparisons → Signal mechanics: technographic depth vs predictive AI The signals diverge more than the marketing suggests, starting with the inputs. HG leans on bidstream web activity and TrustRadius review behaviour; 6sense on its own publisher network, partner feeds such as Bombora and review sites, and first-party de-anonymisation. They diverge further in how the raw data is turned into something a team can act on. HG's approach is composed. Raw signals from partner networks are anchored to a much larger technographic and spend baseline. The intent score is one column in a wider dataset that also carries what the account already owns, roughly what they spend on IT, and (in some tiers) contract renewal windows. A rep or planner interprets the intent uplift against that context. 6sense's approach is modelled. Raw signals are fed through 6sense's AI to produce a predictive account score aligned to a buying stage. The rep primarily sees the score and the stage rather than the underlying breakdown. Where HG expects the buyer to interpret intent against firmographic context, 6sense compresses that interpretation into the model. Neither approach is event-based. Both produce aggregated inferences delivered on refresh cadences that, by our own editorial estimate of the publicly described mechanics, lag the underlying behaviour by days to weeks - signal scale, accuracy and overlap are the dimensions Forrester recommends assessing when evaluating intent providers. Both are useful for planning; both trail the specific fourteen-to-thirty-day buying windows that decide competitive-displacement outcomes. Pricing and procurement HG Insights now publishes starting prices: Starter from $22,000 per year and Growth from $45,000, with Enterprise on custom terms, all on a two-year term (one-year terms are 10% higher). 6sense does not publish list pricing. Industry aggregators and public sales-battlecard research place both in the enterprise bracket. Dimension HG Insights 6sense Entry-level pricing (public research) Roughly $24,000 to $150,000+ per year, median around $52,000 (Vendr contract data) Roughly $50,000 entry for sales-intelligence packages; $60,000 to $130,000 typical mid-market; $100,000 to $300,000+ for full-platform enterprise deals (Vendr contract data) Contract structure Annual, typical multi-year enterprise cycle Annual, typical multi-year enterprise cycle Implementation time Weeks of data integration Typically one to three months, longer for complex enterprise stacks Primary buyer Strategy / market intelligence / RevOps Enterprise marketing / ABM leadership Both are procured through enterprise sales cycles with legal and finance review. Neither sells the platform itself self-serve, though 6sense offers a limited free tier (50 credits per month, without intent data or predictive scoring); every paid plan on both sides is quote-based. The decision tree The choice usually resolves along three axes. Owner of the buying decision. Data / strategy / RevOps → HG. Marketing / ABM → 6sense. What the platform is for. Planning and reporting → HG. Orchestration and activation → 6sense. Signal shape you value most. Technographic and spend depth → HG. Predictive AI account scores tied to buying stage → 6sense. If two of the three point to the same vendor, that is usually the right call. Mixed answers often mean the team ends up using both, which is more common at large enterprises than either vendor markets. How enterprise teams sometimes use both At the largest enterprises, the two coexist. HG feeds the strategy team's planning outputs (territory design, TAM sizing, adoption curves) and 6sense executes the marketing programme those plans commission. The two datasets sit in different systems and the coordination happens at the account-list level rather than at the platform level. For most mid-market and mid-enterprise buyers, running both is over-engineered. Pick the one whose owner is closer to the buying committee and add other layers (signal execution, contact data, sales workflow) as separate purchases. Where a subscription-intelligence layer fits alongside Both HG and 6sense answer questions at the planning altitude: which accounts should be in the programme, what is the shape of the segment, which are researching this category. That is a market intelligence job and both do it well. Neither answers the execution question: which specific account just started trialling your competitor, and where are they in that trial today. Subscription intelligence is a different category of signal - an event-based feed of what accounts are doing right now with their software stack. Where HG surfaces intent as a score and 6sense surfaces intent as a predictive stage, purchase intent of this kind attaches a specific event to a specific account: trial started on day X, vendor switched from A to B, renewal window opening in 45 days. The practical pattern at enterprise teams: HG or 6sense (or both) at the marketing / ABM planning altitude; MarketSizer at the sales-execution altitude, opening its side panel alongside HubSpot, Salesforce and LinkedIn so reps see the timing signal next to the interface they already work in. Full breakdowns at MarketSizer vs HG Insights and MarketSizer vs 6sense . Frequently asked questions Is HG Insights or 6sense better for enterprise ABM? Different jobs. HG is better if the ABM programme is data- and planning-driven, particularly where technographic depth and spend estimates matter. 6sense is better if the ABM programme is orchestration- and activation-driven, particularly where advertising, marketing automation and partner-delivered web personalisation are the primary channels. Most enterprise teams pick based on who owns the programme. What is the difference between HG intent and 6sense intent? The inputs differ: HG's intent is predominantly bidstream-derived plus TrustRadius review activity, while 6sense combines its proprietary publisher network, partners such as Bombora, review-site intent and first-party de-anonymisation (and states it does not use bidstream). HG delivers intent as a topic-level score against a rich technographic and spend baseline. 6sense delivers intent as a predictive buying-stage score inside an orchestration platform. HG expects the buyer to interpret the score against context; 6sense compresses that interpretation into the AI model. How much do HG Insights and 6sense cost? HG Insights publishes starting prices (Starter from $22,000 per year, Growth from $45,000, on a two-year term); 6sense does not publish list pricing. Third-party contract data (Vendr) places HG at roughly $24,000 to $150,000+ per year with a median around $52,000, and 6sense at roughly $50,000 entry for sales-intelligence packages, $60,000 to $130,000 for typical mid-market deals, and $100,000 to $300,000+ for full-platform enterprise deployments, with a reported median around $55,000. Both are procured through enterprise sales cycles, typically with multi-year commitments. Can HG Insights and 6sense be used together? Yes, at large enterprises. HG feeds strategy and planning outputs while 6sense executes the marketing programme. For most mid-market and mid-enterprise buyers, running both is over-engineered. Does either detect competitor trials? Not directly. Both surface aggregated intent scores that suggest research is happening; neither fires on the specific event that a target account started trialling a specific competitor. That is a different signal category - see How to detect competitor trials . Which one is better for sales reps directly? Neither is primarily built for reps, though both are moving that way: 6sense sells a Sales Intelligence product with a rep-facing Chrome extension, and HG markets intent-driven leads since acquiring TrustRadius. In practice HG's output goes mostly to strategy and RevOps, 6sense's to marketing operations, and reps consume both largely through CRM records that other functions push over. Execution-facing signals (Chrome-extension side panels alongside HubSpot, Salesforce, LinkedIn or Apollo) are a job neither platform is built for. Is 6sense predictive scoring more accurate than HG intent? Neither publishes independently audited accuracy figures. Both attract similar false-positive complaints characteristic of aggregated third-party intent. Which model fits a team's workflow better tends to matter more than accuracy claims either vendor makes. What replaces both if I want event-based signals instead of aggregated intent? Subscription-intelligence platforms deliver event-based signals (trial started, vendor switched, subscription approaching renewal) instead of aggregated scores. See What replaces intent data in 2026 . Sources All quantitative claims in this piece trace to public sources. Where a number is a range, we cite the third-party research that produced it. HG Insights homepage - positioning language, product breadth. HG Insights Buyer Intent product page - how the intent product describes itself. HG Revenue Growth Intelligence Fabric - HG's own data-layer positioning and coverage figures. HG Insights acquires TrustRadius (June 2025) - second-party review intent and intent-driven leads. Disclosure: HG Insights has owned TrustRadius since June 2025, so TrustRadius data about HG is not an independent source. HG Insights and 6sense compared, by HG and HG Insights + 6sense Better Together - how HG itself positions the two tools, including the spend and contract-intelligence differentiators. 6sense homepage - positioning language, product breadth. 6sense intent data page - 6sense's own description of its intent sources. 6sense CTO on data collection - 6sense's on-record statement that it does not use bidstream data. 6sense buying-stage glossary - the Target, Awareness, Consideration, Decision, Purchase taxonomy. 6sense + Drift and 6sense + Folloze - web personalisation delivered through partner integrations rather than natively. 6sense onboarding documentation - implementation scope and timeline. G2: 6sense Revenue Marketing vs HG Insights - user ratings, ease-of-setup and implementation-complexity comparison. Vendr - HG Insights and Vendr - 6sense - third-party contract data behind the pricing brackets above. HG Insights publishes starting prices on its pricing guide (viewed 6 October 2026); 6sense does not publish list pricing. Forrester - How To Evaluate Intent Data Providers - the evaluation framework (signal scale, accuracy, overlap); the latency estimates in this piece are our own. The Forrester Wave: Intent Data Providers For B2B, Q1 2025 - the category's current analyst benchmark, in which 6sense is named a Leader. MarketSizer platform capabilities and data-coverage claims (70M+ subscription records, 17M+ active subscriptions, 2. 5M+ active trials, 200+ vendors indexed, 140+ countries) - internal Claim Registry , sourced from live production tables and refreshed quarterly. Available on request via a demo . Recommended reading MarketSizer vs HG Insights - side-by-side breakdown. MarketSizer vs 6sense - side-by-side breakdown. What is HG Insights intent data? - explainer piece. Best purchase-intent data platforms for 2026 - the full landscape. What replaces intent data in 2026? - why event-based signals are displacing aggregated intent for execution teams. How to detect competitor trials - the four detection methods and the buying-window math. ## Blog: Apollo vs ZoomInfo: Which Is Better for Your Team? Apollo and ZoomInfo compared honestly - dataset depth, intent signals, pricing and procurement. Which one wins for SMB outbound, which one wins for enterprise ABM, and how teams end up running both. Apollo and ZoomInfo are both B2B sales intelligence platforms with contact data, intent signals and outreach modules, but they optimise for different buyers. Apollo is a self-serve, sequence-first tool sized for SDR and AE teams at SMB and mid-market. ZoomInfo is an enterprise data platform sized for revenue teams that want the deepest contact-and-firmographic dataset and a full engagement stack in one procurement. Neither is a wrong answer; the right one depends on where the primary bottleneck sits. If timing signals matter as much as contact volume, an event-based signal source layers cleanly on top of either - see MarketSizer vs Apollo and MarketSizer vs ZoomInfo . What each tool actually is Apollo positions itself as the end-to-end AI sales platform . Its core is a large B2B contact database (Apollo publishes coverage in the 275M contact range) bundled with email, phone and LinkedIn sequencing in a single self-serve seat. The free plan is one of the most widely used entry points in B2B SaaS, and Apollo's published pricing starts at $49 per user per month on Basic and $79 per user per month on Professional on annual billing ($69 and $99 billed monthly). ZoomInfo positions itself as the go-to-market platform, powered by the world's most accurate B2B data . Its dataset covers hundreds of millions of professional profiles with org-chart, hierarchy and firmographic depth built out for enterprise use, and its platform extends into engagement, intent and workflow modules bought as one package. Pricing is not publicly listed; independent research by Fifty Five and Five places fully-loaded contracts (with intent add-on) in the $25,000-$58,000+ per year range. Both tools do overlapping things. Both have contact data. Both offer intent signals. Both integrate into HubSpot and Salesforce. The differences show up in dataset depth, workflow surface and how a team pays for it. Where Apollo wins Apollo wins wherever speed of adoption matters more than depth of dataset. The free plan lets a whole outbound team start without procurement, which counts for a lot when a purchase has to survive a bottom-up adoption curve. Once the team is in, the same seat covers data lookup, email sequencing, a dialer and a meeting scheduler - the whole tool chain for a single-motion SDR team collapses into one login. The economics point the same way. At $49-$79 per user per month on annually-billed paid plans (roughly $600-$950 per user per year), Apollo fits into a mid-market SDR budget without ZoomInfo's contracting friction. And for teams that pitch on the back of a specific role posting, Apollo's hiring-event scraping is fresh enough (days, not weeks) to build a real outreach cadence around. The common thread is speed. Apollo's value grows with how quickly a team wants to move from "we should buy something" to "reps are sending sequences. " Where ZoomInfo wins ZoomInfo's case is strongest in the enterprise rooms Apollo rarely reaches. Enterprise contact depth. ZoomInfo's dataset is broader and deeper on large enterprise accounts, and its org-chart and hierarchy data supports multi-thread outbound plays that Apollo is not built for. Single-vendor GTM stack. If a revenue team wants data, intent, engagement, workflow and analytics in one procurement instead of five, ZoomInfo's platform breadth is genuinely useful. There is one contract, one integration surface and one place to manage the stack. Regulated or complex procurement. ZoomInfo has an established procurement path for enterprise finance and IT. That matters when the buying committee involves security review and multi-year commitment. Compliance-heavy verticals. ZoomInfo publishes detailed compliance documentation (data-provenance, GDPR, DNC handling) that lands well with legal review in regulated industries. The common thread is scale. ZoomInfo's value grows with the size and structure of the buying organisation. Signal quality: intent, hiring and funding Both tools ship intent products, and both are built on similar mechanics: aggregated account-level scores derived from web-behaviour partners, refreshed periodically. Apollo intent bundles topic-level scores with hiring-event and funding-event feeds. Reps can filter a list on topic intent alongside firmographic criteria, or trigger sequences off a fresh hiring event. See What is Apollo intent data for the full breakdown. ZoomInfo intent is a paid add-on to the base platform and is deeper in signal categories, particularly for enterprise topics. Third-party review data suggests the false-positive rate is a persistent complaint among power users - independent research by Fifty Five and Five reports 52% of ZoomInfo sales-intent users seeing frequent false positives, with 29% specifically flagging misattributed IP tracking. That does not make the signal worthless, but it is a real constraint on how reps use it. The honest read: neither tool's intent product is event-based. Both are aggregated inference. Which one fits better is less about intent quality than about the surrounding platform. Pricing and procurement The two tools sit at very different price points and buy through very different processes. Dimension Apollo ZoomInfo Entry-level pricing (published / researched) $49/user/month Basic, $79/user/month Professional (annual billing); free plan ~$14,995/year Professional; ~$24,995/year Advanced with intent Fully-loaded pricing Around $10,000-$15,000/year for a small team on Professional $25,000-$58,000+/year with intent + global add-ons (per Fifty Five and Five) Contract structure Month-to-month or annual, cancel anytime Annual minimum, often 2-3 year commitments Procurement path Self-serve credit card Enterprise sales cycle, legal + finance review Neither is right or wrong; they are shaped for different buyers. Apollo's pricing supports a team that wants to move fast and iterate. ZoomInfo's supports a team that wants a single strategic vendor for the GTM data layer. Total cost of ownership: a 15-rep worked example Concrete math for a mid-market SaaS team of 15 outbound reps, using each vendor's published or independently-researched pricing. Line item Apollo (Professional) ZoomInfo (Advanced, with intent) Base seat / platform 15 $99/mo 12 = $17,820 ~$24,995/yr (per Fifty Five and Five, 2025) Intent add-on Bundled Included in Advanced Global data add-on Included +$10,000/yr (typical range) Onboarding / setup Self-serve, effectively $0 Setup and onboarding typically bundled into first-year contract Contract structure Month-to-month or annual Annual, often 2-3 year commitment Year-one all-in ~$17,820 ~$35,000 (single-year), higher if multi-year uplifts stack The comparison flips at scale. At 40+ reps or a genuine enterprise footprint with international coverage and multi-thread ABM plays, ZoomInfo's bundle economics improve and the enterprise features start to earn the price. At 15 or fewer reps focused on mid-market outbound, Apollo's unit economics are hard to beat. Note this is a like-for-like on the platform layer. Teams typically add outreach optimisation (Lemlist, Instantly), signal layers, or timing intelligence separately regardless of which platform they pick as the base. The decision tree The choice usually resolves cleanly along four axes. Team size. Under 20 reps? Apollo is the safer bet on unit economics. 40 or more? ZoomInfo starts to earn its bundle. Buying committee. Self-serve credit-card procurement? Apollo. Legal, finance and IT security review? ZoomInfo has the procurement path built. Motion. Volume outbound where the bottleneck is contact volume and sequencing? Apollo. Enterprise ABM with hierarchy data, territory design and org-chart plays? ZoomInfo. Contract tolerance. Prefer to cancel or downgrade after 60 days? Apollo. Comfortable committing 2-3 years for volume discount? ZoomInfo. The blunt version: three-out-of-four on the left column of each axis points to Apollo; three-out-of-four on the right points to ZoomInfo. Mixed results usually mean the team ends up using both, which is more common than either vendor markets. Compliance and procurement, side by side This is where the vendors diverge more than most demos surface. Data provenance. ZoomInfo publishes detailed documentation on contact-data sourcing (self-declared professional data, community contribution, third-party enrichment, machine-learning inference). Apollo also publishes provenance but with less depth on the enterprise-compliance frame. For a regulated buyer, ZoomInfo's documentation carries less legal-review load. Regional compliance. Both operate under GDPR and CCPA. ZoomInfo publishes region-specific data-handling documentation; Apollo publishes a shorter privacy overview. Both offer DPA templates; ZoomInfo's legal negotiation surface is more mature for enterprise procurement. SOC 2 / ISO / security posture. Both hold SOC 2 Type II. ZoomInfo additionally maintains ISO 27001 and publishes vulnerability-disclosure and penetration-testing documentation as part of its enterprise sales motion. Do-not-contact list handling. ZoomInfo publishes a public opt-out and the removal workflow is documented for both individual and organisation-level requests. Apollo offers opt-out but with less enterprise-grade documentation. Procurement path. Apollo: self-serve credit card, terms of service, no negotiation required. ZoomInfo: MSA + DPA + order form, typical enterprise cycle of 4-12 weeks with legal and finance review. For a regulated-industry buyer (finance, health, government), ZoomInfo's procurement documentation and compliance depth is often the deciding factor - not intent quality or dataset breadth. Apollo can serve regulated industries but frequently loses on the legal-review lift alone. Contact data plus timing signals Keep Apollo or ZoomInfo for contact data. Add MarketSizer for the timing layer neither one covers - trials, switches, renewals and churn events attached to the account. Free trial, live data on your ICP. Book a demo → See all comparisons → How teams end up using both Many teams end up with both tools, and the pattern is more coherent than it sounds. ZoomInfo often lives as the enterprise-account data layer - the tool that carries deep firmographics, org charts and compliance-cleared contact data for the top of the target list. Apollo often lives as the mid-market outbound engine - the tool that carries volume contact data and the sequencing surface where reps actually work. The overlap is real (both hold contact data on the same accounts), and consolidation conversations happen at renewal, but the two tools rarely serve identical jobs at scale. Teams that force one to replace the other frequently end up unhappy on one axis (enterprise depth) or the other (rep-level speed). Where a subscription-intelligence layer fits alongside Both Apollo and ZoomInfo answer the question who to reach out to. Neither answers when . Subscription intelligence is a different category of signal: an event-based feed of what accounts are doing right now with their software stack - trials starting, vendors switching, subscriptions approaching renewal, tools being churned. Where Apollo and ZoomInfo score accounts by inferred activity, subscription-intelligence signals attach a specific event to a specific account - purchase intent you can name and date, sitting on top of the ICP scoring that decided the account was worth watching in the first place. The practical pattern: contact data lives in Apollo or ZoomInfo; timing signals live in a subscription-intelligence tool; the two overlay in the rep's workflow so account selection and outreach message are informed by both. MarketSizer's Chrome extension opens alongside Apollo and CRMs like HubSpot and Salesforce, so a rep working an Apollo list sees the timing signal in a side panel beside Apollo's data. Full breakdowns at MarketSizer vs Apollo and MarketSizer vs ZoomInfo . Frequently asked questions Is Apollo cheaper than ZoomInfo? Yes, by a substantial margin. Apollo publishes paid plans at $49 per user per month (Basic) and $79 per user per month (Professional) on annual billing, with a free tier, per apollo. io/pricing . ZoomInfo fully-loaded contracts (with intent add-on) sit at $25,000-$58,000+ per year, per Fifty Five and Five's 2025 pricing research . The comparison is not clean per-user because ZoomInfo bundles platform modules that Apollo does not, but on any measure Apollo is the lower entry point. Which one is better for SMB outbound? Apollo, in most cases. The free plan, self-serve pricing and same-seat sequencing make it the more common fit for SDR teams at growing companies. ZoomInfo is disproportionately shaped for enterprise revenue organisations. Which one is better for enterprise ABM? ZoomInfo, in most cases. The dataset depth, org-chart data and platform breadth support the kind of multi-thread ABM programme where Apollo runs out of coverage. Does Apollo have intent data like ZoomInfo? Yes, but it is a different shape. Apollo bundles topic intent with hiring and funding signals inside its main sales interface. ZoomInfo intent is a paid add-on with deeper category coverage and a stronger enterprise data footprint. Both are aggregated and inferential; neither is event-based. See What is Apollo intent data . Can I use Apollo and ZoomInfo together? Many teams do. ZoomInfo carries enterprise-account depth; Apollo carries mid-market volume and the sequencing engine. Consolidation typically happens at renewal rather than day one. What does MarketSizer do differently from Apollo and ZoomInfo? MarketSizer is a subscription-intelligence platform, not a contact database. It surfaces event-based buying signals - trials, switches, renewals, churn - and attaches each signal to the underlying event. Apollo and ZoomInfo answer who ; MarketSizer answers when . Teams typically keep one of Apollo or ZoomInfo for contact data and add MarketSizer as the timing-signal layer. Which one is better for regulated industries? ZoomInfo. Its compliance documentation, SOC 2 and ISO 27001 postures, and enterprise procurement path (MSA + DPA + order form) carry substantially less legal-review load than Apollo's lighter self-serve materials. This is often the deciding factor for finance, health and government buyers regardless of what intent quality either tool offers. Does Apollo integrate with ZoomInfo? Not natively, but many teams pass data between them via their CRM (HubSpot or Salesforce). Contact updates in ZoomInfo push to the CRM; Apollo consumes from the CRM; sequencing runs from Apollo. This is a common pattern for teams that keep both. Which one has better data accuracy? Both publish accuracy claims and neither publishes an independently audited number. ZoomInfo's enterprise coverage on large companies is generally deeper; Apollo's coverage at SMB and mid-market is generally broader. False-positive rates on the intent products are similar - Fifty Five and Five research quantifies 52% of ZoomInfo intent users seeing frequent false positives, and the aggregated-intent category as a whole runs at broadly similar precision. Is there a free tier on either? Apollo has a well-established free plan. ZoomInfo does not offer a free tier; access starts with a paid contract (typically with a trial arranged through sales). What replaces both if I want event-based signals instead of aggregated intent? Subscription-intelligence platforms - MarketSizer is one - deliver event-based signals (trial started, vendor switched, subscription approaching renewal) instead of aggregated topic scores. These do not replace the contact-data layer, but they replace the aggregated-intent layer with something that fires when a specific buying event happens. See What replaces intent data in 2026 . Sources Every quantitative claim in this piece traces to a public source. Where a number is a range or a bracket, we cite the third-party research that produced it. Apollo homepage - positioning language, contact-database size (275M range). Apollo pricing page - published per-user-per-month tiers and free-plan availability. ZoomInfo homepage - positioning language, product breadth. Fifty Five and Five - ZoomInfo pricing guide 2025 - independent research on fully-loaded ZoomInfo contract sizes. Fifty Five and Five - ZoomInfo intent review - source for the 52% false-positive statistic and misattributed-IP-tracking finding. Forrester - How to evaluate intent data providers - broader context on false-positive rates across the aggregated-intent category. MarketSizer platform capabilities and data-coverage claims (70M+ subscription records, 17M+ active subscriptions, 2. 5M+ active trials, 200+ vendors indexed, 140+ countries) - internal Claim Registry , sourced from live production tables and refreshed quarterly. Available on request via a demo . Recommended reading MarketSizer vs Apollo - side-by-side breakdown. MarketSizer vs ZoomInfo - side-by-side breakdown. What is Apollo intent data? - what Apollo's intent product actually is. What is HG Insights intent data? - the enterprise-shaped alternative to ZoomInfo intent. Best purchase-intent data platforms for 2026 - the full landscape. What replaces intent data in 2026? - why event-based signals are displacing aggregated intent. ## Blog: What Is Apollo Intent Data? Apollo's intent product bundles hiring, funding and topic signals inside the same platform reps use for contact lookup and sequencing. Here is what it actually is, how it works, and where it fits. Apollo intent data is a set of account-level signals bundled inside Apollo's sales platform, primarily hiring activity, funding events, news mentions and topic-level buyer-intent scores. It sits alongside Apollo's contact database and email sequencing to help outbound teams prioritise which of the accounts already in their list to work first. It is well suited to SDR-led motions where the primary bottleneck is contact volume. It is less well suited when the deciding question is when to reach out rather than who to reach out to; for that, see how event-based signals compare in MarketSizer vs Apollo . What is Apollo intent data? Apollo positions itself as the end-to-end AI sales platform. Intent data is one component of that platform - a set of buying-signal inputs delivered inside the same interface where Apollo users search for contacts, build sequences and run outbound. The intent product covers several signal types under one label: Hiring signals. A target account posts a role that maps to a job function relevant to your product. Funding events. A target account raises capital, expands operations or announces a growth milestone. News mentions. Media coverage that hints at strategic direction (leadership changes, product launches, market entry). Topic-level intent. Aggregated buyer-intent scores per topic, sourced from Apollo's own network and third-party data partners. The output is delivered as filters and score columns in Apollo's People Search and Companies views. A user can build a list scoped to companies showing hiring activity in specific roles, or with a topic-intent score above a threshold, and push that list into a sequence in the same tool. How it works Apollo's intent signals originate from a mix of first-party scraping and third-party data partnerships. The specific composition depends on the signal type: Hiring signals come from continuous scraping of job boards and company career pages. Latency is short - a new posting appears in Apollo within days of going live. Funding and news signals come from press-release feeds, business data providers and Apollo's own enrichment layer. Coverage is broad for larger companies and thinner for early-stage SMB. Topic intent scores are aggregated from web-activity partner networks and mapped to Apollo's topic taxonomy. Scores are refreshed periodically rather than in real time. The signals are exposed as filters in the platform's search UI. A rep working in Apollo can layer hiring activity, funding stage and topic intent on top of a firmographic filter to narrow a list before sequencing. Who it is built for Apollo is built for SDR and AE teams running high-volume outbound. The self-serve pricing, the popular free plan and the same-seat sequencing all point to a workflow where a single rep does everything from contact lookup to email send. Intent data slots into that workflow as a way to narrow the top of the funnel. When the primary bottleneck is which of the many contacts in the database to work first, Apollo's hiring, funding and topic signals give the rep a defensible way to sort a list. Apollo's own pricing page lists a free plan alongside paid tiers starting at $49 per user per month on Basic and $79 per user per month on Professional on annual billing ($69 and $99 billed monthly). Compared to enterprise data platforms this is a low-friction adoption path, and it explains why Apollo shows up in nearly every mid-market outbound stack. Timing signals, not just topic scores Apollo tells you which accounts might be researching your category. MarketSizer tells you which accounts started trialling your competitor nine days ago. See the signal live on your ICP - Free trial, no card required. Book a demo → MarketSizer vs Apollo → Where it works well Three motions where Apollo intent lands cleanly. High-volume outbound at the SMB and mid-market end. When a rep needs to work 200 accounts a week and needs a way to sequence them, hiring plus topic intent is a defensible filter. Signal-plus-persona plays. A specific hiring event (VP of Sales just posted a role) is a strong prompt for a sales-tech pitch. Apollo pairs the signal with the contact, so the rep can reach out in the same interface. Growth-stage accounts. Funding-stage signals catch companies at inflection points where budget expands and buying decisions accelerate. Where it falls short Two limitations that show up when the deciding question changes from who to when . Hiring and funding are indirect signals of buying. A company posting a role or raising a round is one step removed from the buying decision itself. It signals capacity, not action. That gap is fine when the outreach is a broad category pitch; it is a real ceiling when the message needs to reference the specific behaviour that made the account interesting. Topic intent is aggregated and score-based. Same properties as the wider intent-data category: rolled-up account-level scores generated from partner networks, refreshed on a slower cadence than the buying window itself. A rep sees the score, not the underlying event. That works for prioritisation, less well for a timing-based competitive-displacement outreach. The practical version: Apollo's intent data helps a rep decide which of the accounts in Apollo to work next. It does not tell the rep that a specific account just started trialling a competitor, or that a specific subscription is approaching renewal. Those are different signal types, and they come from a different data category. Worked example: building a hiring-signal list in Apollo To make the mechanics concrete, take a hypothetical outbound motion: a sales-tech company targeting VPs of Sales at mid-market B2B SaaS. Here is how an SDR would use Apollo intent to build a list, step by step, based on the flows Apollo documents in its own product materials. Filter by hiring signal. In People Search, open filters and select "Recent job posting" with the role set to "VP of Sales" or "Head of Revenue" and the posting age set to the last 14 days. That already narrows Apollo's database to accounts actively adding sales leadership. Layer firmographic filters. Add employee count (200 to 1,000), industry (Software, SaaS), and country. This trims the hiring-signal set to accounts that also match the ICP. Layer topic intent. Add "Buying intent" filter with topics like "sales enablement" or "sales productivity" and set the intent-score threshold above the baseline (Apollo exposes intent as a numeric score per topic per account). Save the account view. Save as a dynamic view - Apollo re-runs the filter continuously as new postings appear, so the list refreshes automatically. Push to sequence. From the account view, select all contacts matching the persona (VP of Sales, CRO, CMO) at each account, then push into an existing sequence. Apollo handles the send in the same seat. The whole flow lives inside Apollo. There is no export, no separate sequencing tool, no data hand-off. That single-seat workflow is Apollo's core advantage over a stack that composes contact data, enrichment and sending across three or four vendors. The trade-off shows up when the deciding question changes. If the SDR needs to reach an account the moment it started trialling a specific competitor - the fourteen-to-thirty-day window that decides most competitive-displacement outreach - Apollo's hiring and topic filters do not surface that. Different signal category. Signal quality by type Apollo bundles four signal types under the intent-data label, and they are not equal on freshness or precision. Rough characteristics based on the mechanics each signal type uses. Signal type Source Latency Best for Hiring events Continuous scraping of job boards and company career pages Days Sales-tech, HR-tech, learning-tech pitches triggered by a specific role Funding events Press releases, business data providers, Apollo enrichment Days to weeks Growth-stage or budget-expansion plays; broader for larger companies News mentions Media coverage feeds Days Leadership-change or M&A plays where the signal is a named event Topic intent Aggregated web-behaviour scoring across partner networks Weeks List narrowing at the top of the funnel; not a trigger for individual outreach The practical read: hiring, funding and news are event-shaped and fresh enough to act on individually. Topic intent is aggregated and slower - useful as a list filter, less useful as a signal that anything specific happened. Apollo unit economics for a 10-rep team Straightforward math based on Apollo's published pricing : 10 reps on Basic ($49/user/month, annual billing): $5,880 per year. 10 reps on Professional ($79/user/month, annual billing): $9,480 per year. 10 reps on Organization ($119/user/month, annual billing): $14,280 per year. Compare with a stack that composes an enrichment tool ($10-20k), a sequencing tool ($5-8k) and a signal tool ($5-10k) - three vendors, three contracts, three integrations. Apollo's bundle collapses that to one seat. The cost of the bundle is that every capability lives at Apollo's depth. Contact-data completeness, signal freshness and workflow flexibility are all what Apollo has built, not what the best-in-class specialist has built for that layer. For a team where speed and unit economics matter more than best-in-class depth on any single capability, that trade sits in Apollo's favour. How MarketSizer signals compare MarketSizer's purchase intent signals are event-based rather than inferred. Each signal on an account corresponds to a specific observable event - a competitor trial detected, a vendor switch, a renewal window opening, a churn indicator. The signal is attached to the event, not to a score. Three practical differences that matter for outbound teams already using Apollo: The signal is inspectable. A rep can see what event triggered the alert and reference it in outreach. That works particularly well for competitive-displacement plays where knowing the specific competitor being trialled changes the whole message. Latency is short. Signals fire when the event occurs, not on a weekly aggregate cadence. That is what unlocks the fourteen-to-thirty-day trial window. The two tools compose. MarketSizer's Chrome extension opens alongside Apollo, so a rep building an Apollo list sees MarketSizer's event signals in the side panel. The workflows layer rather than compete. The full breakdown, including feature-by-feature comparison and pricing, is in MarketSizer vs Apollo . Frequently asked questions What is Apollo intent data? Apollo intent data is a set of buying signals bundled inside Apollo's sales platform. It includes hiring activity, funding events, news mentions and topic-level buyer-intent scores, delivered as filters and score columns in Apollo's People Search and Companies views. Reps use it to prioritise which accounts in the Apollo database to sequence first. How does Apollo intent data compare to Bombora? Bombora is a specialised topic-level intent provider whose data is used by many downstream tools, including Apollo in parts of its intent product. Apollo bundles Bombora-style topic intent with its own hiring, funding and news signals inside one interface. If a team already runs Bombora natively, Apollo is not a substitute so much as an activation surface for it. Is Apollo intent data event-based? Not primarily. Hiring signals are event-shaped (a specific role posted on a specific day) but the topic-intent element is aggregated and score-based. Event-based buying signals - competitor trials, vendor switches, renewals - come from a different category of tool; see What replaces intent data in 2026 for the split. Do Apollo and MarketSizer work together? Yes. Most teams keep Apollo for contact data and sequencing and add MarketSizer for event-based buying signals. MarketSizer's Chrome extension opens alongside Apollo so reps see the signals in a side panel beside the same interface. Full breakdown at MarketSizer vs Apollo . Who uses Apollo intent data? Predominantly SDR and AE teams running high-volume outbound at SMB and mid-market. Apollo's self-serve pricing, free plan and same-seat sequencing make it a common starter stack for growing outbound teams. What is Apollo intent data best for? Prioritising which accounts in Apollo's database to work first. It is well-suited to volume-led outbound where the deciding question is which contact to sequence next. It is less well-suited when the deciding question is when a specific account enters a buying window. How much does Apollo intent data cost? Intent signals are bundled into Apollo's paid tiers rather than sold standalone. Per Apollo's pricing page , that is $49 per user per month on Basic and $79 per user per month on Professional on annual billing. There is no separate intent add-on line item for most teams. Is Apollo intent data as good as ZoomInfo intent? Different shapes, similar mechanics. ZoomInfo intent has deeper enterprise topic coverage; Apollo intent is bundled into a self-serve platform with a free tier. Both are aggregated third-party intent and both attract false-positive complaints - Fifty Five and Five research found 52% of ZoomInfo intent users seeing frequent false positives. Which one fits depends on the surrounding platform, not on intent quality alone. See Apollo vs ZoomInfo . How often does Apollo refresh intent scores? Hiring, funding and news signals refresh within days of the underlying event. Topic-intent scores refresh periodically (typically weekly) because they are derived from aggregated partner-network activity. The refresh cadence is fine for list-building; too slow for event-triggered outreach on a specific competitor. Can I export accounts from Apollo intent into another tool? Yes. Apollo lets you save account views and export them via CSV or push them into an integrated CRM (HubSpot, Salesforce, others). Most teams keep the sequencing inside Apollo but push flagged accounts into their CRM for pipeline reporting. Does Apollo intent detect competitor trials? Not directly. Apollo intent covers hiring, funding, news and topic-level scores. Detecting that an account started trialling a specific competitor is a different data category - see How to detect competitor trials and What replaces intent data in 2026 for how event-based subscription signals differ from aggregated intent. Sources All quantitative claims in this piece are traceable to public sources. Where a claim requires interpretation, we note it inline. Apollo homepage - positioning line, product description. Apollo pricing page - published per-user-per-month pricing tiers and free-plan availability. Apollo sales engagement product page - hiring signals, funding events, topic-intent inputs. Forrester - How to evaluate intent data providers - background on false-positive rates across the aggregated-intent category. MarketSizer platform capabilities and data-coverage claims (70M+ subscription records, 17M+ active subscriptions, 2. 5M+ active trials, 200+ vendors indexed, 140+ countries) - internal Claim Registry , sourced from live production tables and refreshed quarterly. Available on request via a demo . Recommended reading MarketSizer vs Apollo - side-by-side breakdown of the two tools. Best purchase-intent data platforms for 2026 - the full intent-tool landscape, categorised. What replaces intent data in 2026? - why event-based signals are displacing aggregated intent. How to detect competitor trials - the four detection methods and the fourteen-to-thirty-day window. Purchase intent vs intent data - the definitional split. ## Blog: What Is HG Insights Intent Data? HG Insights intent data is an aggregated account-level score inside the Revenue Growth Intelligence Fabric. What it actually is, how the score is built, who it's for, and where event-based signals fit alongside. HG Insights intent data is an aggregated, account-level score that estimates how likely a company is to be researching a given technology category, delivered as part of HG's broader Revenue Growth Intelligence platform. The signal is generated primarily from bidstream web activity - ad-exchange level browsing mapped to companies - plus verified second-party review behaviour from TrustRadius, which HG acquired in June 2025, interpreted against HG's technographic and spend datasets. It is designed for enterprise ABM and market-intelligence teams working at the account-planning altitude, not for reps who need to know which specific accounts to work this week. If timing signals and rep-level actionability matter more than dataset breadth, an event-based signal source is a better fit; see MarketSizer vs HG Insights . What is HG Insights intent data? HG Insights positions its platform as AI-powered Revenue Growth Intelligence, built on its Revenue Growth Intelligence Fabric data layer. Inside that platform, intent data is one signal type layered on top of a much larger technographic and spend dataset. Where technographics tell you which technologies a company already owns, HG's intent product estimates which technologies the same company might be researching now. The output is an account-level score. For a given topic or product category, a target account is assigned a value that reflects how much recent research behaviour HG has observed for that topic from people at the account. Sales, marketing and RevOps teams typically pull the score into their CRM or account-planning tools to surface accounts showing higher-than-baseline research activity. Two properties of the score matter more than most buyers realise: It is aggregated. The score reflects rolled-up research behaviour across many people at the account. You do not see the specific person, session or content that produced the reading. It is inferential. The score is a probability that research is happening, not a record that a specific buying event occurred. Both properties are deliberate. Aggregating protects privacy and produces smoother signals for account-planning; inference makes the score usable across many categories where direct evidence is impossible. The trade-offs matter when the signal is used for prioritisation instead of planning, which is where most execution teams get stuck. How the score is built HG Insights describes its intent data as predominantly derived from bidstream data, contextualised against its technographic and spend datasets, and - since its June 2025 acquisition of TrustRadius - complemented by verified second-party review intent. The exact inputs vary by product tier and category, but the general pattern looks like this: Bidstream web-behaviour signals. Ad-exchange level browsing activity, mapped to companies and office locations using firmographic data. HG's scoring model measures the frequency of defined topics on pages viewed, the number of locations showing activity, and the trajectory over time. Second-party review signals. Verified research behaviour on TrustRadius - category, product and comparison-page activity from identified buyers - folded in since HG acquired TrustRadius. Technographic context. The company's existing tech stack, spend estimates and firmographics, used to interpret raw behaviour (a company that already owns your competitor reading about you means something different from one that does not). AI models and topic taxonomies. HG's topic library groups the raw signals into research categories that map to how buyers describe their evaluations. The score is refreshed weekly - HG describes taking in nearly 2 billion intent records each week - and delivered via CRM sync, native connectors for Salesforce and Snowflake, data snapshots or an API for downstream tooling. The exact cadence and delivery depend on the customer package. Who it is built for HG Insights is enterprise-heavy by design. The dataset shape, product surface and go-to-market posture all point at strategy, marketing and RevOps teams working at the account-planning altitude - people who build territories, size markets, structure ABM programmes and set annual GTM strategy. HG publishes starting prices on its pricing guide : Starter from $22,000 per year and Growth from $45,000 on a two-year term, with Enterprise on custom terms. Industry pricing aggregators place HG's enterprise packages in the tens of thousands per year, reflecting the depth of the technographic and spend dataset alongside the intent product. That price point makes sense for a strategy-facing tool where the return is measured in years of programme design, not in weekly rep prioritisation. The typical customer is a large B2B technology company running structured ABM. Roughly, if your GTM function has a dedicated ABM team, a market-intelligence function or a formal territory-planning process, HG will land inside your existing motions cleanly. Event-based signals for execution teams HG excels at planning. If your reps need to know which accounts are trialling a competitor right now , event-based subscription signals sit at a different altitude. Free trial, live data on your ICP. Book a demo → MarketSizer vs HG Insights → Where it works well HG intent shines in three specific jobs. Territory design and TAM sizing. Combining tech-install breadth with an intent overlay lets an enterprise strategy team estimate which segments of the addressable market are actively researching adjacent categories, and size territories accordingly. Long-cycle ABM programmes. When a marketing team runs a six-month ABM programme against a fixed set of a few hundred accounts, an intent overlay helps decide sequencing and content pacing across the programme. Analyst-facing reporting. The dataset breadth and structured taxonomy make it easy to build the kind of reports market-intelligence teams present internally: adoption curves, category maturity, competitive share. Where it falls short for sales execution The same properties that make HG intent good for planning make it awkward for the rep-facing execution surface where most GTM teams actually spend their week. The score is aggregated , which means a rep cannot see what evidence produced it. There is no underlying event to reference in outreach, no timing anchor, no specific behaviour to build a message around. The signal helps you rank accounts but not open a conversation. The score is inferred , so it does not tell you that anything happened. It tells you the account's recent behaviour looks like accounts that have historically bought. That is a useful prior, but it is not a buying event. The score is refreshed slowly . Weekly cadence works for a quarterly programme; it does not survive a two-week trial window on a specific competitor. And the tool is enterprise-shaped . The setup, procurement and price make it a heavy lift for a mid-market team where the buyer is a Head of Sales and the deployment target is a working SDR. Worked example: how the score composes for one account To make the mechanics concrete, take a hypothetical target account - a 500-employee B2B SaaS company selling into finance. The GTM team has that account on a watchlist for the topic "customer support automation. " Here is an illustrative sequence, consistent with the scoring dimensions HG describes (topic frequency, number of locations, trajectory over time). The specific numbers, thresholds and score scale below are hypothetical. Week 1. Two employees at the account read three articles on customer-support automation across HG's content-partner network. The IP addresses map cleanly to the account's known office ranges. No score change yet - in this illustration, the volume is below the level that moves the score. Week 2. A researcher at the account downloads a buyer's guide on the topic from a review platform. Another employee visits three vendor comparison pages. Signal volume crosses the threshold. The account's topic-intent score for "customer support automation" ticks up from a baseline of, say, 20 to around 45. Week 3. The signal continues but does not accelerate. Score holds around 45. HG's topic taxonomy also cross-references adjacent topics ("live chat" and "helpdesk software") and lifts those scores modestly. Week 4. The customer's CRM sync runs. The rep who owns the account sees the score in HubSpot: "Customer support automation: 45 (baseline 20)" . What the rep does not see: which two people read what, which vendors they compared, or whether the buying committee has actually convened. Two things worth noticing. The lag between the first web-behaviour signal and the score being visible to a rep is roughly 10 to 14 days in this example. And the rep gets a topic bucket plus a delta - not the underlying content, not the specific people, not the specific vendors under evaluation. Signal latency, in weeks Latency stacks up across four stages between a real buying behaviour and a score change that a rep can act on. The ranges below are our own editorial estimates based on the publicly described mechanics of aggregated third-party intent - signal scale, accuracy and overlap are the dimensions Forrester recommends assessing when evaluating providers: Behaviour to partner-network capture. 0 to 2 days. IP-level web activity is captured close to real time on well-covered publisher networks, longer where partner coverage is thin. Capture to aggregation. 2 to 5 days. Raw sessions are rolled up into account-level signal counts before mapping to topics. Aggregation to topic-mapping and modelling. 3 to 7 days. Signals are matched to HG's topic taxonomy and pushed through modelling to produce a score. Score to rep-visible surface. Up to 7 days. Weekly-cadence refresh from HG to the customer's CRM or account-planning tool. End-to-end that is roughly 7 to 21 days between a buying behaviour and a rep-visible signal. Fine for a quarterly programme; often too slow for the fourteen-to-thirty-day trial window that decides a competitive-displacement outreach. How to interpret an HG score before acting Three things to check before a rep touches an account on the back of an HG intent score. Look at the trend, not the level. A single high score is less informative than a rising delta from baseline. A jump from 20 to 45 says something new started happening; a flat 55 could be background noise for a large account whose employees always browse the topic. Cross-reference the technographic layer. HG's underlying strength is technographic and spend data. An intent uplift on "customer support automation" from an account that already runs the incumbent for years is a stronger buying signal than the same uplift from an account with no relevant install-base context. Rank against ICP fit, not against each other. Topic intent alone tells you an account is researching. It does not tell you whether they can afford your product or whether they fit your target segment. Combine with an ICP score and a win-likelihood layer before a rep gets the account. See Qualified Opportunity: the three-part definition . How MarketSizer signals compare MarketSizer sits at a different altitude. Rather than infer research intent from browsing patterns, MarketSizer's purchase intent detects observable subscription events - trials, switches, renewals, churn - and attaches each signal to the specific event that produced it. A signal on an account is a record that something happened, not a probability that something might be happening. Three practical implications: Explainability. Every signal is traceable to the underlying event. A rep can see what triggered it and reference the specific behaviour in outreach. Timing. Signals fire when the event occurs, not on a weekly aggregate. That matters when the account's buying window is fourteen to thirty days. Coverage shape. MarketSizer indexes deeper history per vendor (200+ vendors, with deep subscription-event history) rather than the wide surface HG covers. The comparison is not a rip-and-replace argument. Enterprise teams often keep a broad intent overlay for planning and add an event-based signal source for execution, with market intelligence covering the account-planning altitude HG is bought for. The full breakdown lives at MarketSizer vs HG Insights . Frequently asked questions What is HG Insights intent data? HG Insights intent data is an aggregated account-level score that estimates whether a company is researching a given technology category. It sits inside HG's Revenue Growth Intelligence Fabric alongside their technographic, spend and firmographic datasets. It is generated primarily from bidstream web activity plus verified second-party review signals from TrustRadius, and refreshed on a weekly cadence. How is HG intent different from other intent data providers? Most third-party intent providers - HG, Bombora, 6sense, ZoomInfo intent - work with similar mechanics: aggregate account-level web-behaviour signals delivered as a score, though the source mixes differ (bidstream, publisher co-ops, review platforms). HG's difference is the depth of technographic and spend context sitting alongside the score. This makes HG particularly useful for enterprise account-planning where the intent overlay needs to be interpreted against install-base data. Who uses HG Insights intent data? Predominantly enterprise strategy, marketing and RevOps teams. HG's pricing (roughly $24,000 to $150,000+ per year with a median around $52,000, per Vendr contract data ) and product surface are built for structured ABM, territory design and market-intelligence functions rather than day-to-day rep prioritisation. Is HG Insights intent data event-based? No. HG intent is inferential and aggregated. The score reflects probability that research is happening at the account, not a record of a specific buying event. Event-based signals - a trial started, a vendor switched, a subscription approaching renewal - come from a different category of data source. See What Replaces Intent Data in 2026 for the shift towards event-based signals. How fast is HG intent data refreshed? Typically weekly, depending on the customer package. That refresh cadence supports quarterly and annual planning motions but is often slower than the fourteen-to-thirty-day evaluation windows that decide competitive-displacement outreach outcomes. Can HG Insights and MarketSizer be used together? Yes. HG's aggregated intent and technographic-plus-spend overlay work at the planning altitude; MarketSizer's event-based signals work at the execution altitude. Enterprise teams commonly keep both. Full breakdown at MarketSizer vs HG Insights . How is HG Insights different from Bombora? Bombora is a specialised topic-level intent provider built on a co-op of B2B publishers. HG partners with Bombora on advertising audiences, but Bombora does not feed HG's intent score - HG generates its own intent from bidstream and TrustRadius review activity, bundled with its wider technographic, spend and contract dataset. If a team already uses Bombora natively, HG is often adding the context around the intent signal (what does the account already own, what would they spend) rather than a replacement for it. How does HG intent compare to 6sense? Both target enterprise ABM teams and both deliver aggregated account-level intent. 6sense leans further into predictive scoring across the full funnel with its own AI models; HG leans further into the underlying technographic and spend depth. See HG Insights vs 6sense for the full comparison. Does HG Insights include contact data? Historically HG was company-level intelligence - technographics, spend, contracts and intent - with contact enrichment left to an integrated ZoomInfo, Cognism or LinkedIn Sales Navigator layer. Since acquiring TrustRadius in June 2025, HG also markets verified contact and buying-centre intelligence and intent-driven leads, though a deep contact database is still not its core product. How is HG intent priced? HG publishes starting prices: Starter from $22,000 per year and Growth from $45,000 on a two-year term (one-year terms are 10% higher), with Enterprise custom. Packages sit in the tens of thousands per year, reflecting the depth of the technographic and spend dataset and the enterprise procurement path. Individual product modules (intent, spend, contracts) are typically bundled rather than sold standalone. Is HG Insights intent data usable for outbound SDRs? Partly, and HG would argue yes: since acquiring TrustRadius it markets intent-driven leads and competitive-displacement plays. Our view is that the core score remains aggregated and refreshed weekly, which struggles with the fourteen-to-thirty-day trial windows that decide most outbound competitive-displacement outreach. It is most useful as a territory-planning input that decides which accounts SDRs should have on their list at all. Sources All qualitative and quantitative claims in this piece are traceable to public sources. Where a specific number is cited, we link to the source or note that we are describing the enterprise-pricing bracket rather than a specific quote. HG Insights homepage - positioning line, product description. HG Insights Buyer Intent product page - how the intent product describes itself. HG Revenue Growth Intelligence Platform and RGI Fabric - HG's own platform-versus-data-layer positioning. HG Insights Contextual Intent launch announcement - the bidstream-based methodology, weekly refresh and the nearly-2-billion-records-per-week figure. HG Insights acquires TrustRadius (June 2025) - the second-party review intent, intent-driven leads and contact intelligence additions. Disclosure: HG Insights has owned TrustRadius since June 2025, so TrustRadius content about HG is not an independent source. HG Insights integrations page - delivery via native connectors, API and data snapshots. How HG Insights partners with Bombora - the advertising-audience partnership described in the Bombora FAQ. HG Insights resource library - published product briefs and dataset documentation. GZ Consulting on Contextual Intent - independent analyst writeup of the scoring mechanics. Forrester - How To Evaluate Intent Data Providers - the evaluation framework (signal scale, accuracy, overlap) referenced in the latency section; the day-range estimates there are our own. The Forrester Wave: Intent Data Providers For B2B, Q1 2025 - the category's current analyst benchmark. Vendr - HG Insights marketplace data - source of the contract-price range and median cited above. HG's starting prices come from its own pricing guide (viewed 6 October 2026); wider brackets are third-party contract data and review-site aggregators ( TrustRadius , G2 ). MarketSizer platform capabilities and data-coverage claims (70M+ subscription records, 17M+ active subscriptions, 2. 5M+ active trials, 200+ vendors indexed, 140+ countries) - internal Claim Registry , sourced from live production tables and refreshed quarterly. Available on request via a demo . Recommended reading MarketSizer vs HG Insights - side-by-side breakdown of the two tools. Best purchase-intent data platforms for 2026 - the full intent-tool landscape, categorised. What replaces intent data in 2026? - why event-based signals are displacing aggregated intent for execution teams. Purchase intent vs intent data - the definitional split between behavioural aggregation and buying events. How to detect competitor trials - the four detection methods and the fourteen-to-thirty-day window that decides outreach outcomes. ## Blog: How to Prioritise SDR Outreach with Purchase Intent The three-part priority stack for SDR outreach: ICP fit as a filter, purchase intent as the primary sort, win probability as a tie-breaker. And the daily workflow that runs it. SDR outreach prioritisation should sort on purchase intent (observable buying events) before ICP fit, not the other way round. Most teams do the opposite: rank the list by firmographic fit, then work it top-to-bottom. That produces the classic "we called every ICP-match account and none of them were ready" outcome. The fix is a three-part stack that filters ICP first (yes/no), then sorts the survivors by purchase intent (highest first), then breaks ties with win probability. Fewer accounts, faster cycles, better conversion. This is the SDR-specific view of the framework described across our inbound lead prioritisation and outbound account prioritisation playbooks; this post is the SDR daily-workflow version. Why SDR outreach fails on priority, not volume The dominant explanation for why SDR pipelines miss target is that reps did not send enough messages. It is almost never true. Reply rates have roughly halved since 2019 (from around 8. 5% to 3-5% on cold email in most B2B benchmarks), and layering more volume on top of a broken prioritisation model just accelerates the decline. The real issue is that most SDR target lists are ordered by firmographic ICP scoring alone. A list ranked by employee count, industry, and technographic fit is a list of accounts that could buy at some point, not accounts that are buying right now . Working that list top-to-bottom, an SDR spends most of their outreach on companies who are not in the market. The reply rate reflects that. Purchase intent inverts the sort. It answers a different question: which accounts are showing observable evidence of a buying motion this week? An account with strong ICP fit and no buying signal is worse target for outreach today than an account with weaker ICP fit and a live competitor trial. The first will still be there next quarter. The second closes to somebody in the next four weeks. For the deeper argument on why observable evidence beats inferred interest, see Purchase Intent vs Intent Data . This post assumes the argument and focuses on how to run it inside an SDR's daily workflow. The three-part priority stack for SDR outreach Three filters, applied in this order. Skipping the order is where teams get stuck. Filter 1: ICP fit, applied as a yes/no cutoff, not a ranked score. Every account either meets the ICP criteria or does not. Those that do not, drop off the list. Those that do, move to Filter 2. The mistake most teams make is treating ICP fit as a ranking dimension (score 0-100 by fit) instead of a gate - which is the difference between lead qualification and lead ranking. Ranking accounts by "62% ICP fit vs 58% ICP fit" is meaningless in a world where all the buying-motion signal lives elsewhere. Filter 2: purchase intent , applied as the primary sort order. Rank the ICP-qualifying accounts by observable buying events. Highest signal first: a live competitor trial detection, an open renewal window, a recent tool churn. Medium signal next: structural triggers (leadership changes, funding events, vendor incidents). Low signal last: engagement signals (content downloads, email opens, page views). This ordering is the entire game. Filter 3: win probability, applied as a tie-breaker among high-intent accounts. Two accounts both showing a live competitor trial: which one has historical patterns matching accounts we actually won against that competitor? Win probability data comes from historical competitive outcomes across the vendor set. When two accounts are equally in-market, working the winnable one first is worth doing. See the Qualified Opportunity definition for the full three-part framework this stack sits inside. The output of this stack is a much shorter working list than the starting one. That is the point. A well-prioritised list of 40 accounts converts more pipeline than a poorly-prioritised list of 400. The daily SDR workflow with purchase intent An SDR working the three-part stack has a repeatable daily rhythm. It looks like this. Morning: 20 minutes triaging the signal queue. The SDR opens their CRM (or a signal dashboard that syncs to it) and reviews accounts where a new purchase intent signal fired in the last 24 to 48 hours. These are the accounts to prioritise today. Not the accounts they were going to touch anyway; the accounts the data says entered a buying motion since yesterday. Rest of morning: outreach to the fresh-signal accounts. These get personalised outreach that references the specific signal ("we noticed you started evaluating X, here is where teams in your category find our tool lands better on Y"). Not a template. The named vendor is what makes the line land: an SDR working an account that started an Intercom trial nine days ago opens differently to one working an account that just churned off Zendesk. The rep spends the two seconds before sending each message reviewing the underlying evidence for the signal. Afternoon: work the persistent-signal accounts. Accounts where a purchase intent signal has been live for more than a week get followed up on cadence, not re-personalised each time. If the signal has been live for 30+ days and no reply has come back, the account moves off the active list. Persistent no-response is a signal too: the trial ended, the decision was made, and the door is closed. End of day: log outcomes on any account that responded. The signal layer only improves if outcomes are captured. Meeting booked, no-reply, opt-out, wrong-contact - each of these is a data point that either validates or invalidates the signal type the rep worked from. Six months of this discipline is what separates SDR teams that get better at purchase intent from teams that plateau. What is missing from this workflow: prospecting cold accounts without a signal. That is deliberate. Cold prospecting on ICP-only fit has a role - top of funnel, market development, category education - but it is not what an SDR working purchase intent should spend the majority of their day on. If the ratio is inverted, the priority stack is not being followed. What to cut from your sequences Adopting a purchase-intent-first priority stack usually means cutting things. Three specific patterns worth killing. Cut the six-touch cold sequence on ICP-only accounts. Six touches on an account with no buying signal is six touches too many. If the account did not respond to touches one or two, adding four more is not going to change the underlying fact that they are not in the market. Cut sequences to two touches on cold ICP-only accounts and reinvest the saved capacity in higher-signal outreach. Cut the "high-intent" segment based on engagement scoring. If your CRM has a "high intent" segment defined as "opened three emails and visited the pricing page," that segment is not high-intent, it is high-engagement. High-engagement without a buying event is nurture-side. Rename the segment. Do not send outbound outreach to it. Cut the account allocation model that assigns X accounts per SDR by territory. Purchase intent does not respect territory boundaries. If all the fresh signals this week are in three territories, those three SDRs work harder that week, and the reps in the quieter territories work fewer accounts more carefully. Rigid per-rep quotas on cold accounts is a leftover from the volume era. The stack does not care who executes it. An in-house SDR team, a founder doing their own outbound, or an outsourced SDR agency working your list - the sort order is the part that moves the numbers, not who sends the messages. An agency working a signal-sorted list of 40 accounts will beat an in-house team working an ICP-sorted list of 400. If you are briefing an external team, brief them on Filter 2: give them the signal feed and the 24-hour touch window, not a larger list. Measuring whether it worked The three metrics that tell you a purchase-intent-first prioritisation stack is landing: Reply rate on fresh-signal outreach vs cold outreach. The delta is where the case is made. In practice teams see a 2 to 5x reply-rate lift on outreach sent inside a fresh buying window vs outreach sent to cold ICP-match accounts. If your ratio is below 2x, the priority stack is not being followed properly (usually because Filter 2 is being skipped and Filter 1 is being over-weighted). Time from signal fire to first-touch. Fresh signals are perishable. A signal that fires on Monday and does not get touched until Friday is a signal that lost most of its value. Best-in-class SDR teams touch fresh signals inside 24 hours. Median teams take 3 to 5 days. If your team is above 48 hours, the workflow is the bottleneck, not the data. Meetings booked per touch on fresh-signal vs cold accounts. The ultimate output metric. Meetings-per-touch on fresh-signal outreach should be materially higher than on cold outreach; if it is not, the signals are being treated the same way as cold outreach (generic template, no reference to the specific event), and the whole prioritisation stack collapses. Frequently asked questions How do you prioritise SDR outreach with purchase intent? Apply three filters in order: (1) ICP fit as a yes/no gate, (2) purchase intent as the primary sort order for accounts that pass the ICP gate, (3) win probability as a tie-breaker among high-intent accounts. The output is a much shorter working list than a firmographic-only ranking would produce, but conversion on that shorter list is materially higher because every account on it is in an observable buying motion. Should SDRs prioritise by ICP fit or purchase intent first? Purchase intent should be the primary sort; ICP fit is a filter, not a rank. Most teams do this backwards, ranking by ICP score and treating purchase intent as a nice-to-have overlay. That inverted priority is why most SDR pipelines miss target: the highest-ICP account with no buying signal is worse outreach today than a lower-ICP account with a live competitor trial. What is a purchase intent signal in B2B sales? Purchase intent signals are observable buying events at an account: a competitor trial detected, an open renewal window, a recent vendor churn, a formal RFP in progress. These are different from engagement signals (email opens, page views, content downloads), which measure interest, not buying motion. Engagement signals belong in nurture; purchase intent signals belong in outreach prioritisation. How does purchase intent change SDR sequence design? Cadences on purchase-intent-flagged accounts get personalised to the specific signal and touched inside 24 hours. Cadences on cold ICP-only accounts get shorter (two touches, not six) because more touches will not change the fact that the account is not in the market. The saved capacity from cutting long cold sequences funds the personalised outreach on fresh-signal accounts. Can SDRs run purchase intent prioritisation without changing tools? Partially. If your CRM has purchase intent signals synced into it (via a Subscription Intelligence tool or an intent-data provider with observable-event signals), yes: the priority stack runs inside the CRM you already own. If your CRM has only firmographic and engagement data, no: you need a signal layer on top, because the sort order that matters most is not present in the data set you have. What is the daily workflow for an SDR working purchase intent? Morning triage on fresh signals (20 minutes reviewing accounts where a signal fired in the last 24-48 hours). Personalised outreach on those accounts through the morning. Persistent-signal follow-ups in the afternoon on cadence. Cold ICP-only prospecting only after both prior blocks are done, and only in a bounded capacity. End-of-day logging on any account that responded. Recommended reading How to Prioritise Inbound Leads with Purchase Intent - the four-tier framework this SDR outbound view is derived from. How to Prioritise Accounts for Outreach - the outbound account-list version of the priority stack. Qualified Opportunity: the three-part definition - the ICP-fit + intent + win-probability framework the priority stack sits inside. Purchase Intent vs Intent Data - the case for observable events over inferred interest. How to Detect Competitor Trials - the highest-signal purchase-intent event and how to catch it in the first 48 hours. ## Blog: How to Detect When a Company Starts a Competitor Trial Four detection methods ranked by fidelity, plus the 14-to-30-day window that decides whether trial-detection outreach converts or misses the account for a year. The most reliable way to detect that a company is trialling a competitor is to observe the footprint that competitor's product leaves on the trialist's public web presence once it is switched on. Behavioural proxies (job postings, LinkedIn mentions, review-site visits) are noisier and slower. The four detection methods below are ranked by fidelity, with the important constraint that only the top two produce signals fresh enough to act on inside the 14-to-30-day evaluation window when the outreach conversation is still open. Why competitor trials are the highest-signal event in B2B SaaS A competitor trial is the single strongest signal in outbound because it collapses three uncertainties at once: is this account in the market, is our category actually a fit, and are they willing to change vendors? A live competitor trial answers all three affirmatively. Every other buying signal answers only one or two of those questions. A pricing-page visit tells you the account has some curiosity, not that they are actually evaluating. A content download tells you the account is topic-aware, not that they are close to a decision. A conference badge scan tells you they attended, not that they are shopping. A trial start tells you the account is doing the work of switching. The evaluation is underway, the internal alignment has cleared enough hurdles to authorise the trial, and the buyer is actively inside a 14 to 30 day window where their attention is on your category. Outreach inside that window consistently outperforms the same outreach sent outside it - the whole case for timing rests on that gap, and it is the one thing worth measuring on your own pipeline before you trust anyone's number for it. See Timing Intelligence 101 for why the temporal layer matters and what depends on getting it right. What a competitor trial actually looks like (technically) When a SaaS company starts trialling a new tool, the tool leaves an observable footprint on their public web presence. That footprint is not private data; it is the visible side effect of switching a product on, and it is inspectable without any access to the trialist's internal systems. Different product categories leave different footprints - some in the customer-facing frontend, some in public infrastructure, some in how vendor-hosted surfaces are wired into the customer's site. What they have in common: the footprint appears when the product goes live and can be tied to a specific vendor on a specific account. We will not spell out the exact signatures we monitor (that is the part of the craft a detection vendor earns its keep on), but the principle is simple: a product that is running leaves evidence that it is running. The critical distinction: parts of the footprint that appear early in a trial versus parts that only appear once installation is complete. Fast detection means catching a trial in its first days. Slow detection means catching an already-installed tool three weeks later. Both are useful, but only fast detection lands you inside the 14-to-30-day evaluation window. The four detection methods, ranked by fidelity Four ways to detect a competitor trial, from strongest to weakest. Fidelity here means the ratio of true positives to false positives, and the latency between "trial actually started" and "signal fires. " Method 1: Technical signature monitoring. Continuously monitor target accounts for a vendor's product footprint and fire an event when it appears where it was absent in the previous scan. This is the highest-fidelity method and the only one that reliably catches trials early. Fidelity depends on the coverage set (is the vendor being trialled one the tool monitors? ) and the scan cadence (daily is table stakes; weekly is too slow to matter). This is the method underneath most modern Subscription Intelligence tools; see What Is Subscription Intelligence? for the category. Method 2: Review-site activity monitoring. Watching G2, Capterra, TrustRadius, and similar sites for a target account's employees writing reviews of a specific vendor. High confidence when it fires (reviews correlate strongly with actual usage), low coverage (most companies do not write reviews), and inconsistent latency (reviews often lag installation by weeks). Useful as a corroborating signal, weak as a primary trigger. Method 3: Job-posting inference. A target account posts a role that explicitly names a competitor tool ("must have hands-on experience with Zendesk"). Reasonable proxy for tool consideration or use, but ambiguous: does it mean the tool is already installed, or that the account is considering it? Latency is long (roles are open for weeks before they signal a real decision). Useful for account-level qualification, weak for timing. Method 4: LinkedIn / social mention scanning. Watching for employees at a target account posting or engaging with a competitor's content. Directionally useful, but heavily noise-prone. A rep at the account "liking" a competitor's LinkedIn post is not evidence of a trial. Best treated as a nurture-side input, not a prioritisation input. The practical stack for a serious detection motion combines Method 1 as the primary trigger with Method 2 as a corroborator. Methods 3 and 4 belong in the nurture layer. The 14-to-30-day window: why timing decides the outcome Once a trial fires, the outreach window is short. Most SaaS trials run 14 or 30 days. Inside that window, the buyer is actively comparing, budget conversations are in motion, and the vendor being trialled has a rapidly-closing lead. Outreach inside the window has three properties that outreach outside the window does not. The buyer is expecting to be sold to. The buyer's attention is on your category. The buyer's alternative (staying with their current vendor) is being actively questioned by the trial itself. All three of those tail off the moment the trial ends and a decision is made. Miss the window and the account goes cold for the length of whatever contract the winning vendor signs, usually a year or three. That is the specific cost of a slow detection method: not "we missed one deal," but "we cannot enter this account's evaluation again for 12 to 36 months. " The math on that is why detection latency matters so much. A daily-refresh signal that fires on day two of a 30-day trial gives you 28 days to act. A weekly-refresh signal that fires on day nine gives you 21 days. A monthly signal that fires on day 25 gives you five. Same underlying data, wildly different conversion outcomes. What to do when a trial detection fires Four moves. In this order. 1. Verify the signal. Every detection method has a false positive rate. Before a rep touches the account, look at the underlying evidence: what technical signature fired, when, and how confident is the detection. A well-designed Subscription Intelligence tool exposes this directly. See Explainable AI in B2B Sales for why inspectable evidence beats explanation-shaped text on this specific problem. 2. Frame the outreach around the specific competitor being trialled. Generic "we heard you might be evaluating" outreach lands as spam. Specific "we noticed you started a trial of X, here is where our category tends to deliver better outcomes on Y" outreach reads as informed. The difference is what the rep does in the two seconds before hitting send. 3. Prioritise on the trial plus the ICP fit, not the trial alone. A trial detection on a company that is not in your ICP is a distraction, not an opportunity. Combine the trial signal with your existing ICP scoring and the Win Probability layer that scores how winnable an account historically is against that specific competitor. See Qualified Opportunity: the three-part definition for the full ICP-fit + purchase-intent + win-probability framework. 4. Log the outcome, whatever it is. The detection layer is a system; systems improve when their outputs are measured. If a trial detection produced a meeting, note it. If it did not, note that too. Six months of outcome tags is how a team distinguishes signals worth acting on from signals worth deprioritising. Compare detection tools with MarketSizer Direct comparisons with tools mentioned above: Bloomberry , BuiltWith , Wappalyzer . Teams usually arrive here from a sales platform that does not detect trials at all, so the two comparisons worth reading next are MarketSizer vs Apollo and MarketSizer vs ZoomInfo . Full index at MarketSizer compared with other tools . Frequently asked questions How do you detect that a company is trialling a competitor? The most reliable method is monitoring the observable footprint the competitor's product leaves on the trialist's public web presence once it is switched on. When that footprint appears where it was absent in the previous scan, a trial has started. Behavioural proxies (job postings, review-site activity, LinkedIn mentions) are noisier and typically lag installation by days to weeks. How fast can a competitor trial be detected? Technical signature monitoring with a daily scan cadence catches most trials within days of installation. Weekly scans catch them within about a week. Monthly scans routinely miss the 14-to-30-day trial window entirely. Freshness is the whole point: a signal that fires after the trial has ended is a retrospective, not a buying signal. What is the difference between detecting a trial and detecting an installed customer? Parts of a product's footprint appear as soon as a trial starts; others only once the tool is fully installed and configured. Trial detection relies on the first class of evidence; installed-customer detection uses both. Only trial detection lands you inside the buying window; installed-customer detection is more useful for renewal-timing plays. Can I detect competitor trials at scale without accessing the trialist's data? Yes. Every method described here relies exclusively on publicly observable evidence. Nothing scans private data, nothing accesses the trialist's internal systems, and nothing depends on a partnership with the vendor being trialled. The footprint is an observable side effect of installation, not a private record. What is the false positive rate on competitor trial detection? Rate depends on the detection method and the vendor's coverage set. Method 1 (technical signature monitoring) on well-covered vendors runs at high precision; Method 2 (review-site) runs higher precision but much lower recall. Any vendor claiming 100% accuracy on trial detection is either lying or does not understand their own product. The honest thing to do is not to quote a single headline accuracy figure at all - it varies by vendor and by how well-covered that vendor is - but to let reps inspect the evidence behind each signal so they can weight it accordingly. What tool detects competitor trials for B2B GTM teams? Subscription Intelligence platforms detect competitor trials as one of their primary signal types. MarketSizer covers 200+ Customer Support, Live Chat, and adjacent martech vendors with daily-refresh technical signature monitoring; the Bloomberry and TheirStack products cover overlapping but different vendor sets. Choose based on which vendors matter most in your competitive set and what the tool's coverage looks like against your target account list, not on marketing claims. Recommended reading What Is Subscription Intelligence? - the underlying data category that makes trial detection possible at scale. Timing Intelligence 101 - the pillar on why buying windows decide outreach outcomes. Qualified Opportunity: the three-part definition - the ICP-fit + intent + win-probability framework that trial detection feeds into. Explainable AI in B2B Sales - why inspectable evidence matters for verifying a trial signal before acting. The Signal-Led GTM Playbook - the operating model that runs trial detection as one of its four core moves. ## Blog: What Is Explainable AI in B2B Sales? (And What It Is Not) EU AI Act Article 50 lands 2 August. Sales AI is exempt - but buyers now have to do the transparency work themselves. Here is the distinction that matters. The EU AI Act's transparency obligations, live from 2 August 2026 as currently scheduled, apply to "high-risk" AI in hiring, credit, and public administration. AI in B2B sales is outside their scope. That does not mean explainability stops mattering; it means the pressure to prove it will now come from buyers, not regulators. The distinction that matters this year is between AI tools that output explanation-shaped text (a model narrating why it scored something the way it did) and AI tools that expose inspectable evidence (the specific event, source, or record behind the score). The first is a UX feature. The second is a category of product. Only the second holds up under a sceptical buyer's questions. What Article 50 actually requires (and what it does not) The EU AI Act's transparency obligations under Article 50 come into force on 2 August 2026, as currently scheduled. Article 50 requires providers of certain AI systems to disclose that users are interacting with AI and to label AI-generated content; the Act's separate high-risk chapter requires those systems to be transparent enough for the people affected to understand how they operate. "High-risk" is a defined term. It covers AI used in hiring and worker management, credit scoring, biometric identification, essential public services, law enforcement, migration, and administration of justice. The list is deliberately narrow. The reason it is narrow is that these are the domains where an AI decision materially changes someone's rights, and where the individual affected has no other recourse. B2B sales AI is not on the list. Not because it is trusted, but because it does not affect an individual's rights. A tool that decides "call Account A before Account B" changes a rep's workflow, not a citizen's access to credit. The practical consequence is that vendors selling AI-driven sales prioritisation, lead scoring, next-best-action, or agentic outreach tools face no regulatory transparency floor. Whatever explainability they offer is a market choice, not a legal one. The exemption sales tools enjoy The exemption is not a scandal. It is a coherent regulatory choice: constrain AI where wrong decisions damage people, leave AI alone where wrong decisions damage quarterly targets. But the exemption creates a subtle problem for the sales AI category. Buyers evaluating an AI-driven sales tool now have to do the transparency work themselves. There is no regulatory framework to lean on when they ask "how did you arrive at this account score? " and no compliance officer to answer to. Every AI sales vendor gets to define its own explainability standard, and most of them are defining it downwards. The industry consensus has drifted to "explainability" meaning "the model can generate a paragraph describing why it produced its output. " That is a very low bar. A language model can produce a plausible explanation for almost any score, correct or not, because generating plausible explanations is exactly what language models are built to do. Explanation-shaped text vs inspectable evidence The distinction is worth naming clearly, because most sales AI vendors will now claim explainability without meeting the harder standard. Explanation-shaped text is what happens when an AI system generates a narrative paragraph justifying its output. "This account scored 87 because its industry, employee count, and recent hiring activity match your ICP. " The paragraph reads coherent, the numbers add up, and the reasoning is legible. What it is not is verifiable. There is no way to click into "recent hiring activity" and see the specific job postings the model used. The paragraph is a language model doing what language models do: producing prose that sounds like an explanation. Inspectable evidence is different. It exposes the specific record, event, or source that drove the output. "This account scored 87 because we detected a competitor trial signature on their subdomain on 12 August, the account entered its renewal window on 3 September, and their last three months of engagement match the pattern of accounts that converted in our Win Probability training set. " Every clause in that sentence points to something a rep can click into and see. The functional difference is what happens when a rep pushes back. On explanation-shaped text, the rep pushes back, the model regenerates the explanation, and the rep is left with two paragraphs of prose neither of which they can verify. On inspectable evidence, the rep pushes back, opens the underlying record, and either agrees with the score or discovers a specific reason the record is wrong. Both are useful; only one moves the tool from "narrator" to "colleague. " The regulatory floor Article 50 sets, in the domains where it applies, is closer to inspectable evidence than explanation-shaped text. That is the standard sales AI vendors will not be required to meet by regulation, and that a serious buyer should now demand by contract. What buyers should actually ask their AI sales vendors These are the questions that separate the two categories. If a vendor cannot answer them in a demo, the tool is producing explanation-shaped text. 1. Can I click into a specific score and see the underlying evidence? Not a description of the evidence. The evidence itself: the record, the event, the source URL, the timestamp. If the answer is "the model can generate an explanation," the tool is narrating. If the answer is "yes, here it is, right-click and open," the tool is exposing. 2. If I disagree with a score, what changes in the system? The correct answer is either "you can override it and the model learns from that override" or "you can flag the underlying data as wrong and it gets corrected in the next refresh. " An answer of "you can add a note" is not an answer. The rep's disagreement has to route back to the data, not just to a comment field. 3. What percentage of your scores can you not explain? Every AI system has an epistemic edge, cases where the model cannot cleanly account for its output. A vendor that says "100% of scores are explainable" is either lying or does not understand their own product. A vendor that says "roughly 5% of scores rely on model interactions we cannot decompose, so we flag those" is telling you the truth and telling you they know their limits. 4. When a score is wrong, how do I find out? The mechanism matters. If the vendor's answer is "you will find out when the deal does not close," the feedback loop is too slow to be useful. If the answer is "we cross-check flagged accounts against a control set weekly, and here is our accuracy dashboard," the vendor is treating accuracy as a metric, not a marketing claim. 5. What is your policy on adversarial explainability testing? This is the sceptic's question. Does the vendor have an internal or external process for stress-testing their explanations, asking "if a buyer really pushed on this, would we survive? " Vendors that have thought about this will describe a process. Vendors that have not will pivot to talking about their model architecture. The half of the story explainability does not solve There is a version of this argument that would end here, with "insist on inspectable evidence and you are safe. " That version would be wrong. Explainability, even inspectable-evidence explainability, is a necessary but not sufficient condition for trusting AI in sales. The specific failure mode explainability does not catch is this: the evidence can be fully inspectable and the score can still be wrong, because the evidence itself was wrong. A recent demo (paraphrased, no identifying detail) surfaced this pattern live. A prospect pushed on how a Win Probability score was computed, asked to see the underlying evidence behind the number, and the platform surfaced the specific accounts and outcomes that fed the score. The evidence was fully inspectable. The prospect then pointed out that a company he personally knew to be a customer of his own product was not showing up in the data at all. The evidence was inspectable. The evidence was also incomplete. That failure mode is real. It is not solved by better narration, and it is not solved by exposing more evidence. It is solved by naming the limitation of the underlying data explicitly, running it against ground-truth checks that the buyer can also see, and being honest about the coverage gaps the tool cannot close. Explainability without ground-truth checks is a demo, not a system. The distinction to hold onto: explanation-shaped text is dishonest by design (it produces prose that sounds like an explanation regardless of whether the underlying reasoning is right). Inspectable evidence is a real category improvement. Neither is a substitute for the vendor being willing to say, on the record, "here are the specific things our tool does not see. " Article 50 goes live on 2 August. The buyers who take advantage of the moment are not the ones who ask AI sales vendors "are you explainable? " They are the ones who ask "show me a case where your score was wrong, and how you found out. " Frequently asked questions Does the EU AI Act cover B2B sales AI? No. Article 50 transparency obligations apply to AI systems used in defined high-risk domains (hiring, credit, biometric ID, essential public services, law enforcement, migration, administration of justice). B2B sales AI is not on the list. Vendors selling AI-driven sales tools face no regulatory transparency floor and any explainability they offer is a market choice, not a legal one. What is "explainable AI" in a sales context? Two categories are being called by the same name. The first is explanation-shaped text : an AI system generates a narrative paragraph describing why it produced its output. Reads legible, is not verifiable, cannot be pushed back on. The second is inspectable evidence : the tool exposes the specific record, event, or source that drove the output, and a rep can click into it. Only the second holds up under a sceptical buyer's questions. How do I know if a sales AI tool is really explainable? Ask five questions in the demo: (1) can you click into a specific score and see the underlying evidence? (2) if you disagree with a score, what changes in the system? (3) what percentage of your scores can the vendor not explain? (4) when a score is wrong, how do you find out? (5) what is the vendor's policy on adversarial explainability testing? Vendors that produce explanation-shaped text pivot to model architecture on these questions. Vendors that expose inspectable evidence give direct answers. Is explainability enough to trust AI in sales? No. Explainability, even the strong inspectable-evidence version, is necessary but not sufficient. The failure mode it does not catch is that the evidence itself can be incomplete. A tool can expose all its evidence and still miss real signals because its underlying data was wrong. Trustworthy AI in sales requires both explainability and honesty about the coverage gaps the tool cannot close. What is the difference between AI transparency and AI explainability? AI transparency is the disclosure layer: this is an AI system, this is what it does, this is what it is trained on. AI explainability is the reasoning layer: why did the system produce this specific output. Article 50 mandates transparency for high-risk systems and stops short of mandating deep explainability. In a sales context, both matter, but the harder standard is explainability, and specifically the inspectable-evidence version of it. When did the EU AI Act come into force? The Act (Regulation (EU) 2024/1689, full text on EUR-Lex ) entered into force on 1 August 2024. Its provisions have phased in over the following two years. Article 50's transparency obligations become applicable on 2 August 2026, alongside most obligations for high-risk systems. Full enforcement, including administrative fines, follows in subsequent phases. Recommended reading What Is Subscription Intelligence? - the underlying data layer that makes inspectable-evidence AI possible in a sales context. What Replaces Intent Data in 2026? - the broader category shift from inferred to observable buying signals. Purchase Intent vs Intent Data - the signal-side distinction that shapes what AI can honestly explain. How to Build a Signal-Led GTM Motion - the operating model that treats AI as a colleague, not a narrator. ## Blog: What Replaces Intent Data in 2026? Traditional intent data is being replaced by evidence-based buying signals: observable subscription events that record what an account did, not what a cookie inferred. Traditional intent data is being replaced by evidence-based buying signals: observable subscription events (competitor trials, renewal windows, tool churn) that record what an account actually did, not what a cookie inferred it might be thinking. For how the evidence-based approach stacks up against 6sense, Bombora and ZoomInfo tool by tool, see the comparisons . The replacement is not a better version of the same category; it is a different data type. The 2026 stack looks like: real subscription events at the top of the funnel as purchase intent , timing intelligence layered over them, and inferred intent data reduced to a nurture-side input rather than a prioritisation input. If you sell into B2B SaaS in 2026 and your prioritisation still runs on page views and keyword surges, the accounts your reps are chasing are usually cold by the time they hit the CRM. Why intent data is being replaced Intent data was built to solve a specific 2015-era problem: which anonymous website visitors are likely in-market for our category? In its original context, it worked. Ten years of shifting behaviour has broken the assumptions underneath it. Three things changed: 1. Content engagement stopped correlating with buying behaviour. Buyers now research inside Slack channels, peer communities, and reference calls that leave no digital trail. By the time a buyer visits your website, they are often validating a decision they have already nearly made. Page views tell you the last 5% of the journey. See Purchase Intent vs Intent Data for the full argument. 2. False positive rates broke prioritisation. Forrester found that 50% of B2B teams see too many false positives from intent signals, and 60% struggle to identify actual members of the buying team within flagged accounts. When half the accounts your tool surfaces are not in-market, prioritising on that signal makes rep effort worse, not better. 3. Timing became the bottleneck. A buyer surge signal that arrives a week after the buying window opens is not a buying signal, it is a retrospective. Batch-cycle intent data (weekly aggregation, monthly refresh) misses the 30-90 day evaluation window that most B2B SaaS deals actually close inside. See Wrong Time GTM: Why Intent Data Misses the Window . None of these is a vendor problem. They are structural. The data category is being replaced because the assumptions underneath it no longer hold. What replaces intent data: three shifts Shift 1: from inferred to observable. The next generation of buying signals records what actually happened at an account, not what a model inferred. A competitor trial started. A tool churned. A renewal window opened. Each event is a specific commercial action that can be inspected, timestamped, and mapped to a rep's outreach motion. This is the Subscription Intelligence category, and it is the largest single shift. Shift 2: from account-level to event-level. Old intent data flags an account as "showing intent" without saying what happened. Evidence-based signals name the event: "trial started on 12 August," "renewal window opens 14 October," "churned from vendor X on 3 January. " An event-level signal gives a rep something concrete to reach out about. An account-level score gives a rep another cold call. Shift 3: from static score to timed window. The old model: run intent scoring monthly, act on the top decile. The new model: react to events inside their commercial window. A trial detection is actionable for 14 to 30 days, then closes. A renewal window opens 90 days out and closes at signature. The output is not a leaderboard, it is a schedule. All three shifts point at the same underlying change: the data layer for prioritisation is moving from inference to evidence, and from static ranking to time-bound intervention. The comparison: old vs new Dimension Traditional intent data What replaces it in 2026 Signal type Inferred interest from web behaviour Observable subscription events Evidence quality Correlation with topic engagement Direct record of commercial action Latency Days to weeks Real-time or near-real-time Grain Account-level score Event-level with timestamp Inspectable? Black box Auditable Best use Nurture-side prioritisation Outreach and retention timing False positive rate High (Forrester: 50% of teams overwhelmed) Low (signals are events, not inferences) The comparison is not a vendor battle. Intent data still has a role, on the nurture side, where "who has been showing category interest" is a useful pool to warm up. The category shift is that prioritisation and outreach timing should not run on inference-based data any more. What this changes for GTM teams Sales: account prioritisation moves from monthly intent scoring to real-time event triggers. A rep working a 200-account target list stops running the same six-week sequence across all 200; instead they prioritise the six accounts showing observable buying events this week and act on those first. Marketing: ABM budget shifts from targeting look-alike audiences to targeting confirmed in-market accounts. The same paid budget aimed at 40 event-flagged accounts converts at a materially different rate to the same budget aimed at 4,000 firmographic look-alikes. The look-alike definition itself moves too, from a static segment to ICP scoring maintained against observed wins, with the wider category view coming from market intelligence rather than an intent feed. Customer Success: retention gets an early-warning layer. A competitor trial signal on an existing customer at day 60 before renewal is a save opportunity. The same information at the renewal call itself is a churn confirmation. See the B2B winback playbook for how CS teams operate this in practice. RevOps: the signal layer becomes shared infrastructure that both Sales and CS pull from. The argument about which team owns which account softens because both teams see the same evidence and the same timing, at the same time. What to do this week Three specific moves, in decreasing order of effort: 1. Audit your current intent data by false-positive rate. Pull the last 60 days of accounts your intent tool flagged as "high intent. " How many actually engaged with a rep? Of those, how many produced a genuine opportunity? If the ratio is under 20%, the tool is not the problem, the category is. Move it to the nurture side of the stack rather than the prioritisation side. 2. Pick one event type and act on it exclusively for two weeks. The most common failure of teams switching from intent data to evidence-based signals is trying to act on everything at once. Pick one event: competitor trials, renewal windows, or tool churn. Build the process around that single trigger, log outcomes, then layer the next one in. This is the change-management shape that actually converts a team over. 3. Rewire one existing motion around a timing window. Instead of running your standard outbound cadence at all accounts on your list, run it only inside the 30-90 day window around a specific event (renewal, trial, churn). The conversion-rate gap between "outreach inside the window" and "outreach outside the window" is large enough that this one change often pays for the entire data layer on its own. Rather than trust a headline multiplier from anyone (including us), run it as a two-week A/B on one motion and measure the delta on your own numbers. None of these three require a new tool by tomorrow. They require a decision that inferred intent data is a nurture-side input, not a prioritisation-side one, and that prioritisation moves to whatever event data you can genuinely observe. Frequently asked questions What is replacing intent data in 2026? The dominant replacement is evidence-based buying signals: observable subscription events (competitor trials, renewal windows, tool churn, competitive migrations) that record actual commercial actions rather than inferred web-behaviour interest. This category is often called Subscription Intelligence in a B2B GTM context. It is a structurally different signal class: event-level rather than account-level, and time-bound rather than static. Is intent data dead? No, but it has moved role. Intent data is still useful as a top-of-funnel nurture input (which categories are getting attention, which topics are heating up in the market). It has stopped being reliable as a prioritisation input for outreach and retention timing, because false positive rates and latency both broke the assumptions the prioritisation use case depended on. What is the difference between intent data and subscription intelligence? Intent data infers interest from anonymous web behaviour (page visits, content downloads, keyword surges). Subscription Intelligence uses observable subscription events (trials, renewals, churns) as direct evidence of commercial action. Intent data tells you someone might be curious. Subscription Intelligence tells you something demonstrably happened. Different data type, different appropriate use. Do I need to rip out my intent data vendor to move to evidence-based signals? No. The common failure pattern is treating the shift as a vendor swap. It is not. Intent data stays in the stack on the nurture side; evidence-based signals get added on the prioritisation side. Most teams end up running both, with the boundary drawn at the point where a rep or CS manager actually reaches out. How fast do evidence-based signals refresh? Best-practice refresh cadence is daily, with a maximum five-day lag between an event happening and the signal firing. Any evidence-based signal layer that batches on a weekly or monthly cycle re-introduces the timing problem that broke intent data. Freshness is the whole point. Where do I start if I have never used evidence-based signals before? Pick one event type (competitor trial detection, renewal window, or tool churn) and one team (net-new acquisition, expansion, or retention). Run that combination for two weeks, log outcomes, then layer in the next event or the next team. Turning everything on at once fails predictably: the team cannot tell which signal is producing which outcome, and adoption collapses. Recommended reading What Is Subscription Intelligence? - the definitional pillar for the replacement category. Purchase Intent vs Intent Data - the case that content engagement is not a buying signal. Why Intent Data Made B2B Outreach Worse - the "high-volume intent surge" failure mode and its downstream cost. Intent Data Didn't Break - The Wrong Signals Were Sold as Intent - the category-error argument in full. Wrong Time GTM: Why Intent Data Misses the Window - the timing-side of why intent data underperforms. Timing Intelligence 101 - the temporal-layer pillar that runs on top of evidence-based signals. ## Blog: Welcoming cloudmotion, Moonside, and Gallabox to the MarketSizer Community Three companies joined MarketSizer this quarter - cloudmotion, Moonside Consulting & Services, and Gallabox. Welcoming each of them to the community. Three companies joined the MarketSizer community this quarter. All three build in categories where buying moments happen faster than most GTM teams can see them. All three arrived with the same question: how do we know who to talk to, and when, before the window closes? Here is who they are. cloudmotion - customer experience in the Zendesk ecosystem Ralph Heeneman and the team at cloudmotion bring customer service operations, customer experience consulting, Zendesk expertise, automation, AI, and digital transformation into a single offer across the Zendesk ecosystem. As those disciplines converge, the organisations that understand what is happening in their market first hold a real advantage. That is what Ralph and the team are building with MarketSizer - a live view of who is evaluating alternatives, who is expanding, who is at risk, and who is entering a buying cycle. Ralph has since described how that works in his own words - read how cloudmotion finds companies evaluating Zendesk . Moonside Consulting & Services - technology consulting built for speed Rens van Gool founded Moonside in 2020. Since then, the team has grown to 140+ professionals supporting more than 20 clients across Europe, combining local consulting expertise with delivery from their capability centre in Tunisia. Technology consulting has changed - it is less about delivering projects and more about helping organisations move faster, scale smarter, and access the right expertise wherever it exists. Which organisations are evaluating new partners? Who is investing in digital transformation? Who is expanding? Those are the signals Rens and the team are using to reach the right conversations at the right moment. Gallabox - conversational AI on the channels customers actually use Sam Gajendra Boopathi and the team at Gallabox help businesses automate conversations across WhatsApp and other messaging channels, so sales, marketing, and support teams can engage customers at scale. Conversational AI is no longer about responding to demand. It is about anticipating it - which companies are evaluating new platforms, which are expanding their customer engagement stack, which are replacing existing vendors. Those are the market signals Sam and the team are now surfacing through MarketSizer. Sam has since walked us through how he works a target list - read how Gallabox prioritises a 10,000+ account target market . Why all three, at once The convergence pattern is not accidental. Conversational AI, technology consulting, and customer experience are collapsing into overlapping categories - where the same buyer is often evaluating a Gallabox, a Moonside, and a cloudmotion at the same time, on different dimensions of the same problem. The GTM teams that win in that environment are not the ones with the biggest contact database. They are the ones who see the buying motion earliest. That is what MarketSizer is built for. And that is what Sam, Rens, and Ralph are now using to find the right accounts at the right moment - before their competitors know the door is open. Welcome to the community. We are excited to see what you build next. Curious what signal-led GTM could look like for your team? Start free - 500 qualified opportunities, no credit card. ## Blog: The Winnable Account Is Warm, Not Hot High intent can be late intent. The most winnable accounts often look warm - an unresolved need, a churned trial, a renewal window - not the ones already deep into buying. The instinct to chase heat is old and hard to argue with. If a dashboard says an account is showing high intent, the rational move looks like doing something about it right now. The trouble is that "hot" and "winnable" are not the same word. By the time an account looks unambiguously hot - loud engagement, a named RFP, a visible shortlist - most of the decisions that determine whether you can win it have already been made. The account is on someone's list. It might not be yours. The more useful prioritisation question is not "which accounts look hottest today", it is "which accounts still have room for us to influence the decision". Once you sort on that, a different kind of account rises to the top: warm, not hot. An unresolved problem, a churned trial, an upcoming renewal, a quiet competitor evaluation. Less obvious in the dashboard. More winnable in the pipeline. High intent is often late intent A high-intent account is doing something. That is the good news. The bad news is that what they are doing is usually a late-stage activity: shortlist review, contract negotiation, procurement approval. The kinds of behaviours that trigger a bright red score on an intent platform - repeat visits to a comparison page, a pricing PDF download, a demo request on a competitor's site - are almost by definition downstream of the buyer's first move. Event-based purchase intent fires earlier because it watches the stack rather than the funnel. By the time those behaviours surface, three things have already happened at the account. A shortlist has been drawn up. An implementation timeline has been floated. Internal consensus has usually formed around one or two vendors already in the room. A late arrival can still win, but the odds are worse and the effort required is higher. Gartner's research on the B2B buying journey puts the amount of time a buyer spends with any single sales representative at around 5% of the total purchase decision - the rest is internal work, vendor research, and consensus-building the seller never sees. By the time the visible signals arrive, most of that hidden work is finished. This is the point the purchase intent platform directory makes at the level of category: aggregated intent tools tend to surface the wrong end of the funnel. They catch accounts once buying is well under way. They rarely catch accounts where the buying motion is still open to influence. Why hot accounts attract too much attention There is a reason hot accounts get the disproportionate share of pipeline attention. They are obvious. They are visible. They are easy to justify inside a QBR. A rep who spent the week on a red-scored account can point at the dashboard and defend the choice. A rep who spent the same week on a warmer, less visible account with a renewal window in 92 days has more explaining to do. The behavioural pattern this creates is well documented. Reps optimise for what looks defensible in a pipeline review. Managers reward action on visible signals over judgement calls on quieter ones. The team ends up over-indexed on the accounts everyone else is also working, and under-indexed on the accounts where the timing is quietly ideal. Scoring win likelihood against the incumbent makes the quieter account defensible in the same QBR. The other problem is coordination. Hot accounts show up on every intent tool at once. As Nick Bennett pointed out on Episode 1 of Intent, Decoded , the same underlying bidstream and content-syndication layers feed most vendors. If a red-scored account looks hot in your tool, it looks hot in three or four of your competitors' tools as well. The buyer receives coordinated attention from all of them in the same week. The seller who arrives second, third, or fourth is fighting for a slot in an already crowded conversation. Why the hottest list is not the most winnable list Three structural reasons the hottest accounts often turn out to be the least winnable ones. The shortlist may already be formed. Late-stage signals mean the buyer has done their initial research. Vendors are already in play. Some have had first calls. The role of an inbound cold outreach at that stage is usually to add friction, not to change the outcome. Implementation may already be underway. The nature of B2B software is that a lot of the "buying" decision is made before contracts are signed. Integrations are scoped. Champions are picked. Change-management work has started. A vendor arriving late does not just have to sell against the incumbent choice - they have to sell against sunk cost. Internal consensus may already exist. The buying committee has usually aligned before the loudest external signals appear. A rep pitching an alternative at that point is trying to unstick a decision, not shape one. That is a harder sale on every dimension: message, timing, effort. The buying group behind that consensus is not small either - Harvard Business Review's coverage of the CEB research on the new sales imperative puts the average B2B buying group at 6. 8 stakeholders, and once those stakeholders have aligned, unpicking that alignment is a very different conversation from being present when it was forming. None of this means high-intent accounts should be ignored. They can be won. But the prioritisation logic that says "the hottest account is the most valuable use of a rep's time" understates how much of the decision has already run its course by the time the account looks hot. What a warm account actually looks like A warm account is one where the buying motion has started but is still open to influence. It usually shows up as a combination of signals, not one loud one. Any of the following are useful starting points. A churned trial with a named competitor. The problem is still there. The chosen solution is not. The buyer has spent time and energy validating the category. The next attempt is where you belong on the shortlist. The deep-dive on running this specifically against the churned base sits in the B2B winback pillar . An unresolved need in the buying committee. A CS team quietly frustrated with the incumbent. A Support VP whose CSAT metric is missing target. A RevOps lead who has been asked to fix a specific gap and has not yet started procurement. The organisation knows something is wrong. Nobody has decided on the fix yet. An upcoming renewal window. A customer of a competitor is 60 to 120 days from a contract decision. Switching costs are lowest inside that window. The window closes when the contract renews. A competitor evaluation that has not yet consolidated. An active trial that has not converted, a proof-of-concept that has stalled, a pilot that has been extended twice. Signs the incumbent choice is not yet locked in. Adjacent category movement. A change in the wider stack that reshapes the buying committee - a new CRM, a new CDP, a new CS platform. The reshaping usually triggers a follow-on review of adjacent tools. These are not glamorous signals. They rarely produce a bright red flag in the dashboard. What they have in common is timing: they mark the point at which the buyer's mind is open, not the point at which it is closing. That is where a seller earns the right to be part of the conversation. On Episode 2 , Leslie Venetz called this the "deposit before ask" pattern - the seller who is present with something useful before the moment of decision is a different animal from the one who arrives during it. Fit, signal, timing, action - a practical framework Sorting on winnability rather than heat needs a small piece of scaffolding. Four questions, in order, work well as a filter for a weekly working list. 1. Fit. Is this account inside the ICP you actually sell to? Firmographic and demographic filters go first. A warm account outside the ICP is a distraction. Fit is what makes the signal worth acting on at all. This is the same point the prioritisation framework makes at a general level: fit precedes signal, not the other way round. 2. Signal. Is there observable evidence of a buying motion at this account? A trial started, a trial churned, a renewal window opening, a technology change, a vendor churn. Context (a page visit, a topic surge) is not enough on its own. The signal is the reason to act now rather than next quarter. 3. Timing. Is the moment still open? A renewal 92 days out is timing. A renewal in three days is not. An active trial in week two is timing. An active trial in week eight is a shortlist review. Timing is what decides whether the effort of an outreach is likely to pay back. 4. Action. What is the appropriate move for this stage? A vendor-aware buyer wants a qualified conversation. A problem-aware but vendor-unaware buyer wants an insight, not a demo. An opportunity-aware buyer wants shortlisting help. Matching the action to the buyer's awareness stage is the difference between an email that lands and an email that adds noise. This is the POV framework Leslie Venetz described on Episode 2. Warm accounts pass all four filters more often than hot accounts do. Hot accounts frequently pass filters one and two, fail on timing (already too late), and get an inappropriate action (a demo invite when a shortlist has already formed). This four-question filter is the rep-workflow version of the more formal account-scoring model in the Qualified Opportunity framework - ICP Fit × Purchase Intent × Win Likelihood. Same underlying logic, different unit of measurement: the QO framework scores accounts multiplicatively for the CRM; the four-question filter is what a rep runs down a working list on a Tuesday morning. The same variables show up on the inbound side in how to prioritise inbound leads with purchase intent , where the tiers are hot / warm / triage / deprioritise rather than a heat score. Find the warm accounts before they turn hot See which accounts have fit, signal, and timing lined up right now Start a free trial to sort your working list on winnability rather than heat. MarketSizer surfaces the churned trials, renewal windows, and competitor evaluations you can still influence - not the shortlists that already closed. No credit card required. Start for free A worked starting play: renewals plus competitor trials One of the practical suggestions Niall made on Episode 2 is worth turning into an example, because it is a good starting point for a team new to signal-led prioritisation. The play: identify customers of a specific competitor whose contracts are due for renewal in the next 90 to 120 days, and who are also showing evidence of trialling one of your named alternatives. Two signals stacked together, not one on its own. The shape of the workflow is deliberately small. Universe: customers of a named competitor inside your ICP. First filter: those with a renewal window opening in 90 to 120 days. Second filter: those with observable evidence of a trial of a named alternative (yours or a competitor's). Trigger: when both conditions are true, add to the working list. Action: outreach anchored on the named competitor trial ("we saw your team started a Zendesk trial nine days ago") plus a warm reference to the renewal window as a natural moment to consider the alternative. This is a much smaller list than a heat-based one. It is also much more winnable. The buyer's mind is already open (they have started a trial). Switching costs are near a local minimum (the renewal window is close). The seller arrives with information the buyer recognises about themselves. Every ingredient of a warm account is present. Niall's caution on Episode 2 is worth repeating: do not turn every signal on at once. Start with one repeatable play. Prove the lift. Then add the next. Signal-led prioritisation only compounds when the workflow is simple enough for a team to actually run. Where the specific play is aimed at former customers rather than net-new prospects, the winback pillar is the deeper motion. The most winnable account is not the hottest The prioritisation logic behind signal-led GTM is not "chase every signal". It is "prioritise the accounts where fit, timing, and context stack together". Sometimes that account is loud. More often it is quiet. A warm account with unresolved need, churned trial activity, a competitor evaluation in flight, and a renewal window three months out is - measured on winnability - a better use of a rep's Tuesday morning than a red-scored account whose shortlist closed last week. The best account is not always the hottest. It is the one where your team still has room to influence the decision. That is the account most likely to become a customer. Frequently Asked Questions Are you saying high-intent accounts should be ignored? No. High-intent accounts still have to be worked. The point is that the hottest accounts are not automatically the most winnable, because a lot of the decision-making has usually already happened by the time the account looks hot. A balanced working list mixes hot and warm accounts, with an eye on where the effort is most likely to pay back. What makes an account "warm" rather than "hot"? A warm account shows evidence that a buying motion has started but is still open to influence: a churned trial, an upcoming renewal window, an unresolved need in the buying committee, a competitor evaluation that has not yet consolidated. Hot accounts show late-stage signals like RFPs, shortlist reviews, and pricing-page visits after a competitor has already engaged them. Is medium intent always better than high intent? No. Intent tier on its own is not the right sort. Winnability depends on fit, evidence of a live buying motion, timing (is the window still open), and buyer awareness stage. High-intent accounts inside the ICP with an open decision window are absolutely winnable. Warm accounts with strong evidence and better timing are often more winnable. How do we find warm accounts in practice? Stack two or three observable signals together instead of relying on one score. A common starting play is customers of a named competitor whose contracts renew in 90 to 120 days and who are also showing evidence of trialling a named alternative. The Subscription Intelligence data layer produces the observable signals; the workflow sits on top of it. Why does the renewal window matter so much? Switching costs are lowest in the window immediately before a renewal, and highest immediately after one. A prospect 90 days from a renewal is in a much better position to consider an alternative than the same prospect 90 days after signing a new two-year contract. That is why renewal windows sit at the top of the observable signal stack. How do we start without turning every signal on at once? Pick one play, run it, prove the lift, then add the next. Renewal-window-plus-competitor-trial is a common starting point because both signals are dated and observable, and both point to a live buying moment. The mistake most teams make is trying to switch every signal on at day one; that produces the same "everything is hot, so nothing is" problem the intent data category ended up with. Episode 2 of Intent, Decoded covers this pattern in detail. Where does this fit into the broader signal-led GTM playbook? Warm-versus-hot prioritisation is one specific move inside a larger motion. The full playbook - find, prioritise, act, measure - is covered in how to build a signal-led GTM motion without replacing your sales stack . The prioritisation logic in this piece is the "prioritise" step, done properly. Recommended reading From Signal to Trust: How Sellers Earn the Right to Reach Out - the Episode 2 recap with Leslie Venetz on how signals turn into permission to sell. Intent Data Didn't Break. The Wrong Signals Were Sold as Intent. - the companion piece on why context got dressed up as evidence, and what to sort on instead. What Is a Qualified Opportunity? - the formal three-part account-scoring model that the fit / signal / timing / action filter sits on top of. B2B Winback: Recover Churned Customers on Competitor Renewal Timing - the deep dive on the churned-trial-plus-renewal signal specifically. How to Prioritise Inbound Leads with Purchase Intent - the same warm-versus-hot logic applied to the inbound queue. The Account Prioritisation Framework That Goes Beyond ICP Score - the general prioritisation approach this piece builds on. How to Build a Signal-led GTM Motion - the wider playbook this prioritisation move sits inside. What Is Subscription Intelligence? - the data category that produces the observable signals a warm-account motion needs. Intent, Decoded - the podcast hub - both episodes and the wider conversation about how to use signals well. ## Blog: Intent Data Didn't Break. The Wrong Signals Were Sold as Intent. Content downloads, keyword surges and page visits are context, not evidence. The market is not rejecting signals - it is rejecting weak proxies dressed up as buying intent. For the compact answer, see What Replaces Intent Data in 2026? This post is the long-form category-error argument that piece consolidates. The prevailing story about intent data right now is that the category is tired. Reply rates are down, procurement is sharper about renewals, and every quarter another RevOps leader posts a version of "we cancelled our intent tool and nothing broke. " The story is almost right. What actually failed was not intent data as a concept. It was the practice of treating a keyword search, a content download, a title change or a page visit as evidence that an account is ready to buy. Those things are context. They were sold as evidence. That is the category error the market is now correcting. This piece is not another "intent data is dead" argument. It is narrower and more useful. Once you separate context from evidence, the pattern reads differently. Teams do not need less signal. They need to stop treating weak proxies as buying intent, and start acting on the observable events that show a buying motion is under way. The category error at the heart of intent data The original promise of intent data was straightforward. Firmographic targeting and a seller's own engagement data missed accounts that were in-market but not yet engaged. Third-party intent - bidstream activity, content syndication, keyword surges - was meant to close the gap. Reasonable in principle. What broke was the definition of the word "intent". Somewhere between the deck and the dashboard, "intent" stopped meaning "evidence of a buying motion" and started meaning "any behaviour the vendor could measure". A page visit is intent. A whitepaper download is intent. A keyword surge on the aggregator's own network is intent. A job change at a target account is intent. Once the category is that loose, the score is measuring interest in a topic, not a buying decision at an account. The two are related. They are not the same thing. This is the practical version of the point Nick Bennett made on Episode 1 of Intent, Decoded : intent data on a cold account with no ICP validation and no prior engagement is a reason to send a bad email faster. The platform did not fail. The definition did. Context is not evidence The simplest way to run a sanity check on any signal is to ask what it proves. Context tells you a topic is on someone's mind. Evidence tells you an account is actually doing something that changes its buying position. The two are useful in different ways, and they belong in different parts of the workflow. Take four common signals as examples. A keyword search or a category page visit. Context. Someone at the account is curious about a topic. It does not tell you whether a decision is under way, who is driving it, or when a purchase might land. A content download. Context. It shows the topic is being researched. It does not tell you whether the download was for a live project, a personal learning goal, or a comparison exercise that will not close for eighteen months. A title change at a target account. Context. A new CRO or a new Head of Support often triggers procurement reviews. It does not, on its own, mean this one will. A competitor trial starting inside a customer of yours, or a churned trial with a named competitor. Evidence. A buying motion has left a footprint that can be observed and dated. The category error is treating the first three the same way as the fourth. That is what most intent products encouraged, and it is why reps stopped trusting the score. As Leslie Venetz put it on Episode 2 of Intent, Decoded , a signal creates curiosity, not permission - and clicking a webinar link seven times is not a licence to send a demo invite. Curiosity is context. Permission comes from something stronger. What the false positives cost the pipeline The practical failure mode is well documented inside sales organisations, even when it does not make it into vendor case studies. When most of what a score flags is context rather than evidence, three things happen in sequence. Reps chase false positives. The first month of a new intent layer usually produces a small lift, then flattens. Reps run down the "high intent" list, most of it produces no reply, and the working assumption inside the team becomes that the list is only marginally better than a well-built ICP list. Forrester's guidance on evaluating intent data providers flags false positives as one of the main structural risks. Trust in the score drops. Once a rep has worked a dozen "hot" accounts that turned out to be cold, the score stops driving behaviour. It stays in the CRM. Nobody sorts on it. We wrote about this pattern in more depth in why CRM data is inaccurate : the signal field survives longer than the trust behind it. The team stops acting on the data. After a couple of quarters, the layer becomes shelfware in everything but name. Marketing still reports on it. Sales quietly reverts to territory lists, referrals, and hand-raisers. The lift the score was supposed to unlock never compounds. That is not intent data failing; it is a definition problem that shipped into a workflow. The measurable consequence is a pipeline where the highest-scoring accounts have similar conversion rates to unscored ones. The score sorts on something the buyer's actual behaviour does not reward. This is downstream of a wider pattern in B2B: on average only about 5% of any category's buyers are in-market at any moment, per Professor John Dawes's Ehrenberg-Bass 95-5 rule research . Scoring on inference and treating the top decile as buying-ready inevitably surfaces a majority of accounts that are still 12 to 24 months from a decision. What an observable signal actually looks like An observable signal is one that both parties could, in principle, describe the same way. A competitor trial has a start date. A renewal window has an end date. A vendor churn has a switch date. A technology change - an app installed, a stack swapped out - has a footprint you can point at. Five signals do the heavy lifting for most GTM teams once context and evidence are separated. Installs. An account has just deployed a specific tool. It marks the entry into a buying motion for adjacent products and the start of the clock for switching costs. Active trials. An account is currently trialling a named vendor. This is a live evaluation, dated and observable. It has a natural window (usually 14 to 90 days) and a clear intervention point. Churned trials. An account trialled a named vendor and dropped the trial. The problem is still there. The chosen solution is not. Winning back interest is materially easier than convincing a happy customer to switch. Renewal windows. A customer of a competitor is 30, 60, or 90 days out from contract renewal. The window in which switching costs are lowest is short and predictable. Technology movement. An adjacent stack change (a new CRM, a new CDP, a new CS platform) that reshapes the buying committee, the integrations required, and the timing of a follow-on decision. None of these signals require a rep to infer motive. Each one has a date on it, a named vendor attached, and a natural next action. This is the layer Subscription Intelligence was built to produce, and it is the reason evidence-based signals sort differently from inferred ones. The same signal set is the temporal component of the Qualified Opportunity framework - ICP Fit × Purchase Intent × Win Likelihood - and the broader taxonomy is covered in the purchase intent platform directory . Score on evidence, not context Which of your target accounts are showing observable signs of buying right now? Start a free trial to see the competitor trials, renewal windows, and vendor churns happening across your ICP. MarketSizer surfaces the accounts where a rep can open with a dated event, not a value prop. No credit card required. Start for free From more intent to who, why, and when The right question is not "which accounts are showing intent". It is three separate questions the CRM does not answer on its own. Who to prioritise. Firmographic and demographic fit still comes first. A signal on an account that does not fit is a distraction. Fit filters the universe; the signal decides which of the fit-qualified accounts is worth acting on this week. On Episode 2 , Leslie was explicit about the order of operations: ICP filters first, signals on top. Reversing that produces a bigger list, not a better one. Why now. A dated event tells the rep what changed and what to open with. "You started a Zendesk trial nine days ago" is a specific opening line that a buyer recognises. "Our platform tells us you're showing intent in the customer support space" is not. The named event is what lets the outreach earn its place in the inbox. What action is appropriate. The same signal justifies a different move depending on the buyer's awareness stage. A vendor-aware buyer visiting your pricing page is ready for a qualified conversation. A problem-unaware account triggering a keyword signal is ready for an insight, not a demo request. Signal-led prioritisation only works if the action matches what the buyer is actually doing. Niall's framing on the show is worth borrowing directly: MarketSizer's job is to help teams work out who to focus on, why now, and what to say. Not to add another intent score to the dashboard. The future is evidence-led prioritisation The corrective is not more intent. It is fewer signals, sorted on evidence, matched to buyer stage. Two moves change the shape of the workflow. Move intent above the funnel, evidence to the trigger point. Keep the intent layer if it is useful for audience segmentation, territory planning, or paid media targeting - that is a market intelligence job, and the aggregators built for it, ZoomInfo and HG Insights among them, are good at it. Stop asking it to decide which account a rep dials on Monday morning. The trigger for outreach should be an observable event: trial started, renewal opened, vendor churned, install detected. The intent score becomes a filter above the trigger, not the trigger itself. Standardise on one repeatable use case first. The teams that get value fastest do not turn on every signal at once. They pick one - usually customers due for renewal in the next 90 to 120 days who are also trialling a named competitor - and build the play around it before adding the next. This is the point Niall made on Episode 2: sophisticated GTM teams routinely skip the fundamentals. Fixing the fundamentals is usually where the quick wins live. The deep-dive on that specific play, for churned accounts on the winback side, is in the B2B winback pillar ; the inbound equivalent - using the same account-level signals to sort form fills - is covered in how to prioritise inbound leads with purchase intent . Once the workflow anchors on observable evidence, the argument about intent data quietly resolves itself. Context is still useful upstream. Evidence is what triggers action. The teams that separate the two get to keep the good parts of the category, without paying for the noise. Frequently Asked Questions Is intent data still worth paying for? Yes, if it is doing an audience or segmentation job rather than a prioritisation job. Intent layers can be useful for narrowing a large universe of accounts down to a research priority, or for informing paid media audiences. They are not the right layer to decide which account a rep should reach out to this week. Evidence-based signals do that job better. What is the difference between context and evidence in a buying signal? Context is a data point that suggests a topic is on someone's mind - a page visit, a content download, a keyword surge, a title change. Evidence is a dated observation of a specific buying motion at a specific account - a competitor trial started, a renewal window opened, a vendor churned. Both are useful. They belong in different parts of the workflow. Are keyword searches and page visits useless? No. They are useful as inputs to segmentation and audience decisions, and as one input among several when a rep is deciding what to research. They should not, on their own, trigger outreach. A page visit tells you a topic is being researched. It does not tell you an account is ready to buy. What counts as an observable buying signal? Any signal that both parties could describe the same way and that has a date attached. Competitor trial starts, active trials, churned trials, renewal windows, vendor churns, technology installs, and stack changes are all observable. They can be timestamped, attributed to a named vendor, and connected to a natural next action. How do we start using evidence-based signals without ripping out our current stack? Pick one repeatable use case first. A common starting point is a play that identifies customers of a specific competitor whose contracts renew in the next 90 to 120 days and who are also trialling one of your named alternatives. Build the workflow, prove the lift, then add the next signal. Turning every signal on at once is what makes the layer feel noisy again. How is this different from Episode 2 of the podcast? Episode 2 makes the case that a signal is not permission and that sellers have to earn the right to act on it. This piece runs the same argument one level up: a lot of what got sold as "intent" was context rather than evidence in the first place, and the market is now sorting the two out. The Episode 2 recap covers the seller's side of the same problem. What should replace an intent-only sales prioritisation motion? An evidence-led motion where a dated buying event triggers outreach, an ICP filter decides whether the account is worth the effort, and the message matches the buyer's awareness stage. The evidence layer is what Subscription Intelligence produces. The mechanics of running the workflow on top of it are covered in how to build a signal-led GTM motion . Recommended reading From Signal to Trust: How Sellers Earn the Right to Reach Out - the Episode 2 recap with Leslie Venetz on the seller's side of the same argument. The Winnable Account Is Warm, Not Hot - what evidence-led prioritisation looks like when you sort on winnability rather than heat. What Is a Qualified Opportunity? - the formal three-part definition (ICP Fit × Purchase Intent × Win Likelihood) that the evidence layer plugs into. B2B Winback: Recover Churned Customers on Competitor Renewal Timing - the deep dive on the renewal + competitor-trial play, applied to the churned base. How to Prioritise Inbound Leads with Purchase Intent - the same three account-level signals applied to inbound rather than outbound. Purchase Intent vs Intent Data - the definitional pillar behind the context-versus-evidence argument. Why Intent Data Made B2B Outreach Worse - the effect of the category error on outreach quality, and how to unwind it. What Is Subscription Intelligence? - the data category that produces the evidence-based signal layer. Intent, Decoded - the podcast hub - both episodes and the wider conversation about how to use signals well. ## Blog: How to Prioritise Inbound Leads with Purchase Intent Not every inbound lead is a good lead. Here is the four-tier framework that scores inbound at the account level - and stops your SDR team from drowning in the 95% that never buy. Inbound feels safer than outbound. Someone at the account raised their hand. They filled a form or downloaded a piece or requested a demo. Compared to the cold cadence, that has to be higher-quality pipeline. Which is why most B2B teams route every inbound lead into the same follow-up motion and are surprised when the conversion math doesn't hold. The problem isn't that inbound is broken. It's that "an inbound lead is a good lead" was true when inbound volume was small and buyer intent was scarce. In a market where every seller runs content and every buyer fills a form for a whitepaper they never read, inbound quality now varies wildly - and lead-level scoring can't tell you which of this week's 200 form submissions are the 15 accounts actually buying. That takes a different unit of measurement, at the account level, using the same signals you'd score outbound on. Tier 1 ICP-fit + in-market Route immediately, ideally within minutes. This is hot pipeline masquerading as an inbound form. Tier 2 ICP-fit, not in-market Nurture patiently. Monitor for Purchase Intent signals. Move to Tier 1 when a buying window opens. Tier 3 In-market, not ICP-fit Triage carefully. Often researchers, competitors, or fringe fit. Occasionally a real deal - but the base rate is low. Tier 4 Neither ICP-fit nor in-market Deprioritise. Marketing analytics only. Zero rep time. Same inbound queue, four very different pipelines underneath. Why "an inbound lead is a good lead" is a fantasy Fifteen years ago, an inbound lead was a real event. B2B content marketing was thin, gated whitepapers were unusual, and the person who filled a form for one was probably close to a buying motion. The default assumption that "inbound leads are hot leads" reflected that scarcity. That world is gone. Every seller runs content, every category has ten competing gated assets, and buyers routinely fill forms for reasons that have nothing to do with buying - to see a competitor's pricing page, to research for a school project, to add material to a personal reading list, to satisfy an internal ask, or because a colleague sent them the link. Inbound volume has grown while the density of real buyers inside that volume has fallen. The average HubSpot marketing benchmark puts B2B MQL-to-closed-won conversion in the low single digits - which means somewhere between 95% and 98% of leads flagged as "qualified" by inbound activity never buy. The problem this creates isn't that inbound is wasted spend. It's that inbound at scale drowns the SDR team in low-quality follow-up, which means the 2-5% of leads that are at accounts actually buying get worked at the same speed and priority as the 95% that aren't. SDR fatigue rises, response time on the real opportunities lags, and the conversion math quietly stops holding. The fix isn't to filter more aggressively at the form. It's to score inbound the same way you'd score outbound - at the account level, using ICP Fit and Purchase Intent and Win Likelihood - and let the form-fill be one data point among several, rather than the deciding one. This isn't a hypothetical use case. In the MarketSizer sales pipeline, primarily-inbound teams - especially mid-market SaaS running paid and organic inbound at scale - ask the same question repeatedly on discovery calls: "can this help us qualify our inbound? " The answer this piece describes is the account-level scoring model that produces yes - but not in the way most teams expect, because the score doesn't attach to the form-filler, it attaches to the account they work at. What "prioritising inbound" actually means Most inbound scoring today runs on lead behaviour. Email opens, page visits, content downloads, form completeness, job title inferred from the form field. All of these describe what one person did on the seller's own properties. None of them describe what the account itself is doing in the outside world. That distinction matters because the buying motion happens at the account level, not the individual level. A form fill from a marketing intern at a great account that just started a competitor trial is a Tier 1 lead - the intern isn't buying, but the account is, and the intern will surface as one of the six-to-ten stakeholders in that buying committee within weeks. A form fill from a VP at an account that just renewed a three-year contract with your primary competitor is a Tier 4 - the VP is engaged, but the account isn't buying, and no amount of follow-up will change that until the competitor contract nears renewal. Prioritising inbound means shifting the scoring lens from "which lead engaged the most" to "which account is actually doing something to buy. " The lead-level engagement stays useful as context (it tells the rep what to say and to whom), but it stops being the primary sort. The account's real-world state becomes the primary sort. This is the same underlying framework used in a signal-led GTM motion for outbound - which is the point. Inbound and outbound should be scored on the same variables. What differs is the routing rule that fires afterward. The four tiers of inbound leads Every inbound lead sits in one of four tiers, defined by whether the underlying account clears the two thresholds that actually predict conversion: ICP Fit and Purchase Intent . Those two thresholds are the whole of lead qualification here; everything else is routing. Each tier gets a different routing rule. Tier 1 - ICP-fit and in-market: hot pipeline The account matches your best-fit customer profile, and there's an observable buying event at the account right now - a competitor trial started, a renewal window opening, a vendor recently churned, a tech-stack change. The person who filled the form may or may not be the decision-maker, but that's secondary; the account is buying, and you have a chance to be part of that decision. These are the leads where the classic Harvard Business Review research on inbound response time genuinely applies - the older study found that reaching a lead within five minutes of form submission produces a step-change in conversion versus reaching them within an hour. The reason that finding held up for so long is that it was measuring exactly this cohort - real in-market leads where speed compounds. On the other 95% of inbound, response time barely moves the needle because the underlying account was never buying. Typically 3-8% of inbound volume in a mature B2B SaaS motion sits in Tier 1. Small in absolute terms, but the tier where nearly all inbound-attributed pipeline actually comes from. Tier 2 - ICP-fit but not in-market: nurture The account matches your ICP, but there's no observable buying event happening right now. The lead engaged with content because they're educating themselves on the category, benchmarking against alternatives, or nurturing an internal project that might become a purchase later. These are the leads most marketing nurture programmes actually work for - because the buyer is genuinely interested but not in a decision cycle yet. The right move is a light-touch nurture cadence anchored on category education, plus continuous monitoring for Purchase Intent signals at the account. When a buying event fires (usually 3-18 months after the initial form fill), the lead promotes to Tier 1 and gets routed to a rep. Typically 20-35% of inbound volume sits in Tier 2. Real revenue future; not this quarter's pipeline. Tier 3 - In-market but not ICP-fit: triage carefully The account is showing observable buying signals, but doesn't cleanly fit your ICP. This tier gets misread more often than any of the others - the buying activity is real, so the temptation is to work the lead like a Tier 1. But if the account is genuinely outside your ICP, the win probability collapses regardless of how in-market they are. Three common Tier 3 patterns worth naming: (1) researchers - analysts, consultants, and journalists who fill forms as part of their own work, (2) competitors - competitor employees studying your product, and (3) genuine fringe-fit accounts where you might win but the sales cycle length and product fit are both worse than your best-fit cohort. The base rate for closing Tier 3 is much lower than Tier 1, so the routing should be a lighter-touch outbound cadence rather than immediate AE assignment. Typically 8-15% of inbound volume. Occasional wins, low priority. Tier 4 - Neither ICP-fit nor in-market: deprioritise The account doesn't match your ICP and there's no observable buying event. The lead is content-consuming, not category-shopping. Marketing leaders sometimes call this bucket "spam leads" - not because the person is a bad actor, but because from a sales-conversion perspective, the volume is functionally noise. There's no realistic path to a deal in a horizon short enough to justify rep time. These leads shouldn't get rep touch. They should get marketing analytics attention (they're useful for measuring content reach and category awareness) and possibly a low-effort automated nurture, but sending an SDR into this tier produces the "sequenced to death for no reason" experience that erodes trust in the seller's brand more than it produces pipeline. Typically 50-70% of inbound volume - the majority. Which is exactly the point: the volume-led inbound motion routes all of these to SDRs, and the resulting activity noise buries the 3-8% of leads that were actually Tier 1. The three signals that reveal an inbound lead's tier Three signals, scored at the account level, determine which tier a given inbound lead belongs in. Each is a different data lookup at the moment of form submission. Signal 1 - ICP Fit of the account, not the lead The lead is the person who filled the form. The account is the company they work at. ICP scoring should happen at the account level - firmographics, technographics, historical closed-won pattern-matching - not at the lead level. Most inbound scoring tools default to lead-level ICP (job title, seniority, function) which is a weaker signal than account-level ICP. A junior person at a great account is still a great account, and they'll bring the buying committee to the eventual sales conversation. A senior person at a poor-fit account is still a poor-fit account, and their seniority doesn't move the deal odds. The ICP Fit score should attach to the account record and route from there, not to the individual lead record. Enrichment tools like Clearbit (now HubSpot) or ZoomInfo produce the underlying firmographic data; scoring against a closed-won cohort turns that data into a usable tier signal. Signal 2 - Purchase Intent at the account level This is the signal that separates Tier 1 from Tier 2 and Tier 3 from Tier 4. Is something buying-relevant happening at the account right now, independent of the form fill? Observable events: a competitor trial started at the account, a renewal window opening on their current vendor, a vendor recently churned, a tech-stack component installed or removed, a regional expansion. Each is a time-bound event that describes what the account is doing, not what the lead is reading. The full argument for why these signals outperform inferred intent is in the Purchase Intent vs Intent Data pillar ; the operational point for inbound is that Purchase Intent is what elevates a Tier 2 lead to a Tier 1 the moment the buying window opens. Evidence-based purchase intent platforms surface these events at the account level. Where the account has an active signal, the lead's tier upgrades regardless of whether the person filled a "priority" form or a "casual research" form. Signal 3 - Win Likelihood against the incumbent The third signal is competitive. Given the specific competitor set at the account (which vendors they already run, which they're evaluating), does the seller have a defensible chance of winning this deal? Win Likelihood matters for inbound the same way it does for outbound: an account that's ICP-fit and in-market but historically loses 96% of the time against the incumbent isn't a Tier 1 lead - it's a very hopeful long shot. The routing should reflect that. Typically Win Likelihood is a lighter-weight signal for inbound scoring than for outbound (because the lead has already self-identified interest), but it still shifts the routing rule at the margins. The three signals combined - account-level ICP Fit, account-level Purchase Intent, and Win Likelihood - are the same three that produce the Opportunity Score . Which is intentional: inbound and outbound should share a qualification framework. What differs is what happens next. Score inbound like you'd score outbound See which inbound leads sit at accounts actually buying right now Start a free trial to enrich every inbound lead with account-level Purchase Intent. MarketSizer surfaces which of the accounts submitting forms this week are in an active buying motion - and which are content researchers who won't buy anything for two years. No credit card required. Start for free How to route each tier The routing rule per tier is deliberately simple. Complexity in the routing logic is where most inbound programmes quietly break down. Tier 1 - Immediate AE or senior SDR assignment, within five minutes. The lead lands in the CRM with the Purchase Intent context attached. The rep's first outreach references both the form fill and the observable account event: "Saw you downloaded our comparison guide, and noticed your team started evaluating Vendor X last week - want to compare notes before the trial ends? " Tier 2 - Marketing nurture with Purchase Intent monitoring. The lead enters a light-touch education cadence (category-relevant content, not product-heavy). The account is watched for Purchase Intent signals; when a signal fires, the lead promotes to Tier 1 and gets routed to a rep. This is where marketing automation earns its keep. Tier 3 - Light-touch SDR outbound, one or two touches maximum. A brief probe to confirm whether the account is genuinely in-market despite the ICP mismatch. If yes, hand off to AE with a caveat that this is fringe fit. If no reply within two touches, drop back to nurture. Tier 4 - Zero rep touch. Automated marketing nurture only. Track for content reach and category awareness metrics, not pipeline. If a Purchase Intent signal ever fires at the account, the lead promotes to Tier 3 or Tier 1 depending on ICP status. Two structural points that matter across all four tiers: Tier assignments are dynamic. A lead can move up (Tier 2 to Tier 1 when a Purchase Intent event fires) or down (Tier 1 to Tier 2 if the buying window closes without conversion). The tier isn't a stamp; it's a state. The routing rule is about who acts, not who watches. Marketing has visibility into all four tiers - the tier just determines which team spends rep time. Reporting still tracks lead volume across all tiers so the top-of-funnel picture stays complete. Two low-friction operational surfaces this actually runs on inside customer teams: CSV enrichment on the raw inbound queue. Export the week's inbound leads to a CSV, enrich each row with account-level Purchase Intent, and route by tier before the queue lands in the SDR seat. This is the pattern most inbound-heavy MarketSizer customers use in their first month - it takes minutes to run and produces an immediately re-sorted pipeline. Chrome-extension lookup at the moment a rep opens a lead. When a rep clicks into an account in their CRM, a browser extension surfaces the account's real-world state (which vendors are present, whether a competitor trial is live, whether a renewal window is open) before the rep decides whether to fire a Tier 1 response. Skips the "wait for the CSV to run" latency for individual leads. Neither surface requires a new dashboard or a workflow the team hasn't seen before. What to stop doing with inbound leads The patterns that most B2B teams accumulate over time and that quietly compound into the "inbound isn't converting" complaint: Stop routing every inbound to an SDR. The default "if it fills a form, sequence it" rule sends the 95% into an SDR cadence that produces nothing but activity metrics. Route by tier, not by form. Stop scoring on lead behaviour alone. Page views, email opens, and content downloads describe interest, not buying motion. Score at the account level. Lead behaviour is context for the message, not the primary sort. Stop treating MQL volume as pipeline signal. An SDR team celebrating "MQL count up 40%" without knowing what happened to those MQLs downstream is measuring the wrong thing. Track MQL-to-Opportunity conversion by tier, not by count. Stop applying the five-minute response rule to every lead. The classic HBR research was measuring a specific cohort - real Tier 1 leads. Applying it to Tier 3 and Tier 4 traffic is how SDR fatigue starts. The five-minute rule is Tier-1-only. Stop assuming inbound quality is stable across cohorts. The mix of Tier 1 through Tier 4 shifts as your content shifts, your paid mix shifts, and your category matures. Audit the tier distribution quarterly; changes in that distribution are a leading indicator that a channel or campaign has changed shape. Stop measuring marketing on lead count and sales on lead conversion. That split incentivises marketing to raise volume and sales to reject leads - the classic misalignment. Both teams should share the Tier 1 pipeline number as the metric that matters. Compare tools with MarketSizer Direct comparison with the enrichment platform mentioned above: MarketSizer vs ZoomInfo . Full index at MarketSizer compared with other tools . Frequently Asked Questions What is inbound lead prioritisation? Inbound lead prioritisation is the practice of ranking inbound leads by the underlying account's real-world state - ICP Fit, Purchase Intent, Win Likelihood - rather than by the individual lead's engagement behaviour. It's how modern B2B teams tell which of the 200 form fills this week are the 15 at accounts actually buying. Isn't every inbound lead worth reaching out to? No. In current B2B markets, only 3-8% of inbound volume typically sits in Tier 1 (ICP-fit and in-market). The rest is a mix of nurture-worthy but not-yet-buying (Tier 2), fringe-fit or researcher (Tier 3), and neither (Tier 4). Routing all four to the same SDR cadence produces activity, not pipeline. How is inbound prioritisation different from outbound prioritisation? The underlying scoring framework is the same - ICP Fit × Purchase Intent × Win Likelihood. What differs is the routing rule after scoring. Inbound Tier 1 gets a fast rep response referencing both the form fill and the account event; outbound Tier 1 gets a cold touch referencing only the account event. See the signal-led GTM playbook for the outbound side. Does the five-minute lead response rule still work? Yes, but only for Tier 1. The original HBR research measured a specific cohort - accounts that were genuinely in-market. On Tier 3 and Tier 4 traffic, response time barely moves the numbers because the underlying account wasn't buying. Applying the five-minute rule universally burns SDR bandwidth on cohorts where it produces no lift. What percentage of inbound leads typically fall into each tier? In mature B2B SaaS motions: Tier 1 usually 3-8%, Tier 2 usually 20-35%, Tier 3 usually 8-15%, Tier 4 usually 50-70%. The exact mix shifts by channel, content type, and category maturity. Track your own distribution quarterly - shifts in the mix are a leading indicator of channel quality changes. Can I use just my CRM to run this framework? You can start with just your CRM if you have ICP scoring already in place - the framework can run on custom fields and workflows. You'll be blind to the Purchase Intent signal until you add a data layer that surfaces account-level buying events; the purchase intent platform directory covers who produces those signals. How should the SDR team respond to Tier 1 leads specifically? Within five minutes if possible. Reference both the form fill and the account-level event - "Saw you downloaded our comparison guide, and noticed your team started evaluating Vendor X last week - want to compare notes before the trial ends? " The dual-anchor makes the message specific enough that the buyer recognises it as considered rather than automated. Recommended reading Purchase Intent vs Intent Data - the definitional pillar that grounds the Signal 2 scoring layer in this framework. How to Build a Signal-led GTM Motion - the outbound counterpart to this piece, running on the same three signals. Best Purchase Intent Data Platforms for 2026 - the directory of platforms that produce the account-level Purchase Intent signal. Why Qualified Leads Don't Close (And How Timing Kills Conversion) - the funnel-level cost of routing every inbound the same way. How to Implement Signal-led Prioritisation in Salesforce and HubSpot - the tactical CRM guide, applicable to inbound routing as well as outbound queuing. From Signal to Trust - Episode 2 companion piece on why a signal (inbound or otherwise) isn't automatically permission to sell. ## Blog: When Every Tool Has the Same Signals Funding rounds, hiring, job changes - the standard signals now ship in every tool, so every rep calls the same account on the same day. Jack Porter and Niall O'Gorman on what is left once that happens. Brendan Hughes booked this one expecting a fight. Two founders who sell competing answers to the same problem, one episode title asking whether intent data is dead, and a subtitle promising that two signal companies would argue it out. What he got was Jack Porter of WhiteWhale and MarketSizer's Niall O'Gorman agreeing on almost everything, which Niall eventually had to call out on air: "again, we're meant to be fighting here. " The agreement turned out to be the interesting part. When two people building in different directions land on the same enemy, the enemy is probably real. Here is what came out of Episode 3 of Intent, Decoded . The Supply of Keyword Intent Is Drying Up Jack's framing of the category is the cleanest version of it I have heard. There are three generations of trying to predict which account is worth a rep's morning. The first is what most people mean when they say intent data: this company searched this keyword. It is the model behind the familiar names, and it works by watching someone from a company IP address land on a page in a content network. The problem is structural rather than commercial. Buyers increasingly do that research inside a model instead of a search engine, and a model reading a blog post on your behalf does not leave the trace the whole method depends on. The supply is thinning out. That is a different failure from "the data was never any good", and it is not one a vendor can fix with better matching. The second generation is the response to that: the pivot from intent to signals. Funding rounds, hiring, job changes, champion tracking. Useful events, and genuinely better than a keyword surge. But they are also the easiest signals to build, which is why they are now in every tool on the market. Everyone Has the Same Signals, So Everyone Calls the Same Day This is where Jack landed the line of the episode. "Everyone with a Clay table, everyone with an Apollo account, everyone with a ZoomInfo account emails the exact same prospect the exact same day at the exact same time. And there is no worse time to reach out than when everyone else is calling. " It is worth sitting with, because it inverts how most teams think about signal quality. A commoditised signal is not merely unhelpful. It is actively expensive. It concentrates the entire market's outbound onto the same accounts in the same 48 hours, which means a signal every tool ships is a reliable predictor of a crowded inbox rather than of a receptive buyer. The funding announcement that told you to call is the same announcement that told forty other vendors to call. Niall's version of the same complaint comes at it from the data side. Aggregated datasets get refreshed on a vendor's schedule, not the buyer's: weekly for the large sites, every six months for the small ones. "That is not helpful, because the data is going to be stale before it is even received. " Then the current fashion is to point a language model at that stale data, which adds fabrication on top of being out of date. His phrase for the result: the wild west. So the shared enemy is not intent. It is static data that everyone owns and nobody can check. We have made the longer version of that argument in intent data didn't break, the wrong signals were sold as intent . Work Backwards From the Buying Moment Jack's answer to the commoditisation problem is to stop starting from the signal. Start from the moment. His worked example: you sell a customer experience or customer support product. When does a company actually buy one of those? Not continuously. There is a tipping point where the pain of doing support manually becomes bigger than the hassle of buying software. Maybe it is crossing a thousand customers. Maybe it is hiring the first customer success person. Maybe it is landing an enterprise deal large enough to eat the team's week. Once you can describe that moment, you can work backwards to the observable events that indicate an account has arrived at it. A first CX hire. An expansion into a new market. A deal announcement that changes the support load. None of those are intent in the classic sense. All of them are evidence that the pain is rising. The exercise is free, and it is the part most teams skip. Write down the single sentence that describes when a company becomes a buyer of your product. If you cannot write it, no signal feed will supply it. What the Account Does, Not What Is Said About It This is where the two companies genuinely diverge, and it is a difference in the question being asked rather than a disagreement about the answer. WhiteWhale watches what is said about an account: news, hiring, filings, earnings calls, company updates, read back with the quote and the source attached. MarketSizer watches what the account does with software. Niall's side of it is subscription movement: when a technology was first seen on an account, when it was last seen, and what the gap between those two dates implies. An active trial looks different from a churned trial. A trial that flipped to a subscription looks different again. From there you can place an account on a lifecycle - new, developing, established, loyal - and the placement changes the play. His example of why that matters is the most practical thirty seconds in the episode. "If somebody has just renewed with a competitor, you put them into nurturing until you are 120 days out from the next renewal. If your own customer starts implementing a named competitor, you pick up the phone as fast as humanly possible. " Two accounts, both showing competitor activity, opposite responses. A list that treats both as "high intent" has destroyed the only information that mattered. Both of those are still evidence rather than inference, which is the thing they share. The detection side of it is covered in how to detect competitor trials , and the renewal-window play in the B2B winback deep dive . Timing Is a Spectrum, Not a Binary Brendan pushed on this and got the reframe the episode needed. Most tools present timing as a one or a zero: this account is hot, go. Jack's objection is that nobody buys that way. "Practically, it is not a binary. That is not how you and I buy things. We think about things, we sleep on it, we wait six months, we forget about it, and then we need to go buy it. " The honest version runs from an account with no competing technology in the stack at all, through an account evaluating, to an account fully committed to a competitor where any deal is a rip and replace. Each position calls for a different move. An account already locked into a competitor gets an air-cover warm-up: education on the differences, run patiently against a renewal date you can approximate. An account building out the function right now, having just put real salary behind it, is go time. Same target list, two completely different motions. A tool that only tells you "hot" cannot tell you which one to run. That is the argument we made from the buyer's side in the winnable account is warm, not hot . The Signal Is Half the Job. Routing Is the Other Half. Niall's contribution here is the one most likely to be ignored and the cheapest to fix. Every signal has a correct recipient, and the default of "send it to the SDR" is wrong most of the time. A named competitor being trialled inside an account where you are the incumbent is not an SDR signal. It goes to the CSM, the account manager, or whoever owns retention, and it goes there today. An account doing early-stage category research is not a sales signal at all; it is a marketing signal, and it is not even a qualified lead yet. Sending it anywhere near a rep guarantees a premature pitch to someone who has not decided they have a problem. His framing of the work order is deliberately unglamorous: most of the value is extracted before any signals arrive. What are you trying to achieve, who are the personas, how do you identify them, in what format does the signal need to surface, which system does it need to land in, and who acts on it. That is a whiteboard exercise, not a purchase. The implementation detail for CRM-side routing is in how to build a signal-led GTM motion . No Source, No Belief Both founders had the same answer to the reliability question, which tells you something about where the category is heading. Jack: "A signals business does not exist unless the data is reliable. As soon as that data becomes unreliable, people stop trusting it and they stop listening to it. " He traces the collapse of trust in first-generation intent to a specific experience every rep has had: you call the account the platform flagged, and the person on the phone has no idea what you are talking about. Technically someone at that IP address did search the term. That fact is useless to the person dialling. So WhiteWhale attaches the source to every signal, whether it is a job post, a press release or a news article, and the rep can read it before they act. Niall's version is the timeline. Nobody believes a subscription record until they can click into it and see when the technology was first detected, when it was last detected, when the competitor went live. The Chrome extension adds a second layer: a live detection on the site you are looking at, checked against the historical record, so the aggregate claim and the current reality are verified against each other. The same idea shows up in how both companies price. Jack's is month to month with an explicit "if the data is bad, cancel", which he contrasts with a multi-year contract paid up front. Niall went one further: when MarketSizer tries to enrich something and gets it wrong, the credit is returned automatically with an audit trail recording the miss, before the customer has to ask. Admitting a failure in public is an odd growth strategy. It is also the fastest route to being believed by someone who has been sold unverifiable data before. Why Not Just Build This Yourself? Brendan asked the question every founder in this category gets asked weekly: I have a model, a few connectors and an afternoon. Why do I need either of you? Jack's answer is operational rather than technical. You can ask a model about a thousand accounts this morning. Will you do it tomorrow morning? And the morning after? How do you know what changed between runs? The manual version does not survive contact with a Tuesday, and the automated version burns through more in credits than either product costs. In practice, he said, most of their customers do both: take the structured data in, and use the model for the writing, the call prep and the research on top. Niall, who is candid about using these tools daily, made the guardrails argument. However the data is produced, it has to come back in a predictable shape. If an SDR has to work out how to read the answer every single time it arrives, in a new format, with different charts, the efficiency gain has gone. You can build that consistency yourself if you are willing to maintain what you build. Most organisations decide that the maintenance is not their core business. His closing image for it was a strip mall with two barbers. One sign reads six dollar haircuts. The other reads we fix six dollar haircuts. Signals Multiply. They Do Not Rescue. The closing advice is the part to take to a planning meeting, and both founders arrived at it independently. Jack: signals and intent data are a multiplier. Point one at a working outbound motion and it gets substantially better - his example was a team going from five meetings a week to fifteen, because the targeting finally matched the message. Point one at a motion that is not working and nothing happens. "If you multiply anything by zero, it is still zero. Intent data is not going to save a bad offer. " The three things that have to be solved first are the offer, the infrastructure and the pitch. A team arriving at a signal product as a life raft, having decided outbound is broken, is close to the worst-case customer, and he tells them so. Niall's version starts even further upstream: rubbish in, rubbish out, and master data is the piece nobody wants to own. His practical instruction for anyone overwhelmed by the tooling is to turn the computer off, take a sheet of paper, and draw the customer journey. Left is where they start, right is where they finish, and the messy middle is the actual work. Do that first, and the tools stop being a menu of options and become a set of answers to questions you have already asked. Go straight to a model instead and you get an impressive plan you had no part in creating, do not understand, and cannot repeat. Which lands them both in the same place: it comes back to the ICP. What do we sell, who do we sell it to, and what is the value proposition. Nothing layered on top works without that, whether it is signals, intent, or a coaching platform. Brendan's summary was more concise. Get the basics right, then add the magic beans. Intent, Decoded is MarketSizer's podcast series on the mechanics of purchase intent and what it actually takes to use it well. Watch Episode 3 on YouTube , listen on Spotify , or catch the full LinkedIn Live recording here . Follow the show on Spotify or YouTube to catch future episodes, or visit the podcast hub for everything in one place. Recommended reading Episode 3: Intent Data Is Dead. Or Is It? - the full conversation, with chapters, quotes and the video. Intent Data Didn't Break. The Wrong Signals Were Sold as Intent. - the long-form version of the shared-enemy argument in this episode. What Replaces Intent Data in 2026? - the compact answer to the question the episode title asks. How to Detect Competitor Trials - the detection methods behind the first seen, last seen timeline. B2B Winback: Recover Churned Customers on Competitor Renewal Timing - the deep dive on the 120-days-out renewal play. Purchase Intent vs Intent Data - the definitional pillar behind the split between what is said and what is done. The Winnable Account Is Warm, Not Hot - why the hottest account is rarely the most winnable one. From Signal to Trust - the Episode 2 recap with Leslie Venetz, on earning the right to a buyer's time. Most GTM Teams Use Intent Data at the Wrong Time - the Episode 1 recap this conversation builds on. Intent, Decoded - the podcast hub - every episode in one place. ## Blog: From Signal to Trust: How Sellers Earn the Right to Reach Out A signal is not permission to sell. Leslie Venetz on why the right to a buyer's time is earned, not extracted, and what that looks like in practice. There's a moment in most GTM teams where an intent platform lights up on an account, someone drops it into a sequence, and everyone hopes that will be enough. It rarely is. That's roughly the pattern our host Brendan Hughes, MarketSizer's Niall O'Gorman, and this episode's guest Leslie Venetz spent an hour pulling apart on Episode 2 of Intent, Decoded . Leslie has spent a decade in outbound. She's a USA Today bestselling author, a LinkedIn Top Voice in sales, and has personally made a quarter of a million cold calls, which is either terrifying or admirable depending on your tolerance for phone calls. (I say that with affection. Both, probably. ) The thread she keeps pulling on is a phrase she coined: earn the right . And once you sit with it, most of the mistakes GTM teams make with intent data become obvious. A Signal Is Not Permission The single strongest idea from the conversation is this: a signal is a piece of context, not a licence to sell. Someone clicked a webinar link seven times. Someone changed roles. Someone's company raised a round. None of that is an invitation. It's a data point about what might be relevant to them, if you can connect it to something they actually care about. Leslie's litmus test is worth stealing: before you send an email, ask what you have actually done to earn the right to send it. To ask for a thirty-minute discovery call. To ask a stranger to spend their social capital pushing your product internally. To ask them for their money. Most outreach fails that test on the first line. Niall summed it up: "signals essentially create curiosity, but not necessarily the permission to reach out on them. " This connects to something we've written about at length: intent data mostly scaled the volume of bad outreach rather than improving its quality. When teams treat a signal as a starting gun, they end up sending faster, worse emails to more accounts. The signal was never the problem. The interpretation was. ICP First, Signal Second (and the Move from Product to Problem) The next move Leslie makes is order-of-operations. Firmographic and demographic ICP filters go first. Signals layer on top. Not the other way around. "I try to build in firmographic and demographic filters to my list, and then layer a signal on top of that," she said. Meaning: don't scroll an intent tool looking for spikes and then work backwards to whether the accounts are a fit. Build the tight, well-defined list first, then use signals to decide who inside that list is worth reaching now. Once you have the right list, the second failure mode kicks in: product-centric copy. Leslie runs a workshop she calls FABs (features, advantages, benefits) that pushes reps to keep asking "so what? " on every product claim until they land on something that actually matters in the buyer's day. It sounds simple. It's the thing almost no outbound sequence does. Most emails stop at the feature. A few push to the advantage. Almost none get to the impact on the person reading it. The other reframe worth stealing from this section is the distinction between something being true and something being relevant . Personalisation, done badly, is a parade of true statements about the buyer: they went to this university, they were promoted last month, their company raised a round. Every fact is verifiable. None of it is a reason to reply. Relevance is different. It ties a specific thing you know about the buyer to a specific reason it matters to them right now. True gets a rep past the spam filter. Relevant gets a reply. Most outbound stops at true, which is why so much of it feels vaguely creepy. The exercise is easy to run on your own team's cadences. Pull one sequence. Every sentence that describes what your product does gets a "so what? " written next to it. If you can't answer that in one line, the sentence isn't ready to send. Every "personalised" line about the buyer gets a "so what to them? " written next to it. If the honest answer is "nothing", the line is decoration, not relevance. The POV Framework: Meet Buyers Where Their Awareness Actually Is Leslie thinks about buyers in three buckets, which she calls the POV framework: problem-unaware, opportunity-aware (or option-aware), and vendor-aware. It sounds like a marketing model. It's really a sales operating instruction. If someone is problem-unaware, they don't yet know they have a gap. A demo invitation is a rounding error. What works is education: a point of view, a piece of research, a benchmark they can compare themselves against. If someone is opportunity-aware, they know they have a problem and are scoping solutions. That's the moment for insight and shortlisting help, not a hard pitch. If someone is vendor-aware and looking at your pricing page, the play is different again: get to the qualified conversation quickly. The same signal reads three different ways depending on which bucket the buyer is in. A visit to your pricing page from a vendor-aware buyer is a green light. From a problem-unaware buyer, it's just curiosity. The teams that get outreach right are the ones tuning their message to the stage the buyer is actually at, not the stage the sequence assumes. This is also where signal-led ABX earns its keep. It's not more coverage. It's the right message meeting the right awareness stage at the right moment. AI Belongs in Research, Not in the Send Button The AI section of the conversation was the sharpest. Leslie is not against AI in outbound. She's used a sequence-generation tool called Reggie for four years. What she pushes back on is a specific pattern: outbound teams pointing an LLM at a prospect list and shipping the output. Her framing landed hard: "LLMs are statistical average generators. Even if they're getting ranked 96 out of 100, they still sound like the same garbage everybody else is sending at scale. " At scale, statistically average copy converges on identical copy, and the entire prospect universe starts receiving variations of the same email. Where AI genuinely helps is upstream of the send. Understanding a persona. Mapping a buyer ecosystem. Pressure-testing whether your value proposition holds up in a specific vertical. Role-playing a discovery call before you dial. Niall's addition was that AI can be a fantastic prep partner for a human seller, and a terrible replacement for one. It's the difference between using a signal to sharpen your thinking and using it to skip thinking entirely. Fundamentals Beat Sophistication: Deposits Before Asks The moment in the conversation that surprised me most was when Leslie said she still finds sophisticated, well-tooled GTM teams missing basic hygiene. Companies with seven-figure tech stacks who haven't tagged their churned customers, don't flag when a past champion changes jobs, and give their reps no time to prep before they dial. The fundamentals are where the biggest signals often live, and they're the least glamorous to fix. Her closing framing is one to write down: "How can I make deposits before asks, so that I am always putting more in the bank than I am ever trying to withdraw? " A deposit is a piece of custom research. A first-hundred-days plan for a newly-appointed CPO. An intro that costs the seller their social capital, not the buyer's. When you've made enough deposits, the ask lands as a natural next step. When you haven't, it lands as spam. Niall's line at the end brings it all together: "All the sophistication means nothing if you're sending the wrong message at scale, at the wrong time, to the wrong person. " That's the whole conversation in one sentence. The path from signal to trust runs through interpretation, order-of-operations, awareness, restraint, and effort. Not through automation. Start With One Repeatable Signal, Not All of Them The other practical instruction from the episode is one worth writing down: do not turn every signal on at once. Sophisticated GTM teams routinely lose to their own tooling here. Twelve intent sources, six intent scores, four scoring models, a heat map, a leaderboard - and reps who stop trusting any of it. The fix Niall offered is a small one, and it's the right place for a team new to signal-led work to start. Pick one signal-based use case, prove it works, then add the next. His worked example was concrete: customers of a specific competitor whose contracts are due for renewal in the next 90 to 120 days, and who are also showing evidence of trialling one of your named alternatives. Two observable signals stacked together. A universe small enough for a rep to actually work. A trigger that anchors the outreach on something the buyer would recognise about themselves. The reason that specific play works as a starting motion is that every element passes Leslie's litmus test. The renewal window is a dated event, not a guess. The competitor trial is observable evidence, not a keyword surge. The account is inside the ICP because it's a customer of a named competitor you already sell against. The action - a warm reference to the renewal window with an anchor on the competitor trial - is matched to a vendor-aware buyer who is already scoping alternatives. Fit, signal, timing, and relevance line up before anyone opens an email. We covered the same prioritisation logic from the buyer's side in the winnable account is warm, not hot , and the wider argument about why context is not evidence in intent data didn't break; the wrong signals were sold as intent . The deep-dive treatments are in the B2B winback pillar for the churned side of the play, and how to prioritise inbound leads with purchase intent for the inbound side. Once that one play is running and producing reply rates you'd trust, add the next one. Not before. Signal-led prioritisation compounds when the workflow is simple enough for a team to actually run each morning; it collapses when the workflow is a dashboard. Intent, Decoded is MarketSizer's podcast series on the mechanics of purchase intent and what it actually takes to use it well. Watch Episode 2 on YouTube , listen on Spotify , or catch the full LinkedIn Live recording here . Follow the show on Spotify or YouTube to catch future episodes, or visit the podcast hub for everything in one place. Recommended reading Intent Data Didn't Break. The Wrong Signals Were Sold as Intent. - the companion piece on the category error that turned context into "intent", and what to sort on instead. The Winnable Account Is Warm, Not Hot - a practical prioritisation essay on why the hottest account is rarely the most winnable one. What Is a Qualified Opportunity? - the formal three-part definition (ICP Fit × Purchase Intent × Win Likelihood) that this episode's "signal is not permission" argument sits inside. B2B Winback: Recover Churned Customers on Competitor Renewal Timing - the deep dive on Niall's renewal + competitor-trial play, applied to the churned base. How to Prioritise Inbound Leads with Purchase Intent - the inbound version of the same account-level signal set the episode discusses. Why Intent Data Made B2B Outreach Worse - the deeper argument for why signals need interpretation, not automation. Purchase Intent vs Intent Data - the definitional pillar behind the point that not every click is a buying signal. Most GTM Teams Use Intent Data at the Wrong Time - the Episode 1 recap and the piece this episode builds on. How to Build a Signal-led GTM Motion - the corrective playbook once you stop sorting on intent alone. Intent, Decoded - the podcast hub - every episode and the wider conversation about how to use signals well. ## Blog: B2B Winback: How to Recover Churned Customers Using Competitor Renewal Timing Most B2B winback is broken because the outreach lands at the wrong time. Here is the three-data-point playbook - where they went, when the new vendor renews, why they left - that changes conversion. Most B2B teams have a "churned" tag in their CRM and a vague plan to "revisit those accounts eventually. " The eventually never comes, because the accounts sit in a queue with no trigger, no owner, and no clear sense of when the window to win them back is actually open. The result is a graveyard of former customers who might buy again but are never contacted at the moment they'd say yes. Winback done well is one of the highest-return motions in B2B SaaS. It's cheaper than acquisition (the buyer already knows the product), faster than expansion (they're already in-category), and defensive against competitors (every churned customer you recover is one your competitor loses). The reason most teams don't do it well isn't lack of will - it's that they don't have the three data points that turn "churned account" into "winnable account this quarter. " This piece is about those three points, the four-move playbook they unlock, and the specific timing window that makes winback actually work. Day 0 Customer churns Signs with competitor. Contract typically 12-36 months. Months 1-6 Honeymoon New vendor active, expectations high. Winback outreach lands as noise. Middle Reality sets in Some accounts start regretting the switch. Signals begin to surface. -90 to -30 days Renewal window Competitor's contract expiring. Winback outreach lands harder than acquisition outreach ever will. The winback window isn't when they churned. It's when the vendor they went to is about to renew. Why B2B winback is mostly broken The default B2B winback motion looks like this: once a quarter, someone in marketing pulls the churned-account list, drops it into a nurture sequence, and hopes something comes back. Reply rates hover around cold outbound territory. The occasional account converts, everyone congratulates the campaign, and the list gets shelved for another quarter. What's actually happening: the churned list is being worked at the wrong time. A customer who churned three months ago is still in the honeymoon phase with their new vendor - reaching out then is the sales equivalent of showing up at a wedding to remind the bride about her ex. A customer who churned 30 months ago has almost certainly renewed the competitor and won't seriously reconsider for another 12 to 24 months. The winback sequence hits both windows equally, and neither productively. The retention economics behind winback done well are severe. Bain and Company's research on customer loyalty economics - the foundational work by Fred Reichheld - established that a 5% increase in customer retention can lift profits by 25 to 95%, because retained customers cost less to serve, buy more over time, and generate referral revenue. Winback is retention economics applied in reverse: recovering a customer captures a version of the same lift, minus the churn cost you already paid. The reason most teams miss this isn't that they don't believe the math. It's that they don't have visibility into the three data points that would tell them which churned account is in a winnable window right now, versus which one is a fond memory that isn't buying anything for another two years. Without those three points, winback stays a "spray the list once a quarter" activity - and produces the outcomes such activities produce. What makes winback different from acquisition and expansion Winback shares vocabulary with acquisition ("targeting accounts", "outreach cadence") and with expansion ("existing customer", "upsell moment"), but it's structurally a different motion. Confusing it with either produces the wrong plays and the wrong metrics. Acquisition is about finding accounts that fit your ICP and haven't yet chosen a vendor. Every rep on your team runs some version of it. Reply rates on cold acquisition outbound typically sit at 1-3% depending on quality of signal underneath. Expansion is about growing existing customers. It runs off product usage signals, contract dates, and CS relationships that already exist - the retention and expansion motion. Reply rates are functionally close to 100% because the customer is already engaged. Winback sits between the two. The buyer already knows your product - they made a considered decision to leave. That means acquisition-style value-prop messaging doesn't work (they've heard it) and expansion-style relationship messaging doesn't work either (there's no active relationship). What works is a specific, evidence-anchored reason to reconsider, delivered inside the narrow window when they're already evaluating alternatives - which, for most churned customers, is the 30-90 days before their current vendor's contract expires. The other structural difference: winback has a defensive component. Every churned customer you win back is one your competitor loses at renewal. That competitive angle doesn't exist in acquisition (where you're often competing against "do nothing") or expansion (where you're competing against yourself for wallet share). Winback is inherently competitive, and the messaging + timing have to account for that. The three data points that unlock a real winback motion Every effective winback motion depends on knowing three things about each churned account. Miss any of the three and the motion collapses back into spray-and-hope. Point 1 - Where the customer went Knowing that a customer churned isn't enough. Knowing which vendor they went to is what turns winback from generic "come back" outreach into a specific competitive play. If the churned customer went to Vendor A, the winback motion knows what Vendor A is bad at, which case studies land against them, and what the competitive gap the customer is likely feeling looks like. Most B2B CRMs don't track this. The churn reason field, when filled in at all, usually says "cost" or "product fit" - vague enough to be actionable-adjacent but not enough to build a play on. What's missing is the specific replacement vendor, which is externally observable (via web technology signals, licensed data feeds, or account-level product detection) but not routinely captured inside the CRM. The evidence-based purchase intent platforms that produce this signal at the account level are the same ones that produce new-business winback signals. Knowing "Account X churned to Vendor A" is the input the whole rest of the motion depends on. Point 2 - When the new vendor's renewal window opens A churned customer under contract with Vendor A isn't in market. A churned customer whose Vendor A contract is 60 days from expiring is very much in market, whether they've told anyone yet or not. The renewal window is the temporal layer of winback. It's usually opaque - most B2B SaaS contracts aren't public - but it's estimable from the churn date (Vendor A contracts are typically 12, 24, or 36 months long) and observable from tech-stack signals when the customer starts evaluating alternatives before the formal renewal. Track this at scale and the winback list stops being 500 accounts to work eventually and becomes 15 accounts entering a live renewal window this quarter. This is where the Vendor A renewal timing becomes what a competitor trial start is for acquisition - the trigger event that anchors the entire motion. Point 3 - Why they left in the first place The reason a customer churned is what the winback message has to address. If they left because your product missed a feature that's since been shipped, the message is "here's what changed. " If they left because your pricing didn't fit their scale at the time, the message is "here's how our packaging has moved. " If they left because a specific product manager they liked left the company, the message is different again. Most CRMs capture some version of this at the churn moment, though the quality varies wildly. The point isn't to have a perfect churn-reason database - it's to have enough context per account to write outreach that doesn't sound like it came from a generic winback sequence. Even a lightly-tagged reason (product / price / relationship / competitive) is enough to route the account to the right message. The three points together - where they went, when their new vendor renews, why they left - are what let a winback motion produce reply rates and conversion rates that dwarf cold acquisition. Any two of the three, and you have a nurture campaign. All three, and you have a real motion. The winback playbook: four moves Once the three data points are in place, the operational playbook has four moves. Each is straightforward on its own; the compounding effect comes from doing all four inside the renewal window. Move 1 - Segment the churned base by winbackability Not every churned account is a winback candidate. Segment the full churned base into four buckets: Cold - Cannot Recover. The customer explicitly said never contact us again, or the underlying reason they left is structural and hasn't changed (they moved out of your ICP, they were acquired by a competitor, the founder who bought is no longer there). Cold - Wrong Timing. They just renewed with the competitor. Winback outreach now is noise. Set a reminder to re-evaluate them 18-24 months before their next renewal window. Warm - Watching. They churned within the last 12 to 24 months, their competitor's contract is estimable, they're still in your ICP, and the reason they left is no longer structurally true. This is the working list. Hot - Renewal Window Open. The competitor renewal window is within 90 days. This is the queue that gets worked this week. Most B2B teams find that only ~10-20% of their churned base is in the Warm or Hot buckets at any given moment. That's the point - a small, prioritised list produces better results than a large, undifferentiated one. One additional signal worth escalating within the Hot bucket: churned accounts that operate across multiple entities, regional sub-brands, or product lines in the tracked category. A winback at a multi-entity account isn't one deal - it's a template for winning back several. When both a parent brand and its regional or product-line extensions show up in the churned base with overlapping renewal timing, the account moves to the top of the queue. Move 2 - Track the competitor renewal window For each account in Warm or Hot, track the estimated renewal window on the competitor's contract. Where the exact date is unknown, use the customer's churn date + typical contract length in your category (usually 12, 24, or 36 months) as the baseline, then adjust based on any observable tech-stack signals suggesting they're already evaluating alternatives. MarketSizer surfaces this at scale for the tracked vendor categories - the Winback Opportunities feature is built specifically to identify churned customers whose current vendor's renewal is approaching. The signal is treated identically to a Purchase Intent event on any other in-market account: it's a time-bound trigger that moves the account onto the queue, not a permanent flag. Move 3 - Time outreach to the renewal decision cycle Enterprise B2B renewals aren't decided at the renewal date - they're decided in the 60 to 120 days before. That's the window where the customer's procurement team is reviewing alternatives, the CS lead on the competitor's side is scheduling QBRs, and the champion is deciding whether to fight for a renewal or start looking around. Winback outreach should land in that window, not at it. A first touch 90 days before Vendor A's renewal lands as thoughtful (the buyer sees "they've been paying attention to us"). A first touch two weeks before the renewal lands as opportunistic (the buyer sees "they must have called every churned account this quarter"). The former converts; the latter poisons the well for a second attempt in the following cycle. The cadence itself compresses inside the window. Instead of a 6-week acquisition cadence, run a 3-4 touch sequence over 6-8 weeks, each touch anchored to something specific the buyer would recognise. The signal-led GTM playbook covers the underlying operating model; winback is a specific application of it against the churned base. Move 4 - Anchor the message on what changed since they left The single most consistent failure mode in winback outreach is repeating the value prop the buyer already rejected. If they churned because your product was missing a feature, telling them how great your product is now doesn't work - they've heard it before. What works is telling them what specifically changed that addresses the reason they left. The message structure that lands: The specific reason they left (acknowledged, not defended). What's changed since then (concrete, dated, ideally referenceable). Why now specifically matters (their competitor's renewal is coming, the market has shifted, a champion of theirs has moved companies). A low-friction next step (not "book a full demo"; usually "worth a 15-minute conversation to see what's different"). This structure works because it presumes the buyer isn't stupid - they left for a real reason, and the outreach has to acknowledge that reason before making a new pitch. Winback outreach that pretends the churn didn't happen produces the polite-decline reply pattern that most winback campaigns generate. The rule of thumb from teams running this motion well: churned buyers don't come back because they like you again. They come back because something specific has changed that produces a genuine business reason to reconsider. Which is exactly what the outreach has to name - a product change, a pricing shift, a market change, or the observable fact that their current vendor's renewal is coming up. If the message can't point at that "specific something," the sequence isn't ready to go out. See who churned and where they went Which former customers just entered a competitor renewal window? Start a free trial to track your churned base against competitor renewal timing. MarketSizer detects when the vendor they went to is nearing renewal - and surfaces the winback opportunities worth pursuing this quarter. No credit card required. Start for free Common winback mistakes The patterns that repeat across teams that have a winback motion in name but not in outcome: Working the whole churned list on a fixed quarterly cadence. The renewal windows across the churned base don't align to your fiscal quarter. Sequencing every churned account on the same schedule guarantees that most of the outreach lands outside the renewal window on any given account. Reusing the acquisition value prop verbatim. The buyer heard it and left. Repeating it says the seller hasn't understood why they left, which reinforces the original decision rather than reversing it. Treating winback as marketing's problem. Winback needs the same cross-functional discipline as acquisition - a specific owner (usually a small SDR pod or a CS-adjacent AE), a defined queue, weekly review of active windows. When it lives entirely in marketing, it becomes a nurture campaign, not a motion. Ignoring the champion's current company. A champion at the churned account often moves jobs before the renewal window opens. If they moved to a company you don't currently sell to, that's a follow-them-into-the-new-account play, not a winback play. Different motion, related data. Skipping the honeymoon window entirely, then flooding the renewal window. Total silence for 12 months followed by three touches in 3 weeks reads as opportunistic. The honeymoon window is where you build the light-touch presence (community, useful content, occasional check-ins) that makes the renewal-window outreach feel continuous rather than surprising. Not tracking what worked. Winback conversion rates are typically low enough that a single quarter of data isn't sufficient to know what's working. Track cohort-level winback conversion by segmentation reason (product / price / relationship / competitive), by renewal-window timing, by messaging variant. It takes 3-4 quarters to have signal; skipping the measurement means the motion never improves. What good winback looks like in metrics Winback conversion rates run higher than cold acquisition and lower than expansion. The specific numbers vary by category, but the shape is consistent across teams that run the motion well. Reply rate on evidence-anchored winback outreach: typically 8-15% versus 1-3% on cold acquisition outbound. The buyer knowing the product raises the reply floor materially. Meeting-to-opportunity conversion from winback replies: typically 40-60%, higher than acquisition because the buyer already understands the category and the seller. Opportunity-to-close on winback deals: category-dependent, but typically 20-30% - lower than expansion (where the customer is captive) but higher than acquisition against the incumbent (where the seller starts from zero). Cost per recovered customer : typically 40-60% of cost per new acquisition, because the sales cycle is shorter and the marketing spend is lighter. Time from first winback touch to closed deal : typically 45-75 days when the renewal window is properly targeted, versus 90-180 days for equivalent acquisition deals in the same category. These metrics compound: a 12% reply rate × 50% meeting-to-opportunity × 25% close rate = ~1. 5% churned-account-to-closed-won ratio. On a churned base of 300 accounts with 20% in a renewal window, that's roughly 60 accounts worked and ~1 recovered per quarter. Modest at first glance, but compounding across quarters and pooled against a small cost base, it usually pays back inside two quarters. To put a scale on it: in one MarketSizer customer's first winback pull, the platform surfaced ~220 opportunities against a churned base of a few thousand accounts. Only a fraction sat inside the 90-day renewal window, but that fraction was the working queue - the accounts where a rep could open with "your current vendor is coming up for renewal" and mean it. The customer's response after seeing the list captured the mechanic well: "who's the current competitor and when they're renewing - that's all you need. " Frequently Asked Questions When is the best time to reach out to a churned customer? The best time to reach out to a churned customer is 60 to 120 days before their current vendor's renewal window opens. That is when the buying committee is actively re-evaluating, budget is being reviewed, and switching costs are being weighed against benefits. Reaching out at the point of churn (too early: they just made the decision) or at their next renewal call (too late: the decision is being made without you) both underperform the 60-120-day window by a wide margin. What is B2B winback? B2B winback is the practice of recovering former customers who left for a competitor. Unlike acquisition (net-new accounts) or expansion (growing existing customers), winback targets accounts that already know your product and made an active decision to leave. Done well, it produces reply rates and conversion rates significantly higher than cold acquisition because the buyer already understands the category. When is the right time to reach out to a churned customer? The 60-120 days before their current vendor's renewal window closes. During the honeymoon phase (the first 6-12 months after they switched), winback outreach lands as noise. During the renewal window, the buyer's procurement team is actively reviewing alternatives, which is when a specific, evidence-anchored winback message can genuinely land. How do I know when a competitor's contract is up for renewal? Most B2B SaaS contracts aren't publicly disclosed, but the renewal window is estimable from the customer's churn date plus typical contract length in your category (usually 12, 24, or 36 months). Observable tech-stack signals, licensed data feeds, and vendor-state detection platforms - see the purchase intent platform directory - can narrow the estimate substantially. What percentage of churned customers can realistically be won back? In a mature B2B SaaS motion, 5-15% of the Warm and Hot segments of the churned base are recoverable per cycle - which usually corresponds to 1-3% of the total churned base per quarter, since only a fraction of churned accounts sit in a renewal window at any moment. The rate scales with the quality of the three underlying data points (where they went, when the new vendor renews, why they left). Who owns winback - sales, marketing, or customer success? Ownership varies by team but the motion needs a single owner. Most B2B teams that do this well put winback under sales - typically a small dedicated SDR pod or a CS-adjacent AE - with marketing providing the enablement and messaging, and CS surfacing former-customer relationships and context. If winback lives in marketing alone, it becomes a nurture campaign; if it lives in CS alone, it becomes a check-in call. Neither converts. How is winback different from a churn-prevention motion? Churn-prevention runs against your active customer base to reduce departures. Winback runs against your churned base to recover departures. Both use similar signal types - competitor trial detection, tech-stack changes, engagement decay - but at different stages of the customer lifecycle. Churn-prevention is a defensive play on the retention side; winback is an offensive play on the recovery side. What should the first winback touch say? It should reference three things: the specific reason the buyer left (acknowledged, not defended), what's changed since then that addresses that reason (concrete and dated), and why now specifically matters (usually the competitor's renewal window). Ask for a low-friction next step - a 15-minute conversation to see what's different, not a full demo booking. Recommended reading How to Build a Signal-led GTM Motion - the wider operating model that winback is a specific application of. Best Purchase Intent Data Platforms for 2026 - the directory of platforms that produce the three data points winback depends on. Purchase Intent vs Intent Data - the definitional argument behind evidence-based signals like competitor renewal timing. Why Customer Success Teams Fail on Timing, Not Effort - the CS-side companion on catching accounts before they churn in the first place. What Is Subscription Intelligence? - the data category that produces churn detection and competitor renewal signals at scale. ## Blog: What Is a Qualified Opportunity? The Three-Part Definition GTM Teams Need MQL and SQL describe lead status. A Qualified Opportunity describes the account. Here is the three-part framework - ICP Fit, Purchase Intent, Win Likelihood - and why all three must clear. A Qualified Opportunity is an account, not a lead. That single distinction is what most GTM teams still get wrong. The vocabulary they inherited - MQL, SQL, PQL - describes what a person did on the seller's own properties. The vocabulary a modern signal-led motion needs describes what the account is doing in the outside world. Those are different measurements. They produce different pipelines. A Qualified Opportunity is defined by three things happening at once at the account level: ICP Fit (the account matches the customers you already win), Purchase Intent (something buying-relevant is happening at the account right now), and Win Likelihood (you have a defensible chance of winning it against the incumbent vendor). All three must clear. Any account that clears all three earns a rep's time. Any account that doesn't - regardless of how good it looks in a lead-scoring dashboard - doesn't. This isn't a theoretical framework. It's the qualification system MarketSizer walks GTM leaders through on sales calls, and it's the one every account in the platform is scored against. What follows is the anatomy of it - each of the three parts, why they combine the way they do, and how a team actually runs on Qualified Opportunities in the CRM they already own. ICP Fit Could buy? Purchase Intent Buying right now? Win Likelihood Can you win it? QUALIFIED OPPORTUNITY Three thresholds. An account only earns a rep's time when all three clear. What is a Qualified Opportunity? A Qualified Opportunity is an account that has cleared three distinct thresholds simultaneously: it matches your Ideal Customer Profile, it's showing observable evidence of a live buying motion, and you have a defensible chance of winning it given the competitive context. Only when all three clear is the account worth a rep's time this week. The term is deliberately account-level rather than lead-level. A single person at a company downloading a white paper doesn't produce a Qualified Opportunity - it produces a lead, which may or may not correlate with what the account itself is doing. A Qualified Opportunity describes the state of the company, not the behaviour of one person inside it. The three thresholds are structured to answer three different questions that GTM teams have historically confused with each other. ICP Fit answers a static question about identity. Purchase Intent answers a temporal question about timing. Win Likelihood answers a strategic question about competitive positioning. An account can clear one or two of these without being a Qualified Opportunity - which is why the framework is multiplicative, not additive, and why so much conventional lead scoring quietly fails at pipeline generation. Why MQL and SQL no longer describe the unit of work The traditional B2B qualification stack - Marketing Qualified Lead, Sales Qualified Lead, Product Qualified Lead - was built for a world where the seller's own engagement data was the strongest signal available. A lead read three emails, downloaded a white paper, attended a webinar. That accumulated engagement produced an MQL. A conversation with an SDR confirmed budget, authority, need, timeline. That produced an SQL. In a market with limited buyer-side visibility, this was a reasonable framework. The problem is that these categories describe the state of the seller's interaction with a single person, not the state of the buyer. Account-level lead qualification inverts that: the account is the unit, and the person who filled the form is context. An MQL tells you a person engaged with your marketing. It doesn't tell you whether the account they work at is buying anything, whether the person is the actual decision-maker, or whether the account already just signed a three-year contract with your primary competitor. The measurement gap this creates is visible in every mid-funnel pipeline review. The B2B buying group has grown - Harvard Business Review's coverage of the CEB research on the new sales imperative found the average buying group already runs to 6. 8 stakeholders, and only a fraction of them ever consumes the seller's marketing content. At the same time, buyers are pulling back from human sales interactions altogether - McKinsey's B2B Pulse research tracks a steady multi-year shift toward digital-first, self-serve buying journeys where most of the evaluation happens before a rep is ever contacted. Scoring on person-level engagement misses most of the buying group by definition, and misses the accounts that aren't in a buying motion at all. What actually predicts a sale, in current B2B SaaS markets, isn't a person's engagement with the seller - it's the account's own behaviour in the outside world. A competitor trial. A renewal window. A vendor churned. A tech stack changing. These are events at the account level, not the person level, and they exist independently of whether anyone at the account ever downloaded a white paper. Which is exactly why they predict pipeline better than MQL/SQL scoring does. A pattern that comes up repeatedly in sales conversations with GTM leaders: there's a difference between activity and opportunity that most B2B teams have never quite named. Activity is a rep working a list. Opportunity is a rep working the accounts that are actually doing something to buy. Activity metrics rise and fall with headcount. Opportunity metrics rise and fall with signal quality. The vocabulary of MQL and SQL was built for a world where those two things looked identical from the seller's chair; the current market has broken that overlap. One sales rep we've spoken to described the daily reality this way: working 100 leads a day when you know only three will pick up isn't productivity, it's a rep working more and closing less. The rep isn't inefficient. The prioritisation layer underneath is. The three parts of a Qualified Opportunity Each of the three parts answers a different question. Each is scored independently. All three must clear before the account is a Qualified Opportunity. Part 1 - ICP Fit: could this account buy? ICP Fit answers the identity question. Does this account look like the customers you already win? The score combines firmographic data (industry, employee count, geography, revenue band), technographic data (existing tech stack, tools present, integrations in use), and pattern-matching against the seller's own historical closed-won cohort. ICP Fit is the most stable of the three scores. It changes slowly - most accounts don't fundamentally shift what they look like on a monthly basis. It's also the most familiar, because it's the closest to how B2B teams have been scoring accounts for a decade. The difference in a Qualified Opportunity framework is that ICP Fit is necessary but not sufficient . A perfectly ICP-fit account with no buying motion is a future opportunity, not a current one. A perfectly ICP-fit account you can't win against the incumbent is a long shot, not a queue priority. ICP Fit's job is to filter out the accounts that couldn't buy under any circumstances - the industries you don't sell into, the sizes you don't serve, the geographies you don't cover. It's the wide filter at the top of the funnel. Everything downstream depends on it being right; nothing downstream is enough on its own. Part 2 - Purchase Intent: is something buying-relevant happening right now? Purchase Intent is the temporal layer. It answers whether the account is actively in a buying motion at this moment - not whether the account might be interested in general. Purchase Intent is calibrated on observable buying events, not inferred behaviour. A competitor trial that just started. A renewal window opening in 47 days. A vendor recently churned. A tech-stack component installed or removed. A regional expansion that activates a new category need. Each of these is a time-bound event that describes what the account is doing , not what someone at the account is reading . The distinction is covered in more depth in Purchase Intent vs Intent Data , but the operational point stands here: intent data infers; purchase intent evidences. Purchase Intent is the most volatile of the three scores. Accounts move in and out of active buying motions constantly, and a signal that fires today may close in 14 or 60 or 90 days depending on what triggered it. Because of that volatility, purchase intent is also the score that most needs to be time-bound : an event without a window-close date is a permanent-flag failure mode that erodes rep trust in the layer. Part 3 - Win Likelihood: can you actually win it? Win Likelihood is the competitive layer. It answers whether, given the specific competitive context at this account, the seller has a defensible chance of winning the deal if they engage. Most B2B teams have historical patterns that make some accounts very winnable and others very unwinnable, and they usually know which is which without articulating it. Win Likelihood formalises that knowledge. It scores against which competitor is incumbent at the account, the seller's historical win rate against that competitor in similar-shape deals, the tenure of the incumbent relationship (a three-year contract signed six months ago is very different from one signed 30 months ago), and the seller's own product fit relative to the specific competitive context. Win Likelihood is what prevents a signal-led motion from wasting time on in-market accounts where the seller has structural disadvantages. An in-market account where the seller wins 4% of deals against the incumbent isn't a Qualified Opportunity - it's a long shot. An in-market account where the seller wins 40% of deals against the incumbent is. Same buying motion, different priority. Why all three must align: the multiplicative argument The three parts are combined multiplicatively, not additively. This is the single most important operational detail in the framework, and the one most easily missed by teams porting the Qualified Opportunity vocabulary into an existing lead scoring system. An additive score treats a strong showing on one dimension as compensating for weakness on another. If ICP Fit is 90 out of 100 and Purchase Intent is 20 out of 100 and Win Likelihood is 30 out of 100, an additive score reads as 140 out of 300 - a middling opportunity worth working. A multiplicative score reads as (0. 9 × 0. 2 × 0. 3) = 0. 054 - a poor opportunity that shouldn't earn a rep's time until at least one of the low sub-scores changes. Multiplicative scoring reflects the underlying reality of B2B pipeline generation. A perfect-fit account with no buying motion is a future prospect, not a current one - you can't will an account into being in-market just because it looks like your best customer. An in-market account you can't win against the incumbent is a resource sink - it will keep flagging as "high intent" every quarter and the rep will keep working it and it will keep failing to close. Only accounts that clear all three thresholds simultaneously produce a real pipeline motion. The operational discipline this introduces: reps can no longer sort their queue by any single dimension. A "high intent" list that isn't filtered by fit and win likelihood produces the same volume-led problem as an "ICP-fit" list that isn't filtered by intent. The queue has to be sorted by the composite score, and that composite has to be a multiplication rather than an average. How a Qualified Opportunity differs from MQL, SQL, and PQL Traditional B2B qualification categories describe what a lead did inside the seller's funnel. A Qualified Opportunity describes what the account is doing in the outside world. These aren't the same measurement dressed up differently - they measure different things and produce different pipelines. Category What it measures Unit of work Failure mode MQL (Marketing Qualified Lead) A person's engagement with the seller's marketing (content downloads, email opens, form fills) A single person, at a single moment, on the seller's own properties Overweights content consumers; underweights the actual buying committee SQL (Sales Qualified Lead) A person's stated readiness in a sales conversation (BANT criteria - budget, authority, need, timeline) A single person, self-reporting through a conversation Depends on rep judgement + honest buyer self-reporting; conflates conversation quality with buying motion PQL (Product Qualified Lead) A user's in-product behaviour (feature adoption, usage volume, activation milestones) A single user, inside the product Requires a PLG motion to exist; blind to accounts that aren't already users Qualified Opportunity An account's real-world state - fit + observable buying motion + winnable competitive context The account itself, in the outside world, at the current moment Depends on the signal layer producing high-quality evidence; goes stale if any of the three parts is neglected The three lead-based categories aren't wrong - they just describe a different unit of work. MQL and SQL describe funnel progression on a per-person basis. PQL describes user-level engagement inside the product. A Qualified Opportunity describes the account itself, in the market, right now. Modern GTM motions increasingly use both: lead-based categories to track individual buying-team members, Qualified Opportunities to track which accounts are worth working at all. See this in your own market Which accounts in your ICP are clearing all three thresholds right now? Start a free trial to test the framework on your real accounts. MarketSizer scores every account against ICP Fit, Purchase Intent, and Win Likelihood - and surfaces the Qualified Opportunities worth working this week. No credit card required. Start for free How to score a Qualified Opportunity in practice The scoring machinery for a Qualified Opportunity looks like this in practice - the specifics vary by platform, but the operational shape is consistent. 1. Score ICP Fit as a percentage (0-100). Base it on firmographic and technographic match against the seller's closed-won cohort. Refresh quarterly - firmographics don't change fast enough to require a shorter cadence. Automate the scoring where possible; manual ICP tagging is where most sales teams introduce the largest amount of inconsistency. 2. Score Purchase Intent as a percentage (0-100) with a window-close date attached. Base it on observable buying events - trials, renewals, churn, tech stack changes - with each event carrying its own decay curve. A trial that started five days ago scores higher than one that started six weeks ago. A renewal opening in 30 days scores higher than one opening in 120 days. The window-close date is what makes the score actionable rather than a permanent flag. 3. Score Win Likelihood as a percentage (0-100). Base it on historical closed-won and closed-lost data against the specific competitor set present at the account. Where historical data is thin (a new market, a new competitor), start with a conservative default (30-40%) and update as data accumulates. 4. Combine multiplicatively into a single Opportunity Score. Multiply the three sub-scores (as decimals, 0-1) and multiply the result by 100 to get a normalised 0-100 composite. An account scoring 80/70/60 across the three sub-scores produces a composite of (0. 8 × 0. 7 × 0. 6) × 100 = 33. 6 - modest but working. An account scoring 90/20/50 produces (0. 9 × 0. 2 × 0. 5) × 100 = 9 - almost certainly not worth a rep's time this week. 5. Set a threshold that reflects available capacity, not a fixed number. A high-capacity team can work accounts scoring above 25. A low-capacity team should only work accounts above 50. The threshold is a business decision, not a technical one, and it should move as coverage capacity changes. The composite score is what feeds the rep's queue. The three sub-scores are what feed the rep's outreach. Both are needed - the composite tells the rep which accounts to work, and the sub-scores tell the rep what to say about each one. For a walkthrough of how this actually gets wired into the CRM, see How to Implement Signal-led Prioritisation in Salesforce and HubSpot . What changes when your GTM motion runs on Qualified Opportunities Adopting the Qualified Opportunity framework changes what the GTM team sorts on, what the rep says on a first touch, and what the pipeline review measures. Each of these compounds. For SDR and AE teams: the queue changes from a static ICP list to a dynamic list sorted by composite Opportunity Score. Reps work the highest-composite accounts first, and each first touch anchors on the specific event that pushed the account onto the queue - not a generic value prop. Reply rates lift because the outreach references something the buyer recognises. The wider operating model is covered in the signal-led GTM playbook . For marketing: the audience definition shifts from "all ICP-fit accounts" to "ICP-fit accounts with an active Purchase Intent signal. " Paid media targets narrower cohorts with higher in-market density. Nurture sequences track to Purchase Intent decay curves rather than fixed schedules. MQL contribution is reported alongside Qualified Opportunity contribution - the two metrics coexist, but Qualified Opportunity is the one that predicts booked pipeline. For customer success: the same three scores that identify new-business Qualified Opportunities also flag at-risk customers. An existing customer with a competitor trial detected is a churn risk regardless of how their QBR went last month. The score becomes an early-warning system on the retention side. For pipeline reviews: the meeting shifts from "how many meetings did each rep book" to "how many Qualified Opportunities did each rep convert to first meeting. " Activity metrics still get tracked, but they're no longer the primary sort. The team learns to measure whether the signal layer produced accounts that closed, which is the only metric that ultimately matters. Common misapplications Teams that adopt the Qualified Opportunity vocabulary but don't see the lift usually trip on one of the patterns below. Adding the scores together instead of multiplying. An additive composite creates the same accumulate-signals-and-hope failure mode as a traditional lead score. A single strong dimension can pull the composite high enough to earn attention on accounts that shouldn't be worked. Multiplicative scoring forces all three thresholds to clear. Using inferred behaviour to score Purchase Intent. If Purchase Intent is scored on content downloads, ad clicks, or bidstream signals, the score is measuring interest rather than buying motion - and the whole framework degrades into a rebranded MQL score. Purchase Intent has to be anchored on observable events for the composite to hold together. Skipping Win Likelihood because "we can win anywhere. " Every seller has structural disadvantages against certain competitors in certain contexts. Ignoring that data means the team works winnable accounts and unwinnable accounts at the same intensity, which is a silent cost of pipeline that shows up 60-90 days later as low conversion rates on flagged accounts. Never adjusting the threshold. The threshold above which accounts earn a rep's time isn't fixed - it depends on team capacity, sales cycle length, and the density of Qualified Opportunities in the addressable market at any given moment. Teams that set the threshold once and never revisit it either overwork low-quality accounts (threshold too low) or leave real opportunities unattended (threshold too high). Reporting Qualified Opportunities to marketing but not tracking rep behaviour. If marketing reports "we generated 200 Qualified Opportunities this quarter" but sales still works the CRM list they built in Q1, the framework has become a marketing metric rather than an operating discipline. Reps have to actually sort their queue on the composite score - otherwise the vocabulary exists in decks but not in workflows. Treating the composite as a black box. The three sub-scores need to be visible per account, alongside the composite. When a rep works a Qualified Opportunity, they should be able to see why it's on their queue - the specific event, the ICP match reasons, the competitive context. Black-box scoring breaks rep trust the first time a flagged account fails to convert. Frequently Asked Questions What is a Qualified Opportunity in B2B sales? A Qualified Opportunity is an account that has cleared three thresholds at the same time: it matches your Ideal Customer Profile, it's showing observable evidence of a live buying motion, and you have a defensible chance of winning it against the incumbent vendor. Only accounts that clear all three earn a rep's time. It's an account-level unit of work, not a lead-level one. How is a Qualified Opportunity different from an MQL or SQL? MQL and SQL describe what a single person did on the seller's own properties (marketing content or a sales conversation). A Qualified Opportunity describes what the account itself is doing in the outside world. Modern GTM motions typically track both - lead-based categories for individual buying-team members, Qualified Opportunities for which accounts are worth working at all. Why is a Qualified Opportunity scored multiplicatively rather than additively? An additive score lets a strong showing on one dimension compensate for weakness on another - which produces accounts that get worked even though a critical dimension is failing. Multiplication forces all three dimensions to clear. A perfect-fit account with no buying motion still scores near zero, which reflects the operational reality that fit alone isn't enough. What signals produce a good Purchase Intent score? Observable buying events at the account level: a competitor trial starting, a renewal window opening (typically 60-90 days out), a vendor recently churned, a tech-stack component installed or removed, a regional expansion that activates a new category need. The platform directory covers who produces each signal type. How often should Qualified Opportunity scores refresh? ICP Fit refreshes quarterly (firmographics don't change fast enough to justify a shorter cadence). Purchase Intent refreshes daily or near-real-time (the underlying events are time-bound and stale signals produce false positives). Win Likelihood refreshes monthly (competitive context shifts slowly enough that daily updates would add noise). Does a Qualified Opportunity require an active buying window? Yes. Purchase Intent is the temporal component, and its function is to require that something buying-relevant is happening at the account right now. An account without an active Purchase Intent signal is a Future Opportunity or a Watch List account - useful to track, but not a Qualified Opportunity worth working this week. What is an Opportunity Score, and how does it relate to a Qualified Opportunity? The Opportunity Score is the composite score produced by multiplying the three sub-scores (ICP Fit × Purchase Intent × Win Likelihood). A Qualified Opportunity is any account whose Opportunity Score clears the team's working threshold. The score is the measurement; the Qualified Opportunity is the unit of work above the threshold. Recommended reading Purchase Intent vs Intent Data - the deeper argument for why Purchase Intent must be anchored on observable events rather than inferred behaviour. How to Build a Signal-led GTM Motion - the operating playbook that runs on Qualified Opportunities as the unit of work. How to Implement Signal-led Prioritisation in Salesforce and HubSpot - the tactical guide for wiring Opportunity Score fields into the CRM. How to Prioritise Accounts for Outreach Using Timing and Win Likelihood - a companion piece on the prioritisation side of the framework. Best Purchase Intent Data Platforms for 2026 - the directory of platforms that produce the Purchase Intent signal layer. What Is Subscription Intelligence? - the data category that produces evidence-based Purchase Intent signals. ## Blog: Why Intent Data Made B2B Outreach Worse (And How to Fix It) Intent data was supposed to make outreach smarter. In most companies it just scaled the same bad cadence with more confidence behind it. Here is why - and what to do instead. For the compact 2026 answer, see What Replaces Intent Data in 2026? This post is the long-form "why the outreach got worse" thread inside that argument. Intent data was supposed to fix the outreach problem. Most teams have more data on their accounts than they've ever had, more signals layered into more dashboards, more "intent flags" lighting up in their CRMs every week. Reply rates haven't moved. Meetings booked per SDR haven't moved. Cost per pipeline dollar has gone up. The argument here isn't that intent data is broken. It's that scoring outreach on inference - rather than on evidence of a buying motion - did to B2B sales what algorithmic feeds did to social media: it scaled the volume of low-quality output without improving the underlying quality of what was sent. We covered the structural argument in the Purchase Intent vs Intent Data pillar . The practical effect on outreach is what this piece is about. The promise Sharper prioritisation Fewer wasted touches Higher reply rates Better-qualified meetings The outcome at most companies More confident outreach over the same accounts Faster sequencing, not better targeting Reply rates flat or declining Reps trusting the layer less each quarter A category that sold prioritisation. A reality that delivered confidence. The promise versus the outcome The original argument for intent data was specific. The B2B funnel was built on firmographic targeting plus the seller's own engagement data, and that combination missed accounts that were actively in-market because they hadn't engaged with the seller yet. Intent data closed that gap: third-party publisher activity, anonymised IP-level behaviour, content syndication signals - all aggregated into a "who's researching your category right now" view. The category sold a single promise: with more signal upstream of the seller's own engagement, sales teams could prioritise outreach against accounts that were genuinely in-market. The metric that mattered was reply rate, because reply rate measures whether the rep is reaching the right buyer at the right time. That's not what happened in most companies. Forrester's evaluation of B2B intent data providers found 50% of teams report too many false positives. Reply rates on cold outbound have stayed flat or declined for most teams over the same period intent data spend has climbed. The category didn't fix the outreach problem. It scaled it. Why scoring on inference makes outreach faster, not better The mechanism is simple. Intent data scores accounts on inferred interest - someone at the account loaded a category page, an anonymised IP appeared in a bidstream, a topic surged in a third-party publisher network. The signal is real in the sense that something happened. It's just not specific enough to predict a buying motion at that account in the next 60 days. A rep who gets a list of "high intent" accounts every Monday morning treats the list the way they used to treat the ICP list - as a queue to work through. The cadence stays the same: day 1 email, day 3 LinkedIn, day 5 call. The messaging stays the same: a value-prop opener wrapped around a content piece. The only thing that changes is that the rep feels more confident the account is in-market, because the score told them so. That confidence is the problem. When the signal is real evidence of a buying motion - a competitor trial just started, a renewal opens in 47 days, a vendor was recently churned - the rep can open with the named event and the message lands harder. When the signal is a score, the rep opens with a generic value prop and the message reads the same as every other outreach the buyer received that week. Same touch, more confidence behind it, no improvement in reply rate. The data layer changed; the cadence didn't. The compounding effect: scoring the same accounts at higher confidence means the team works those accounts harder. More touches per account, more sequence escalation, more inbound from marketing. The signal didn't tell the team to find different accounts. It told the team to push the existing ones harder. The three ways intent data broke outreach Three specific failure modes recur across teams that scaled intent data without rethinking the cadence underneath. Generic touches feel more confident, so reps send more of them A rep who's been told an account is "high intent" sends touches with more conviction than a rep working from an unscored list. That conviction doesn't translate into a different message - the rep doesn't suddenly know what specifically the account is doing - but it does translate into volume. More touches, longer sequences, more aggressive follow-up. The buyer experiences this as more pressure with no new information. The intent score told the rep the account was a priority. The buyer's actual buying motion (or lack of one) hasn't changed. The rep is now pushing harder on an account that wasn't necessarily buying. Reply rate drops. The score the rep relied on starts being correlated with worse outcomes, not better ones. The bidstream-correlated cohort problem Most third-party intent data is sourced from the same underlying signal layer - a small number of bidstream networks, content syndication co-ops, and aggregator publishers. That is as true of the largest providers as the smallest, which is the substance of how MarketSizer compares to ZoomInfo . Two intent vendors selling to two competing sellers in the same category will often flag the same accounts as "high intent" in the same week, because they're seeing the same underlying behaviour. The buyer gets contacted by both sellers (and often a third and fourth) inside the same window with similar messaging. Each seller thinks they're early. They're all arriving simultaneously. Reply rates on whichever seller arrives first are mediocre; reply rates on every subsequent seller are worse. The shared signal layer scales coordinated noise at the buyer without anyone intending to. The intent-confidence loop Most intent data products score recency and intensity together. An account that's surged in the last 30 days gets a higher score than one that surged 90 days ago. A rep who works the high-recency, high-intensity scores will, on average, get marginally better engagement - because the score correlates loosely with actual buying motion. The trouble is that the correlation is just strong enough to keep the team using the layer, but not strong enough to compound. Reply rate on top-decile intent accounts is higher than reply rate on the rest of the list. But it's not high enough to fundamentally change the team's pipeline math. The team gets a small lift, takes the win, and assumes the layer is working. Over time, the team scales harder on the layer (more spend, more reps, more sequences) and the lift doesn't compound - it dilutes. The signal got noisier as more sellers piled into the same vendors. This is the most insidious failure mode because it doesn't look like a failure mode. Reply rate is up - just not by enough to matter at the bottom of the funnel. What the data actually looks like The pattern in practice, across teams that scaled intent data hard: Cold email reply rates of 1-3% on "high intent" accounts - inside the range of cold email rates on any reasonable B2B list. The intent score isn't producing a fundamentally different funnel. 3 in 100 of "high intent" accounts producing a meeting within 30 days - the Tanvi framing from candidate sales calls. The score correlates with interest, not with a near-term buying motion. Most "high intent" accounts are out-of-market on a quarterly cycle and stay that way. Marketing-sourced pipeline contribution flat year-over-year despite higher MQL volumes from intent-driven audience segmentation. The volume goes up; the conversion to opportunity stays the same; net pipeline contribution doesn't grow. Rep adoption of the intent layer dropping in months 4-6 - a leading indicator that reps are walking away from a layer they were told would change their week. The CRM keeps the signal field populated. Nobody sorts on it. None of these data points are damning on their own. Together they describe a category that delivered a small lift sold as a large one, and then over-scaled the small lift into noise. The fix: intent as a filter, evidence as the trigger Intent data has a legitimate use case. It works as a coarse top-of-funnel filter - useful for audience segmentation in paid media, useful for narrowing a 500,000-account universe down to a 50,000-account research priority. The mistake most teams made was using it as the trigger for outreach, not as the filter above outreach. The corrective is to put a different kind of signal underneath the intent layer - one that produces evidence of a real buying motion at a specific account. Subscription Intelligence is one category of that signal layer - it detects observable buying events (competitor trials starting, renewal windows opening, vendors churned, tech stacks changing) at the account level, sourced from observable data, not from inferred behaviour. We've covered the broader category of evidence-based platforms in the Best Purchase Intent Data Platforms for 2026 directory . Put intent on top and evidence underneath, and the workflow inverts. Outreach gets triggered by the evidence - the trial that started, the renewal that opened - not by the intent score. The cadence anchors on the named event. The rep opens with something the buyer recognises, not with a value prop. The score gets used for audience targeting and territory planning, not for sequencing decisions. What fixing it looks like in practice Four moves separate teams that fixed this from teams that scaled the noise. Stop using the intent score as the queue sort. The rep's working list should sort on a time-bound event (renewal opening, trial started, vendor churned), not on a static or rolling intent score. That is what opportunity discovery is for. The score becomes a filter, not the sort. Anchor every cold touch on the named event. "I saw your team started evaluating Vendor X four days ago" lands. "I noticed your company has been showing intent in our category" doesn't. The opening line tells the buyer the rep knows something specific - or it tells them they don't. Audit what the layer flagged 90 days ago. Pull the cohort of accounts your intent layer flagged as high-intent a quarter back. How many converted to an opportunity? How many are still flagged at the same intensity? The delta is the layer's actual signal quality. Pair the intent layer with an evidence layer. Intent for audience and segmentation, evidence-based purchase intent for sequencing and prioritisation. Two layers, two different jobs - not one layer asked to do both badly. Teams that make these four moves see the lift the original intent-data category promised but never delivered. Not because they replaced the intent layer - because they stopped asking it to do a job it was never structured to do. Frequently Asked Questions Is intent data dead? No. Intent data still works as a top-of-funnel filter and as an audience-targeting layer in paid media. It's the wrong signal to base sales prioritisation on, especially in B2B SaaS where the buying window is narrow and the inferred-intent signal sits too far upstream of the actual buying motion. Why have my intent data reply rates dropped? Most likely because more sellers in your category are now sourcing from the same underlying intent data and contacting the same accounts in the same week. The signal didn't degrade - the surrounding competitive density did. The shared bidstream layer scales coordinated noise even when no individual seller intends it. What should I use instead of intent data for sales prioritisation? Evidence-based purchase intent - the observable buying events at an account (trials, renewals, churn, tech-stack changes) - is the layer that produces a trigger a rep can actually anchor outreach on. The pillar piece on purchase intent vs intent data covers the difference, and the platform directory covers who produces it. Can I keep my intent data tool and add an evidence-based layer on top? Yes - in fact that's the recommended stack for most teams. Intent for audience filtering and territory planning; evidence-based purchase intent for sequencing and sales prioritisation. Two layers, two different jobs. They don't compete. The compare pages map exactly where each intent tool sits next to MarketSizer. How do I tell if my intent layer is producing signal or producing noise? Run a 90-day cohort audit. Pull the accounts your intent layer flagged as high-intent 90 days ago. What proportion converted to a real opportunity? What proportion are still flagged at the same intensity without converting? A high "still flagged, not converted" rate means the layer is producing recurring noise on the same population of accounts, not real signal. How long does it take to see results from a signal-led, evidence-anchored motion? Reply-rate lift is usually visible within the first sequencing cycle (30-45 days), because event-anchored opening lines convert better than generic value-prop openers. Pipeline impact tracks to the underlying sales cycle - a 90-day cycle produces a 90-day measurement window. By month four most teams have enough data to confirm the new motion. Is this a Niall O'Gorman / Episode 1 argument? The "intent data accelerated the noise" framing comes from Episode 1 of Intent Decoded , where the conversation centred on intent data as a starting gun rather than a confirmation signal. The full episode is on the podcast hub . Recommended reading Purchase Intent vs Intent Data - the pillar this spoke ladders into. Best Purchase Intent Data Platforms for 2026 - the directory of evidence-based platforms that produce the layer this piece argues for. Most GTM Teams Use Intent Data at the Wrong Time - the Episode 1 recap where the "starting gun, not confirmation signal" framing first ran. What Is Subscription Intelligence? - the data category that produces the evidence-based signal layer. Your Sales Outreach Isn't Broken. Your Timing Is. - the timing-side of the same argument. Why Qualified Leads Don't Close (And How Timing Kills Conversion) - the downstream effect on the funnel of the failure mode this piece describes. ## Blog: How to Implement Signal-led Prioritisation in Salesforce and HubSpot The data model, list views, routing logic, and rep workflow that turn the signal-led GTM playbook into something a team actually runs each morning. The strategic argument for signal-led GTM is covered in the pillar piece on building a signal-led GTM motion without replacing your sales stack . This is the tactical companion: what you actually create inside Salesforce or HubSpot so the rep's queue is sorted by buying-window event instead of by static account list. (MarketSizer connects natively to HubSpot, Pipedrive and Zoho CRM - the full connection list is on the integrations page . ) The level of detail below is platform-specific but stops short of menu-click documentation - both Salesforce and HubSpot rearrange their UIs often enough that step-by-step screenshots age badly. What matters is the data model, the views, the routing logic, and the rep's daily flow once it's live. Field-creation specifics are easy to look up in vendor docs; what's harder to find is the operating shape the implementation has to land in. The four data objects you need to model Signal-led prioritisation needs four things modelled inside the CRM. Most teams already have two of them and just need to add the others. Together they replace stage-based routing with lead qualification that reads the account, not the form fill. Object 1 Account / Company The base record. Already exists. The signal layer attaches to this. Object 2 Signal event The buying event itself - trial started, renewal opening, vendor churned. Time-bound. Has a source. Object 3 Opportunity Score Three sub-scores: ICP Fit, Purchase Intent, Win Likelihood. Composite tells the rep how to sort. Object 4 Window-close date When the buying window closes. Drives the rep's calendar sort. Four data objects. Two already exist in most CRMs. Two get added. Most teams already have the Account/Company object and an Opportunity/Deal object. The two new objects are the Signal event (what changed, when, and where the signal came from) and the Opportunity Score (the composite that sorts the rep's queue). Window-close dates can either be modelled as a property on the signal event or as a derived field on the account, depending on platform - both work. The signal layer (typically MarketSizer or one of the platforms in the purchase intent platform directory ) writes into these objects via the CRM connector. The rep's job is to consume what's written, not to maintain it. Setting up signal-led prioritisation in Salesforce Salesforce gives you the most flexibility but also the most ways to overcomplicate the setup. Stay close to native objects - Account, Opportunity, Task, and the platform's existing queue logic - rather than building a custom object hierarchy. The setup below is the minimum that gets to a working rep workflow. Salesforce: custom fields on Account and Opportunity Six custom fields, each with a clear job: Opportunity Score (Number, 0-100) on the Account. The composite that sorts the queue. ICP Fit Score (Number) on the Account. The first sub-score. Fit assessment against your best-fit customer profile. Purchase Intent Score (Number) on the Account. The temporal layer - observable buying signals . Win Likelihood Score (Number) on the Account. The competitive layer - historical win rate against the incumbent vendor at this account. Active Signal Type (Picklist - Trial Started / Renewal Window / Vendor Churned / Tech Stack Change / Champion Move / Other) on the Account. The named event the rep references in outreach. Window Close Date (Date) on the Account. When the buying window closes - the field reps sort by. Two optional fields worth adding once the core six are in place: Signal Source (Text) - which platform produced the signal, so reps and ops can audit the layer. Signal First Detected (Date/Time) - when the event was first picked up, so you can track time-to-first-touch. Put all eight in a dedicated Account page layout section called something obvious like "Signal Intelligence" so reps know where to look. Don't bury them in a custom related list - they need to be visible in the standard view. Salesforce: list views and queues The rep's primary working surface is a list view called something like My Active Buying Windows : Filter: Active Signal Type is not blank AND Owner = current user. Sort: Window Close Date ascending (soonest first). Display columns: Account Name, Active Signal Type, Window Close Date, Opportunity Score, Purchase Intent Score, Last Activity. A second list view called Unassigned Active Windows with the same filter minus the Owner clause becomes the team queue - any rep without active accounts works from this list, and the team's SDR manager assigns based on coverage rather than turn-by-turn round-robin. For teams running ABM or named-account models, a third view called Named Accounts in Window filters to accounts on the strategic list AND with an active signal, so AEs see which of their assigned accounts just opened a buying window. Salesforce: routing and alerts Routing in Salesforce can be handled inside flows or via the assignment rules used elsewhere - the choice depends on team size and complexity. Minimum routing logic: When Active Signal Type changes from blank to a value AND the account has an existing Owner, post a Chatter notification to that Owner. When Active Signal Type changes AND there is no Owner, run the same assignment logic the team uses for marketing-source leads. When Window Close Date is within 14 days, escalate to the team channel for visibility. Alert routing should go where the rep already works - Chatter, Slack via the connector, or a Salesforce mobile notification - not into yet another tool. The signal layer is only useful if it reaches the rep in time. Setting up signal-led prioritisation in HubSpot HubSpot's strength here is the workflow engine - the visual workflow builder makes signal-based routing simpler to wire up than equivalent Salesforce flows. The data model is otherwise close enough that the move-by-move logic carries across with small naming differences. HubSpot: custom properties on Company and Deal The equivalent six properties on the Company object: Opportunity Score (Number, 0-100) - the composite. ICP Fit Score (Number) - your ICP scoring output. Purchase Intent Score (Number) - observable buying signal strength. Win Likelihood Score (Number) - competitive odds. Active Signal Type (Dropdown) - same set of options as the Salesforce picklist. Window Close Date (Date picker) - when the window closes. Add the same two optional properties for source attribution and detection time. Group all eight into a Company property group called "Signal Intelligence" so they sit together in the record view. HubSpot: saved views and workflows HubSpot's equivalent of the Salesforce list view is a saved Company view. Three to create: My Active Buying Windows : filter on Active Signal Type is known AND Company Owner = current user, sort by Window Close Date ascending. Unassigned Active Windows : same filter without the owner clause. Named Accounts in Window : layer in a "Target Account" boolean property if you run a named-account programme. Save each as a default view for the relevant team role so SDRs and AEs land on them when they log in. HubSpot: routing and alerts HubSpot workflows handle the routing natively. Three workflows are enough to cover the operational logic: "New active signal - assigned owner" : triggered when Active Signal Type changes from blank to any value AND Company Owner is not blank. Action: send the owner a HubSpot task linked to the company, plus a Slack notification via the connector. "New active signal - unassigned" : triggered when Active Signal Type changes AND Company Owner is blank. Action: route via the existing lead-assignment workflow. "Window closing soon" : triggered when Window Close Date is within 14 days AND the deal isn't already in late stage. Action: post to the team Slack channel, escalate to the owner. Keep workflows simple. Two of the most common failure modes for HubSpot signal-led implementations are workflow sprawl (twenty automations all triggering off the same property change) and silent automation conflicts (one workflow updates a property that triggers another). Three workflows is enough. The daily rep workflow once it's wired in The point of the implementation is what the rep does every morning once the layer is live. The flow looks roughly like this: Open the CRM and land on the My Active Buying Windows view. The list is sorted by window-close date, so the accounts with the tightest deadlines are at the top. Pick the top three accounts. For each one, check the Active Signal Type and the Window Close Date. The rep should be able to articulate, in one sentence per account, what changed and when. Open the first account. The Signal Intelligence section on the record shows the named event, the source, the sub-scores. The rep uses this as the opening reference in outreach. Send the first touch. The opening line of the email or call references the named event - "I saw your team started evaluating Vendor X six days ago" - not the generic value prop. Log the touch and move on. A successful touch updates Last Activity, which feeds into the team's coverage view. The whole flow per account takes 5-10 minutes once the rep has done it ten times. The lift comes from two things: the rep is working accounts that are actually buying, and the opening line references something the buyer recognises. Both compound. Common implementation traps The traps that recur across teams that get the data model right but the operational logic wrong: Creating fields no one looks at. Custom fields buried five tabs deep get ignored. If the signal data isn't in the rep's primary view within a week of going live, the project quietly stalls. Put the fields on the default record layout and make the list view the rep's home page. Letting the signal layer write without supervision. The signal source needs an audit trail - which platform wrote the value, when, and based on what. Reps quickly stop trusting fields they can't trace. Skipping the unassigned queue. Teams that route every signal to the existing owner miss the accounts that don't have one. The Unassigned view is what catches new-business accounts before they become a missed-opportunity pipeline review note. Modelling signals as a custom object instead of properties on the account. Custom objects look cleaner in a data model but create a workflow tax - every report, view, and automation has to handle the join. Start with properties on the Account/Company and only upgrade to a custom object if you genuinely need many-to-many signal-to-account relationships. Triggering on every property change. If three workflows all trigger when Active Signal Type changes, you get three notifications per event. Pick one trigger workflow and let downstream automations fan out from there. Forgetting the integration handshake. The signal-source platform needs the right CRM permissions to write back. Check the integrations layer before reporting to the wider team that the implementation is live - the most common "it doesn't work" report is actually "the connector isn't authenticated. " Frequently Asked Questions Do I need a custom object for signal events? Usually not for the first implementation. Start with custom properties on the Account/Company object - the operational logic works the same way and the workflow complexity is lower. Upgrade to a dedicated custom object if you need to track signal history at the event level rather than the most-recent-signal level. How do I write the Opportunity Score back to the CRM? Most signal-intelligence platforms write the composite score via their native CRM connector - the score lands as a number in the field you created, refreshed on the platform's standard cadence. If your signal platform doesn't have a native connector, write via the CRM API and run the sync as a scheduled job. Either way, the field that holds the score should be flagged read-only for reps to prevent manual overrides. Should reps be able to edit the signal fields? No. The signal layer is the source of truth for those fields; reps editing them creates an audit trail that nobody trusts. The fields the rep can edit are downstream of the signal - outreach status, next action, notes - not the underlying signal data. How does this affect existing lead-routing rules? The signal layer should sit above existing routing, not replace it. When a new active signal fires on an account that already has an owner, the existing routing applies. When a new active signal fires on an account with no owner, the existing lead-assignment logic kicks in. The signal layer changes which accounts qualify, not how they're assigned. How long does this take to set up? For a team that's already on the target CRM, the data-model and view setup is typically half a day to a day of admin work. The longer piece is rep enablement - making sure SDRs and AEs land on the new view as their default, understand the Signal Type vocabulary, and know how to reference the named event in outreach. Plan a two-week rollout, not a single launch day. Can I run this without the signal platform's native connector? Yes, via CSV import on a daily or weekly cadence. The trade-off is freshness - a CSV update once a day means the rep is working with up-to-24-hour-old data, which is fine for renewal windows but starts to miss the edge of a 14-day competitor trial. Live API integration is the operating standard once volume is past pilot. What if my team uses a CRM other than Salesforce or HubSpot? The data model carries across. Pipedrive, Close, and Microsoft Dynamics all support custom fields, list views, and workflow triggers - the names of those primitives are different but the operational logic is the same. Six fields, three views, three automations. Recommended reading How to Build a Signal-led GTM Motion (Without Replacing Your Sales Stack) - the strategic pillar this tactical piece sits underneath. Best Purchase Intent Data Platforms for 2026 - the directory of platforms that produce the signal layer the CRM consumes. Purchase Intent vs Intent Data - the definitional argument for why the signal layer matters in the first place. Why CRM Data Is Inaccurate - what the signal layer fixes inside the data CRMs already hold. How to Prioritise Accounts for Outreach - the prioritisation principles the four-move motion runs on. ## Blog: The Signal-Led GTM Playbook: Four Moves That Run Inside Your Sales Stack The pillar guide to signal-led GTM for B2B SaaS teams: what it is, how it differs from ABM and intent-led, and the four-move playbook that runs inside the stack you already own. Most GTM teams don't have an account problem. They have a prioritisation problem. The list of 5,000 ICP-fit accounts is fine. The 50 accounts inside that list that are actually doing something to buy this quarter - that's the gap pipeline falls into. Signal-led GTM closes that gap by changing what the team sorts on. Not engagement. Not firmographic score. Observable buying events at the account: a competitor trial started, a renewal window opening, a vendor churned, a tech stack changing. The job below is to explain what a signal-led motion looks like, how the four moves work, what changes for each team, and why you can run all of it inside the sales stack you already own. Volume-led GTM Sort by: firmographic fit + engagement Unit of work: the list Cadence: time-based - day 1 email, day 3 call Success metric: activity throughput Signal-led GTM Sort by: observable buying events Unit of work: the buying window Cadence: event-based - trigger, evidence, then sequence Success metric: reply rate, win rate, time-to-window-close The difference isn't the tools. It's what the team sorts the list on. Why volume-led GTM is structurally broken Volume-led GTM is the playbook most teams still run by default. Build a list against an ICP. Push the list into a sequencing tool. Run the cadence. Track activity. The implicit bet is that enough touches over enough accounts will produce enough pipeline - and the rep's job is to make sure the touches actually happen. The bet was reasonable when the alternative was no signal at all. It hasn't held up. The structural problem is buyer scarcity. The LinkedIn B2B Institute's research with the Ehrenberg-Bass Institute on the 95-5 rule found that only ~5% of B2B buyers are actively in-market at any given moment. SaaS-specific analysis puts the figure closer to 24% across an annual cycle, but the operational shape is identical: the majority of any reasonable ICP list isn't buying anything for months or years. Running a volume cadence over that list spends rep time on accounts that aren't going to move, regardless of how good the messaging is. The compounding problem is that volume-led GTM scales the wrong variable. More reps means more touches means more low-quality outbound. Gartner's future-of-sales research already shows buyers spend only 17% of their evaluation time meeting with potential suppliers - the rest is self-serve research. Adding more outbound touches into that 17% doesn't improve conversion; it gets your team filtered out faster. The third problem is morale. When a rep works a 200-account cadence and lands two meetings, they stop trusting the list. When a rep works a 50-account cadence and lands ten meetings, they trust the list and the layer underneath it. Same person, same effort, different signal - that's the deciding variable, not coaching or rep skill. Signal-led GTM doesn't fix volume by doing more of it. It fixes volume by sorting on the variable that actually predicts a sale - whether something buying-relevant is happening at the account right now. What signal-led GTM actually is Signal-led GTM is a motion where the unit of work is a buying window, not an account on a list. Each account gets worked when an observable event signals an open window - a competitor trial, a renewal opening, a vendor churned, a tech stack changing, a champion moving. When no window is open, the account waits. The rep's calendar is sorted by which windows close soonest, not by which accounts were assigned at the start of the quarter. The promise is concrete: a rep who works only accounts with an open buying window has a higher reply rate, a higher win rate, and a shorter time-to-meeting than the same rep working a static ICP cadence. Not because the rep is better. Because the underlying signal is closer to a buying motion. The data layer that makes this work is purchase intent - the observable evidence that an account is in a buying motion. We've covered the underlying argument in Purchase Intent vs Intent Data: Why Content Engagement Is Not a Buying Signal ; the data category that produces it is Subscription Intelligence . Signal-led GTM is what you do once that layer is in place. How signal-led GTM differs from ABM and intent-led motions Signal-led GTM, ABM, and intent-led prospecting overlap in how they're described in vendor decks. They don't overlap in what they sort on. ABM sorts on account fit. The list comes first, the signals come later. Most ABM programmes run a fixed account list against a marketing cadence and expect engagement to fill in around it. The list is the lever; everything else is execution. Intent-led prospecting sorts on inferred behaviour. The list comes from anonymous web activity, content syndication, or third-party publisher data. The signal is interest, not action - which means the prioritisation is over a population of researchers, not buyers. Signal-led GTM sorts on observable events. The list is the by-product of the signal - accounts surface because something changed, not because they fit a profile or showed interest. Fit and Win Likelihood layer in as filters, but the temporal signal is what moves an account into "work this week. " The practical effect: signal-led GTM produces a shorter list with higher conversion, while ABM and intent-led produce longer lists with thinner per-account context. Most teams running ABM today already have most of the inputs to run a signal-led motion - they're just sorting on the wrong field. The four moves of signal-led GTM Four moves take a team from volume-led to signal-led. Each replaces a step the existing motion already runs - none of them require a new sales stack. Move 1 Find Build the list from observable signals, not just firmographics. Move 2 Prioritise Sort by Opportunity Score - Fit, Intent, Win Likelihood combined. Move 3 Act Tie outreach to the named event, not the day-of-the-cadence. Move 4 Measure Track time-to-window-close, not engagement throughput. Move 1 - Find: list-building from observable signals The volume-led motion starts with an ICP filter, runs it across a contact database, and pulls a list of 5,000 accounts. Signal-led starts the other way around: filter the universe down to accounts where something observable just changed, then layer ICP fit on top as a qualifier. Observable signals that produce a list worth working: A competitor trial starting at the account. A renewal window opening on a tracked vendor (typically 60-90 days out). A vendor recently churned, creating a replacement window. A new tech stack component installed or removed. A champion changing companies (carrying preference for your product to a new account). A regional expansion or new entity creating an in-category buying need. The list this produces is shorter than an ICP-filtered one, often dramatically so. That's the point. A team that works 50 in-market accounts a week converts better than the same team working 500 ICP-fit accounts where 95% aren't in a buying window. The list shrinks; the conversion rate rises; the time per converted account falls. The shift in inputs: instead of starting with a contact database, start with the signal layer. Subscription Intelligence platforms like MarketSizer produce these events at the account level - we cover the broader category in Best Purchase Intent Data Platforms for 2026 . Move 2 - Prioritise: an Opportunity Score, not an activity score A list of accounts with open buying windows still needs to be sorted. Not every in-market account is worth calling today. The Opportunity Score is the prioritisation layer signal-led teams use. Three sub-scores, multiplied not added: ICP Fit answers could this account buy? Purchase Intent answers is something buying-relevant happening right now? Win Likelihood answers can you actually win it against the incumbent vendor? An account has to clear all three before it earns a rep's time. A perfectly ICP-fit account with no active signal is a future opportunity, not a current one. An in-market account where you historically win 4% of deals against the incumbent is a long shot, not a queue priority. Only the accounts that score on all three become Qualified Opportunities - the unit of work the motion actually runs on. The discipline this introduces: a rep can no longer work an account because it looked good in the CRM or because marketing flagged it for engagement. The account has to earn its place across the three layers, which means the prioritisation is auditable. When a rep asks "why is this account at the top of my queue? ", the score points at the trial that started, the firmographic match, and the historical win rate against the competitor - in that order. Move 3 - Act: tie outreach to the signal, not the cadence Volume-led cadences are time-based. Day 1 email, day 3 LinkedIn, day 5 call. The structure is calendar-driven; the content is template-driven; the rep's job is to make sure each touch goes out on time. Signal-led cadences are event-based. The first touch references the event that triggered the account onto the queue. "I saw your team started evaluating Vendor X four days ago - want to compare notes before the trial closes? " lands. "I noticed your company has been showing intent in our category" doesn't. The difference isn't writing skill. It's that one touch references something the buyer did, and the other one references something the rep noticed. Two follow-on rules that compound the first: Sort the rep's calendar by time-to-window-close. A 30-day-old trial event is closer to a buying decision than a six-week-old white paper download. The rep should work the trial first, regardless of which account was assigned first. Let the signal carry the cadence. When the underlying event is a renewal opening 60 days out, the cadence is sequenced over those 60 days with reference points along the way (45 days, 30 days, two weeks). When the event is a trial that runs for 14 days, the cadence compresses into that window. Cadence length tracks signal length, not a fixed playbook. This is the move that converts. Cold outreach reply rates aren't broken because of messaging - they're broken because the messaging arrives untethered from anything the buyer recognises as relevant. Tying outreach to the signal solves it without needing to rewrite the cadence. Move 4 - Measure: window-close, not engagement The metric volume-led GTM optimises against is activity throughput - emails sent, calls dialled, sequences completed. The metric signal-led GTM optimises against is window-close: the proportion of in-market accounts the team converts before the buying window closes. Engagement isn't useless. It's a leading indicator of the signal layer working - if the team is responding to signals correctly, reply rates and meeting-to-discovery conversion should both rise. But engagement is downstream of the signal layer, not a substitute for it. A team can run high engagement on accounts that aren't in-market and still produce no pipeline; a team can run lower engagement on accounts that are all in-market and outperform the first one significantly. Four metrics worth tracking on top of standard pipeline reporting: Signal-to-meeting conversion - of accounts flagged with an event, how many converted to a discovery call within 30 days. Window-close rate - of accounts in an active buying window, how many closed before the window closed. Time-to-first-touch - from event detection to the rep's first outreach. Should be measured in days, not weeks. False-positive rate - of accounts flagged 90 days ago, what proportion never produced a real buying motion. A rising false-positive rate is the earliest signal that the signal layer is degrading. What changes for sales, marketing, and customer success Signal-led GTM isn't a sales-only motion. The signal layer changes what each function prioritises - including the parts of GTM where outbound cadences don't apply. Sales: from cadence-driven to event-driven SDR and AE workflows shift from "work this list of accounts in this order" to "work the accounts where something changed, in window-close order. " The list isn't assigned at the start of the quarter; it surfaces continuously, driven by the signal layer. Account prioritisation moves from a quarterly exercise to a weekly one . The hardest cultural shift here is on the rep side: a rep used to controlling their own list now lets the signal layer set the queue. The way most teams reconcile this is by giving the rep authority over how they work each account but letting the signal layer decide which account they work next. Reps keep their cadence muscle; the data layer handles the prioritisation. Marketing: from MQL volume to in-market account quality The marketing function in a volume-led motion is judged on MQL throughput - leads generated, meetings booked, pipeline contributed by attribution. In a signal-led motion, the metric becomes the proportion of MQLs that come from in-market accounts. A team generating 1,000 MQLs from accounts with no observable buying signal contributes less to pipeline than a team generating 200 MQLs from accounts in an active window. Operationally, this means marketing's audience strategy shifts from broad ICP coverage to in-market segments. Paid media targets accounts in the signal layer; nurture campaigns track to renewal windows; content distribution prioritises the segments where signals are firing. Same channels, different sort. Customer success: from quarterly review to early-warning system The CS function gets the most direct benefit from the signal layer because the same evidence that flags new buyers flags existing customers who are quietly evaluating alternatives. CS teams typically fail on timing, not effort - a customer that started a competitor trial three weeks ago is a churn risk regardless of how their QBR went. The shift for CS: from quarterly health checks to event-triggered intervention. A competitor trial alert at an existing customer triggers a CS motion immediately, not at the next scheduled review. An expansion signal - new region, tech-stack addition, organisation growth - triggers an expansion play before the customer thinks to ask. The signal layer turns CS from defensive to proactive without adding headcount. Why you don't need to replace your sales stack The single most common objection to signal-led GTM is the assumption that it requires replacing the tooling that's already in place. It doesn't. The motion runs inside the workflow your team already uses. The work most teams already do: Build lists in a contact database (ZoomInfo, Apollo, Clay, LinkedIn Sales Navigator). Enrich those lists with firmographic and contact data. Push selected accounts into a CRM (Salesforce, HubSpot). Run outreach through a sequencing tool (Outreach, Salesloft, Apollo, native CRM). Review pipeline weekly or fortnightly. Signal-led GTM doesn't replace any of these. It changes what feeds into them. The signal layer surfaces accounts with an open buying window; those accounts flow into the existing contact-database workflow, get enriched the same way, land in the CRM the same way, and run through the same sequencing tool. The sequencing template just gets a different opening line - one that references the named event rather than a generic value prop. The tactical follow-on - how to wire the signal layer into Salesforce or HubSpot, what fields to add, how to route alerts, what the rep's daily flow looks like - is covered in the companion piece on How to Implement Signal-led Prioritisation in Salesforce and HubSpot . The high-level point matters here: a signal-led motion is an additive layer over an existing stack, not a replacement for it. This matters operationally for two reasons. First, adoption: a tool that demands a new process gets adopted in week one and abandoned by week six. A layer that improves the existing process keeps its adoption because the rep doesn't have to relearn anything. Second, cost: most signal-led platforms cost less than the contact database they sit on top of, and replace nothing. The decision is "add this layer to the stack we already have," not "swap our stack for this one. " Common failure modes when adopting signal-led GTM Teams that try signal-led GTM and don't see the lift usually trip on one of the patterns below. Treating the signal as the answer, not the trigger. A flagged account isn't a closed deal. It's the start of a sales motion that still requires good messaging, the right rep, and the right follow-through. The signal opens the conversation; the rep wins it. Stacking the signal layer on top of a broken ICP. Signal-led prioritisation can't rescue a target account list that was wrong before the signals layered in. If the ICP definition includes accounts you don't actually win, you'll see in-market signals at those accounts and still lose the deal. Letting the layer go quiet. If the signal layer flagged 100 accounts in Q1 and only flagged 30 in Q2, something has changed - either the dataset has gone stale, your ICP has shifted, or the macro environment has compressed. Treat dropping signal volume as a leading indicator, not a quiet quarter. Mixing event-based outreach with cadence-based metrics. If the team is measured on activity throughput, the cadence will quietly drift back to time-based touches over a fixed list. Either align the metric to the motion, or watch the motion regress to the metric. Not aligning sales, marketing, and CS on a shared in-market definition. Marketing's in-market and sales' in-market are usually different definitions inside the same company. Pick one before the signal layer goes live, or you'll spend three quarters arguing about why the dashboards disagree. Buying for category coverage rather than for a specific gap. "We need an intent platform" is procurement language. "We don't know which of our ICP accounts is in a renewal window this quarter" is a job to be done. Buy for the latter. Compare tools with MarketSizer Side-by-side comparisons for the platforms mentioned above: Apollo , ZoomInfo , Clay . Full index at MarketSizer compared with other tools . Frequently Asked Questions What is signal-led GTM? Signal-led GTM is a sales motion that sorts and prioritises accounts based on observable buying events - competitor trials, renewal windows, vendor churn, tech-stack changes - rather than firmographic fit or engagement. The unit of work is a buying window, not an account on a list. Reps work accounts in window-close order. How is signal-led GTM different from ABM? ABM sorts on account fit and works a fixed list. Signal-led GTM sorts on observable buying events and works a dynamic list that updates as new events fire. Most ABM programmes already have most of the inputs to run signal-led, but sort on the wrong field. Do I need to replace my sales stack to adopt signal-led GTM? No. Signal-led GTM is an additive layer on top of the existing stack - the contact database, CRM, and sequencing tool stay the same. What changes is what feeds into them. The signal layer surfaces accounts with an open buying window, which then flow through the existing workflow. How long does it take to see results from a signal-led motion? Reply-rate lift is usually visible within the first sequencing cycle (typically 30-45 days), because event-anchored outreach lands harder than generic outbound. Pipeline impact tracks to the sales cycle - a 90-day cycle produces a 90-day measurement window. By month four most teams have enough data to recalibrate the motion. What's the right team to adopt signal-led GTM first? SDR teams running outbound usually see the fastest lift because their primary metric (reply rate, meetings booked) is most directly affected by signal quality. AE-led motions follow once the inbound from SDR shifts to event-anchored opportunities. Marketing and CS layer in as the signal vocabulary becomes shared across teams. What's the difference between signal-led GTM and intent-led prospecting? Intent-led prospecting sorts on inferred interest - anonymous web behaviour, content downloads, third-party publisher signals. Signal-led GTM sorts on observable events at the account - a trial started, a vendor churned, a renewal opening. Both are useful at different stages of the funnel; signal-led is closer to a buying motion and produces a higher per-account conversion rate. How do I measure success in a signal-led motion? The four metrics worth tracking on top of standard pipeline reporting: signal-to-meeting conversion, window-close rate, time-to-first-touch, and false-positive rate. The first three measure how well the team responds to the signal layer; the fourth measures whether the signal layer itself is producing real signal or quiet noise. Recommended reading How to Implement Signal-led Prioritisation in Salesforce and HubSpot - the tactical companion to this pillar, with the CRM data model and rep workflow specifics. Purchase Intent vs Intent Data - the underlying argument for why observable events outperform inferred behaviour as a buying signal. Best Purchase Intent Data Platforms for 2026 - the directory of platforms that produce the signal layer signal-led GTM runs on. What Is Subscription Intelligence? - the data category that produces evidence-based purchase intent signals at scale. Timing Intelligence 101 - the operational complement, focused specifically on the temporal layer of the Opportunity Score. Why Customer Success Teams Fail on Timing, Not Effort - what signal-led looks like inside CS specifically. Intent Decoded - the podcast where the underlying conversations around signal-led GTM happen. ## Blog: The Best Purchase Intent Data Platforms for 2026 Most "intent" tools score interest, not buying motion. Here is how the purchase intent platform market actually divides - and how to pick the right layer. The B2B "intent" market is now crowded enough that two platforms can both call themselves purchase intent tools and capture entirely different things. One detects an account starting a competitor trial. Another detects an IP at the account loading a third-party blog post. They sell into the same procurement cycle. They produce wildly different pipeline. The list below sorts every major purchase intent platform by the kind of signal it actually produces - direct evidence, aggregated signals, inferred behaviour, review-site activity, or de-anonymised visitor identification. Pick the category that fits the question your pipeline can't answer today, then pick the tool. Skipping that first step is how teams end up with three intent tools that all flag the same accounts and none that flag the ones that matter. Category 1 Evidence-based purchase intent Observable buying events. Trial started, renewal opening, vendor churned, tech stack changed, champion moved. Category 2 Aggregated signal intelligence Multi-source aggregation. Community, product-usage, and first-party signals combined and ranked into account-level priority. Category 3 Inferred intent data Third-party bidstream and content syndication. Inferred from anonymous web behaviour and topic surges. Category 4 Review-site purchase intent Buyer activity inside software review platforms. Comparisons, profile views, category searches. Category 5 First-party visitor identification De-anonymising visitors to your own site. People and company names attached to first-party engagement. Five categories of purchase intent platform. Most teams need two or three, not one of each. What are the best purchase intent data platforms for 2026? The best purchase intent platforms in 2026 are the ones that produce signals you can act on within the buying window they describe. By category: MarketSizer , HG Insights , and BuiltWith for evidence-based subscription and tech-stack signals; Common Room , Pocus , and Default for aggregated signal intelligence; Bombora , 6sense , and Demandbase for inferred third-party intent; G2 Buyer Intent and TrustRadius for review-site intent; RB2B and Warmly for first-party visitor ID. Skip the bloated all-in-ones - the right purchase intent stack is two or three platforms across complementary categories, not one with eight half-built features. What changed in purchase intent tooling in 2026 Three shifts have reshaped the purchase intent market over the last two years - and most procurement teams haven't caught up. The first is the rise of evidence-based signals as a distinct category. For a decade, "intent" meant inference: an IP at an account loaded a page, an anonymised user appeared in a bidstream, a topic surged across third-party publishers. The implicit promise was that enough inferred signals add up to a real buying motion. That promise has worn thin. Forrester's own evaluation of B2B intent providers found that 50% of teams report too many false positives. Evidence-based platforms - which detect observable events like competitor trial starts, renewal windows, and tech-stack changes - have moved from "interesting alternative" to "the layer everything else gets compared against. " We've broken the underlying argument down in Purchase Intent vs Intent Data: Why Content Engagement Is Not a Buying Signal ; the practical effect on the stack is everywhere. The second is consolidation in the legacy intent-data layer. Clearbit is now fully integrated into HubSpot following the 2023 acquisition and evaluated as part of the wider HubSpot product rather than a standalone enrichment tool. Bizible has been retired by Adobe and replaced by Adobe Marketo Measure. Bombora and Foundry remain the two heavyweight third-party intent suppliers, but the days when a single bidstream feed could pass as "intent" are over. Buyers want to see what produced the signal before they let a rep act on it. The third is the rise of signal intelligence as its own category. Tools like Common Room, Pocus, Default, and LoneScale don't claim to be a single source of intent - they aggregate signals from product usage, community activity, first-party engagement, and selected third-party feeds, then rank accounts by composite priority. The category sits between the evidence-based platforms (which produce specific events) and the inferred intent providers (which produce topic surges). For teams running a product-led or community-led motion, it's often the most useful layer to add first. The practical implication: stop buying "an intent tool" and start buying intent layers . A modern stack typically combines one evidence-based platform with one signal aggregator, plus a third-party feed if the addressable market is broad enough to need it. Three tools, three different signal types, one qualification layer underneath. Real evaluation signals, not page-view scoring While most intent platforms score anonymous web visits, MarketSizer tracks vendor-specific evaluation events: trials, renewals, competitive displacements. 70M+ historical subscription outcomes, every score inspectable down to the signal that fired it. Compare the data → Book a demo → 6 quick tips to choose the right purchase intent platform Decide which signal type your pipeline actually needs before reading vendor decks. Prioritise tools you can audit - if you can't see what triggered a signal, you can't act on it credibly. Check signal latency against your sales cycle. A weekly refresh is too slow for a 14-day trial. Integrate into the workflow you already run - CRM, Chrome, CSV - not into a new dashboard. Watch out for tools that bundle "intent" with enrichment to mask thin signal quality. Test against your real ICP, not the vendor's demo dataset. What to look for in a purchase intent data platform The category is loose enough that two vendors can both honestly call themselves purchase intent platforms while producing entirely different outputs. The features below cut through the marketing and tell you what the tool actually does. Key features to look for in a purchase intent platform Signal type, named clearly. Does the platform detect specific observable events (a trial start, a renewal window) or infer interest from digital behaviour (page views, content downloads)? Both are valid, but you should know which one you're buying. Inspectable evidence per account. Every flagged account should come with the event or signal behind the flag. Black-box scoring kills rep trust the first time a "high intent" account fails to convert. Latency that matches your sales cycle. If your buying window is 14 to 60 days, a weekly-refresh feed will miss the moment. Daily refresh or near-real-time is the operating standard. Workflow-native delivery. Signals should land in the CRM, the Chrome extension, the CSV export - wherever your team already works. A platform that demands a new dashboard is a platform that gets abandoned. Coverage that matches your addressable market. Vendor coverage matters more than total record count. Ten million accounts mean nothing if your ICP isn't well covered in the dataset. Integration with the rest of your qualification stack. Purchase intent without ICP fit and win likelihood is half a story. Look for platforms that surface the temporal layer of an Opportunity Score , not just a stand-alone intent flag. The 2026 purchase intent stack: category-by-category breakdown The five categories below cover the full purchase intent market. Most teams need two or three platforms across complementary categories, not five overlapping ones. Evidence-based purchase intent platforms The platforms in this category detect specific observable events - a competitor trial start, a renewal window opening, a vendor churned, a tech stack change, a champion moving company. They give the rep a named event to reference in outreach instead of an aggregate score. This is the category that's matured the most in the last two years. Tool Signal type Best for MarketSizer Subscription events - competitor trials, renewal windows, churn, vendor migration - across 200+ tracked vendors and 17M+ active subscriptions, refreshed daily with a maximum signal lag under five days. Delivered as the temporal layer of an Opportunity Score alongside ICP Fit and Win Likelihood . B2B SaaS vendors covering acquisition, winback, inbound qualification, and expansion against observable buying events. Strongest in customer support, live chat, CRM, marketing automation, and martech adjacencies. HG Insights Technology installs, IT spend signals, and intent layered on top. Strongest in technographic depth - which products are deployed where, at what scale, on what contract. Enterprise sellers whose pitch hinges on the target's existing tech stack, especially in security, infrastructure, and IT operations. BuiltWith Public-source detection of web technologies in use - analytics, ad tech, CMS, payment, hosting. Best treated as a tech-stack lookup, not a real-time signal feed. Teams building lists by tech stack - typically web tech, marketing tech, and e-commerce platforms. Wappalyzer Lightweight technology detection from public web data. Chrome extension and bulk lookup. Overlaps with BuiltWith at the simpler end of the use case. SDRs doing per-account tech stack lookups on the fly, or marketers building one-off lists. UserGems Champion-movement signals - when a former customer or champion changes company. Pulls from LinkedIn and other public sources. Teams whose strongest wins come from following champions into new accounts. Strong winback and expansion use case. Champify Customer and champion identification - surfaces who at an account already knows you or your competitor. Closer to a CRM enrichment layer than a real-time event feed. Teams focused on warm intro paths via existing relationships, especially in renewals and expansion. Bloomberry Real-time tech stack signals and trigger events. Detects when a company installs or removes a tracked technology - closest direct comparison point to MarketSizer in the evidence-based category, with the trade-off being tech-stack-only versus full subscription lifecycle coverage. Sellers whose use case is tech-stack-triggered - "tell me when this account installs X. " The trade-off across this category: depth versus breadth. Tools like HG Insights and MarketSizer go deeper into category-specific signal density. Tools like BuiltWith and Wappalyzer go broader but produce static profiles rather than time-bound events. The right choice depends on whether your motion is event-triggered or list-triggered. Aggregated signal intelligence platforms This category aggregates signals from multiple sources - community, product usage, first-party engagement, sometimes third-party intent - and ranks accounts by composite score. The output is a prioritised feed of accounts rather than a single event. Strong for teams running PLG or community-led motions where the relevant signals are scattered. Tool Signal type Best for Common Room Community, product, and 1P signals aggregated into a single account view. Successor positioning to the original commsor, now centred on signal intelligence and AI-driven prospecting workflows. Teams running community-led or PLG motions who need to surface in-product and community signals at the account level. Pocus Product-led sales platform. Scores accounts on product-usage and intent signals, then routes the highest-priority accounts to sales with recommended plays. PLG and hybrid-sales teams converting free users into paid pipeline. Strongest where there's a real product usage signal to score. Default AI-native signal capture and routing. Pulls form fills, intent signals, and CRM data into a single pipeline for account routing and qualification. Inbound-heavy teams where the friction is routing speed rather than signal quality. LoneScale Real-time intent and firmographic signals. Lighter-weight than Common Room or Pocus; useful as a signal source feeding a CRM rather than a full account-priority layer. Mid-market teams who want intent signals without buying a full ABM platform. Factors. ai Account-level engagement analytics. Tracks first-party engagement, attribution, and account journeys. More analytics than activation. Teams that already have signals but need a way to score and report on them. The aggregator trade-off: the quality of the composite score depends entirely on the quality of the underlying signals. An aggregator over a noisy signal layer produces a confident-looking score that still isn't a buying event. Worth asking the vendor which signal sources they actually pull from, and how recent. Inferred / third-party intent data platforms This is the category most people mean by "intent data. " Bidstream activity, content syndication, anonymised IP-level web traffic, and topic-surge analysis aggregated from third-party publisher networks. The signal is large-scale and broad, but inferred rather than evidenced. Useful for top-of-funnel discovery; weaker as a basis for prioritising who to call this week. Tool Signal type Best for Bombora The canonical third-party intent provider. Content consumption signals aggregated across a co-operative of B2B publisher sites. Topic-level intent surges at account level. Enterprise marketing teams running broad ABM programmes who need topic-level intent to feed audience segmentation. 6sense Predictive analytics combined with intent data. Builds account-level buying-stage models from anonymised behaviour, third-party intent, and CRM history. Larger teams that want intent and predictive scoring inside a single ABM platform. Demandbase Intent signals, account insights, and audience segmentation. Similar positioning to 6sense - intent inside a broader ABM platform. Multi-channel ABM teams running paid media, audience segmentation, and intent-driven outreach from a single platform. Foundry Technographics and intent built on the legacy IDG publisher network. Strong in enterprise IT and tech-buyer signal sets. Enterprise tech sellers - cloud, security, infrastructure - where IT decision-maker reach matters. TechTarget Priority Engine Account-level intent from TechTarget's enterprise IT publisher network. Surfaces in-market accounts with named active research projects. Enterprise IT and B2B tech vendors selling into a known publisher audience. ZoomInfo Intent Intent layer attached to ZoomInfo's contact and firmographic database. Combines third-party intent signals with enrichment for a single workflow. Teams already on ZoomInfo for enrichment who want intent without adding a separate vendor. The third-party intent trade-off: scale versus specificity. These platforms see far more activity than any single seller could, but the signal type - inferred from anonymous behaviour - sits one layer removed from an actual buying motion. Best treated as a top-of-funnel filter feeding a more specific layer underneath. Review-site purchase intent platforms A narrower category, but a sharp one. Review sites see buyers actively comparing vendors, reading peer reviews, and shortlisting alternatives - which is closer to a buying motion than topic-level content consumption. Useful as a complement to other categories, especially in competitive deals. Tool Signal type Best for G2 Buyer Intent Account-level activity inside G2 - category pages viewed, competitor comparisons, profile views. Signals are tied to specific buyer actions, not just topic interest. Vendors whose category has meaningful G2 traffic and whose buyers shortlist via peer reviews. TrustRadius Similar mechanic to G2 - buyer activity inside TrustRadius surfaces accounts in active research. Strong in enterprise software categories. Enterprise vendors targeting categories where TrustRadius is the more credible review source. Review-site intent is one of the cleaner inferred signals because the underlying behaviour is closer to evaluating a vendor than to reading content. The limitation is coverage - if your buyers don't use the review site, the signal isn't there to capture. First-party visitor identification platforms Platforms in this category de-anonymise your own website traffic. They tell you which companies (and increasingly which individuals) are visiting your site even before they fill in a form. Useful as a first-party engagement signal, but not a replacement for purchase intent against accounts that haven't visited yet. Tool Signal type Best for RB2B Person-level identification of anonymous website visitors. Captures around 70 to 80% of US-based traffic via a tracking script. Free at small volumes. Inbound-heavy SDR teams who want to engage high-intent visitors before they convert through a form. Warmly Real-time website visitor identification combined with intent signals. Routes engaged visitors to sales via Slack and Chrome notifications. Mid-market teams running inbound where speed-to-engage on a hot visitor is the conversion lever. Clearbit (now HubSpot) First-party enrichment combined with anonymous visitor reveal. Now evaluated as part of the wider HubSpot product rather than a standalone tool. HubSpot customers wanting native visitor enrichment without adding a separate vendor. Koala Second-party and product signals plus website visitor identification. Sits between visitor ID and signal aggregation. Product-led teams that need to combine in-product activity with website signals to spot the right accounts to engage. The visitor ID trade-off: depth on the accounts that already know you, blindness to everyone else. Pair it with a category-1 or category-3 platform if your funnel needs to find net-new accounts as well as engage the ones already on your site. Compare individual tools with MarketSizer Side-by-side breakdowns of MarketSizer against the intent-tool cluster named above. Each page quotes the vendor's own positioning, sticks to observable facts, and says when to choose them instead. MarketSizer vs Apollo - contact database plus intent inside a self-serve sales platform vs event-based subscription signals. MarketSizer vs ZoomInfo - enterprise contact depth with aggregated intent vs event-based subscription signals. MarketSizer vs HG Insights - technographic-and-spend intelligence for enterprise ABM vs execution-ready buying-event signals. MarketSizer vs Clay - programmable enrichment workbench vs pre-built subscription-event signals. MarketSizer vs BuiltWith - technology install snapshot vs subscription state and change events. See all comparisons . Common pitfalls when buying purchase intent data The patterns that recur across teams that end up with intent budgets and no pipeline: Buying for category coverage rather than for a specific gap. "We need an intent tool" is procurement language. "We don't know which of our ICP accounts is in a renewal window this quarter" is a job to be done. Buy for the latter. Stacking two tools that produce the same signal. If you already have Bombora and you're evaluating Foundry, ask what new signal type you're adding. If it's the same bidstream pulled from a slightly different publisher network, you're paying twice for one layer. Mistaking enrichment for intent. Some platforms bundle intent with enrichment and let the brand of the platform stand in for the quality of the signal. Look at the signal source independently of the company logo on the slide deck. Treating the score as the answer. A composite score with no inspectable evidence behind it is a black-box ranking. The first time a "high intent" account fails to convert, reps stop trusting the layer - and once that trust is gone, the budget keeps spending but the workflow stops following. Ignoring latency. A weekly-refresh feed is fine for category awareness. It's too slow for a 14-day competitor trial. Match the refresh cadence to the buying window the signal describes. Not aligning on what "in-market" means. Marketing's in-market and sales' in-market are usually different definitions. Pick one before procurement starts. Tips for selecting the right platform for your team Name the gap first. Acquisition? Winback? Inbound qualification? Expansion? Each leans on a different signal layer. Pick the category before the vendor. Two evidence-based platforms are not a stack. One evidence-based plus one aggregator plus one inferred third-party feed is. Check signal coverage against your real ICP. A platform with 50M accounts means nothing if your 5,000 best-fit accounts have thin coverage. Test the workflow before you buy the platform. If the proof-of-value asks reps to log into a new dashboard, the pilot will look fine and adoption will collapse after week six. Make adoption a procurement criterion. Track which reps actually opened the platform in week four, not who attended the kickoff. Build in a quarterly false-positive audit. Pull the cohort of "high intent" accounts the platform flagged 90 days ago. How many converted? How many are still flagged? The delta tells you whether the layer is producing signal or producing noise. Benefits of getting the purchase intent layer right Outreach reply rates that move. Cold outbound referencing a specific observable event - "I saw your team started evaluating Vendor X four days ago" - lands. Generic intent claims don't. A shorter, sharper account list. When the intent layer is doing real work, reps work 50 accounts that are in-market instead of 500 that fit the ICP. Same time, higher conversion. Earlier visibility into churn and expansion. The same signal layer that finds new buyers flags existing customers who are actively evaluating alternatives - timing intelligence inside customer success , not just outbound. A defensible reason to engage every account. An auditable signal trail is the difference between "trust the score" and "here's what changed at this account in the last 30 days. " Sales, marketing, and CS working from the same priority list. The single biggest win from getting the purchase intent layer right is alignment. Same accounts, same evidence, same Qualified Opportunity definition - applied across teams. Frequently Asked Questions What is the best purchase intent data platform for small teams? For small teams, the strongest single-platform picks are MarketSizer if your motion is event-triggered (you need to know when accounts are in a buying window), or Common Room if your motion is community or product-led and you need to aggregate multiple soft signals into a single priority view. Avoid enterprise-priced bundles - the ROI rarely lands at small-team volumes. How is purchase intent data different from intent data? Purchase intent is the observable evidence that an account is in a buying motion right now - a trial started, a renewal opening, a vendor churned. Intent data is the broader category of inferred signals from digital behaviour - page views, content downloads, third-party research. The full breakdown is here . Are review-site intent signals more reliable than third-party intent? Generally yes - review-site activity is closer to an actual buying behaviour (comparing vendors, reading reviews, shortlisting) than topic-level content consumption. The limitation is coverage. If your buyers don't shortlist via the review platform, you don't see the signal. Best treated as a complement, not a replacement. Can I use just one purchase intent platform? Sometimes, but rarely optimally. Most GTM teams benefit from two or three platforms covering complementary signal types - typically one evidence-based, one aggregator or third-party feed, and one visitor ID layer. The trick is choosing categories that don't overlap; two platforms producing the same kind of signal is paying twice for one layer. What's the right refresh cadence for a purchase intent feed? Match the refresh to the buying window the signal describes. A renewal window 60 days out tolerates a weekly refresh. A competitor trial that runs for 14 days needs daily or near-real-time detection. Anything slower and the rep is reaching out after the window has closed. How should purchase intent feed into the rest of my sales workflow? Purchase intent works best as the temporal layer of an account qualification score, not as a stand-alone alert. Combine it with ICP Fit (does this account look like the customers you already win? ) and Win Likelihood (can you actually win it? ) - the three layers together form an Opportunity Score , and only accounts that clear all three earn the call. How do I avoid false positives from a purchase intent platform? An auditable signal trail is the strongest guard. Every flagged account should come with a named signal - the trial that started, the renewal opening, the vendor that churned. Black-box scores erode rep trust the first time a flagged account doesn't convert. Run a quarterly cohort audit on flagged accounts versus actual outcomes. Final thoughts: buy evidence, not inference Purchase intent has become a category broad enough to mean almost anything. Two vendors can both call themselves intent platforms and produce signal types that share nothing except the marketing label. The buyers who pick well are the ones who frame the choice as which signal type, for which gap , and then buy two or three platforms across complementary categories rather than one platform that claims to do everything. The shift worth making this year is from "intent" to purchase intent . Intent tells you something might be happening. Purchase intent tells you something is. The pipeline difference between them is the whole reason this category exists. If you're trying to figure out where your team should start, read the pillar piece on purchase intent vs intent data first - it makes the underlying argument these category choices flow from. The methodology behind MarketSizer's own evidence-based platform is on Our Data and in the Precision Intent Whitepaper . ## Blog: Purchase Intent vs Intent Data: Why Content Engagement Is Not a Buying Signal Intent data infers interest from web behaviour. Purchase intent is the observable evidence that a company is actually buying. The difference is your pipeline. TL;DR: No, not on its own. Content engagement (page views, downloads, webinar attendance) shows interest in a topic. It does not show that an account is buying. Purchase intent (an active competitor trial, an open renewal window, a recent vendor churn) is the evidence layer content engagement is missing. Use content engagement to nurture. Use purchase intent to prioritise outreach. See What Replaces Intent Data in 2026? for the full category shift. Most B2B sales teams are working with a signal that was never built to predict a sale. Intent data was built to score interest. Purchase intent is the evidence that someone is actually buying. The two aren't the same, and treating them as if they are is the single biggest reason qualified pipelines stall before they convert. The fix isn't more intent. It's a different category of signal underneath the prioritisation layer - one that observes what an account is doing rather than infers what someone there might be thinking. The intent gap: why most sales teams are working with the wrong signal The intent gap is the distance between an account that looks interested and an account that's actually doing something to buy. Most intent stacks are tuned to find the first. Pipeline is built from the second. For a decade, the B2B sales playbook has treated digital engagement as a proxy for buying readiness. A page visit, a content download, a third-party research session - all catalogued, scored, and pushed to reps as "intent. " But reading isn't evaluating. Someone consuming your content isn't the same as someone trialling your competitor. The first is interest. The second is a buying motion in progress. The cost of conflating the two shows up on every pipeline review. Reps work the flagged accounts. The accounts don't convert. The team blames the messaging, the ICP, the cadence - and rebuilds the same stack with a slightly different filter. The structural issue - that the underlying signal was inference, not evidence - never gets named. Forrester's research on B2B intent data providers identifies the same gap from the buyer side: 50% of B2B teams report too many false positives from their intent data, and 60% struggle to identify actual buying-team members within flagged accounts. When half the accounts your tool surfaces are not in-market, "intent" stops being a signal and starts being noise. Interest (inferred) Content view Ad click Anonymous IP visit Third-party research session Buying motion (observed) Competitor trial started Renewal window opening Vendor recently churned Evaluation under way The gap between Interest and Buying motion is where pipeline stalls. Closing the intent gap needs a different category of signal - one that observes what an account is doing rather than inferring what it might be thinking. That category is purchase intent. What is purchase intent? Purchase intent is the observable evidence that a company is in a buying motion right now - a live trial of a competing product, a renewal window opening, a vendor recently churned, a new entity expanding into a category. It describes what the company is doing , captured from observable signals - not what an individual at the company is reading. It answers a different question to intent data. Intent data asks "who might be thinking about buying? " Purchase intent asks "who is actually doing something right now? " It isn't a semantic difference. It's a different kind of evidence: Intent data records that someone at an account did something digital - visited a page, downloaded a piece, appeared in a bidstream. Purchase intent records that the account itself moved in a buying-relevant way - started a trial, churned a vendor, entered a renewal window, expanded into a category. Purchase intent is one of three layers in the MarketSizer Opportunity Score : ICP Fit (does this account look like the customers you already win? ), Purchase Intent (is something buying-relevant happening at the account right now? ), and Win Likelihood (can you realistically win the deal? ). Purchase intent is the layer that tells you why now. How purchase intent differs from buyer interest Buyer interest is what a person at an account engages with. Purchase intent is what the account does. The two correlate at the long end of the funnel, but the correlation is weak in the short term - not strong enough to prioritise outreach off. A marketing director who reads your blog every week might be a future buyer, or might just be a peer studying the space. A CS lead who tries your competitor's free tier is a buying motion in progress, whether or not they've read a single piece on your site. Conflating them is how pipeline reviews fill up with accounts that look engaged but never convert. What is intent data? Intent data is the broad category of signals that infers buying interest from digital behaviour - usually a mix of IP-resolved web traffic, content syndication, third-party publisher data, and bidstream activity. It surfaces accounts that look engaged based on what people at those accounts read, view, or click across the open web. When it arrived in the mid-2010s, it filled a real gap. Before intent data, B2B teams had firmographics, contact databases, and their own first-party engagement. Intent data added a third layer: signals from outside the seller's own properties. A marketing team could finally see that an account researching their category had been engaging across the web, not just on their own site. The limitation is what intent data can infer, and what it can't. It infers interest. It can't evidence a buying motion. Why intent data was designed for a different problem Intent data was built for top-of-funnel discovery in an era when the alternative was no signal at all. It works as a coarse filter for who to add to a long list. It fails as the basis for prioritising who to call this week - because the signal it captures sits too far upstream of the buying decision to drive that level of prioritisation. That's not opinion. It's baked into how the signal is sourced: Latency. Bidstream and content syndication signals refresh weekly. A live trial of a competitor is a real-time event. By the time a content download lands in your CRM, the buyer has often already shortlisted vendors. Attribution. IP-level attribution maps a page visit to a company, not to a person and not to a buying motion. Gartner's research on the future of B2B sales finds buyers now spend only 17% of their evaluation time meeting potential suppliers - the rest is research, often done by people who aren't the decision-maker. Tracking that research at the IP level produces volume, not insight. No audit trail. Most intent providers can't expose the underlying source of a signal. A rep sees "Account X is showing intent on topic Y" and can't dig into what actually happened. There's no event to point at. None of this makes intent data useless. It makes it the wrong signal to prioritise on when something sharper is available. The five differences between purchase intent and intent data Intent data is inference. Purchase intent is evidence. Five ways that plays out in practice: Dimension Intent data Purchase intent Signal type Inferred from digital behaviour Observed from buying-relevant events Unit of evidence A person at the account engaged with something The account itself moved in a buying-relevant way Latency Days to weeks behind the underlying behaviour Detected within days of the event occurring Audit trail Aggregate score; underlying source rarely exposed Named event a rep can reference in outreach False positive rate High; flagged accounts often have no live buying motion Low; the event either happened or it did not Inferred signal Page view on /pricing White paper download Ad impression Bidstream activity What a rep can say "I noticed your team has been showing intent in our category. " Observed event Competitor trial started - 4 days ago Renewal window opens - in 47 days Vendor X churned - 11 days ago New region launched - 22 days ago What a rep can say "I saw your team started evaluating Vendor X four days ago - want to compare notes before the trial ends? " The signal underneath changes what a rep can credibly say in outreach. That last row is the one that compounds. False positives don't just waste outreach - they wear down trust in the signal layer itself. Once a sales team has spent a quarter working flagged accounts that didn't convert, the next batch gets ignored regardless of quality. Why content engagement is not a buying signal Content engagement is a useful signal. It isn't a buying signal. The difference matters because most marketing automation stacks treat the two as interchangeable - and most pipeline shortfalls trace back to that conflation. Reason 1: Engagement is not time-bound to a buying window A buying window is a finite period. A renewal sits 60 days out. A trial runs for 14 days. A churn event opens a ~90-day replacement window. These windows are narrow, dated, and closeable. Content engagement has none of that. A reader who downloads a white paper might be evaluating today, evaluating in 18 months, or never evaluating at all. The act of engagement carries no temporal information, so it can't be sorted against time-bound events without losing the sort. Timing Intelligence exists because you can't pull a buying window out of a content download. Reason 2: The person consuming the content is rarely the buyer The same Gartner research that found buyers spend 17% of their time with suppliers also found B2B buying groups now run between six and ten stakeholders. The marketing director who downloads the white paper is rarely the person who signs the renewal or owns the budget. And the people who consume content skew towards roles that consume content - marketing, ops, junior buyers, researchers, peers, students of the category. The people who decide to switch vendors are often the ones who never touch your top-of-funnel assets. Score on content engagement and you systematically overweight the wrong people. Reason 3: Engagement scales with your content output, not the buyer's readiness Publish more content and engagement goes up. Publish less and it goes down. The volume of "intent signals" you generate is a function of your marketing throughput, not the market's buying behaviour. That's a useful metric for marketing. It's a terrible one for sales prioritisation. A rep who works the highest-engagement accounts each week is working the accounts that happened to encounter the most marketing collateral, not the accounts most likely to buy. One is a content distribution problem. The other is a pipeline problem. They're not the same problem. The hidden cost of treating inference as evidence The cost rarely shows up on a pipeline review line item, which is why it persists. It shows up as low reply rates, stalled mid-funnel deals, and reps who quietly stop trusting the intent layer. It also shows up in the research. The LinkedIn B2B Institute's work with the Ehrenberg-Bass Institute on the 95-5 rule found that at any given moment, only ~5% of B2B buyers are actively in-market. SaaS-specific analysis puts the figure closer to 24% across an annual cycle, but the takeaway is the same: most of the accounts your intent stack flags as "engaged" aren't buying anything for months or years. That's Signal Blindness in a single sentence - working accounts that look engaged but show no evidence of a real buying motion. The team works. The pipeline doesn't convert. Nobody on the call can quite explain why. The fix isn't more intent data. It's a different category of signal sitting between intent data and the CRM - one that captures what accounts are doing , not what people at those accounts are reading. That category is what Subscription Intelligence is built to produce: real subscription events - trials, renewals, churn, expansion - turned into direct evidence of buying motion across an addressable market. Where purchase intent fits inside the Opportunity Score Purchase intent is one of three layers in the Opportunity Score - the way MarketSizer surfaces Qualified Opportunities for GTM teams. Each layer answers a different question, and an account has to clear all three before it earns a rep's time. ICP Fit answers could this account buy? It scores firmographics, technographics, and pattern-matches against the customers you already win. Purchase Intent answers is something buying-relevant happening right now? It detects active evaluations, renewal windows, churn events, and vendor movement across the account's stack. Win Likelihood answers can you actually win it? It scores against the specific competitive context - which vendor's incumbent, your historical performance against them, the structural odds. These layers don't add up. They multiply. A perfectly ICP-fit account with no active buying motion isn't a Qualified Opportunity - it's a future one. An in-market account where you historically win 4% of the time isn't a Qualified Opportunity - it's a long shot. Only the accounts that clear all three earn the call. ICP Fit Could buy? Purchase Intent Buying right now? Win Likelihood Can you win it? QUALIFIED OPPORTUNITY Example purchase intent events Competitor trial started Renewal window opening Vendor churned Tech stack change Purchase intent is the layer that makes the score time-sensitive. Strip it out and you're left with a static fit assessment - useful for list-building, useless for prioritising who to call this week. With it, the score sorts accounts by who's doing something this month, not who looks good on paper. How to act on purchase intent inside your existing workflow Acting on purchase intent doesn't mean replacing your sales stack. The signals are valuable to the degree they fit the workflow your team already runs - CSV lists, CRM workflows, manual outreach, Chrome-extension lookups. A signal that sits in a separate dashboard is a signal that gets ignored. Four shifts get a team from intent-driven to evidence-driven without changing the tooling layer: Swap the "engaged accounts" filter for an "events this week" filter. Pipeline reviews surface accounts where something observable changed in the last seven days - a trial started, a renewal window opened, a vendor churned. Engagement is the tiebreaker, not the primary sort. Tie outreach to the event. The strongest cold outbound references something the account did, not something you noticed. "I saw your team started evaluating Vendor X" lands. "I noticed your team has been showing intent" doesn't. Score outreach against time-to-window-close, not engagement. A 30-day-old trial event is more actionable than a six-week-old white paper download. The rep's calendar should be sorted by which windows close soonest. Audit your false-positive rate quarterly. Pull the cohort of "high-intent" accounts your stack flagged 90 days ago. How many converted? How many are still flagged? The delta tells you whether your intent layer is producing signal or producing noise. MarketSizer plugs these shifts in at the data layer - feeding observable buying events into the workflow your team already runs. The underlying methodology is on Our Data , and the longer read is the Precision Intent Whitepaper . Frequently Asked Questions What is the difference between purchase intent and intent data? Purchase intent is observable evidence that an account is engaged in a buying motion - an active trial, an open renewal window, a recent vendor churn. Intent data is inferred interest based on digital behaviour - content engagement, page visits, third-party research signals. The first is evidence of action. The second is inference of attention. Is content engagement a buying signal? No. Content engagement is useful for top-of-funnel awareness and nurturing, but it isn't a buying signal. It isn't time-bound to a buying window, it's rarely produced by the actual buyer, and its volume scales with the seller's content output rather than the buyer's readiness to purchase. Does this mean intent data is useless? No. Intent data works as a coarse top-of-funnel filter and as a way to track category-level awareness. It's the wrong signal to prioritise sales on when something sharper - purchase intent - is available for the same account. What is an example of a purchase intent signal? A live trial of a competitor's product, a renewal window opening 60 to 90 days out, a vendor churn event creating a replacement opportunity, an expansion into a category that activates the relevant tech stack, or a tech stack change that points to an underlying buying motion. How does purchase intent fit into account scoring? Purchase intent is the temporal layer of the Opportunity Score. It sits between ICP Fit (could this account buy? ) and Win Likelihood (can you win it? ). All three need to clear before an account counts as a Qualified Opportunity worth a rep's time. What data sources produce purchase intent signals? Observable, public-source data - in-page technology detection, public DNS records, published subprocessor pages, and licensed third-party feeds - combined into events that describe what an account is doing across its software stack. Purchase intent doesn't require or use private internal systems or anonymous web tracking. How quickly do purchase intent signals appear after the underlying event? For active trial and subscription signals, the maximum lag is under five days. The data refresh runs daily, so purchase intent works as a weekly prioritisation input, not a quarterly report. Recommended reading What Is Subscription Intelligence? Real Buying Signals Explained - the cornerstone explainer for the data category that produces purchase intent signals. Timing Intelligence 101 - how to operationalise time-bound buying signals across SDR, AE, marketing, and CS workflows. Beyond the 95/5 Rule - why most of your market is out-of-market at any given time, and what that means for prioritisation. How to Know When a Prospect Is Ready to Buy - the specific signals that separate a browser from a buyer. Most GTM Teams Use Intent Data at the Wrong Time - the conversation from Episode 1 of Intent Decoded on where intent data fits in a signal-led motion. ## Blog: Why MarketSizer Defers Half Its Fee Until You Win Most B2B software charges you regardless of outcomes. Here is how MarketSizer built its pricing model differently - and what it took to make it work. MarketSizer's job is to put qualified opportunities in front of your team: accounts in your ICP, showing real purchase intent right now, and likely to be winnable for you based on outcomes in your segment. Nothing that fails any of those three filters reaches your list - ICP fit, real-time intent, and win likelihood are scored together, and only the accounts that clear all three appear at all. The pricing model is built to complement that. Half of every opportunity's cost is deferred until MarketSizer detects a conversion. If the account never becomes a paying customer of yours, that half is never charged. That is the model in one sentence - and the rest of this post is the reasoning behind it. The starting constraint was simple: the full cost of an opportunity should only be collected when the customer has evidence it was worth pursuing. Everything else - the upfront fee, the data surfaced, the narratives generated - had to be priced in a way that shared the risk. That constraint ruled out most of the obvious options immediately. Most B2B software charges customers regardless of outcomes. Seat licences, platform fees, per-API-call meters: the vendor collects revenue whether the product delivers value or not. The buyer carries all the risk, the vendor carries none of it, and the relationship is built around an annual commitment that is settled long before anyone knows whether it worked. MarketSizer's pricing model was designed the other way around. The deferred half is the success fee, and it is the entire credibility lever of the model. What Outcome-Based Pricing Means for Sales Intelligence Intercom recently published their thinking on outcome-based pricing for Fin for Sales. Their core principle: you pay when Fin delivers value. For a sales AI, they landed on the qualified lead as the right unit - €10 per prospect that clears your criteria and gets routed to a rep. It is a sound model, and the reasoning behind it is worth reading. But it illustrates something important about where most outcome-based pricing in sales AI currently draws the line: at readiness for human action. Fin qualifies the lead. The rep takes it from there. The pricing boundary sits at the handoff. Intercom is not alone in pushing pricing closer to outcomes. Emergence Capital's framework for pricing AI software argues the same direction from a different angle: the more autonomous a product's actions and the more attributable their outcomes, the further pricing should move from seats and toward results. MarketSizer operates at a different point in the funnel with a different data position, which is what makes a further-deferred fee structurally possible. The outcome MarketSizer charges for is not the qualified lead. It is the converted customer. Three Models Considered and Rejected Three alternatives were stress-tested before MarketSizer landed on the current model. Per-seat or per-user. This is how Apollo, ZoomInfo, and most legacy data tools charge: a flat annual fee regardless of how much of the platform you use or whether any deal ever closes. The incentive structure is misaligned by design - the vendor collects full revenue whether the product delivers outcomes or not, which puts all the risk on the customer. Per-API-call or pure consumption. You pay for every signal surfaced, every account enriched, every intent flag raised. Consumption pricing is now mainstream in B2B SaaS - OpenView Partners tracks it as the dominant evolution in software pricing - and it works well for infrastructure tools where every call is value delivered. It is the wrong fit for sales intelligence. It penalises teams for exploring the market thoroughly: the more accounts you look at, the more you pay, even when most of those accounts were correctly assessed as unwinnable and left alone. It also charges you for noise alongside signal, which is the opposite of what good data should do. Revenue share. The purest version of outcome-based pricing: take a percentage of closed revenue from accounts MarketSizer surfaced. Intercom considered and rejected this for Fin, citing two problems: attribution (between qualification and close, many things outside the AI's control affect the outcome) and measurement (you need deep CRM integration to track deals to closure). MarketSizer arrived at the same conclusion, for the same reasons, plus one more: a revenue percentage creates a conflict of interest around which opportunities get surfaced. If the vendor earns more on larger deals, the platform has an incentive to weight itself toward them. That incentive does not belong in the model. The Four Moments MarketSizer Charges For MarketSizer's pricing has four moments. Each one is tied to a deliberate action the customer took. Identify - 1 credit (€0. 10). The customer views a qualified opportunity: ICP fit, purchase intent signals, win probability, estimated contract value. Unqualified accounts are free and unlimited. This fee exists because surfacing a pre-scored, pre-ranked account that fits your criteria and is actively in market has real value - but it is priced below the cost of missing it. Activate - 5 to 50 credits, scaled by Opportunity Score. The customer decides to pursue the account and unlocks the full picture: competitive context, AI-generated narratives, battle cards, workflow assignment. This is half the total cost of an opportunity. The other half is deferred. Contact - 2 credits per verified email, 10 credits per verified phone number. Unlocking the specific people to reach. Priced à la carte because outreach is selective - you do not need every contact at every account. Convert - equal to the Activate fee. Charged only when MarketSizer detects that an account you Activated has become a paying customer of yours. The relationship between Activate and Convert is the core of the model. When you activate a 4. 0-scored opportunity, you pay 40 credits. If it converts, you pay 40 more. If it never converts - if you worked the account for months and it went cold - you pay nothing beyond that initial 40. The success fee is deferred indefinitely until the outcome is observable. If it never lands, neither does the fee. How MarketSizer Detects a Conversion Revenue-share pricing fails partly because of attribution and partly because of measurement. To know when a deal closes, you need to read from the customer's CRM - and as Intercom noted, that is a significant integration burden. MarketSizer needed to solve the measurement problem without putting it on the customer. Conversion detection works differently here. The primary path is objective subscription data: MarketSizer maintains a subscription intelligence dataset of 72 million historical subscription outcomes , tracking when companies start and stop paying for software products. When an account you Activated begins paying for your product, MarketSizer observes that transition in its own data - no self-reporting required, no CRM read access needed, no dependency on your pipeline updating on time. Where the subscription dataset does not cover the market - non-SaaS products, off-platform contracts, vendors not yet ingested - you can mark a conversion manually in-app with the destination deal ID and contract date. That is the fallback, not the primary mechanism, and that distinction is what makes the model credible: the 72-million-row dataset is what removes the "you are just billing for whatever you say closed" objection. When a conversion is detected, customers receive an in-app and email notification with the signal type, the account, the timestamp, and the credit cost. They have 30 days to dispute it. If the evidence is inconclusive, MarketSizer defaults to refunding the customer. The Risk on the Other Side The honest critique of any model that bills on detected outcomes is symmetric to the critique of revenue-share. If MarketSizer earns more by detecting more conversions, MarketSizer has an incentive to over-detect: to call conversions that did not really happen, or to attribute conversions to accounts that closed for unrelated reasons. That risk is real, and the model has to take it seriously to be credible. The mitigations are deliberate. Detection is grounded in objective subscription data, not self-reported pipeline. Every detected conversion comes with an audit-trail entry showing the signal type, the timestamp, and the Activate record it traces back to. The 30-day dispute window is generous, and the tie-break defaults to the customer: where the evidence is inconclusive, the charge is refunded. The reasoning behind that policy is straightforward - a refunded conversion costs less than a lost customer. None of this eliminates the asymmetry. It just keeps the cost of getting it wrong squarely with the vendor. Why the Fee Scales with Quality The Opportunity Score is a composite of three signals: ICP fit (40%), purchase intent (35%), and win likelihood (25%). It runs from 0. 0 to 5. 0 in half-point steps. The Activate fee scales directly with it. A 5. 0-scored opportunity costs 50 credits to activate. A 2. 5 costs 25. A 0. 0 costs nothing. This matters for two reasons. First, pricing reflects expected value at the moment of activation, not in hindsight. A 5-star account is more expensive because it converts at a higher rate - the fee is proportional to what the data suggests it is worth. Customers who activate lower-scored accounts are taking a more speculative bet, and they pay less for it. Second, scaling the fee with quality keeps the scoring honest in both directions. A flat activation fee would create an incentive to inflate scores to justify the cost. Scaling means the scoring has to be calibrated correctly: over-scoring would make the product expensive for no reason, and customers would notice. The total cost of a full-funnel 5-star converted opportunity is around €10. That is deliberate. The per-credit price is set at the customer's subscription tier, and the Activate-Convert symmetry is the credibility mechanism - not the extraction ceiling. Pricing below the unit cost of a lost deal is the whole point. What You Never Pay For It is as important to say what the model does not charge for as what it does. You do not pay to browse the market. Unqualified accounts are free and unlimited to view - you can explore your entire ICP universe before spending a credit. You do not pay a success fee after 12 months. The conversion window closes a year after activation. A deal that closes three years later, through channels disconnected from the account MarketSizer surfaced, does not trigger a fee. You do not pay a second fee on expansions. If an Activated account converts and later expands, the conversion fee is not charged again. Re-Activating the same account for a different product line or business unit is a new Activation and a new conversion fee may apply - but the Activation record, not the company, is the unit of accounting. You do not get a refund if the customer churns. The conversion fee fires once, at the moment of conversion, and is not clawed back if the account subsequently churns. The fee was for delivering the conversion, not for retention. Retention is your domain, not MarketSizer's. You do not pay for seat licences or platform presence. Every credit charge is tied to an action you chose to take. The Model in Practice The critique of outcome-based pricing from the vendor side is usually that the vendor is carrying too much risk. Revenue is unpredictable. Long sales cycles mean fees land months after the work was done. Both are true. The model accepts them as part of the deal. The case for accepting them is that the incentives line up throughout. MarketSizer has a direct financial interest in the quality of the opportunities surfaced, not just in the volume. A direct interest in accurate scoring, because inflated scores make the product expensive relative to outcomes. A direct interest in detection quality, because a conversion missed is revenue never collected. Every one of those interests is aligned with the customer's. For a 5-star opportunity that converts with a verified email contact unlocked, the total is 103 credits end-to-end: 1 to identify, 50 to activate, 2 for the contact, 50 when MarketSizer detects the conversion. At entry-band pricing that is €10. 30. At volume that is less. For a 5-star opportunity that never converts: 51 credits . That is your downside on a top-rated account. Your downside is capped at the Activate fee. Your upside is not. That is the model. The subscription intelligence data MarketSizer has built is what makes it possible to defer further than any other sales-intelligence vendor we have observed. If the model works for you, the pricing page has the full breakdown. If it does not, that is worth knowing before you commit to anything - which is exactly why the free trial requires no card. Start free to see the accounts in your market that are actively evaluating right now, or book a demo if you would rather be walked through it. ## Blog: Most GTM Teams Use Intent Data at the Wrong Time Intent data isn't broken. Most teams are just using it as a starting gun instead of a confirmation signal. For the compact 2026 answer, see What Replaces Intent Data in 2026? This post is the long-form timing argument inside that piece. There's a pattern that plays out so often it's almost a rite of passage. RevOps buys an intent data platform (not cheap, as anyone who's been in that procurement meeting knows). Marketing pulls a list of accounts spiking on relevant topics. They hand it to sales as a priority list. Sales works the list. Conversion is flat. Everyone blames the data. But the platform didn't fail. The team skipped the foundation. That's the thread that ran through the entire first episode of Intent, Decoded - our new podcast series where we get into the mechanics of what's actually happening inside GTM teams right now. Our host Brendan Hughes sat down with Nick Bennett, a B2B marketing consultant who's built over $6 million in pipeline through LinkedIn organic, and Niall O'Gorman, co-founder of MarketSizer and someone who spends a concerning amount of time thinking about purchase intent signals. (I say that with affection. It's also kind of the whole business. ) The conversation surfaced something that's obvious in retrospect but easy to ignore when you're under pressure to show pipeline: intent data only works when it layers on top of existing account context. Not as a reason to reach out. As confirmation that it's already time. The Signal Is Not the Starting Gun Nick put it plainly: an account that's never engaged with you, has no ICP fit validation, and no existing relationship - even if it's spiking on a relevant keyword - is just a reason to send a bad cold email faster. Intent data on a cold account is noise dressed up as signal. What a legitimate trigger looks like is different. An account that's been in your nurture sequence, visited your pricing page twice, and is now spiking on a competitive comparison keyword - that's worth moving on. The intent signal confirms what the other data was already suggesting. But you need the other data first, which means you need the foundational work done before you ever open the intent platform. The uncomfortable implication here is that most teams buying intent tools haven't done that work. They're reaching for a shortcut before they've built the road. (For how event-based signals differ from the big intent platforms tool by tool, see the comparisons . ) It's the same dynamic we've written about elsewhere - outreach doesn't break because of bad copy , it breaks because the timing was wrong before the first word was written. Intent Data as a Filter, Not a List Builder One of the sharper observations from Nick was about how ABM teams are using intent tools to build bigger lists rather than tighter ones. The logic sounds reasonable - more data means more coverage, more coverage means more opportunities. But ABM isn't supposed to scale like that. If you're running 2,000 accounts in your "ABM programme," you're not doing ABM. You're doing targeted marketing with extra steps. The framework he keeps coming back to is three inputs for account selection: fit, engagement, intent. That is the same shape as opportunity discovery - a shortlist built from the overlap rather than from any one input. All three need to be high before you run a coordinated play - and by coordinated, he means sales and marketing moving on the same account at the same time with messaging built around what the signals are actually suggesting. Not an email sequence. A play. There's also an underused application on the field marketing and events side. Intent signals should be informing invite lists for executive dinners and regional events - not just who fits the ICP, but who's actively showing signs of pain or pressure right now. An account spiking on topics relevant to your product three weeks before your event is worth getting in the room. Most teams are building invite lists off CRM filters and rep suggestions, with no signal layer underneath any of it. (We've made the broader case for this approach in our piece on signal-led ABX . ) The Data Foundation Problem Niall's framing from the episode is worth sitting with. He drew a parallel to ecommerce: getting a listing up on a marketplace is the easy part. Making it convert is the hard part, and the foundation of that is master data quality. Bad data in, bad outcomes out. The same logic applies to intent infrastructure. Most of the rich data that teams need is already sitting inside their CRM or marketing automation platform - but extracting the right signal at the right time, in the right context, is where things fall apart. The promise of AI and GTM intelligence tools isn't to replace that foundation. It's to help you do more with what you've already built, if you've built it properly. And if you haven't - if your CRM is a graveyard of stale contacts and inconsistent firmographic data - then no intent platform is going to save you. You're just running more volume against a broken foundation. We've gone deeper on this in why CRM data is inaccurate - the short version is that the missing layer is rarely more contacts and almost always sharper signal. Why This Keeps Happening There's a structural reason GTM teams keep making this mistake, and it's not that they're bad at their jobs. Niall and Nick both touched on it: people are burnt out. Marketing teams are doing the job of three people. Budgets have compressed. The pressure to show pipeline activity - any activity - is relentless. So teams lean into whatever's fastest. Intent data platforms sell on the promise of finding buyers who are ready now, which is exactly what a time-starved marketing team wants to hear. The result is that the tool gets used as a starting gun rather than a confirmation signal, and when the conversion numbers come back flat, the vendor gets the blame. It's a pattern that cheap VC capital made worse - it was easy to fund spray-and-pray outreach when money was free. Now that unit economics matter again, the feedback loop is closing. The teams that figure out how to use intent data properly will have a genuine structural advantage over those still treating it as a list builder. (For the broader argument, see why timing beats volume . ) The One Thing to Take Away Nick's closing line from the episode is worth writing on a sticky note: "Stop using intent data to decide when to reach out. Start using it to decide what changed and whether you have something worth saying because of it. " If you can't answer what changed in that account and why it matters to them right now, the signal isn't a reason to act. It's a reason to watch. The outreach comes when you have something relevant to say - not because the platform told you the account is hot. That distinction - between a reason to act and a reason to watch - is probably the clearest line between GTM teams that are using intent data well and those who are still pouring lighter fluid on a dead pipeline. Intent, Decoded is MarketSizer's podcast series on the mechanics of purchase intent and what it actually takes to use it well. Listen to Episode 1 on Spotify , watch on YouTube , or catch the full LinkedIn Live recording here . Follow the show on Spotify or YouTube to catch future episodes, or visit the podcast hub for everything in one place. Recommended reading Purchase Intent vs Intent Data - the definitional pillar behind the argument this episode made. Why Intent Data Made B2B Outreach Worse - the deeper critique of the "intent as starting gun" pattern. How to Build a Signal-led GTM Motion - the corrective: what to do once you stop sorting on intent alone. Best Purchase Intent Data Platforms for 2026 - the directory of platforms that produce the evidence layer underneath. ## Blog: Your Sales Team Isn't Failing Pipeline, Your Data Infrastructure Is Why pipeline problems are actually signal problems - and what to do about it. The Q1 review lands and the numbers are short. Again. The pipeline was there - at least it looked like it was there. The forecast had enough. The calls were happening. The sequences were running. But somewhere between "strong opportunity" and "closed won," the deals bled out. And now comes the same conversation: more coaching, more headcount, different messaging, another sales methodology certification. Before you go there - consider that the reason your B2B sales team keeps missing pipeline targets might have nothing to do with the team. The Diagnosis Nobody Wants to Make When a sales team misses targets, the default assumption is execution. The reps aren't sharp enough. The manager isn't coaching enough. The process has gaps. These might be true - but they're also easy to blame because they're visible and they point at people. The harder diagnosis - and the more common root cause - is structural: your team is being asked to hit pipeline targets with fundamentally incomplete data. Salesforce's 2024 State of Sales report found that 84% of sales reps missed quota last year and only 28% hit their annual target - the lowest attainment rate in six years. The instinct is to blame the people. The data points elsewhere. They know who to target (ICP lists). They know what to say (messaging frameworks). What they don't know - what nobody has told them and no tool in their current stack can show them - is when to target whom. Without timing signals, sales becomes a volume game. More calls, more emails, more sequences. Some deals close because you happened to reach the right person at the right moment by accident. Most don't close because you reached the right person at entirely the wrong moment, and they politely told you to follow up in Q3, and Q3 became Q4, and Q4 became "not this year. " Your reps aren't lazy. They're blind. What Your Team Is Working Without Most B2B sales teams operate with three layers of information: Layer 1: Company data (static) Firmographics - industry, size, tech stack, location. Your CRM, LinkedIn Sales Navigator, ZoomInfo. This tells you who they are. Layer 2: Contact data (semi-dynamic) Job titles, email addresses, recent role changes. This tells you who to reach. Layer 3: The missing layer - market signals (real-time) What is this company doing in the market right now? Are they trialling a competitor? Is their renewal approaching? Have they recently reduced seats on their current tool? Did a new executive join who typically switches the tech stack? Layer 3 is what separates outreach that lands from outreach that bounces off. It's also what your CRM is structurally missing - and no amount of data hygiene or CRM switching will add it. Your team has layers 1 and 2. Layer 3 either doesn't exist in their workflow, or it's a vague intent score that says "this company is surging on the topic of customer support software" - which is nearly useless for deciding whether to call this week or next quarter. The Real Reason Forecasts Are Wrong If you've ever sat in a pipeline review where half the deals you were counting on slipped to next quarter, the reason is almost always the same: the deals entered the pipeline at the wrong moment. The prospect was ICP-fit. They engaged early. They had genuine interest. But there was no real urgency signal behind them - no competitor trial, no renewal pressure, no trigger event. They went through the motions of an evaluation at their pace, which turned out to be much slower than your quarter. This is not an edge case: Forrester research consistently finds that 86% of B2B purchases stall during the buying process, and nearly two-thirds of B2B leads take at least three months to decide. The pipeline looked healthy. The urgency was never there. A pipeline built on ICP fit + engagement is not the same as a pipeline built on ICP fit + timing signals. The first produces a healthy-looking forecast that slips. The second produces a more conservative-looking forecast that closes. This is the core of why qualified leads don't close at the rate your pipeline suggests they should. Most teams are optimising for the look of the pipeline, not the quality of it. What Happens When You Add the Missing Layer Here's what changes when your team operates with real-time market signals alongside their CRM: Prioritisation becomes evidence-based Instead of starting Monday with a static list of 40 accounts and choosing the 10 to work based on gut or recency, your reps start with a dynamic priority list ranked by signal strength. The accounts at the top have competitor trials, approaching renewals, or visible trigger events. The accounts at the bottom have perfect ICP fit and zero timing signals. Your reps immediately know where to spend their time. Outreach becomes contextual, not cold "We noticed you might be evaluating tools in this space" is not a guess. It's based on a real competitor trial signal. That context changes the nature of the conversation. The prospect doesn't feel cold-called - they feel found at the right moment. Forecasting becomes more honest When deals enter the pipeline because of a timing signal (competitor trial detected, renewal in 60 days), they have a real urgency basis. Forecast reliability improves because the foundation of each deal is a real market event, not just an ICP match and some engagement activity. Churn risk becomes visible before the conversation For CS teams, the same signal layer that surfaces new pipeline opportunities also surfaces at-risk accounts. A customer reducing seats while a competitor trial runs in their org is not a green account - regardless of what the health score says. Is This a Headcount Problem or a Signal Problem? Before your next quarterly planning conversation - and before you read the signal-led ABX framework that shows what the alternative looks like - consider this question: if you doubled the size of your sales team tomorrow - same stack, same data, same workflow - would you close twice as much? If the answer is "probably not," then the constraint isn't headcount. It's the quality of information the team is operating with. More reps working from incomplete data produce more outreach to the wrong accounts at the wrong time. The conversion rate doesn't improve. The cost per deal goes up. Gartner's B2B buying research reinforces this: B2B buyers spend just 17% of their total purchase time talking to potential vendors - meaning outreach that isn't timed to an active buying window is almost certainly wasted. The teams that are consistently hitting B2B pipeline targets aren't always the ones with the biggest headcount or the most sophisticated messaging. They're the ones whose reps know which 10 accounts out of 500 are worth pursuing this week - because a signal told them. Compare tools with MarketSizer Direct comparison with the enrichment platform mentioned above: MarketSizer vs ZoomInfo . Full index at MarketSizer compared with other tools . Frequently Asked Questions Why does my B2B sales team keep missing pipeline targets? The most overlooked cause of missed B2B pipeline targets is a missing data layer - the real-time market signals that tell your team which accounts are in a genuine buying window right now. Without timing signals (competitor trial activity, renewal windows, trigger events), sales teams prospect on ICP fit alone, which produces pipelines full of accounts that engage but don't close on your timeline. How can I improve B2B sales pipeline conversion? The highest-leverage intervention is adding timing intelligence to your qualification process. Identify accounts that are in active buying windows - showing competitor trial activity or approaching renewal - and prioritise these over ICP-fit accounts with no real-time signals. This typically produces a smaller but significantly higher-converting active pipeline. What causes B2B deals to stall in pipeline? Deals most commonly stall when they entered the pipeline at a low-urgency moment - the prospect was interested but had no immediate pressure to decide. Without a real timing trigger (renewal, competitive pressure, trigger event), prospects engage politely and slow-roll the decision indefinitely. The fix is qualifying on timing signals, not just engagement and ICP fit. Is missing sales targets a headcount problem? Occasionally, but less often than assumed. When the root cause is analysed, most persistent target misses in B2B sales teams trace back to a data quality problem - not enough information about which accounts are in a buying window right now - rather than insufficient outreach volume. Adding headcount to a team working from incomplete signals typically increases cost per deal without improving conversion. What data does a B2B sales team need to hit targets? At minimum: company fit data (ICP criteria), contact data (reaching the right stakeholders), and timing signals (evidence that the account is in a buying window). Most teams have the first two layers. The third - real-time market signals like competitor trial detection and renewal windows - is the layer that determines whether pipeline converts or drifts. The Team Isn't the Problem. Give Them the Signals. Your sales team is working from a map with half the information missing. They can see who to target. They can't see when. That's not a motivation problem or a skill problem. It's a data infrastructure problem - and it's fixable. ## Blog: How to Find Companies That Are Actively Evaluating New Software Stop guessing who might be in-market. Learn how to identify companies actively evaluating solutions like yours. Most B2B sales advice tells you to track job postings, watch for funding rounds, or monitor LinkedIn activity. And yes, those are signals. But here's the problem your pipeline has been paying for quietly: by the time you see a job post for a "Head of Customer Experience" or a Series B announcement, so has everyone else in your space. Your outreach lands in a crowded inbox. Your SDR is the fifth call that week. The timing was never yours to begin with. There's a better signal. And most teams have never seen it. Why Standard Prospecting Signals Miss the Actual Buying Window When a company is genuinely evaluating new software, they don't announce it. They don't post "we're reviewing our tech stack" on LinkedIn. What they do instead is start a trial. A competitor trial is the clearest, most direct evidence of active software evaluation that exists in B2B. It's not a prediction. It's not a proxy. It's a company that opened an account, entered card details, and started running a product through its paces. That's a buying motion, visible, real, and time-bound. The standard signals, e. g. job posts, funding alerts, intent content downloads, are valuable, but they're downstream of the actual decision. A company in active evaluation mode isn't reading blog posts about your category. They're comparing your product against three others. If you reach them during a competitor trial, you're in the conversation. If you reach them two months after they renewed, you're wasting a call. That gap between when evaluation starts and when you find out, is where pipeline goes to die. What Does "Actively Evaluating" Actually Look Like? Understanding how long the evaluation window actually stays open matters more than most teams realise. Here's what it looks like in practice for a B2B SaaS company making a software switch: Week 1-2: Internal frustration reaches a tipping point. A stakeholder requests demos or signs up for a free trial. Evaluation begins quietly. Week 3-5: The team is comparing two or three tools. Stakeholders are attending demos, building business cases, pulling pricing. Week 6-8: A decision is close. Budget approval is sought. The window for a competitive insertion is nearly closed. Most teams discover the evaluation is happening somewhere around week 7. The prospect is already mentally committed. Your outreach feels like an interruption rather than a solution. The companies worth chasing are in weeks 1-4. That's your window. The question is: how do you see into it? How to Find Companies That Are Evaluating New Software Right Now Here's how the most effective GTM teams identify in-market accounts, ordered from least to most reliable: 1. Job postings and funding news (lagging signals) Monitoring for "VP of Customer Success" hires or new funding rounds can indicate a team is building out infrastructure, and that infrastructure might include new software. Use tools like LinkedIn Sales Navigator, Crunchbase, or news alerts. The limitation: These are correlation signals, not causation. A new hire doesn't mean evaluation. A funding round doesn't mean your category. And there's typically a 30-90 day lag between the trigger event and the job posting going live, Indeed's Hiring Lab data shows the average time-to-fill a position is 41-48 days, meaning a posted role already represents weeks of elapsed decision-making before it's publicly visible. Where the signal does hold up: a role-specific board reads cleaner than a general feed, because the roles are already filtered to the function you sell into. The niche boards where candidates find GTM jobs double as a public record of which revenue teams are staffing up and in which direction. Three SDR roles and a RevOps lead is a team building an outbound motion, not backfilling a leaver. Treat that as context on an account, not as a trigger to sequence on. 2. G2 and review site activity (weak real-time signal) Some intent data platforms surface when a company's employees are browsing competitor profiles on G2 or Capterra. This indicates category awareness, someone is looking around. The limitation: This is anonymous web behaviour. You can't verify who was browsing, whether they're a decision-maker, or whether they're in active evaluation vs. just curious. Intent scores based on review site activity have a high false-positive rate, Forrester research on intent data providers found that 50% of B2B teams using intent data report seeing too many false positives for accounts showing intent. 3. Content consumption signals (weakest intent) A company downloads your competitor's "ultimate guide to customer support software. " Sounds promising. But content downloads correlate almost as often with existing customers doing research, junior employees learning the category, or analysts building benchmarks. The limitation: Content consumption doesn't indicate purchase readiness. It indicates topic awareness. These are not the same thing. 4. Competitor trial detection (strongest signal) When a company starts a trial of software in your category, they are by definition evaluating. There's no ambiguity. No inference needed. The signal is: this company, at this point in time, is actively testing a tool like yours. If you're a Customer Support SaaS vendor and your prospect just started a trial of Intercom or Zendesk, you have a short, precise window to make your case. Competitor trial detection is the closest thing to real-time, evidence-based evaluation intelligence that exists, and the strongest signal that a prospect has entered a genuine buying window . For a deeper look at how timing signals map across GTM roles, see Timing Intelligence 101 . What Makes Competitor Trial Signals Different From Intent Data? This distinction matters if you're building a prospecting stack and wondering where to allocate budget. One side of it is purchase intent you can name and date; the other is market intelligence that tells you how the category is moving but not which account to call. Signal type What it detects Evidence quality Latency Job postings Org growth / change Indirect 30-90 days Funding news Budget availability Indirect Weeks G2/review site activity Category interest Anonymous, inferred Days Content downloads Topic awareness Anonymous, inferred Days Competitor trial detection Active software evaluation Direct, observable Real-time Traditional intent data platforms (Bombora, 6sense, G2 Buyer Intent) aggregate anonymous web behaviour signals and score them. Those scores tell you a company is "surging" on a topic, not that they're evaluating. Competitor trial detection tells you a company has entered a buying motion. That's a fundamentally different conversation you're walking into. How to Build Your "Evaluating Right Now" List Once you understand what signals to chase, here's the practical process: Step 1: Define your evaluation window. For your category, how long does a typical software evaluation take? In Customer Support SaaS, active evaluations typically run 30-60 days. That's your outreach window. Step 2: Identify which competitors your prospects trial first. Not every competitor trial is equal. A company trialling your closest alternative is a much warmer signal than one trialling a tangentially related product. Know your competitive set and prioritise accordingly. Step 3: Layer ICP fit on top of the signal. A trial signal without ICP context wastes effort. A 10-person startup trialling Zendesk is not the same opportunity as a 150-person CS team doing the same thing. Combine the timing signal with firmographic fit to score and rank accounts using a Opportunity Score model . Step 4: Reach out in the first two weeks. This is the most important step most teams skip. The longer you wait after trial detection, the lower your odds. In the first two weeks, the prospect is still forming opinions. After week four, they're usually close to a decision. Step 5: Lead with the signal, not a pitch. "We noticed you might be evaluating tools in this space" is not a cold open. It signals awareness without being creepy, creates instant relevance, and opens a door the prospect is already walking through. What to Do With This Intelligence Inside Your Workflow Finding evaluating companies is only half the job. The other half is making sure the signal reaches your rep at the right moment, not buried in a weekly report they ignore. The most effective sales teams surface evaluation signals: In the CRM record itself : A flag that says "competitor trial detected" next to an account is impossible to miss In Chrome, during prospecting : When an SDR views a prospect on LinkedIn or in HubSpot, the signal appears inline As a daily digest : A prioritised list of accounts that entered evaluation mode in the last 24 hours The signal is only as good as how fast it reaches the person who can act on it. Your Next 10 Pipeline Opportunities Are Already Moving Right now, companies in your ICP are starting trials of your competitors. They're comparing, evaluating, and building business cases. Most of them will make a decision in the next 60 days, with or without ever hearing from you. The question isn't whether the signal exists. It's whether you're seeing it in time. See which companies in your market are evaluating right now. Start with 500 free Qualified Opportunities at marketsizer. io . Compare with intent tools Side-by-side with the intent platforms mentioned above: 6sense , Bombora . For the technographic and install-base route to the same question, see MarketSizer vs BuiltWith and MarketSizer vs HG Insights . Full index at MarketSizer compared with other tools . Frequently Asked Questions How do I find companies that are actively evaluating new software? The most reliable method is competitor trial detection, identifying when a company starts a trial of a tool in your category. This is a direct evaluation signal, unlike indirect proxies like job postings or content downloads. Platforms like MarketSizer surface real-time competitor trial events across 200+ SaaS vendors. What signals indicate a company is ready to buy software? The strongest indicators are: competitor trial activity, renewal dates approaching for their current tool, recent negative reviews posted about their existing vendor, and headcount changes in the relevant team. Competitor trials are the most direct because they represent an active decision process, not a prediction. How early can I identify a software evaluation? With competitor trial detection, you can identify an evaluation within days of it starting, often before the prospect has attended a second demo. With traditional intent data, you typically identify interest 4-8 weeks after evaluation has begun, when intent scores accumulate enough to surface. Gartner's B2B buying journey research confirms that buyers spend only 17% of their total purchase time interacting with any vendor, the rest is self-directed research that traditional intent platforms can only infer after the fact. Is tracking competitor trials legal and ethical? Yes. Subscription intelligence platforms aggregate publicly observable data about software usage patterns, they do not access private company systems. The data reflects market-level signals about which vendors companies are trialling, not individual employee activity. What's the difference between intent data and evaluation signals? Intent data infers interest from anonymous web behaviour, e. g. content consumption, ad engagement, review site browsing. Evaluation signals are direct evidence of a buying motion, like a competitor trial or renewal event. The former tells you a company might be interested; the latter tells you they're actively deciding. ## Blog: It's Not Your CRM, It's the Signal Layer Your CRM Is Missing Your CRM has the data it was given. The problem is what it was never told. Your CRM data is wrong. Not because your team enters it badly - though that's probably also true - but because of something more fundamental: your CRM is designed to capture what has already happened, not what's happening right now in your market. Validity's 2025 State of CRM Data Management report , surveying 602 CRM users and admins across the US, UK, and Australia, found that 76% of organisations said less than half of their CRM data is accurate and complete, and 37% reported losing revenue directly as a consequence. Every time a deal stalls, a churned customer surprises you, or a prospect goes cold without explanation, someone asks the same question: "Why didn't we see that coming? " It's the same reason qualified leads don't close , and why B2B sales teams keep missing pipeline targets . The honest answer is that your CRM can't see it. And no amount of better data hygiene will change that. What Your CRM Was Built to Do (And What It Wasn't) Your CRM is a record of your relationship history. Last call: October 12. Deal stage: Proposal. Health score: Green. Renewal: Q2. That's all accurate. It's also entirely backward-looking. It tells you what happened between you and your customer. It doesn't tell you what's happening between your customer and the rest of the market. The question your CRM can't answer isn't "what did we do last? " It's "what is this company doing right now? " Are they trialling a competitor? Is their renewal coming up and have they started evaluating alternatives? Did they reduce seats last month? Is the champion who bought your product still in the seat? None of this shows up in your CRM, not because your team failed to log it, but because this information doesn't come from your relationship with the customer. It comes from the market. That's the signal layer your CRM is missing. Why "Better Data Hygiene" Doesn't Fix the Real Problem The most common fix proposed for CRM data quality issues is process: enforce mandatory fields, run quarterly data audits, integrate your email to capture touch activity automatically. These are not bad ideas. But they solve for incomplete historical records, they don't solve for the fundamental gap between CRM data and market data. Consider the scenario that plays out in thousands of CS and sales teams every quarter: CRM record: Account status - Healthy. Renewal score: 8/10. Last contact: 6 weeks ago. Reality: The head of customer experience started a Zendesk trial 3 weeks ago. Nobody knows. Your CRM score is accurate based on everything your team has experienced with this account. But the score reflects a conversation that happened weeks ago. The market moved. The CRM didn't. This isn't a data hygiene problem. It's a structural gap. Your CRM captures your signal. It has no visibility into competitor signals. Salesforce's State of Sales report found that reps spend 70% of their time on non-selling tasks - much of it entering data into the CRM - yet approximately only 2% of reps trust the accuracy of what they're maintaining. More entry doesn't produce better signal; it produces more incomplete history. The Three Things Your CRM Will Never Tell You No matter how clean your data is, no matter which CRM you're on, there are three categories of intelligence that CRM systems cannot capture because they're external to your relationship: 1. Competitor trial activity When an account in your pipeline or your customer base starts trialling a competitor, your CRM has no way of knowing. The only way you'd find out is if your champion told you, and they won't, because they're already evaluating their options. This is the most damaging gap. A prospect in a competitor trial is a time-limited opportunity. Every day without contact is a day the competitor spends building familiarity and trust. 2. Renewal windows and switching timing Your CRM knows when your contract renews. It doesn't know when your customer's evaluation of alternatives is likely to begin. Those two things are not the same date. Companies typically begin evaluating alternatives 60-90 days before renewal, SaaS renewal best-practice research consistently recommends starting the renewal process 90-120 days before expiry precisely because that is when customers are actively assessing alternatives. If your CRM alert fires at 30 days out, you're already two months late to the conversation that mattered. 3. Market-level switching patterns Your CRM shows you your own win/loss data. It has no visibility into what's happening across your competitive landscape, which accounts are switching vendors, which competitors are winning market share in your ICP segment, which companies in your pipeline are deep into conversations with your closest competitor. That market-level picture is where your biggest opportunities, and your most urgent risks, are hiding. What the Missing Signal Layer Looks Like in Practice The companies that have closed this gap aren't using a better CRM. They've added a layer beneath the CRM, a subscription intelligence feed that monitors competitor trial activity, renewal windows, and switching patterns across their target market. In practice, it looks like this: Without the signal layer: A rep opens a CRM record for a mid-market account. (The eDesk case study shows what this shift looks like in practice for a real CS/CX team. ) Last call was six weeks ago. Deal stage: stalled at proposal. No obvious next step. The rep fires off a follow-up email and moves on. With the signal layer: The same rep opens the same record. A flag on the account says: "Competitor trial detected - 9 days ago. " The rep knows this account is in active evaluation. The follow-up isn't a generic check-in, it's a specific, urgent reach-out to get back in the room before the trial ends. Same CRM. Same rep. Completely different outcome potential, because the signal layer changed what the rep could see. How to Close the Signal Gap Without Replacing Your CRM You don't need a new CRM. You need a signal layer that sits alongside it and surfaces what the market is doing in real time. The criteria for that signal layer (and the full framework for how to prioritise accounts using timing and win likelihood signals ): 1. It should be CRM-native, not a separate dashboard -> Signals that live in a separate tool get ignored. The intelligence needs to appear in the workflow where your reps already operate, in the CRM record, in the Chrome extension, in the sequence tool. 2. It should surface competitor events, not just your own data -> CRM enrichment tools that add contact data or verify email addresses are useful but not sufficient. The gap is market intelligence, what competitors are doing in your accounts, not better contact data. 3. It should be real-time, not weekly -> A weekly digest of competitor activity delivered every Monday is better than nothing. But trial windows are 30 days long. A signal that arrives 7 days after the event has cost you a third of the window already. Your CRM Isn't the Problem. But It's Not Enough Either. Your team isn't making bad decisions because they ignore the CRM. They're making incomplete decisions because the CRM only tells them half the story, the half they already know. The other half is in the market. In the trials your prospects are running. In the renewals your customers are reconsidering. In the switches happening right now, in real time, that your CRM will never surface. See what the signal layer reveals about your accounts. Start with 500 free Qualified Opportunities at marketsizer. io Frequently Asked Questions Why is my CRM data inaccurate? CRM data is typically inaccurate in two ways: it's incompletely entered by reps (a process problem), and it's fundamentally limited to recording your relationship history (a structural problem). The second issue - the absence of external market signals like competitor trials and renewal windows - cannot be fixed by improving data hygiene. It requires a dedicated signal layer alongside the CRM. What data is CRM missing? CRM systems don't capture competitor trial activity, market-level switching patterns, renewal windows for competitor tools, or changes in product usage behaviour at competing vendors. These signals exist in the market but aren't visible through your CRM because they're external to your customer relationship. How can I improve CRM data quality? For process-level gaps (missing fields, outdated records, incomplete logs), structured data hygiene processes and CRM automation help significantly. For the structural gap, the absence of market signals, the solution is a subscription intelligence layer that feeds competitor and timing data directly into your CRM records. What is a signal layer in B2B sales? A signal layer is a data feed that sits alongside your CRM and surfaces real-time market intelligence - competitor trial events, renewal windows, switching patterns, and product usage changes - that your CRM cannot capture from your own relationship history alone. It fills the gap between "what your team has experienced" and "what the market is doing. " Does a better CRM fix inaccurate data? A better CRM can improve data entry, automation, and historical record quality. It cannot fix the fundamental gap between internal relationship data and external market signals. Switching CRMs doesn't add competitor intelligence to your workflow; a dedicated signal layer does. Recommended reading How to Implement Signal-led Prioritisation in Salesforce and HubSpot - the exact data model, custom fields, and views to add to your CRM so the signal layer actually drives the rep's queue. How to Build a Signal-led GTM Motion - the operating playbook for the layer that fills the CRM-data gap. Purchase Intent vs Intent Data - what kind of external signal actually closes the CRM's blind spot. Best Purchase Intent Data Platforms for 2026 - the directory of platforms that produce the signal layer. ## Blog: Why You Keep Losing Deals to Competitors: What Win Data Actually Shows Win/loss analysis reveals patterns most teams miss. Here is what the data actually shows. The post-mortem is always the same. And if you want the data behind why timing beats volume in competitive situations, the pattern is remarkably consistent. The post-mortem is always the same. You lost a deal to Intercom, or Zendesk, or Freshdesk and when the team debriefs, you hear some variation of: "They went with the bigger brand," or "We got beaten on price," or "They already knew the competitor. " These aren't wrong. But they're rarely the full picture, and they almost never point at what you could have done differently. Across 70M+ subscription outcomes tracking competitive outcomes in the Customer Support and Live Chat software market, a different pattern emerges. One that has much less to do with your product or your pricing and much more to do with when you showed up. Why Competitive Loss Analysis Usually Gets It Wrong Most loss analysis starts with exit interviews and CRM tags. A rep marks a deal "lost to competitor" and notes the reason. The problem with this data isn't that reps are dishonest, it's that they're asking questions after the decision was already made. By the time you're running a competitive loss debrief, the prospect has already committed. They've had a final meeting with the competitor. They've built a business case. They've got stakeholder sign-off. At that point, what they tell you about why they chose the other option is rationalisation, not root cause analysis. The real competitive loss happened weeks earlier and you weren't watching. Here's what the subscription data shows consistently: most competitive losses aren't decided in the final evaluation meeting. They're decided in the first two weeks of a competitor trial, when the prospect is forming their initial impressions and no one from your team is in the room. What Win Data From 70M+ Subscription Records Actually Shows The patterns that predict competitive wins and losses in the CS/Chat software market: Pattern 1: The company that reaches out first during a trial wins more often This is the single strongest predictor of competitive outcome. Not product quality at that point, not pricing, not brand. Timing of engagement. When a prospect starts a competitor trial and receives a relevant, contextual outreach from an alternative vendor within the first 14 days, the probability of that alternative entering the final consideration set is significantly higher than if the outreach comes in week 4 or 5. Harvard Business Review research on lead response found that firms who responded within the first hour were nearly 7× more likely to qualify the lead than those who waited even 60 minutes, a response-speed advantage that maps directly onto competitive trial scenarios. This is why finding companies in active evaluation - before your competitor gets exclusive access - is the highest-leverage competitive play available. The competitor you're losing to isn't always better. They're often just earlier. Pattern 2: ICP-fit alone is a weak win predictor You'd expect that the better your ICP match, the more likely you are to win. And that's true... up to a point. But the data shows clearly that ICP score without timing signals produces a wide range of outcomes. A perfect ICP-fit account that has been on a competitor for 14 months and just renewed is effectively immovable. The same account 45 days before renewal, with an active trial of a third competitor, is a genuine opportunity. ICP fit tells you which companies could be great customers. Timing tells you which ones are ready right now. Pattern 3: Competitive losses cluster around the same window When you look at when competitive losses are effectively decided - not when the deal closes, but when the prospect's preference crystallises - they cluster in a 2-4 week window early in the evaluation process. This window opens when the trial starts and closes when the prospect has a clear frontrunner. Forrester's 2024 Buyers' Journey Survey of 11,352 global buyers found that 92% of B2B buyers start the formal evaluation process with at least one vendor already in mind , meaning preference locks in well before most sales teams register a live opportunity. Outside that window, competitive insertions almost never succeed. Inside it, they succeed at a much higher rate. Pattern 4: Teams that use subscription signals win more head-to-head This is the finding that should concern your RevOps leader: GTM teams that receive competitor trial alerts and act on them within 72 hours win a meaningfully higher proportion of contested deals than teams operating on traditional intent data or no real-time signal at all. Gartner research shows that B2B buyers spend only 17% of their total purchasing time actually meeting with potential suppliers , which means the active window where a vendor can change a prospect's mind is narrower than most sales cadences are built to address. The gap isn't the product. It's the intelligence infrastructure. Why Your Competitive Battlecards Aren't the Problem If your response to losing deals is to improve battlecards, you're solving the wrong problem. Battlecards help in live competitive discussions when a prospect says "we're also looking at Zendesk" and your rep needs to articulate a clear alternative. They're genuinely useful at that point. But battlecards are defensive tools. They help you respond to competition that's already in the room. What most teams are missing is the offensive intelligence: knowing which accounts are currently in a competitive evaluation before you've been invited to compete, and reaching them while they're still forming opinions. A battlecard used on day 35 of a trial is less effective than an outreach on day 8. The battlecard didn't fail. The timing was just too late for the battlecard to matter. The Competitive Losses You Should Have Won Here's the thought experiment worth sitting with: of the last 20 competitive losses your team has recorded, how many of those prospects were in a competitor trial for 3+ weeks before your SDR first reached out? If your prospecting motion is trigger-based on traditional signals (MQL activity, ad engagement, funding news), the honest answer is probably: most of them. The prospects you're losing aren't fundamentally different from the ones you're winning. They came into evaluation at a similar stage, with similar pain, with similar appetite to switch. The difference is often this: your competitor reached them first, stayed in contact, and built credibility before you showed up. You're not losing to better products. You're losing to better timing. What Competitive Win Intelligence Looks Like in Practice Shifting from a defensive to an offensive competitive posture requires one thing: knowing when evaluation is happening before your competitor gets exclusive access. In practice, this means: 1. Monitoring for competitor trial activity in your target accounts -> When a company in your ICP starts a trial of Intercom, Freshdesk, or any other competitor, that's a live competitive event. It should surface in your workflow within 24 hours, not in a weekly pipeline review (see how MarketSizer for sales teams surfaces these signals directly in your CRM and Chrome workflow). 2. Scoring trials by win likelihood, not just ICP fit -> Not every competitor trial is equally winnable. A company 18 months into a strong relationship with your closest competitor is a different opportunity than a company in the first two months of a trial that replaced a tool they clearly weren't happy with. The account prioritisation framework that captures this distinction (ICP fit × purchase intent × win likelihood) is what separates a competitive play worth running from one that isn't. Win likelihood scoring, based on historical competitive outcomes, tells you which trials are worth running a competitive play on. 3. Responding faster than your cadence allows -> Most outreach sequences are weekly at best. Competitor trial windows are 30 days. If your first touch in a competitive situation is 7 days after the signal arrives, you've used a quarter of the window before the first conversation starts. The Next Deal You Lose, It Doesn't Have to Go That Way The competitive loss pattern is consistent. You can map it backwards from almost every deal you've lost: the prospect evaluated quietly, formed an impression early, and your outreach landed too late to change it. That pattern is not inevitable. It's a timing problem and timing problems are solvable with the right signals. Frequently Asked Questions Why do companies keep choosing competitors over us? The most common reason isn't product quality or pricing, it's timing. Most competitive decisions crystallise in the first 2-4 weeks of an evaluation. If your outreach arrives after that window, you're often competing for a decision that's already been made. Earlier, more relevant outreach consistently outperforms better features or lower pricing as a competitive lever. How do I find out why we keep losing deals to competitors? Exit interviews and CRM loss tags give you rationalised explanations, not root cause data. A more reliable approach is analysing when in the evaluation process you entered the conversation, and how that correlates with win/loss outcomes. Teams that enter competitive evaluations early (within 14 days of trial start) win at significantly higher rates. What is competitive win rate and how do I improve it? Competitive win rate is the percentage of deals you win when a named competitor is involved. To improve it, the highest-leverage intervention is earlier engagement in the evaluation window, reaching prospects while they're still forming opinions, not after they have a preferred option. Subscription signal intelligence that surfaces competitor trial events in real time is the most direct enabler. How can I tell when a prospect is evaluating a competitor? Direct indicators include: competitor trial signals surfaced via subscription intelligence platforms, competitor reviews posted on G2 or Capterra by company employees, and job postings that reference specific competitor tools as "preferred experience. " Of these, competitor trial detection via subscription data is the most reliable and most timely. Should I use competitive battlecards to win more deals? Battlecards are useful for live competitive discussions but are insufficient as a competitive strategy. They're defensive tools, they help you respond to a competitor that's already in the room. The higher-value intervention is identifying competitive evaluations before they're established, using trial detection to enter the conversation before a preferred option is formed. ## Blog: How to Prioritise Accounts for Outreach Using Timing and Win Likelihood Not all accounts are equal. Learn how to stack-rank your list using signals that actually predict outcomes. Your ICP list has 500 companies on it. Your SDR has time for 30 outreach sequences this week. How do you decide which 30? Most teams either go by gut ("these feel like the right accounts"), go by recency ("whoever came into the CRM this week"), or go by ICP score alone. And the result is the same: a pipeline that's full of ICP-fit accounts that never close because no one checked if those accounts were actually ready to buy. Gartner research on account-based strategy confirms the pattern: intent data - signals of active buying behaviour - is consistently the missing layer in account prioritisation, with 70% of B2B marketers now recognising it as a necessary complement to firmographic ICP scoring. ICP fit is the baseline. It's not the differentiator. Why ICP Score Alone Fails Your Pipeline Your Ideal Customer Profile captures the firmographic and technographic characteristics of your best-fit accounts - company size, industry, tech stack, team structure. It tells you: if this company buys our product, they're likely to get value from it. That is a real input to lead qualification , just not a sufficient one. What it doesn't tell you is: is this company in a position to buy right now? Two companies can have identical ICP scores with completely different buying realities and this is precisely why qualified leads don't close at the rate your pipeline suggests they should. The Ehrenberg-Bass Institute's 95:5 rule (LinkedIn B2B Institute, 2021) quantifies this: because B2B vendors are typically replaced every five or more years, approximately 95% of your ICP is out of market in any given quarter: Company A: 14 months into a strong relationship with your closest competitor. Just renewed. Active champions at the vendor. No evaluation underway. Company B: 8 months into a difficult implementation with a competitor. Renewal in 45 days. Three seats reduced last month. Champion just left. On your ICP scoring model, these two companies look the same. In reality, Company A is effectively out of market; Company B is one bad renewal conversation away from a switch. If your prioritisation process can't distinguish between these two accounts, you're routing your reps' time to a coin flip. The Opportunity Score Framework: ICP Fit × Purchase Intent × Win Likelihood The framework that closes this gap adds two dimensions to your existing ICP model: Dimension 1: ICP Fit -> What you're already measuring. Firmographic match, tech stack alignment, team size, industry fit. This is your filter, it removes companies that aren't worth pursuing regardless of timing. Dimension 2: Purchase Intent -> Evidence that a company is actively considering a change. For a detailed breakdown of what genuine purchase intent signals look like, and how to distinguish them from casual interest, see how to know when a prospect is actually ready to buy . Not inferred from content consumption - that's weak intent. Direct evidence: competitor trial activity, renewal windows opening, seat count declining, negative vendor reviews posted, product incidents at the current vendor. Gartner's analysis of B2B intent data in ABM draws the same distinction: direct buying signals (competitive research, renewal-stage activity) are strong indicators; passive content engagement is weak. Purchase intent is the timing dimension. It separates accounts that are passively ICP-fit from accounts that are actively in a buying motion right now. Dimension 3: Win Likelihood -> Not every in-market opportunity is equally winnable. A company 6 weeks into a Zendesk trial that's going well is a harder competitive fight than a company 2 weeks into an Intercom trial with visible signs of friction. Win likelihood is derived from historical competitive outcomes: which accounts, in which situations, with which signals, convert to customers? This is where data at scale separates teams that guess from teams that know. The Opportunity Score is the intersection of all three. An account that's ICP-fit, actively in-market, and winnable based on competitive context. That's a Qualified Opportunity - the highest-value unit of work your reps can touch. How to Score Each Dimension Scoring ICP Fit Your ICP scoring model should include at minimum: Company size (employee count, revenue band) Industry and sub-industry Tech stack (current tools in the relevant category) Team structure (presence of dedicated CS/CX function) Geographic market Weight these based on what your best existing customers look like, not based on theory. The most dangerous ICP model is one built on aspiration rather than evidence. Score range: 1-10 per company. Minimum threshold to advance: 6+. For a framework on building the account list that feeds this scoring model, see 6 strategies to build high-quality target account lists . Scoring Purchase Intent This is where most teams are flying blind - and where the most leverage exists. Strong purchase intent signals (score these higher): Competitor trial detected : Direct evidence of active evaluation Renewal window opening : 60-90 days before renewal, decision-making begins Seat count reduction : Usage decline suggests dissatisfaction Champion departure : New stakeholder = fresh evaluation of the incumbent Weaker purchase intent signals (score these lower): Content downloads about your category G2 profile browsing activity LinkedIn engagement with category content Score range: 1-10. Weight heavily toward direct evidence signals (competitor trial, renewal window) over inferred signals. Scoring Win Likelihood Win likelihood is the hardest to calculate without historical outcome data but you can build a proxy: How long has the account been on their current vendor? (shorter = higher opportunity) Which competitor are they with / trialling? (vs. competitive record) Is there a visible trigger event? (new funding, leadership change, product incident) How many competitors are already in the evaluation? (more competition = lower win likelihood) Score range: 1-10. This dimension should narrow your list, not expand it. Putting the Opportunity Score Into Practice: An Example Imagine your weekly account prioritisation review. You have 80 ICP-fit accounts to evaluate. Here's how the Opportunity Score narrows and prioritises: Account ICP Fit Purchase Intent Win Likelihood Opportunity Score Priority Acme Corp 9 8 (competitor trial detected) 7 24 #1 - outreach today Brightworks 8 7 (renewal in 55 days) 8 23 #2 - outreach this week Novatel 7 3 (content download only) 6 16 Hold - monitor Syncsole 9 1 (no signals) 5 15 Back-burner - great fit, wrong time Notice that Syncsole has the highest ICP fit score of the group and sits at the bottom of the priority list. It's a great potential customer. Just not right now. Without the Opportunity Score, your rep might spend their best outreach time on Syncsole because the firmographic fit is perfect. With it, they're spending that time on Acme Corp where a competitor trial has opened an active buying window. Building the Opportunity Score Into Your Weekly Workflow The framework is only useful if it runs automatically, not as a manual exercise that gets skipped when the team is busy. In practice, the Opportunity Score workflow looks like this: ICP list is static : Updated quarterly based on your ICPs. These are the accounts your reps should ever be touching. Purchase intent signals are real-time : Competitor trial alerts, renewal windows, and usage signals surface daily. These update the intent dimension automatically. Win likelihood is semi-dynamic : Updated when competitive context changes (a new competitor enters the account, a key champion departs, a product incident occurs). A prioritised account list is generated weekly : The top 20-30 accounts by Opportunity Score become the outreach focus. No other accounts get sequences that week. The discipline this creates: Reps stop working from a static list of favourite accounts and start working from a dynamic, signal-driven priority stack that changes as the market changes. The Accounts Worth Your Reps' Time This Week The question your prioritisation framework should answer isn't "which accounts are a good fit? " It should be "which accounts are a good fit AND ready right now AND winnable? " That intersection is smaller than your full ICP list. It's also far more valuable because every hour your rep spends on a Opportunity Score account is an hour spent where the market is actually moving. Every hour spent on an ICP-fit account without timing signals is an hour that might produce a good conversation and no deal because the prospect simply isn't in a buying window yet. Stop Prioritising the Accounts That Look Right. Start Prioritising the Ones That Are Ready. Your best customers looked exactly like your ICP before they bought. The ones that didn't close also looked exactly like your ICP. The difference was timing. The Opportunity Score doesn't find you better accounts. It finds you the right accounts at the right moment which is the only version of "better accounts" that closes. And you don't need to figure all of this out by yourself. That's why we built MarketSizer, where all of this intelligence is already baked in. See your highest Opportunity Score accounts right now. Start with 500 free Qualified Opportunities at marketsizer. io . Compare tools with MarketSizer Side-by-side with the intent platforms mentioned above: 6sense , Bombora . Full index at MarketSizer compared with other tools . Frequently Asked Questions How do you prioritise accounts for outreach? The most effective account prioritisation combines three factors: ICP fit (firmographic and technographic match), purchase intent (direct signals like competitor trials and renewal windows), and win likelihood (probability of winning based on competitive context and timing). Prioritising only on ICP fit - the most common approach - ignores whether the account is actually in a position to buy right now. What is account scoring in B2B sales? Account scoring is the process of ranking target companies by their likelihood to buy. Most scoring models use firmographic criteria (industry, size, tech stack). More sophisticated models layer on timing signals like competitor trial activity, renewal windows, and churn signals, to distinguish accounts that fit your ICP from accounts that are actively in a buying motion. What is the difference between ICP score and purchase intent? ICP score tells you whether a company is a good potential customer based on their profile. Purchase intent tells you whether that company is currently in a buying motion. A high ICP score with no purchase intent signals means the company could be a great customer but not right now. The combination of both is what identifies a Qualified Opportunity worth prioritising. How often should I update my account prioritisation list? Purchase intent signals (competitor trials, renewal windows) should update your priority list in real time or daily. ICP scoring criteria should be reviewed quarterly. Win likelihood adjustments should be made when material competitive context changes, like a new champion, a competitor product release, or an account trigger event. What tools help with account prioritisation? CRM systems provide historical relationship context. Intent data platforms (6sense, Bombora) provide inferred interest signals. Subscription intelligence platforms like MarketSizer provide direct evidence signals, e. g. competitor trial detection, renewal windows, and switching patterns, that populate the purchase intent and win likelihood dimensions of account scoring. Recommended reading How to Build a Signal-led GTM Motion - the four-move playbook that puts Opportunity-Score prioritisation into a working sales motion. How to Implement Signal-led Prioritisation in Salesforce and HubSpot - the CRM-level implementation guide, including the custom fields and views that make this scoring actually run inside the rep's queue. Purchase Intent vs Intent Data - the definitional piece behind the Purchase Intent dimension of the score. Best Purchase Intent Data Platforms for 2026 - the directory of platforms that produce the signals the score runs on. ## Blog: Your Sales Outreach Isn't Broken. Your Timing Is. The message is fine. The list is fine. But if your timing is off, nothing else matters. You've refined the subject line. You've personalised the first sentence. You've tested shorter emails, longer emails, video emails, voice notes. You've bought the best sequencing tool. Your SDR is sharp. The response rate is still 2%. And the reason your qualified leads aren't closing at the rate your pipeline suggests they should has the same root cause. Here's the part of this problem that no copywriting guide will tell you: it's almost certainly not your copy. Why 75% of Your Outreach Is Wasted Before It's Sent At any given moment, roughly 75% of the companies in your ICP are not in a buying window. They recently renewed. They're mid-contract with no evaluation underway. Their current vendor is delivering good enough results for them to not want to disrupt anything. Research by the Ehrenberg-Bass Institute for LinkedIn's B2B Institute (2021) puts the out-of-market figure even higher, their 95:5 rule finds that approximately 95% of B2B buyers are not in-market at any given quarter, based on average vendor switching cycles of five or more years. These companies are not ignoring your emails because the subject line was weak. They're ignoring them because there is no reason to respond. Not now. Not this week. Possibly not this year. When you send 100 emails to a cold ICP list, you're statistically reaching approximately 75 people for whom your perfectly crafted message has zero relevance to their current priorities. The 2% response rate isn't a failure of execution, it's an accurate reflection of how many of those 100 are actually in a position to consider what you're selling. See how MarketSizer for sales teams filters your ICP list down to only the accounts in an active buying window. The math doesn't fail you. The targeting does. What Happens When You Reach a Prospect in Their Buying Window Now imagine your 100 emails were sent to the subset of your ICP that is actively in a buying window right now. Companies in a competitor trial. Companies 45 days from renewal. Companies whose current vendor just had a visible service failure. The same email - same subject line, same copy - lands completely differently. The prospect is already thinking about your category. They're already comparing. They may have already mentally begun the process of change. Your email arrives not as an interruption but as a relevant piece of information at a useful moment. Response rates among buying-window prospects are not 2%. They're closer to 15-25%, with the same message. That range is consistent with what B2B cold email benchmark data across 10,000+ campaigns (Built For B2B, 2025) shows for top-decile, tightly targeted outreach, the average sits at 1-3%, but campaigns with strong ICP targeting and timing signals regularly hit 8-12%, and best-in-segment plays exceed 15%. The difference is not what you said. It's when you said it. What "Timing" Actually Means in Outreach Most sales advice treats timing as "send on Tuesday morning" or "follow up after three days. " That's scheduling, not timing. Real timing means reaching a prospect in the window where a decision is live or about to be live. That timing layer sits on top of your sequencing tool rather than replacing it - here is how it pairs with Apollo , for example. For a full breakdown of what those windows look like and how to identify them, see how to know when a prospect is actually ready to buy . That window typically opens when: A competitor trial starts at their company A renewal date is 30-90 days out A key stakeholder in their buying role has recently changed Their current vendor has a visible incident, price change, or product regression Outside these windows, your outreach is ambient noise. Inside them, it's signal. The outreach itself doesn't change. The moment does. Why Better Copy Doesn't Fix a Timing Problem The B2B sales industry spends enormous effort optimising what is said and almost no effort optimising when it's said. RAIN Group's Top Performance in Sales Prospecting research found that it takes an average of 8 touches to generate a meeting but the research also shows that 71% of buyers want to connect with sellers early when they're actively looking for new solutions, meaning timing to an active window dramatically changes receptiveness. There are thousands of guides, courses, and frameworks for writing cold outreach. There are almost none for identifying when a specific prospect is in a genuine buying window, and building your outreach calendar around that, not around your SDR's weekly quota. The result is an industry-wide habit of sending good messages to the wrong moment. Your rep who closes at 25% isn't writing better emails than your rep who closes at 8%. The data behind why timing beats volume makes this pattern impossible to ignore. They're finding better timing - sometimes consciously, often by luck. They're reaching prospects who are about to decide anyway, and being the relevant voice in the room. How to Fix Low Outreach Response Rates Without Rewriting a Word Step 1: Stop prospecting from a static ICP list -> A list of 500 ICP-fit companies is not a prospecting list. It's a universe. Treating all 500 as equally worth outreach right now is the root of the timing problem. Step 2: Add a timing filter -> Before any account enters an active sequence, answer: what is the real-time signal that tells us this prospect is in a buying window this week? The account prioritisation framework shows you exactly how to score and filter on this. Competitor trial activity, renewal windows, and trigger events are the filters. Without one, the account goes to monitoring, not sequencing. Step 3: When a timing signal fires, move fast -> Competitor trial windows are 30 days long. Renewal windows are open for 60-90 days but the highest receptiveness is in the first 30. If your outreach sequence starts 10 days after a signal arrives, you've lost a third of your best window before the first message lands. Step 4: Let the signal shape the message -> A message that references the timing context - "we know teams often evaluate alternatives in the run-up to renewal" - outperforms a generic value prop every time. You don't need to name the specific trigger. You just need to be relevant to the moment. Your Copy Is Fine. Your Prospect List Isn't. The 2% response rate is not a verdict on your skills as a writer or your product's value. It's a verdict on your prospect list, specifically, on how many people in that list are in a genuine buying window right now. Find the window. Show up in it. The copy will do its job. Frequently Asked Questions Why is my sales outreach not getting responses? The most common cause is timing, not copy. Approximately 75% of ICP-fit companies are not in a buying window at any given moment, meaning most outreach reaches prospects with no immediate reason to engage. Focusing outreach on prospects with active timing signals - competitor trials, renewal windows, trigger events - typically produces 5-10x the response rate from the same message. How do I improve my cold email response rate? The highest-leverage fix is improving when you send, not what you send. Identify accounts in active buying windows (showing competitor trial activity or approaching renewal) and prioritise these for outreach over ICP-fit accounts with no timing signals. Response rates among in-window prospects are significantly higher, often 15-25% vs. 2-3% for cold lists. What is the best time to reach out to a B2B prospect? The best time is when a timing signal fires, not a day of the week. Specifically: within the first 14 days of a competitor trial, in the 30-60 day window before renewal, or within two weeks of a trigger event (leadership change, vendor incident, funding close). These windows are where outreach response rates are highest regardless of message quality. Why do sales emails get ignored? Sales emails get ignored primarily because they arrive at a moment when the prospect has no immediate reason to engage. The email may be well-written and relevant in principle but if the recipient isn't in an active buying window, it competes with hundreds of other inputs and loses. Timing-first prospecting reduces this problem by reaching prospects when the category is already on their mind. Does personalisation improve cold email response rates? Personalisation helps at the margin but only once you've solved the timing problem. A highly personalised email sent to a prospect who renewed six months ago and has no evaluation underway will still underperform a moderately personalised email sent to a prospect in an active competitor trial. Fix timing first; then optimise personalisation. Recommended reading Why Intent Data Made B2B Outreach Worse - the deeper argument for why scaling intent without rethinking the cadence accelerated bad outreach instead of fixing it. Purchase Intent vs Intent Data - the signal-quality argument behind the timing-first claim. How to Build a Signal-led GTM Motion - the operating playbook for outreach anchored on observable events instead of cadences. Best Purchase Intent Data Platforms for 2026 - the directory of platforms that produce the timing signals this piece argues for. ## Blog: How to Know When a Prospect Is Ready to Buy (Not Just Interested) Interest is not intent. Learn the signals that separate browsers from buyers. Your prospect is responsive. They open your emails. They took a second call. They said "this looks interesting, let's stay in touch. " And then Q2 ends and they still haven't moved. Sound familiar? The problem isn't that your prospect lied. They were interested. They just weren't ready. And there's a gap between those two states that most B2B sales motions treat as the same thing, at a significant cost to pipeline velocity and forecast accuracy. Learning to read that gap is the difference between a calendar full of interesting conversations and a pipeline that actually converts. Why "Interest" Is Not a Buying Signal Interest means a prospect is aware of a problem and open to exploring solutions. It's a necessary condition for a sale but it's not sufficient. Every quarter, most sales pipelines are full of genuinely interested prospects who don't close. They're not lying when they say it looks good. They're not stringing you along when they take the follow-up call. They're just not in a position where the decision is urgent. The company without a real buying trigger can be interested indefinitely. Interested costs them nothing. Deciding requires effort, stakeholder alignment, budget approval, and the willingness to change something that's currently functioning, even if imperfectly. "Interested" is the neutral state. "Ready to buy" requires a forcing function. Once you know how to read the difference, the next step is prioritising those ready-to-buy accounts above everyone else in your pipeline . That forcing function is either internal, a trigger inside their business or competitive, an outside option that's making the status quo feel more expensive than changing. What a Genuine Buying Window Looks Like A buying window is the period when a prospect has both the motivation and the practical opportunity to make a decision. To find which companies in your market are currently inside one, see how to find companies that are actively evaluating new software . It opens when a trigger event occurs and closes when either a decision is made or the urgency dissipates. Buying windows in B2B SaaS are often shorter than sales teams assume, typically 30-90 days from the moment a real trigger fires. (Forrester's research shows the opposite problem is also real: when no trigger exists, buying cycles stall indefinitely, nearly 90% of global B2B buyers reported their purchase process stalled in 2023 , precisely because no forcing function was present. ) Outside this window, even the best outreach produces polite non-committal. Inside it, a well-timed, relevant conversation can close quickly. The signals that open a buying window fall into three categories: The Three Categories of Purchase Readiness Signals Category 1: Direct evidence of active evaluation These are the strongest signals because they require the prospect to take action, not just feel something. Competitor trial started: A company that has signed up for a competitor's trial is not just interested in your category. They are actively evaluating it. They've given a vendor their contact information, agreed to terms, and started running a product through its paces. This is a buying motion in progress. Reaching a prospect during an active trial is categorically different from reaching them at any other moment. They're already engaged with the problem, comparing options, and forming opinions. You're not starting a conversation, you're joining one that's already happening. RFP issued or demo scheduled: Less common in SMB and mid-market, but in larger deals, the formal procurement process is itself a signal of readiness. If your prospect has invested the effort in building an RFP, the decision is real. Renewed budget approval for the category: When a new budget cycle opens and a prospect is actively allocating resources toward solving the problem you solve, the timing is right. Category 2: Structural triggers that create urgency These are events in the prospect's business that make the status quo untenable - they don't directly indicate evaluation, but they create the conditions for one. Renewal window approaching: The 60-90 days before a software renewal is the highest-concentration buying window in SaaS. The prospect is already thinking about whether to stay or switch. Their attention is on the category in a way it won't be again for 12 months. Gartner's 2023 Global Software Buying Trends report found that only 45% of software buyers renew without consideration, 53% actively research and contemplate alternatives at renewal time. If you reach out with a relevant, contextual message during a renewal window, you're arriving at exactly the moment the prospect is most receptive to alternatives. Champion or decision-maker change: A new head of Customer Experience, a new VP of Sales, a new CTO, any significant leadership change in the buying role for your category creates a natural re-evaluation moment. New stakeholders aren't emotionally committed to the existing vendor. They're often actively looking to put their own stamp on the stack. Russell Reynolds' analysis of Fortune 500 technology leaders found that 53% of incoming top technology officers are external hires, arriving specifically with a transformation mandate and no loyalty to incumbents. Product incident or vendor failure at the current tool: When a prospect's current vendor has a public outage, launches a price increase, removes a feature, or generates a wave of negative reviews, the comfort level with the status quo drops. These are not permanent windows, they close if the vendor recovers the relationship, but in the weeks following a visible incident, receptiveness to alternatives spikes. Company growth or restructuring: Significant headcount growth (20%+ in a quarter), a funding close, a merger, or a restructuring often triggers a tech stack review. The existing tools either scale with the new reality or they don't. Category 3: Behavioural signals These signals are worth noting but are weakly predictive on their own. Treat them as interest indicators, not buying signals: Content downloads and whitepaper consumption Pricing page visits (stronger than content, but still anonymous and unverifiable) G2 or Capterra profile browsing LinkedIn engagement with category content Email open rates These signals tell you a prospect is thinking about the category. They don't tell you they're in a buying window. A category-curious prospect and a buying-window prospect require entirely different engagement strategies, and confusing the two is one of the most common sources of pipeline inflation. How to Use This in Your Outreach: A Practical Framework Once you understand the difference between interest and readiness, your outreach strategy changes: For Customer Success teams , the same framework applies in reverse: If an existing customer shows structural trigger signals (seat reduction, champion change, competitor trial), that's a churn risk, not a sales opportunity. Why CS teams fail on timing, not effort covers that mirror image in full. For prospects with no timing signal: Move to a low-touch monitoring track. Check in quarterly. Watch for trigger events. Don't waste high-intensity SDR time on outreach that will produce polite engagement and no urgency. For prospects with structural triggers (renewal approaching, trigger event, leadership change): Move to an active sequence, but lead with relevance to the trigger, not with a generic pitch. "We know you're likely reviewing your CS stack ahead of your renewal" is a much stronger open than "we help CS teams improve their workflow. " For prospects with direct evaluation signals (competitor trial detected): Move immediately. These are your highest-priority accounts. The window is 30 days or fewer. The first message should acknowledge the evaluation moment, offer something genuinely useful in the context of that evaluation, and create a reason to talk now, not "whenever it's convenient. " The Timing Mistake Most Sales Teams Make The most common mistake in identifying prospect readiness is conflating engagement with intent. A prospect who engaged with three pieces of your content, attended a webinar, and met you at a conference is engaged. They like you. They may respect your thinking. But if there's no real buying trigger, no renewal pressure, no competitive evaluation underway, no internal urgency; they will engage indefinitely without buying. The mistake is treating engagement as evidence of readiness. Engagement earns a follow-up. It doesn't earn a forecast entry. The honest question to ask of every "interested" prospect: what is the real-world event that would make them decide this quarter rather than next year? If you can't answer that question, the deal isn't in your pipeline. It's in your relationship list. Stop Chasing Interest. Start Finding Readiness. Interested prospects are everywhere. They'll take your calls, watch your demos, and tell you it looks great. Then they'll renew with their current vendor because nothing was pushing them to decide. Ready-to-buy prospects are in your market right now, e. g. approaching renewal, evaluating alternatives, experiencing the moment that makes the status quo uncomfortable. The only question is whether you see them in time. See which of your target accounts are in a genuine buying window right now. Start with 500 free Qualified Opportunities at marketsizer. io . Frequently Asked Questions How do you know when a B2B account is ready to buy? A B2B account is ready to buy when it shows a direct evaluation signal (an active competitor trial, an open RFP, a formal evaluation) rather than only engagement signals like content downloads or email opens. Direct evaluation signals indicate the buying window is currently open. Structural triggers such as an approaching renewal, a leadership change, or a vendor incident indicate a window is about to open. Prioritise outreach in that order: direct evaluation first, structural triggers next, engagement signals last. How do I know if a prospect is ready to buy? The clearest indicators are direct evaluation signals: an active competitor trial, an RFP in progress, or a formal evaluation underway. Structural triggers, like approaching renewals, leadership changes, vendor incidents, indicate a window is opening. Engagement signals (content downloads, email opens) indicate interest, not readiness. Prioritise outreach to prospects with direct evaluation or structural trigger signals. What is a buying signal in B2B sales? A buying signal is evidence that a prospect is in an active decision-making process. The strongest B2B buying signals are direct evaluation events (competitor trial started, RFP issued) and structural triggers (renewal window open, key stakeholder change). Weaker signals include content engagement and review site browsing, which indicate category interest rather than purchase readiness. What is the difference between a prospect who is interested and one who is ready to buy? An interested prospect is aware of a problem and open to exploring solutions, but has no immediate forcing function. A ready-to-buy prospect has a real urgency trigger: a renewal forcing a decision, a competitive alternative they're actively evaluating, or an internal event that makes the status quo untenable. The difference matters enormously for outreach timing and forecast accuracy. How long is a typical B2B buying window? In the Customer Support SaaS category, active buying windows typically run 30-90 days from when a trigger event fires. Competitor trial windows are often 30-45 days. Renewal windows typically open 60-90 days before the contract date. Outside these windows, even strong outreach is unlikely to accelerate a decision. What are subscription intelligence signals? Subscription intelligence signals are evidence of what's happening in a company's software subscriptions, e. g. competitor trials started, renewals approaching, seats reduced, tools removed. Unlike anonymous web intent signals (content consumption, ad engagement), subscription events are direct, observable evidence of buying motion. They're the highest-fidelity timing signal available for B2B SaaS outreach. How do I avoid confusing engagement with buying intent? Ask the forcing function question for every pipeline entry: what is the specific event that would cause this prospect to decide this quarter rather than deferring? If the answer is "nothing specific, they seem interested," move the account to a monitoring list. Reserve active sequences for prospects with real timing triggers. ## Blog: What Is Subscription Intelligence? Real Buying Signals for B2B GTM A GTM primer on Subscription Intelligence - the data layer that uses real subscription events at other companies as direct evidence of buying behaviour. Subscription Intelligence is a B2B GTM data layer that uses observable subscription events at other companies (trials started, tools renewed, software churned) as direct evidence of buying behaviour. It is not the same category as subscription-billing analytics; it sits between your CRM (what has happened) and intent data (what might be happening) and answers a different question: which accounts are actively in-market right now, and which of those are winnable. If you sell into a category where prospects trial software before buying, Subscription Intelligence is what tells you the trial started, so your outreach lands inside the buying window, not weeks after it closed. The problem with the signals you're relying on right now Most B2B GTM teams are working with two data layers: their CRM (what's already happened) and intent data (inferred signals of what might be happening). Both are imperfect. The CRM looks backwards. Intent data looks sideways, at anonymous web behaviour that may or may not correlate with real buying activity. The signal quality problem is structural. Forrester's research on B2B intent data identifies data decay, anonymous signals without purchase context, and the failure to map signals to active buying cycles as the core reasons intent data underdelivers, warning explicitly that "intent signals on their own should not replace the qualification process. " The gap between a flagged account and a real buying motion is wide, and most intent tools don't close it. That's not a vendor problem. It's a structural one. Anonymous web signals (content downloads, ad clicks, IP-based page visits) are inferences about intent, not evidence of it. They tell you someone at a company read something once. They don't tell you whether that company is actually evaluating software, who the decision-maker is, or whether you have any chance of winning. Subscription Intelligence is built to fill exactly this gap. What is Subscription Intelligence? Subscription Intelligence is the practice of using real subscription event data (competitor trials started, tools renewed, software churned, evaluation windows opened) as direct evidence of buying behaviour, rather than inferring intent from anonymous web activity. Where traditional intent data asks "who might be thinking about buying? " , Subscription Intelligence asks "who is actually doing something right now? " The distinction matters enormously in B2B SaaS, where the difference between a prospect who read a blog post and a prospect who just started a 30-day trial of your competitor is the difference between a cold outreach and a live opportunity. A note on the term itself: "subscription intelligence" is also used inside the subscription-billing world to describe analytics on your own recurring revenue base (churn rates, MRR movement, cohort behaviour). This piece is about a different, GTM-facing meaning: intelligence about what is happening in other companies' subscriptions, used as a buying signal for your outbound and retention motions. Billing analytics answers "how is my business doing"; GTM Subscription Intelligence answers "which accounts should we talk to this week, and why now". MarketSizer, for example, built GTM Subscription Intelligence on 70 million+ subscription outcomes across 140+ Customer Support and Live Chat software products, spanning trial detection, renewal windows, churn events, and competitive migration patterns. That dataset makes it possible to identify not just who is in-market, but when their buying window opens, and closes. How it's different from intent data Criteria Traditional Intent Data Subscription Intelligence Signal source Anonymous web behaviour (IP tracking, bidstream, content syndication) Direct subscription events (trials, renewals, churn, migrations) Signal type Inferred (someone browsed something) Direct evidence (something actually happened) Latency Days to weeks behind actual behaviour Real-time or near-real-time Inspectability Black box, you can't verify the source Auditable, you can see the specific event False positive rate High. Forrester found 50% of B2B teams experience too many false positives Low, signals are events, not inferences Individual vs account Account-level only Account-level with event specificity The false positive problem is significant. Forrester's evaluation of B2B intent data providers found that 50% of B2B teams see too many false positives from intent data signals, and 60% struggle to identify actual members of the buying team within flagged accounts. When half the accounts your tool surfaces aren't genuinely in-market, your reps waste time, morale drops, and win rates suffer. Where Subscription Intelligence sits in your stack Think of it as a third data layer that sits between intent data and your CRM: CRM ← what has already happened (deals, history, contacts) Subscription Intelligence ← what is happening right now (trials, evaluations, renewals) Intent Data ← what might be happening (inferred from web behaviour) This layer is what makes it possible to build a Qualified Opportunity , an account that isn't just ICP-fit, but is actively in-market and winnable based on competitive context. Without this layer, your CRM tells you who you've already spoken to, and your intent data tells you who vaguely looks interested. Neither tells you who is in a live buying window right now, which is the question that actually drives pipeline. Subscription Intelligence does not replace either layer. It sits between them and makes the other two more useful. Your intent tool flags a keyword surge; Subscription Intelligence tells you which of those flagged accounts also has a competitor trial live in the last 14 days. Your CRM shows an existing customer coming up for renewal; Subscription Intelligence tells you whether they have quietly started evaluating alternatives. What Subscription Intelligence unlocks in practice For Sales: Real-time alerts when a prospect company starts a competitor trial or a current customer shows churn signals. Outreach timed to the start of a buying window, not a content download from three weeks ago. In practice this means an AE working a target account list of 200 companies can prioritise the six that are actively trialling a competitor today, instead of running the same six-week sequence across all 200 and hoping timing lands. For Marketing: Campaign targeting built on accounts that are provably in-market, not firmographic proxies. ABM spend directed at accounts where a trial detection event, not a demographic assumption, confirms buying readiness. The same $50k of paid budget targeted at 40 confirmed in-market accounts converts differently to the same budget spread across 4,000 look-alike matches. For Customer Success: Early warning when a customer trial-starts a competitor, so CS can intervene before a renewal conversation becomes a cancellation conversation. In categories with fast renewal cycles (Live Chat and Customer Support software both trend faster than the SaaS average), the difference between catching a competitor trial at 60 days out versus at the renewal call itself is usually the difference between a save and a churn. For RevOps: A shared signal layer that both Sales and CS pull from, so the outbound motion and the retention motion aren't running on different intelligence. Consolidated signal infrastructure removes the argument about which team should have called an account first. How Subscription Intelligence actually works Subscription events are observable at scale by tracking the technical footprint of subscription-based software. When a company installs, trials, or removes a SaaS tool, that tool leaves an observable footprint on the company's public web presence - one that can be inspected without any private data access. The Subscription Intelligence layer aggregates those signatures across a defined vendor coverage set (in MarketSizer's case, 140+ Customer Support, Live Chat, and adjacent Martech products) and normalises them into subscription lifecycle events: "trial detected," "renewal window open," "vendor churned," "competitor migration detected. " Every event is timestamped, mapped to an account, and joined against historical outcomes so it can be scored for likelihood of a real buying motion. In MarketSizer's dataset, signals are refreshed daily, with a maximum five-day lag between an event happening and the signal firing. That matters because a "renewal window open" signal that arrives 30 days late is not a buying signal, it is a post-mortem. The engineering constraint here is that Subscription Intelligence only works if the underlying detection is honest about what it can and cannot see. Every signal is inspectable, meaning a rep can click into it and see the specific evidence behind the flag. If a signal cannot be inspected, it is not a Subscription Intelligence signal, it is another form of inference. The output: Qualified Opportunities, not signals Signals are only useful if they lead to action. MarketSizer translates Subscription Intelligence into a Opportunity Score , a three-part qualification framework that scores every account across ICP Fit - how well the account matches your best-fit customer profile Purchase Intent - what subscription events indicate active buying readiness Win Likelihood - based on 70M+ historical competitive outcomes, what is the probability of winning this account against this competitor Accounts that score well on all three become Qualified Opportunities , the unit of work your GTM team should be building pipeline around. This matters because not every in-market account is worth pursuing. A company that just renewed a 3-year contract with your primary competitor is technically "in-market", but the win likelihood is near zero. Subscription Intelligence is what makes that distinction visible before a rep spends three weeks on an unwinnable deal. Why now? The sales intelligence market is growing fast. MarketsandMarkets values it at $3. 80 billion in 2025 and projects $7. 35 billion by 2030 (10. 6% CAGR), with multiple independent research firms placing the 2025 figure between $3. 8B and $4. 4B. That growth reflects a market that has outgrown basic contact databases and is looking for something with more signal fidelity. Subscription Intelligence is the answer to what teams have been asking for since intent data disappointed them: direct, inspectable, timely evidence of actual buying behaviour, not another layer of inference on top of inference. Compare with adjacent tools Side-by-side comparisons with the technographic and intent tools this post references: HG Insights , BuiltWith , Wappalyzer . Full index at MarketSizer compared with other tools . Frequently asked questions Is Subscription Intelligence the same as intent data? No. Intent data infers interest from anonymous web behaviour (page visits, content downloads, keyword surges). Subscription Intelligence uses direct subscription events (competitor trials started, renewals approaching, tools churned) as evidence of actual buying activity. Intent data tells you someone might be curious. Subscription Intelligence tells you something has demonstrably happened. Both can be useful, but they are structurally different classes of signal. Is Subscription Intelligence the same as subscription-billing analytics? No. Subscription-billing analytics (Vindicia, Chargebee, and similar platforms) measures your own recurring revenue metrics, churn, MRR, cohort behaviour. GTM Subscription Intelligence looks outward at what is happening in other companies' subscriptions, and uses that as a buying signal for your outbound and retention motions. Different problem, different data, different user. How is Subscription Intelligence different from technographic data? Technographic data (BuiltWith, Wappalyzer, HG Insights) tells you which technologies a company currently uses. That is a static snapshot. Subscription Intelligence adds the temporal layer: when a tool was added, how long it has been in trial, when it is coming up for renewal, and whether it has recently churned. A snapshot answers "what do they use"; Subscription Intelligence answers "what are they doing about it right now". What kinds of subscription events can be detected? The main categories are trial starts, active use of a specific vendor, tool removal or churn, renewal windows, and competitive migrations. Each has a different signal shape and a different appropriate GTM response. A trial start on a direct competitor is an outbound-worthy alert. A renewal window opening on your own customer is a CS intervention. A tool churn is a re-engagement opportunity. How fresh are Subscription Intelligence signals? In MarketSizer's dataset, signals refresh daily with a maximum five-day lag between an event happening and the signal firing. That freshness is the point: a "buying window opened" signal that arrives 30 days after the window opened is not a buying signal, it is a retrospective. Any Subscription Intelligence layer that batches on a weekly or monthly cadence is not solving the timing problem it claims to solve. Who uses Subscription Intelligence, sales or marketing? Both, plus Customer Success and RevOps. The signal layer is shared. Sales uses it to prioritise outbound. Marketing uses it to target ABM spend at provably in-market accounts. CS uses it to catch competitor trials at existing customers before renewal. RevOps owns the shared infrastructure so all three teams work from the same intelligence rather than each running their own signal stack. Recommended reading Purchase Intent vs Intent Data - the signal-side definitional piece: what Subscription Intelligence actually produces as a buying signal. Best Purchase Intent Data Platforms for 2026 - the directory comparing evidence-based platforms against the rest of the intent market. How to Build a Signal-led GTM Motion - the operating model that runs on Subscription Intelligence as the input layer. Timing Intelligence 101 - the temporal-layer companion: when to act on the signals Subscription Intelligence surfaces. ## Blog: Why Qualified Leads Don't Close (And How Timing Kills Conversion) They fit ICP. They engaged. They vanished. Here is why timing is the missing variable. You've done the hard work. The lead is ICP-fit. The prospect has budget authority. They've engaged with content. They've taken a call. Your rep has followed up four times. And then nothing. The deal doesn't die loudly, it just drifts. Emails go unanswered. "Now isn't the right time" becomes a holding pattern that lasts months. Your pipeline numbers look fine on paper because the deal is still technically open. But everyone on the team quietly knows it's not going anywhere. This plays out in B2B SaaS pipelines every week. And the answer everyone reaches for (better messaging, stronger follow-up, more sequences) fixes the wrong problem. Why Messaging and Process Aren't the Root Cause When qualified leads don't close, the instinct is to diagnose the sales motion. Were the discovery questions sharp enough? Was the value prop clearly communicated? Did the follow-up sequence have enough touches? These are worth asking but they're almost never the root cause of stalled pipeline. The same logic explains why B2B sales teams keep missing pipeline targets despite good execution on every other dimension. Here's the more uncomfortable truth: most qualified leads don't close because they weren't actually ready to buy when you reached them. According to Gartner, modern B2B buyers spend just 17% of their total purchase journey interacting with all potential vendors combined and 6sense research (2025) shows that 83% of buyers define their purchase requirements before ever speaking to sales . By the time your rep reaches a prospect, the buying window is often already opening or closing, independently of your outreach. They matched your ICP. That part was right. But ICP fit is a static measure, it tells you a company could benefit from your product. It says nothing about whether the company is currently in a position to act. A perfect-fit prospect who recently renewed with their current vendor for 12 months is not in a buying window. A perfect-fit prospect who is 6 weeks into a painful implementation with a competitor and approaching renewal is a completely different conversation. Your pipeline treats them the same. They're not the same. What "Qualified" Is Missing: The Timing Dimension The standard definition of a qualified lead covers four things: Budget, Authority, Need, Timeline. Your reps check these boxes. The prospect says they have budget, they're the decision-maker, they have the need, and "Q3 looks likely. " But here's where the qualification breaks down: timeline is not the same as timing. Gartner's research on the B2B buying journey shows that buying decisions are nonlinear -> budgets shift, priorities change, and a prospect who says "Q3 looks likely" may genuinely mean it when they say it, only for internal dynamics to push the decision out by six months. The stated timeline tells you their intention; real timing tells you what's actually happening in their business. Timeline is what the prospect tells you. Timing is what's actually happening in their business, the operational pressure that creates genuine urgency. The leads that actually close fast have a real timing signal behind them: Their current vendor just had a major outage Their renewal is in 45 days and they're unhappy They just started a trial of your closest competitor (and that trial isn't going well) A new executive just joined who wants to evaluate the existing stack Without one of these real-time triggers, even a highly qualified lead exists in a low-urgency zone. Your reps are pushing; the prospect is nodding. But nothing is compelling them to move. That's the gap most pipeline analyses never surface: the qualified lead was real, the timing just wasn't there. The 24. 7% Reality: How Much of Your Market Is Actually In-Market Right Now? Research by Professor John Dawes at the Ehrenberg-Bass Institute established the foundational principle: at any given moment, roughly 95% of your potential B2B buyers are not in the market. Only about 5% are actively evaluating. In practice, the exact percentages vary by category, MarketSizer's subscription intelligence data puts the in-market figure at 24. 7% for the Customer Support and Live Chat SaaS category (a finding explored in depth in Beyond the 95/5 Rule: Why SaaS GTM Needs Precision Timing ). The remaining 75. 3% are either locked in, satisfied, or between decision cycles. This number matters more than your pipeline forecast. If your prospecting motion generates 100 leads per month from your ICP, roughly 75 of them are fundamentally not in a buying window, regardless of how well your SDR executes the outreach. They'll take the call, engage politely, maybe even go to second stage, and then stall. The 25% that are in a window will close at dramatically higher rates, not because your product is better, but because the timing is right. The pipeline problem isn't volume. It's that you're filling your pipeline with the 75% and wondering why conversion is low. What Does a Real Buying Signal Look Like? The signals that indicate a prospect is actually in a buying window, not just potentially interested: High-conviction timing signals: A competitor trial has started at the account (direct evidence of evaluation) The renewal date with the current vendor is 30-90 days out A key champion or budget holder has recently changed roles A product incident or support failure at the current vendor Recent seat count reduction or declining usage at the current tool Moderate signals (worth monitoring, not acting on alone): A job posting for a role that would own the product you sell Recent negative review posted about the current vendor on G2 New executive hire with a history of switching tools in this category Weak signals (often false positives): Downloaded a category guide or attended a webinar Viewed your pricing page once Engaged with a LinkedIn ad Most pipeline qualification processes treat all of these the same, "they showed intent. " But the first category is genuinely predictive of near-term close. The last category is curiosity. Mixing them produces a pipeline that looks healthy and converts poorly. The difference in outcome is significant. Forrester research shows that outreach timed to trigger events can improve B2B conversion rates by up to 400% compared to untimed cold outreach, and that 74% of B2B buyers choose the vendor who first delivers relevant, contextual insights at the moment they're evaluating . Getting into the buying conversation early, with the right signal, is not a marginal improvement. It's a different game. The Fix: Qualify on Timing, Not Just Fit The shift that changes conversion rates is simple to understand and hard to implement without the right data: add timing signals to your lead qualification criteria. That means knowing when a prospect is actually ready to buy , not just whether they fit your ICP, and prioritising accounts by timing and win likelihood , not ICP score alone. The second half of that - scoring win likelihood against the competitor actually in the deal - is what stops a well-timed lead from being an unwinnable one. Before a lead enters your active sequences, your team should be able to answer: what is the real-time signal that tells us this account is in a buying window right now? If the answer is "nothing specific, they just fit our ICP," that account should go into a monitoring list, not an active sequence. Your reps' time is too valuable to spend on prospects that are ICP-fit but not in-market. When a timing signal arrives for that account, e. g. a competitor trial detected, a renewal approaching, a trigger event, the account should move immediately from monitoring to active priority. This isn't a harder process. It's a more honest one. It replaces "this looks like a good lead" with "this account is in a buying window right now. " And it produces a pipeline that's smaller, more honest, and far more likely to close. Your Pipeline Isn't the Problem. Your Definition of "Qualified" Is. The leads in your pipeline aren't bad leads. Most of them will eventually buy from someone. The question is whether they'll buy from you and whether they'll buy this quarter or in 18 months. The ones that buy this quarter have a real timing signal. Find them before your competition does. See which of your ICP accounts are in a genuine buying window right now. Start with 500 free Qualified Opportunities at marketsizer. io . Compare tools with MarketSizer Direct comparison with the intent platform mentioned above: MarketSizer vs 6sense . Full index at MarketSizer compared with other tools . Frequently Asked Questions Why don't qualified leads close? The most common reason qualified leads don't close is the absence of a real timing signal. ICP fit tells you a company could benefit from your product, it doesn't tell you they're in a position to act right now. Leads that stall typically matched the profile but lacked a genuine urgency trigger: a competitor trial, a renewal window, a trigger event that creates real pressure to decide. How do I improve my B2B lead conversion rate? The highest-leverage intervention is adding timing signals to your qualification criteria. Rather than prospecting all ICP-fit accounts equally, identify the subset that are in an active buying window, showing competitor trial activity, approaching renewal, or experiencing a trigger event. Focusing outreach on this subset typically produces significantly higher conversion rates from the same lead volume. What is the difference between a qualified lead and a Qualified Opportunity? A qualified lead meets ICP criteria and indicates some level of interest. A Qualified Opportunity meets ICP criteria, is showing active purchase intent signals, and is winnable based on competitive context. The distinction matters because most pipeline problems stem from treating qualified leads as Qualified Opportunities, and building forecasts on accounts that aren't actually in a buying window. Why does my pipeline look healthy but not convert? A pipeline that looks healthy but converts poorly is usually full of ICP-fit accounts that entered at a low-urgency moment, no real timing signal, no immediate trigger. They engaged at early stage (MQL, SQL) because the outreach was good, but there's nothing compelling them to act. The fix is adding timing signals to qualification, not more follow-up sequences. How long should a qualified lead stay in my pipeline before being disqualified? Rather than time-based disqualification, consider signal-based: if no real buying signal has emerged within 90 days (no trial detected, no renewal window, no trigger event), move the account to a monitoring list rather than an active sequence. Return it to active priority when a real timing signal appears. ## Blog: Timing Intelligence 101: How GTM Leaders Can Stop Missing Buying Signals and Start Acting in Real Time The pillar guide to Timing Intelligence for B2B GTM teams: what it is, how it beats intent data, and how to act on buying windows in real time. GTM teams have more data than ever before. You have CRMs brimming with contact information, analytics platforms tracking every click, and intent data providers flagging potential interest. Yet, despite this deluge of information, a critical gap persists. Sales pipelines stall, marketing campaigns miss the mark, and customer churn remains a persistent threat. The problem isn't a lack of data; it's a lack of temporal context. Your teams are operating with "signal blindness". An inability to see when a customer is truly ready to engage, buy, or expand. This guide addresses that blindness by introducing Timing Intelligence and providing a framework for building a GTM engine that captures real buying moments. By understanding "when," organizations can shift from reactive outreach to proactive, precision-guided engagement. And when timing signals are combined with ICP fit and win probability, the result is something more valuable than a signal, it's a Qualified Opportunity. If you want to see the underlying subscription-level datasets behind these timing patterns, download the 2025 Precision Intent Whitepaper . The Clock is Ticking: Why "When" is Your GTM's Underexploited Advantage In the complex orchestration of a modern GTM strategy, teams meticulously focus on the "who" (Ideal Customer Profile), the "what" (value proposition), and the "how" (channel strategy). Yet, the "when", the precise moment of opportunity, is often left to chance, guesswork, or lagging indicators. This oversight is no longer sustainable. The speed of business has accelerated, and buyer journeys are more dynamic and less linear than ever. The difference between capturing a market leader and losing to a competitor often comes down to a matter of days or even hours. Exploiting the temporal dimension of your market is not just a nice-to-have; it's a strategic imperative that separates high-growth organizations from the rest of the pack. The Hidden Cost of Being Blind to Timing Signal blindness is the pervasive condition where GTM teams, despite being surrounded by data, cannot distinguish between passive interest and an active buying moment. The consequences are serious: SDRs chase accounts that look good on paper but are not in market. SDRs spend time chasing companies that aren’t actually in market. AEs miss the narrow window when a competitor’s trial is live or a renewal is imminent. Customer success teams engage too late, when churn risk is already high. Demand generation invests budget on audiences without short‑term buying intent. We break down this gap in more detail in "Timing Beats Volume: Why Most GTM Teams Lose Deals Before They Event Start" . For example, data by MarketSizer suggests that more than 75 % of buyers aren’t actively in market at any given time. That means most GTM efforts are mistimed from the outset. Not wrong, just late. This blindness stems from relying on static or slow-moving data points (firmographics, historical engagement, or generic intent signals) that lack the real-time context of a company's immediate circumstances. Quarterly intent refreshes lag real behaviour. Enrichment fields do not update when a company trials or churns a solution. Website visitors remain anonymous unless they convert. Pipelines stall not because buyers vanish, but because the moment of opportunity was missed. Your teams might see that a company fits your ICP, but they are blind to whether the company needs you right now. This creates a GTM motion based on assumptions, not evidence, leading to wasted effort and missed connections with potential customers. What Causes Signal Blindness in GTM Teams? Signal blindness isn't a failure of individual effort; it's a systemic issue rooted in outdated data strategies, organizational silos, and mismatched processes. For GTM leaders, understanding these root causes is the first step toward building a more perceptive and responsive organisation. The problem lies not in the absence of signals themselves, but in our inability to capture, connect, and act upon them in a timely manner. The modern GTM stack has inadvertently created new walls just as it has torn down old ones, leaving critical insights trapped and moments of opportunity missed. Data Overload, Insight Scarcity: The Paradox of Information The central paradox for modern GTM teams is having access to more data than ever while struggling to extract actionable insights. Your CRM, marketing automation platform, product analytics, and third-party data sources create a tidal wave of information. However, this data is often fragmented, noisy, and lacks the unifying layer of temporal context. Teams become overwhelmed trying to manually connect a website visit with a news article or a support ticket. Without intelligent systems to filter, prioritise, and surface what truly matters right now , the data becomes more of a distraction than an asset. This overload leads to insight scarcity, where the most critical, time-sensitive signals are buried under a mountain of low-value information. Siloed Signals: When Teams Don't See the Full Customer Story Signal blindness is exacerbated by organizational silos. The marketing team sees content engagement and web traffic. The sales team sees CRM activities and email responses. The customer success team sees product usage and support tickets. Rarely do these teams have a unified, real-time view of the entire customer journey. A prospect might be showing strong engagement with marketing content (a marketing signal), while their company simultaneously announces a major expansion (a timing signal). A customer might show declining product usage (a CS signal) just as a key competitor launches a new, aggressive pricing model (a market signal). When these signals live in separate systems and are seen by separate teams, no one connects the dots. This fragmentation prevents a cohesive, timely response, leaving the organisation vulnerable and reactive. Legacy Processes vs. Agile Buyer Journeys: The Speed Mismatch Many GTM processes were designed for a slower, more linear world. Cadences are often time-based (e. g. , "email on day 1, call on day 3") rather than event-based. Territory planning is done quarterly or annually based on static firmographics. These legacy processes create a fundamental mismatch with the speed and agility of modern buyer journeys. A buying moment can emerge and vanish in a matter of weeks. A company's entire strategic priority can shift overnight following an acquisition or a funding announcement. When your GTM process operates on a rigid, pre-determined timeline, it is structurally incapable of adapting to the real-time needs of the market. This speed mismatch ensures that by the time your process directs you to act, the opportunity has often already passed. The Hidden Pain: Stalled Pipelines, Wasted Efforts, and Lost Revenue The pain of signal blindness is felt across the organisation, even if the root cause isn't always identified. It manifests as a sales pipeline with low velocity, where deals stall for "no apparent reason. " It shows up in marketing as low conversion rates from seemingly well-qualified leads. It appears in customer success as unexpected churn from accounts that "seemed happy. " These are the symptoms of a GTM engine running on incomplete information. Teams are putting in the effort, but that effort is misaligned with the customer's actual timeline. This leads to wasted resources, frustrated employees, and, most critically, a significant and often invisible drain on potential revenue. Introducing Timing Intelligence: The Antidote to Indecision and Delay Timing Intelligence is the antidote to this epidemic. It is a strategic GTM discipline focused on systematically identifying, interpreting, and acting upon time-sensitive events within your target market and customer base. It moves beyond passive data collection to create an active, event-driven GTM model. Instead of asking "Who should we talk to? ", Timing Intelligence answers a more powerful question: "Who should we talk to today , and why? " By layering real-time temporal context over your existing data, this approach empowers your marketing, sales, and customer success teams to engage prospects and customers with the right message at the exact moment of need, transforming your outreach from an interruption into a welcome, relevant conversation. What is Timing Intelligence and Why Does it Matter Now? Modern GTM strategy has mastered the who (ICP), the what (value proposition), and the how (channels and plays). The critical missing dimension is the when. Timing Intelligence detects and acts upon moments signaling readiness to buy, renew, expand, or switch, moving beyond probabilistic hints to concrete evidence of change. Examples include: A live trial of a competitor’s product. A subscription that has recently churned. A renewal window that is approaching. A new market entry detected across multiple domains. A tech stack change that reveals a complementary opportunity. How Timing Intelligence connects to Opportunity Intelligence Purchase Intent - the timing layer - is one of three dimensions that together define a Qualified Opportunity. The others are ICP Fit (does this account match your best-fit customers? ) and Win Probability (can you realistically win this one? ). Timing Intelligence without these two dimensions leads to chasing the right moment with the wrong accounts. All three must align. These events define when outreach will be welcomed instead of ignored and when the smallest intervention can prevent churn or activate expansion. Intent data has value, but it is often ambiguous. A company reading about automation does not mean a purchase team is evaluating vendors. Timing Intelligence adds the missing temporal context. It turns vague signals into concrete moments. Why Timing Matters Now More Than Ever Buyer journeys have become faster, quieter and far less linear. PLG motions allow users to trial tools without speaking to a rep. Switching costs continue to fall. AI accelerates evaluation cycles. Renewal processes are shorter and more fluid. In this environment, the window of opportunity compresses. When a prospect starts a trial, the competitive clock begins immediately. When a customer tests an alternative on a secondary domain, churn risk rises quickly. When a brand expands into new geographies, the expansion opportunity does not last long. This is a strategic shift: from aligning to assumed cycles to aligning to real‑time readiness. Timing Intelligence vs. Traditional Intent Data: What's the Difference? Traditional intent data tracks behaviour like content consumption or keyword research. It tells you who might be interested, but not when they’re ready. Timing Intelligence adds that temporal context, when the company is changing : maybe they’ve started a competitor’s trial, maybe their renewal window opened, maybe they expanded into a new region. These events are actionable triggers. Where intent is blurred and probabilistic, timing signals are concrete. They convert interest into readiness. Building Your Timing Intelligence Engine: A Framework for GTM Leaders Overcoming signal blindness requires more than just new technology; it demands a strategic framework for building a Timing Intelligence engine. This engine is the core of a modern GTM operation, designed to systematically ingest, unify, and activate time-sensitive data. For GTM leaders, constructing this engine involves laying a robust data foundation, integrating diverse signal types, and creating a single source of truth for timely action. This framework transitions your organisation from being data-rich and insight-poor to being intelligence-driven, ensuring every GTM motion is powered by a clear understanding of "why now. " 1. Identifying High-Impact Timing Signals The foundation of any Timing Intelligence engine is the quality and breadth of its data inputs. The goal is to move beyond static profiles to a dynamic, real-time understanding of your market. This requires a conscious effort to identify and track the signals that truly correlate with buying moments for your specific product or service. These signals fall into several key categories: behavioural, firmographic, and, most critically, temporal event-based data. For example: A live competitor trial, a renewal window opening, a tech stack change, or a geographic expansion. These are your "when" triggers. Building this foundation means strategically selecting data sources that provide a comprehensive view of a company's journey, structure, and, most importantly, its current circumstances. It's about focusing on the data points that predict future action, not just describe past states. 2. Map the Full Customer Journey Across Teams Capture first touch, website behaviour, content consumption, inbound/outbound interactions and extend that through onboarding, product usage, customer health scores. Only then can you see patterns that matter. 3. Integrate Intent, Firmographics and Zero-Party Data Firmographics give you structure (industry, size, tech stack). Intent and behavioural data give you interest. Zero‑party data (surveys, forms) gives you explicit context. Together you enrich the profile. 4. Create an Ideal Customer Profile This becomes the single source of truth, consolidating signals from marketing, sales, and customer success tied to time-sensitive events. When timing signals activate, the ICP updates and triggers actions. In practice, this means scoring accounts not just on fit, but on three dimensions simultaneously: ICP Fit, Purchase Intent (timing signals), and Win Probability. The combination that defines a Qualified Opportunity. 5. Activate Outreach in Real Time Insights are only useful if they trigger action. That means workflows that send tasks to SDRs, sequences via your MAP, alerts in the CRM, all aligned to the exact moment. How Timing Intelligence Transforms GTM Teams in Practice Timing Intelligence is not theoretical. When used in daily workflows, it changes how every team operates. SDRs & Outbound Teams: Work Smarter with Signal‑Based Lists Instead of generic lists, SDRs target accounts with recent churn, trials or expansion, outreach lands when the buyer is most likely receptive. AEs: Call at the Right Time with Full Context Reps enter calls aware of recent signals: “I saw you started a competitor trial 10 days ago” which improves discovery quality and compresses sales cycles. CS and Account Managers: Spot Churn and Expansion Signals Early Declining usage plus a competitor trial alert = early warning. Success teams act before the visible dashboard shows churn risk. For a CS-specific breakdown, see "Why Customer Success Teams Fail on Timing, Not Effort (And How to Fix It)" . Demand Gen: Build Campaigns That Hit Real‑Time Relevance Rather than broad targeting, marketers build campaigns around segments showing spikes in competitive activity or vendor churn, relevance drives higher engagement . Essential Technologies for Building a Timing Intelligence Stack Building a powerful Timing Intelligence engine requires a modern technology stack capable of ingesting, unifying, and, most importantly, activating data in real time. The right tools and platforms are not just repositories for information; they are the connective tissue that transforms raw signals into orchestrated GTM plays. From predictive AI models to customer success platforms, each component plays a critical role in ensuring that a detected buying moment is never missed. This section explores the key technologies that GTM leaders must leverage to bring their Timing Intelligence strategy to life at scale. AI & Machine Learning : Predictive models analyse diverse datasets (trial data, behaviour, news) to score readiness. Customer Data Platform (CDP) : Unifies data across CRM, MAP, analytics and third‑party feeds. If you're exploring how this plays out in tooling and data models, read our post on how propensity modelling transforms B2B SaaS lead prioritisation . Marketing Automation & Sales Intelligence Tools : Trigger personalised campaigns and alerts based on signals. Customer Success Platforms : Monitor health, usage and subscription signals to detect churn or expansion windows. Technology Intelligence : Track shifts in tech stack (installation, removal) to detect complementary or competitive opportunities. What Does a Fully-Functional Timing Intelligence Engine Require? To build a true Timing Intelligence capability, a GTM organisation needs three layers working together: 1. Evidence based vendor-state signals Signals must ground in observable facts: active trials, churned subscriptions, tech stack changes, geo expansion patterns. MarketSizer tracks 70M+ subscription outcomes across multiple categories in the Martech industry, with 17M+ active subscriptions monitored and signals refreshed daily. This gives GTM teams real evidence to act on, not inferences. Find out more about the MarketSizer platform. 2. A unified customer profile across systems Signals from across the market, product usage and CRM must converge into one real time view. Siloed signals cause blind spots. 3. Workflow-ready activation Timing is useless unless the insight reaches the rep at the moment of action. That means in the CRM, in the sales tools and in the channels where outreach happens. It requires the ability to trigger tasks, sequences and personalised plays instantly. These elements form the backbone of a modern, timing aware GTM engine. Without them, organisations remain trapped in lagging indicators and delayed reactions. The Strategic Advantage: Move from Reactive to Precision GTM Signal blindness is no longer an acceptable cost of doing business. For too long, GTM leaders have been forced to navigate a complex market with an incomplete map, relying on lagging indicators and educated guesses to determine when to engage. The result has been a tremendous waste of resources, missed opportunities, and a frustrating disconnect between effort and outcome. Implementing a Timing Intelligence engine changes how the business thinks, not just what software it runs. It requires a commitment to breaking down data silos, building agile processes, and empowering your teams with a unified, real-time view of the customer journey. The key takeaways are clear: Signal Blindness is the Hidden Drain on Revenue: Your biggest missed opportunities are the ones you never see. The cost of not knowing when a customer is ready is too high to ignore. Timing Intelligence is the Solution: It provides the crucial temporal context that traditional data lacks, turning vague interest into actionable buying moments. Actionability is Everything: The goal is not to accumulate data, but to integrate real-time insights directly into your GTM workflows, triggering immediate, relevant actions. By systematically capturing and activating time-sensitive signals, you can transform your entire organisation: Marketing becomes more efficient. Sales becomes more relevant. Customer success becomes proactive. RevOps becomes predictive. Revenue becomes repeatable. The path forward begins with a conscious decision to end signal blindness. Start by evaluating your current data sources and identifying the gaps. Foster collaboration between your marketing, sales, customer success, and RevOps teams to build a unified view of the customer. Invest in the technology that can centralise your data and activate it at scale. By embracing this approach, you are not just adopting a new strategy; you are building a more perceptive, responsive, and successful organisation poised to capture the real buying moments that define market leadership. Frequently Asked Questions What is Timing Intelligence in B2B sales? Timing Intelligence identifies and acts on events that signal readiness for purchase, renewal, expansion or churn giving you a “when” to accompany the “who” and “what”. What is Timing Intelligence in GTM? Timing Intelligence is the practice of detecting and acting on time-sensitive events that signal a company's readiness to buy, switch, renew, or expand. Unlike static lead scoring, it answers the question "who should we talk to today, and why? " by surfacing concrete signals like competitor trials started, renewal windows opening, tools being swapped, rather than inferred intent. It is the Purchase Intent layer of a complete Qualified Opportunity framework. What is the difference between Timing Intelligence and intent data? Intent data captures behavioural signals like content downloads and keyword research, useful for identifying interest, but typically lagging and probabilistic. Timing Intelligence provides direct evidence of specific buying-relevant events happening right now: a competitor trial has started, a subscription has churned, a renewal is 30 days out. The key difference is evidence vs. inference. How does Timing Intelligence improve sales and marketing alignment? It creates a shared, real‑time profile and workflow across teams. When a signal fires, both marketing campaigns and sales outreach can align instantly. What are examples of real‑world timing signals? Examples: A competitor trial kicking off at a company, a renewal window opening, a tech stack change, a company entering a new market. Can Timing Intelligence replace intent data? No, intent data remains valuable for identifying interest. Timing Intelligence adds the concrete temporal dimension that converts interest into readiness. The most effective approach combines both: intent data for awareness-stage signals, Timing Intelligence for in-market evidence, and win probability scoring to prioritise which opportunities to pursue first. Together, these three layers form a Opportunity Score that identifies Qualified Opportunities. What tools are needed to build a Timing Intelligence engine? You’ll need platforms that capture signals, unify data (CDP/CRM), trigger workflows (MAP/sales engagement), and stitch it all into your operations. AI helps deepen readiness modelling. Recommended Reading Purchase Intent vs Intent Data - the signal-quality argument that sits underneath the Timing Intelligence playbook. How to Build a Signal-led GTM Motion - the operating model that runs on top of timing intelligence. Best Purchase Intent Data Platforms for 2026 - the platform directory by category. ## Blog: Timing Beats Volume: Why Most GTM Teams Lose Deals Before They Even Start More activity does not mean more pipeline. Here is why timing trumps volume every time. There's a persistent belief in B2B GTM that the answer to a pipeline problem is more activity: more calls, more sequences, more SDRs, more campaigns. And the logic seems sound - if your conversion rate is fixed, then doubling inputs doubles output. But conversion rate isn't fixed. It's a function of timing. And timing is something volume, on its own, cannot fix. The teams consistently hitting pipeline targets aren't the ones with the most outreach activity. They're the ones whose outreach is concentrated in windows where a decision is actually live. More sequences into the wrong moment produces more polite non-responses. Fewer sequences into the right moment produces pipeline that converts. This post is about why timing beats volume - and what the win data from 140+ customer support and live chat SaaS products actually shows. The Volume Fallacy in B2B Sales The appeal of volume is understandable. It's measurable, manageable, and under the direct control of sales leadership. Hire more SDRs. Run more sequences. Add more accounts to the top of the funnel. The problem is that the out-of-market problem can't be outrun with volume. At any given moment, research by the Ehrenberg-Bass Institute for LinkedIn's B2B Institute suggests approximately 95% of B2B buyers are not in market - not actively evaluating, not in a buying window, not about to make a decision. In higher-velocity SaaS categories with shorter renewal cycles and more active trial culture, that number is lower - analysis of 70M+ subscription outcomes across 140+ customer support and live chat SaaS products (MarketSizer data, 2022-2025) shows approximately 24. 7% of accounts displaying observable buying signals at any time. But even at 24. 7%, three-quarters of your ICP list is not in a buying window right now. When you send 1,000 outreach sequences to an ICP list: - Roughly 750 reach companies with no immediate reason to engage - Roughly 250 reach companies in an active buying window - Your 2-3% response rate is a mathematical outcome of that ratio The instinct is to blame the copy, the subject line, the timing of the send. But the timing that matters isn't what hour you sent on Tuesday. It's whether the recipient was in a buying window when your message arrived. Static Scores Tell You Who. Timing Tells You When. The account scoring most GTM teams use today is static. ICP fit, lead scoring, engagement scoring - these tell you which companies match your ideal customer profile. They answer the question "who should we be pursuing? " That question has value. But it's only half the picture. The distinction: static scores identify who to target; timing signals reveal when to engage. And win likelihood - based on historical competitive outcomes in similar scenarios - tells you whether it's worth pursuing at all. Qualified Opportunities align all three simultaneously: ICP Fit, Purchase Intent timing, and Win Probability. A perfect ICP-fit account that renewed three months ago and has no evaluation underway is a very different outreach priority from a perfect ICP-fit account that is 45 days from renewal and just started a competitor trial. The static score looks the same. The timing score is radically different. The teams closing consistently aren't working harder on the 95%. They've found a way to identify the 5-25% that are in window - and they concentrate effort there. That narrowing is the job opportunity discovery does before a rep opens their list. What the Win Data Shows About Timing Across 140+ customer support and live chat SaaS products, clear patterns emerge in competitive win/loss outcomes: The first vendor to engage during an evaluation window wins more often The single strongest predictor of competitive outcome is not product quality, price, or brand. It's timing of engagement. When a vendor reaches a prospect in the first 14 days of a competitor trial - before opinions are formed - the probability of entering the final consideration set is significantly higher than engagement in weeks 4 or 5. Volume of outreach correlates weakly with win rate Teams with higher outreach volume don't necessarily win more. Teams with better-timed outreach do. The two variables are independent. High-volume, poorly-timed teams produce high activity and mediocre results. Lower-volume, well-timed teams produce predictable pipeline. Competitive losses cluster in a 2-4 week window early in the evaluation Most competitive losses aren't decided in the final meeting. They're decided when the prospect first uses the competing product and forms an initial impression. By the time your outreach arrives in week 5, the evaluation has a frontrunner. Renewal windows are the highest-conversion timing event The 60-90 days before a software renewal contract expires are the highest-density buying window in SaaS. A prospect in this window is already thinking about whether to stay or switch. ICP-fit outreach arriving in this window converts at significantly higher rates than the same outreach arriving 8 months into a fresh contract. What Good Timing Feels Like in a GTM Team Teams that have added timing intelligence to their workflow describe a specific shift: from "we're running a lot of activity" to "we know exactly which 15 accounts need our attention this week. " The weekly priority list isn't built from gut feel or account recency. It's built from signals: - These 8 accounts have competitor trials running - contact today - These 4 accounts are 45 days from renewal and showing churn signals - escalate to senior rep - These 3 accounts had a champion change last week - reach out before the new leader locks in their tech stack Outreach to these accounts feels different - because it is different. The prospect is in motion. The rep isn't interrupting; they're arriving at a moment the prospect is already navigating. When GTM teams align outreach, messaging, and campaigns to behavioural windows, deals transition from seeming "lucky" to becoming consistently predictable. That predictability has a name: Qualified Opportunities - accounts where ICP Fit, Purchase Intent, and Win Probability align at the same time. How to Shift From Volume to Timing Step 1: Measure your timing quality Before changing anything, understand the baseline. For your last 50 closed won deals: how many entered your pipeline with an identified timing signal (competitor trial, renewal window, trigger event)? For your last 50 closed lost deals: how many had no timing signal at pipeline entry? Step 2: Add a timing gate to pipeline entry Require an identified timing signal before any account moves to an active sequence. "Great ICP fit" is not sufficient on its own. The question is: what is the specific real-world event that makes this account likely to act this quarter? Step 3: Connect a real-time signal feed to your CRM You can't apply timing intelligence without timing data. Subscription intelligence platforms surface competitor trial events, renewal windows, and churn signals directly in CRM records - the same place reps already work. Step 4: Let signals define your weekly outreach priority Rather than working through a static list, let the signal feed surface your weekly priority. The accounts with live signals - competitor trials, approaching renewals, trigger events - move to the top. Everything else moves to monitoring. Step 5: Measure outreach-to-meeting rate by signal type Over time, you'll be able to see which signal types produce the best response rates. Competitor trial signals typically outperform renewal signals. Renewal signals outperform job-posting triggers. This data lets you continuously sharpen your timing criteria. Frequently Asked Questions What is the difference between timing and intent data in B2B GTM? Intent data tells you that an account has expressed interest - content downloads, keyword searches, ad clicks. Timing intelligence goes further: it detects specific, time-sensitive events that signal buying readiness right now, such as a competitor trial starting, a renewal window opening, or a subscription being churned. Timing is the difference between "this account might be interested" and "this account needs to hear from us today. " What is a Qualified Opportunity in sales? A Qualified Opportunity is an account that simultaneously meets three criteria: ICP Fit (they match your best-fit customer profile), Purchase Intent (they are showing active in-market signals right now), and Win Probability (historical data suggests you can win this type of account in this competitive context). Chasing timing signals alone without considering ICP fit or win probability leads to pursuing the right moment with the wrong accounts. Does increasing outreach volume improve conversion rates? Not directly. Conversion rates are primarily a function of timing quality - the proportion of outreach that reaches accounts in an active buying window. Adding volume to a poorly-timed outreach motion produces more activity with the same conversion rate. Improving timing quality improves conversion rate with the same or lower volume. Can timing intelligence improve retention? Yes. In customer support and live chat SaaS, churn risk surfaces in subscription signals weeks before it appears in health score dashboards. MarketSizer monitors 200+ vendors and 250+ products across 70M+ subscription outcomes, detecting competitor trials, usage drops, and renewal risk before CS teams would otherwise see them. This shifts CS from reactive intervention to proactive engagement - one of the highest-ROI timing plays in the GTM stack. How quickly does a buying window close? Competitor trial windows typically run 30-45 days. Renewal windows are typically 60-90 days, but the highest receptiveness to alternatives is in the first 30-45 days of that window. Leadership change windows are typically 60-90 days from the date of the new hire. After these windows close, prospects either make their decision or settle back into the status quo - and your outreach becomes ambient noise again. Volume Gets You in the Door. Timing Gets You the Deal. You can't volume your way to consistent pipeline when 75% of your ICP is out of market at any given time. The math doesn't work. But 25% of your market is in a window right now. They're evaluating. They're approaching renewal. Something happened in their business that opened a door. The only question is whether you see it in time. Figures refreshed May 2026 from MarketSizer's production data. The original analysis was conducted on a smaller, pre-Signal-Lab classification of the dataset; the underlying behaviour patterns are unchanged. For the canonical source of truth, see Our Data . ## Blog: How Propensity Modelling Transforms B2B SaaS Lead Prioritisation Move beyond gut feel. Learn how propensity models help you focus on accounts most likely to convert. Most B2B SaaS teams have a lead scoring problem. Not a lack of lead scores - most teams have plenty of those - but a fundamental issue with what those scores are measuring and how well they predict actual revenue. Standard lead scoring assigns values to behaviours: opened an email (+5), visited pricing page (+10), attended a webinar (+15). Enough points and a lead gets routed to a rep. The logic is that behavioural engagement correlates with buying intent. The problem is that this correlation is weak. And in a market where your reps' time is your most constrained resource, weak correlation translates directly to wasted effort. Propensity modelling is a different approach. And when it's applied well - combining three independent propensities rather than a single composite score - it's the closest thing to a reliable buying-readiness signal that exists for B2B SaaS. What Exactly Is a Propensity Model? A propensity model is a statistical framework that uses historical outcomes to predict future behaviour. Rather than asking "did this account do something that usually correlates with buying? ", it asks "given everything we know about this account and accounts like it, what is the probability that they will buy? " Unlike lead scoring, which assigns fixed values to behaviours, propensity modelling is adaptive. It examines historical outcomes, identifies genuinely impactful signals, and adjusts dynamically. Lead scoring resembles a checklist; propensity modelling functions as a compass. In practice, the most effective B2B SaaS propensity models evaluate three distinct propensities simultaneously: the likelihood of ICP Fit (is this the right type of account? ), the likelihood of Purchase Intent (is it in-market now? ), and Win Probability (can you realistically win this one? ). This three-part framework - when built on real subscription event data - is what separates teams that guess from teams that know. The Anatomy of a Propensity Model A propensity model has three components: Signals (Features) The inputs the model uses to make predictions. In B2B SaaS, high-quality signals include: Firmographic: company size, industry, growth rate, geographic market Technographic: current tool stack, category usage, adjacent software Behavioural: product usage patterns, CRM engagement history, support ticket volume Temporal: days since last purchase, renewal proximity, contract age Competitive / Intent: competitor research, content consumption, and - most powerfully for SaaS - direct subscription signals: active competitor trials detected, vendor churn, renewal windows, and tech stack changes. MarketSizer provides this layer from 70M+ subscription outcomes, refreshed daily. Outcome Labels What the model is predicting. Options include: - Purchase (will this account buy within 90 days? ) - Churn (will this account cancel within 90 days? ) - Expansion (will this account expand their contract within 180 days? ) - Win (will we win this deal if we pursue it? ) Critically, each outcome requires its own model. A model predicting purchase propensity uses different signals than one predicting churn propensity - and combining them into a single score often obscures more than it reveals. Historical Outcomes The training data the model learns from. A propensity model is only as good as the historical outcome data it's built on. Teams without sufficient closed won/lost history often need to supplement internal data with external signals - which is where subscription intelligence data at market scale becomes important. The Three Propensities That Matter Most 1. ICP Fit Propensity The probability that this account matches the profile of your historically successful customers. Built from firmographic and technographic signals. Tells you: "is this account worth investing in at all? " ICP fit is the filter. It removes accounts that, based on historical outcomes, are unlikely to convert regardless of timing or competitive context. In product terms this is ICP scoring : a model fitted to closed-won history rather than a segment someone wrote down once. 2. Purchase Intent Propensity The probability that this account is actively in a buying window right now. Built primarily from subscription and behavioural signals. This is where most propensity models fall short - they use content consumption and ad engagement as proxies for purchase intent, when the highest-fidelity signals are direct: is this company trialling a competitor? Is their renewal 30 days out? Did their champion just leave? The most predictive subscription signals for SaaS buying propensity include: - Competitor trial starts - an account is actively evaluating alternatives - Subscription churn events - a tool has been removed, creating a replacement opportunity - Renewal window timing - a contract is approaching expiry, typically 30-90 days out - Multi-vendor evaluation patterns - simultaneous trials of multiple competitors These signals are available from subscription intelligence platforms built on real market data - MarketSizer tracks these signals across 140+ customer support and live chat products using 70M+ subscription outcomes, refreshed daily. 3. Win Probability The probability that you will win this account if you pursue it, given the competitive context. Built from historical win/loss outcomes in similar competitive scenarios. Win probability is the dimension most teams omit entirely. They know if an account is in-market. They don't know if it's a fight they historically win. A company in an active Zendesk trial is a different proposition from one in an active Freshdesk trial - your win rate against these competitors may be very different, and that difference should determine how much resource you invest. How These Three Propensities Work Together The power of this framework comes from evaluating all three simultaneously rather than collapsing them into a single composite score. A single composite score hides important information: - High ICP fit + low purchase intent = great future customer, not a current opportunity - High purchase intent + low ICP fit = in-market, but not for you - High ICP fit + high purchase intent + low win probability = a live opportunity you're unlikely to win Separating the three propensities makes these distinctions visible. Reps can see not just that an account is high-priority, but why - and can calibrate their approach accordingly. An account that's ICP-fit and in-market but low win probability might still be worth a low-touch touch to maintain relationship. An account that's all three - ICP-fit, in-market, and high win probability - is a Qualified Opportunity worth immediate focus. Building a Propensity Model in Practice For teams with limited internal data If you don't have sufficient closed won/lost history (typically 200+ outcomes minimum), supplement internal data with external subscription intelligence. A market-level dataset covering competitor trials, renewals, and churn events across your category provides the volume and diversity of signals needed to build reliable purchase intent propensity scores. For teams with strong internal data Use your CRM history as the foundation for ICP fit and win probability models. Layer external subscription intelligence on top for the purchase intent dimension, which internal data typically can't capture (you don't know what your prospects are doing with competitors). For CS and expansion teams Run the same three-propensity framework in reverse: ICP fit for the customer (are they still in the profile of a successful long-term customer? ), expansion propensity (are they showing signals of needing more? ), and churn propensity (are they showing signals of looking elsewhere? ). External subscription signals - competitor trials running in a customer account - are often the first and most reliable indicator of churn risk. Conclusion: From Scores to Qualified Opportunities Propensity modelling transcends a simple lead scoring upgrade - it represents movement toward truly predictive GTM strategy. For SaaS companies facing growth pressure, it's transitioning from optional enhancement to essential capability. The end goal isn't a score - it's a Qualified Opportunity: an account where propensity to fit your ICP, propensity to be in-market now, and win probability all align. That alignment is what lead qualification is supposed to test, and what a stage-based checklist never does. That's when GTM effort converts most reliably. Frequently Asked Questions What is a propensity model in B2B SaaS? A propensity model is a predictive framework that uses historical outcome data to estimate the probability that an account will take a specific action - buy, churn, expand, or respond. Unlike lead scoring, which assigns fixed values to arbitrary behaviours, propensity modelling is built on actual outcomes and adjusted dynamically as new data arrives. What is the Opportunity Score in B2B propensity modelling? The Opportunity Score is a three-part propensity framework that evaluates accounts across ICP Fit (how well the account matches your best customers), Purchase Intent (signals that it is actively in-market right now), and Win Probability (likelihood of winning based on historical competitive outcomes in similar scenarios). Rather than a single composite score, this approach treats each dimension independently - an account can be high intent but low win probability, or perfect ICP fit but not yet in-market. All three must align to qualify as a Qualified Opportunity. What subscription signals are most predictive in B2B SaaS propensity models? The most predictive subscription signals for SaaS buying propensity include: competitor trial starts (an account is actively evaluating alternatives), subscription churn events (a tool has been removed, creating a replacement opportunity), renewal window timing (a contract is approaching expiry, typically 30-90 days out), and multi-vendor evaluation patterns (simultaneous trials of multiple competitors). MarketSizer tracks these signals across 140+ customer support and live chat products using 70M+ subscription outcomes, refreshed daily. How is propensity modelling different from lead scoring? Lead scoring assigns fixed point values to behaviours and sums them to a score. Propensity modelling estimates the probability of a specific outcome using machine learning on historical outcomes. Lead scoring is a rule-based system; propensity modelling is an evidence-based one. The practical difference: propensity models adjust to new data automatically, whereas lead scores require manual recalibration. Do you need a large dataset to build a propensity model? For ICP fit and win probability, you typically need 200+ historical closed won/lost outcomes to build a reliable model. For purchase intent, internal data is often insufficient - you can't see what your prospects are doing with competitors. Subscription intelligence data at market scale (70M+ outcomes across 200+ vendors) fills this gap and is available without building a large internal training set. Figures refreshed May 2026 from MarketSizer's production data. The original analysis was conducted on a smaller, pre-Signal-Lab classification of the dataset; the underlying behaviour patterns are unchanged. For the canonical source of truth, see Our Data . ## Blog: Beyond the 95/5 Rule: Why SaaS GTM Needs Precision Timing The 95/5 rule tells you most of your market is not buying. Precision timing tells you who is. The 95/5 Rule is one of the most cited principles in B2B marketing strategy. Developed by Professor John Dawes at the Ehrenberg-Bass Institute, it holds that at any given moment, only around 5% of B2B buyers are actively in-market. The remaining 95% are not evaluating, not ready to decide, and largely unreachable through demand-capture tactics. The implication is significant: most of your sales outreach is reaching the 95% who have no reason to engage. Your brand investment should dominate your demand-capture investment. And when you do target in-market buyers, you should do so with precision. The 95/5 Rule is a useful strategic anchor. But in SaaS specifically, the reality is more complex - and more actionable. Because in SaaS, buying windows are shorter, renewal cycles are more frequent, and trial culture means evaluation activity is directly observable. The in-market proportion is higher than 5%. And critically, you can actually see who's in it. What the 95/5 Rule Gets Right The core insight of the 95/5 Rule is important: the overwhelming majority of your ICP is not evaluating right now. Outbound sequences sent to a cold ICP list are statistically reaching mostly non-buyers. Brand investment that reaches the 95% before they enter a buying window pays long-term dividends that demand capture can't replicate. This reframes the ROI calculation for brand vs. demand spend. A prospect who encountered your brand during the 95% phase - when they were not evaluating - arrives at the 5% phase with familiarity and positive associations. Your demand capture work is easier and more effective because brand investment did its work earlier. The 95/5 Rule also reframes what "good conversion rates" look like. If 95% of your outreach is reaching non-buyers, a 2-3% response rate isn't a failure of execution. It's an accurate reflection of the in-market proportion. Where the 95/5 Rule Understates SaaS Reality The 95/5 Rule was developed on data from categories where vendor switching cycles are long - five-plus years. In markets where companies switch vendors every few years, only 5% might be evaluating at any given quarter. SaaS is different. Subscription-based software has: - Shorter average contract lengths (typically 1-3 years vs. 5+ years for enterprise software) - More active trial culture (free trials create evaluation periods that would not otherwise exist) - Faster churn and switching cycles driven by competition - Annual renewal moments that trigger re-evaluation even without active dissatisfaction The result: a meaningfully higher proportion of accounts show observable buying signals at any given time. Analysis of 70M+ subscription outcomes across 140+ customer support and live chat products spanning 2022-2025 shows that approximately 24. 7% of accounts display observable buying signals at any given time. This is not the same as 24. 7% actively choosing a new vendor - it includes accounts in early evaluation stages, approaching renewal while showing comparison behaviour, or running a competitor trial alongside their current tool. But it's meaningfully higher than 5%. And the actionable implication is significant: in SaaS, the in-market pool is larger and more dynamic than traditional B2B buying models suggest. The Evidence: A Spectrum, Not a Binary In-market vs. out-of-market is not a binary state. Buying readiness exists on a spectrum - and the most useful GTM framework is one that treats it that way. Based on subscription signal data, accounts in a given SaaS category at any given time distribute roughly as follows: High Intent (approximately 4-6% of market): Renewal cycle open, plus multiple active competitor trials underway. These accounts are actively comparing. The decision timeline is typically 30-60 days. GTM response: immediate, high-touch, executive-level engagement. Medium Intent (approximately 8-12% of market): Renewal cycle open, with one active competitor trial. Evaluating, but the process is less advanced. Timeline typically 60-90 days. GTM response: active sequence with strong timing context. Low Intent (approximately 8-10% of market): Early indicators - negative vendor review posted, job posting for a role that owns the category, or renewal approaching without active trial. Interest signals present, urgency unclear. GTM response: moderate-touch outreach, increased monitoring frequency. Not in Market (approximately 70-80% of market): Recently renewed, stable, no evaluation signals. GTM response: brand investment, awareness content, no active sequences. This spectrum maps directly to a Qualified Opportunity framework. An account at High intent isn't automatically a Qualified Opportunity - it also needs to fit your ICP and represent a scenario you can realistically win. Evaluating all three simultaneously - ICP Fit, Purchase Intent (the spectrum tier), and Win Probability based on competitive context - prevents high-intent accounts that aren't winnable from consuming disproportionate GTM resource. Run continuously across the whole addressable market, that evaluation is opportunity discovery rather than list building. From ICP Fit to Urgency: Why Both Matter The most common GTM prioritisation error is conflating ICP fit with buying urgency. An ideally profiled account provides no value if renewal occurred recently. Timing proves equally critical as profile match. And timing alone isn't enough either - a high-intent account outside your ICP, or in a competitive scenario you've historically lost, isn't a Qualified Opportunity. The most effective SaaS GTM teams evaluate three dimensions simultaneously: ICP Fit, Purchase Intent tier, and Win Probability. Only accounts that score well across all three deserve high-priority pursuit. A company with a perfect ICP score that renewed 8 months ago and has no evaluation signals is not worth an active sequence this week. A company with a moderate ICP score that is 45 days from renewal and running a competitor trial might be your best opportunity this month. The ICP score determines long-term potential. The intent tier determines immediate urgency. Win probability determines whether the investment of rep time is justified. All three must be visible simultaneously. Precision Timing in Practice Understanding the 24. 7% figure and the intent tier structure changes how GTM teams allocate effort: For demand capture: Focus ICP-fit outreach on High and Medium intent tiers. Competitor trial detection and renewal window monitoring feed this prioritisation automatically. Don't run sequences to the 70-80% Not in Market segment. For brand investment: The 70-80% Not in Market segment is where brand content earns its return. They're not evaluating today, but they will be. Familiarity and trust established now converts to competitive advantage when their renewal window opens. For CS and expansion: Apply the same intent tier framework to your existing customer base. A customer account that is Not in Market internally (stable, satisfied) but showing signals of market comparison (competitor trial running) has moved up the intent tier - and requires immediate CS attention. For RevOps: Build the intent tier distribution into your forecasting model. Pipeline entries in High and Medium intent tiers have materially different expected conversion rates than entries without intent signals. Separating these in your forecast produces more accurate predictions and clearer conversations about pipeline quality. Frequently Asked Questions What is the 95/5 Rule in B2B marketing? The 95/5 Rule, developed by Professor John Dawes at the Ehrenberg-Bass Institute, states that only around 5% of B2B buyers are actively in-market at any given time. It's a useful heuristic for balancing brand investment against demand capture - but in SaaS, buying windows move faster and more cyclically than the original model accounts for. What percentage of SaaS accounts are in-market at any time? Analysis of 70M+ subscription outcomes across 140+ customer support and live chat products (2022-2025) shows that approximately 24. 7% of accounts display observable buying signals at any given time - distributed across high, medium, and low intent tiers. This is meaningfully higher than the 5% suggested by the traditional 95/5 heuristic, driven by SaaS-specific dynamics: free trials, renewal cycles, and churn. What are intent tiers in SaaS GTM? Intent tiers segment accounts by buying readiness: High intent (renewal cycle plus multiple competitor trials), Medium intent (renewal cycle plus single trial), Low intent (early indicators, renewal without trials), and Not in Market (recently renewed, stable state). Each tier calls for a different GTM response - from rapid executive-level engagement at High, to long-term brand investment at Not in Market. What is a Qualified Opportunity in B2B SaaS? A Qualified Opportunity is an account that meets three criteria simultaneously: it fits your ICP (ICP Fit), it is actively in-market based on observable signals (Purchase Intent), and it represents a scenario you can realistically win (Win Probability). Intent tier alone is insufficient - an account can be High intent but outside your ICP or in a competitive scenario you historically lose. Qualified Opportunities combine all three. How do I know which intent tier an account is in? Intent tier classification requires real-time subscription signal data - competitor trials started, renewal windows opening, tool stack changes. MarketSizer tracks these signals across 200+ vendors and 250+ products using 70M+ historical subscription records, refreshed daily, to assign intent tiers to accounts in your target market automatically. The 5% Is Not Your Whole Market The 95/5 Rule was never meant to suggest that only 5% of your market matters. It was meant to explain why brand investment is essential for long-term growth - because 95% of your potential customers aren't ready yet, and how they perceive you during that period determines whether you win when they are. In SaaS, the in-market proportion is higher and more dynamic. The 24. 7% showing buying signals today won't all be showing them in three months. New accounts will enter that pool. The market is continuously rotating. Precision timing is knowing where accounts are in that rotation - and showing up with the right engagement at the right tier, not just when you feel like prospecting. Figures refreshed May 2026 from MarketSizer's production data. The original analysis was conducted on a smaller, pre-Signal-Lab classification of the dataset; the underlying behaviour patterns are unchanged. For the canonical source of truth, see Our Data . ## Blog: Why Customer Success Teams Fail on Timing, Not Effort (And How to Fix It) CS teams work hard. But without timing signals, effort gets wasted on accounts that were never at risk - or already gone. Customer Success teams are not failing because of insufficient effort. CS teams are, on the whole, working hard - logging calls, building relationships, conducting QBRs, and tracking health scores. The effort is there. What is missing is timing. The signals that would tell CS exactly when to act - on churn risk, renewal opportunities, and expansion moments - are either arriving too late or not arriving at all. That gap is called signal blindness. And it is the real reason CS teams miss the moments that matter most. What Signal Blindness Costs CS Teams Consider what happens when CS doesn't see the signals in time: On churn: A customer starts trialling a competitor. Without a signal, CS learns about this at the renewal conversation - weeks after the prospect has formed strong opinions about the alternative product. By then, retention is an uphill recovery exercise, not proactive management. On renewal: A contract enters the 90-day renewal window. Without a signal, CS follows the standard QBR cadence, treating this account the same as a recently-renewed one. The moment of highest leverage - when the customer is actively thinking about their options - passes without targeted intervention. On expansion: Usage has grown steadily. A new team has joined the company. The conditions for an expansion conversation are ideal. But without a signal, CS moves on the account based on quarterly planning cycles, not on the moment when expansion readiness is highest. Signal-led retention and expansion is the difference between the two cadences. In each case, the signal existed. What was missing was the infrastructure to surface it in time. The Signals That Actually Predict CS Outcomes Health scores are the standard CS tool. But health scores are lagging indicators - they measure what has already happened inside your product, not what is happening in the broader account context. The signals that most reliably predict churn risk, renewal readiness, and expansion opportunity are external: Competitor trial started - a customer has begun evaluating an alternative. This is the single most predictive churn signal, and it typically appears weeks before any internal product metric changes. Renewal window opening - 60-90 days before contract expiry is the highest-leverage period for retention activity. An account entering this window needs a different engagement posture than one that renewed six months ago. Stakeholder change - a champion has left. A new executive has joined who owns the budget. These changes create both risk (the new decision-maker hasn't built a relationship with your product) and opportunity (they haven't committed to an alternative either). Seat reduction - a customer reduces their active seat count. This signal often precedes downgrade or churn by 30-60 days. Miss it and the account moves from a save to a winback opportunity , which is a longer and more expensive motion. Expansion signal - a company has grown headcount in a role category that uses your product, or added complementary tools that indicate growing sophistication and a readiness for your product's advanced features. These signals are not available in health score dashboards. They come from subscription intelligence: the continuous monitoring of what is happening in an account's broader software environment. Moving Toward Signal-Led Customer Success Signal-led CS replaces the question "how is this customer doing inside our product? " with "what is happening in this account that should change our engagement right now? " The infrastructure for signal-led CS requires: A real-time external signal feed - subscription intelligence that monitors competitor activity, renewal timelines, and tool stack changes across the customer base ICP Fit context - is this customer still in the profile of accounts where you have high long-term retention? Or have their circumstances changed in ways that make them a lower-retention-probability account? Win probability by scenario - if a customer is trialling a specific competitor, what is your historical win rate in competitive scenarios involving that vendor? Together, these three dimensions - ICP Fit, Purchase Intent (which includes churn risk signals for existing customers), and Win Probability - form a Qualified Opportunity framework that applies equally to CS as it does to new business acquisition. At MarketSizer, subscription intelligence is built from 70M+ subscription outcomes across 140+ customer support and live chat products - with signals refreshed daily and on-demand verification available. This is evidence, not estimation. The capability delivers: Alerts the moment a competitor enters an existing account Flags upcoming renewals alongside stakeholder shifts Surfaces usage trends that predict upsell moments Automatic prioritisation of accounts by lifecycle urgency Scoring across ICP Fit, Purchase Intent, and Win Probability - so CS knows which accounts are Qualified Opportunities for expansion and which need urgent retention focus The 2025 Imperative: CS as a Revenue Engine The expectations placed on CS have shifted. Net Revenue Retention is now a primary board-level metric. CS owns renewal and expansion outcomes directly - not just customer satisfaction scores. Signal-led CS isn't just an upgrade. It is a competitive advantage. Teams using Opportunity Intelligence report 30-50% less time spent on unqualified accounts - and earlier identification of the expansion and churn moments that define net revenue retention. The teams that will lead on NRR in 2025 are not the ones with the largest CS headcount. They are the ones whose CS teams see the signals earliest and act on them with the greatest precision. Frequently Asked Questions What is Opportunity Intelligence for Customer Success teams? Opportunity Intelligence is the practice of using evidence-based subscription data to identify which accounts are worth prioritising for retention, renewal, or expansion - and when. For CS teams, this means getting ahead of churn risk before it shows on a health score dashboard, catching renewal windows 60-90 days out, and spotting expansion readiness signals before the account asks. It combines ICP Fit, Purchase Intent signals, and Win Probability into a single view. How does subscription intelligence differ from product analytics for CS? Product analytics shows what a customer is doing inside your product. Subscription intelligence shows what they are doing outside it - competitor trials started, tools being swapped, renewal decision timelines. These external signals are often the earliest indicators of churn risk or expansion readiness, and they rarely appear in product analytics dashboards. Together, both views give CS teams the full picture. How does subscription intelligence help CS teams? It delivers real-time signals - competitor trials, renewal windows, usage shifts, stakeholder changes - directly into CS workflows via CRM sync or the MarketSizer copilot. Signals are derived from 70M+ subscription outcomes across 200+ vendors and 250+ products and refreshed daily. CS teams can act on churn risk, expansion opportunities, and renewal timing before these moments become visible in lagging health score dashboards. What is the difference between a health score and a subscription intelligence signal? A health score aggregates product usage data - logins, feature adoption, support tickets - to produce a composite assessment of account health. It reflects what a customer has done inside your product in the recent past. A subscription intelligence signal reflects what is happening in the account right now, externally: a competitor trial that started today, a renewal entering its decision window next week, a stakeholder that moved roles yesterday. Health scores are retrospective; subscription intelligence signals are prospective. Can CS teams use the same Qualified Opportunity framework as sales? Yes. The three-dimension framework - ICP Fit, Purchase Intent, and Win Probability - applies directly to CS workflows. For existing customers, ICP Fit tells you whether this account still matches your ideal long-term customer profile. Purchase Intent signals in the CS context include churn risk indicators and expansion readiness signals. Win Probability tells you how successfully you have historically retained or expanded similar accounts in similar competitive contexts. Figures refreshed May 2026 from MarketSizer's production data. The original analysis was conducted on a smaller, pre-Signal-Lab classification of the dataset; the underlying behaviour patterns are unchanged. For the canonical source of truth, see Our Data . ## Blog: What Is Signal-Led ABX and Why ABM Is Outdated in 2025 ABM was built for a world with less data. Signal-led ABX is built for what we have now. Account-Based Marketing was built for a world where the challenge was identifying the right accounts to target. Find companies that match your ICP. Build a list. Run coordinated campaigns at them. Measure engagement. For many B2B teams, ABM delivered real results when it was introduced. But the market has moved. ICP identification is no longer the bottleneck - most mature GTM teams can define their ICP with reasonable accuracy. The bottleneck now is knowing when those accounts are ready to engage, and whether you can actually win when they are. That is where ABM falls short. And it is why signal-led ABX is emerging as the framework that replaces it. How ABM Has Evolved Account-based marketing has gone through several iterations since it became mainstream in B2B: ABM 1. 0 - List-Based Targeting: Build a static list of target accounts. Run display ads, direct mail, and sales outreach at those accounts simultaneously. Measure lift in account engagement versus a control group. ABM 2. 0 - Intent-Enhanced Targeting: Layer third-party intent data onto the target account list. Prioritise accounts showing interest signals - keyword research, content consumption, ad engagement. Personalise outreach by intent tier. Signal-Led ABX - Evidence-Based Prioritisation: Replace inferred intent with direct evidence of buying readiness. Use real subscription signals - competitor trials, renewal windows, tool churn - to identify the accounts that are in an active buying window right now. Coordinate sales, marketing, and CS around those signals as they fire. The progression represents a shift from static targeting to dynamic, evidence-driven engagement. The account list stops being a fixed input and becomes a live prioritisation signal. What Makes ABM Outdated in 2025 The core limitation of traditional ABM is that it answers the wrong question. ABM answers "which companies should we target? " Signal-led ABX answers "which companies should we engage today, and why? " The practical consequence: ABM runs campaigns. ABX responds to signals. Campaigns have lead times, planning cycles, and predetermined messaging tracks. Signals fire in real time and require immediate, contextually appropriate responses. ABM measures engagement. ABX measures readiness. Content downloads and ad impressions indicate interest. Competitor trials and renewal windows indicate active evaluation. ABM works from fit. ABX works from fit plus timing plus win probability. Fit alone doesn't tell you whether to engage this week. All three dimensions are required. What Is GTM Signal Intelligence? Signal intelligence in a GTM context means replacing probabilistic intent signals with direct evidence of events happening in target accounts. The distinction matters enormously in practice. Traditional intent data captures signals like: - Keywords researched related to your product category - Content downloaded from your website or content networks - Ad impressions and click-throughs These signals tell you an account might be interested. They are inferences from public behaviour. Signal intelligence captures: - Active competitor trials started at a target account - Renewal windows opening at accounts in your ICP - Subscriptions churned, creating a replacement opportunity - Tech stack changes indicating complementary needs - Stakeholder movements that reset buying authority These signals tell you something has actually happened. They are evidence of specific events with direct relevance to buying readiness. For customer support and live chat SaaS vendors, MarketSizer tracks 70M+ subscription outcomes across 200+ vendors and 250+ products - with daily signal refreshes. This gives GTM teams real evidence to act on, not inferences. From signals to Qualified Opportunities Signal intelligence becomes most powerful when it answers a specific question: is this account worth pursuing right now? The most effective signal intelligence translates live signals into a qualification framework - combining ICP Fit, Purchase Intent, and Win Probability (the Opportunity Score) - to surface only the accounts where timing, fit, and competitive context align simultaneously. Built from 70M+ subscription outcomes across 140+ customer support and live chat products (2022-2025), the Opportunity Score moves GTM teams from "there are signals here" to "this is a Qualified Opportunity. " How Signal-Led ABX Works in Practice A signal-led ABX motion has five components: 1. Real-time signal feed - competitor trials, renewal windows, churn events, and tech stack changes surfaced as they happen, not as a weekly batch. 2. ICP scoring on every signal - not every competitor trial is worth pursuing. A 20-person startup trialling Zendesk is a different proposition from a 300-person CS team doing the same thing. ICP scoring separates signals worth acting on from background noise. 3. Win probability layer - historical competitive outcome data determines which signals you are likely to convert. Your response to a Freshdesk trial and a Zendesk trial may be very different depending on your historical win rates. 4. Coordinated multi-channel response - sales, marketing, and CS receive the same signal simultaneously. Sales triggers immediate outreach. Marketing adjusts ad targeting and content delivery. CS flags any existing relationship. 5. Timing gates on all sequences - no account moves to an active sequence without a timing signal. ICP fit is a necessary but not sufficient condition for active pursuit. The output isn't just signals - it's Qualified Opportunities: accounts where ICP Fit, Purchase Intent, and Win Probability align at the same time. Frequently Asked Questions What is Opportunity Intelligence? Opportunity Intelligence is the practice of using evidence-based subscription data to identify and qualify accounts worth pursuing - not just accounts that fit your ICP, but accounts that are actively in-market and competitively winnable right now. It goes beyond traditional intent data by combining fit, timing, and win probability into a single score. What are Qualified Opportunities in B2B GTM? Qualified Opportunities are accounts that simultaneously meet three criteria: they fit your ideal customer profile, they are actively in-market (showing purchase intent signals), and they are winnable based on competitive context. Focusing GTM effort on Qualified Opportunities - rather than all in-market accounts - is how teams reduce wasted time by 30-50% while improving win rates. What is the difference between ABM and ABX? ABM (Account-Based Marketing) is a coordinated sales and marketing approach focused on identifying and targeting specific accounts with personalised campaigns. ABX (Account-Based Experience) evolves this by incorporating CS into the coordination and shifting from campaign-based engagement to signal-led responses. ABX engages accounts when signals indicate readiness, rather than according to a predetermined campaign calendar. Why is intent data outdated in 2025? Traditional intent data captures signals like content downloads and ad clicks - but these are inferred and often weeks behind actual buying behaviour. In 2025, subscription intelligence provides direct evidence of what is happening: competitor trials detected, renewal windows opening, tool evaluations in progress. Paired with ICP Fit and Win Probability scoring, this moves teams from "this account might be interested" to "this is a Qualified Opportunity. " How does signal-led ABX improve conversion rates? Signal-led ABX concentrates outreach into the windows when buying decisions are actually live. Rather than distributing effort across all ICP-fit accounts regardless of timing, it focuses resources on accounts where a specific triggering event has occurred. The result: higher response rates (the prospect is already in motion), shorter sales cycles (the evaluation is already underway), and better pipeline quality (entries are backed by evidence, not by a rep's intuition). Figures refreshed May 2026 from MarketSizer's production data. The original analysis was conducted on a smaller, pre-Signal-Lab classification of the dataset; the underlying behaviour patterns are unchanged. For the canonical source of truth, see Our Data . Recommended reading How to Build a Signal-led GTM Motion - the operating playbook that puts signal-led ABX into practice across sales, marketing, and CS. Purchase Intent vs Intent Data - the signal-quality argument that underpins the ABM-to-ABX shift. Best Purchase Intent Data Platforms for 2026 - the directory of signal platforms by category. How to Implement Signal-led Prioritisation in Salesforce and HubSpot - the tactical CRM-level implementation. ## Blog: The Best ABM Tools for 2026: Build a Smarter, Signal-Led Tech Stack A practical guide to the ABM tools worth evaluating - and how to think about your stack. Account-Based Marketing (ABM) has changed. The list of target accounts is no longer the differentiator; what separates effective teams now is knowing which of those accounts are actually in-market this quarter. The shift is from basic personalisation toward identifying Qualified Opportunities: accounts that are ICP-aligned , showing observable buying signals , and winnable based on competitive context. The best ABM tools in 2026 do one of three things: help you find the right accounts, help you act on them at the right time, or measure whether either of those is working. The stack below is grouped that way. Whether you are a fast-moving startup or an enterprise demand gen team, the gap between cutting through and burning budget is usually the timing layer, not the channel mix. What are the best ABM tools for 2026? The best ABM tools in 2026 combine signal intelligence, ICP fit, and timing data into a single qualification layer. By category: 6sense and Demandbase for end-to-end ABM platforms; MarketSizer for subscription-based timing signals; Common Room , Pocus , and Default for AI-native signal intelligence; Clay and Apollo. io for enrichment and outreach; HockeyStack and Dreamdata for attribution. Skip the bloated all-in-ones - the signal-led stack below is what actually drives pipeline. What changed in ABM tooling in 2026 The ABM tooling market has consolidated faster than most teams have refreshed their stacks. Clearbit is now fully integrated into HubSpot following the 2023 acquisition, and its standalone enrichment value has been largely subsumed into the wider HubSpot product. Bizible , once the default for multi-touch B2B attribution, has been retired by Adobe and replaced by Adobe Marketo Measure. commsor has rebranded and repositioned as Common Room , leaning into community and signal aggregation. 6sense and Demandbase remain the independent giants, but they are under genuine pressure from a new wave of signal-native challengers built for a smaller, faster, more competitive go-to-market motion. The biggest shift is the rise of signal intelligence as a distinct category. Where "intent data" was about probabilities - this account looked at a topic, therefore they might be interested - signal intelligence is about evidence. Tools like Common Room, Pocus, Default, and MarketSizer capture concrete events: a competitor cancellation, a champion changing roles, a new trial signup, a renewal date approaching. The output is not a score on a list of accounts; it is a ranked queue of qualified opportunities with a recommended next action and a defensible reason to engage. For a deeper look at how this category took shape, see our breakdown of signal-led ABX and why ABM is outdated in 2025 . Underneath this, AI-native entrants like Clay, Default, and Persana have collapsed enrichment, signals, and outreach into a single workflow. Where in 2023 a typical ABM stack ran six or seven specialised vendors, in 2026 a high-performing team can run on three or four - if those tools combine ICP fit with purchase intent and timing into one qualification layer rather than three separate dashboards. The practical implication: stop buying tools by category. Start buying for the moment in your funnel that is leaking. For most GTM teams in 2026, that moment is timing - finding the in-market 5% before competitors do. Add the timing layer your ABM stack is missing Most ABM tools tell you who matches your ICP. MarketSizer shows which of those accounts are evaluating competitors right now - active trials, renewals, switches - so your reps work the 5% who are actually buying. See how it slots in → Book a demo → 6 Quick Tips to Choose the Right ABM Tools Align tools to your ABM strategy not the other way around. Prioritise integration into your CRM and workflows. Focus on lifecycle, not just firmographics. Skip overpriced "all-in-one" bundles if you only need a slice. Ensure team adoption with shared goals across Sales, Marketing & CS. Build a stack that can scale with your growth. What to Look for in an ABM Tool An effective ABM tech stack is built with intention, not assembled from whichever vendor pitches loudest at the conference. The categories below cover the three jobs an ABM stack actually has to do: find the right accounts, act on them at the right time, and measure the result. Key features to look for in ABM tools The features that matter cluster around three questions: who, when, and what to do next. Who to target ( Fit ) : ICP scoring built on observable behaviour - tech stack, vendor history, switching patterns - not just firmographics. Firmographic match alone leaves you with a long list and no priority. Why now ( Timing ) : Real, observable signals - trial starts, renewal windows, churn events, competitive switches. Content downloads and anonymous web visits are not buying intent; they are research. What to say next ( Context ) : Which vendors are present in the account, what changed recently, and which historical scenarios you tend to win. Without this layer, every outreach reads the same. Integration into the workflow you already use : CRM sync, Chrome surface, CSV in and out. A tool that demands a new process gets abandoned in week six. Inspectable evidence : Every score should come with the signals behind it. A black-box ranking that says "act on this account" with no reason is a tool reps stop trusting after the first false positive. These five are what separate a stack that delivers from one that looks impressive in a procurement deck. The 2026 ABM Tech Stack: Category-by-Category Breakdown Tools to find the right accounts in ABM Category Tool Key Features Ideal For Intent Data Clearbit (now HubSpot) Enriches CRM data with firmographics and intent insights. Now fully integrated into HubSpot following the 2023 acquisition - evaluate as part of the wider HubSpot product rather than as a standalone tool. HubSpot customers wanting native enrichment. RB2B Identifies 70 - 80% of anonymous website traffic (people + companies) by installing a tracking script. Sales and marketing teams who want to proactively engage high-intent visitors before they convert. Apollo. io Combines intent insights with outbound email capabilities. Sales-driven ABM teams focused on outreach. factors. ai Provides granular analysis of account engagement signals. Companies prioritising deeper signal visibility. Data Enrichment Leadspace Offers AI-driven enrichment and account prioritisation. Companies optimising sales - marketing alignment. LeadiQ Streamlines lead capturing and qualification for outbound efforts. Sales teams optimising lead generation. Lusha Simplifies contact data enrichment and validation for prospecting. Small to medium-sized sales teams. 1P/2P/3P Signals Lantern Aggregates 1P and 2P data for granular targeting. Teams looking for precise targeting signals. Foundry Delivers deep technographic insights from 3P data. Companies building technology-specific ABM lists. champify Activates IP signals by identifying customer champions. Teams focused on renewal and expansion. LoneScale Delivers actionable intent and firmographic insights. Companies needing real-time ABM data. koala Tracks second-party signals for dynamic ICP adjustments. Teams refining their targeting strategies. Relationship Mapping The Swarm Analyses team networks to uncover key stakeholders. Teams mapping organisational hierarchies. SalesIntel Maps decision-maker hierarchies with firmographic details. Teams refining account-based prospecting. Sales and Marketing Activation Tools for ABM Success Category Tool Key Features Ideal For Content Creation & Personalisation Folloze Create account-specific landing pages and experiences. Personalised ABM content at scale. Uberflip Centralise and curate personalised content experiences for accounts. Content hubs for specific account groups. Navattic Interactive product demos to engage target accounts. Driving engagement with product-focused buyers. Hushly Convert anonymous visitors with dynamic, personalised content. Engaging unknown website visitors. Wynter Test and refine ABM messaging with target audiences. Optimising content relevance for ABM campaigns. Unbounce Build targeted, high-converting landing pages. Campaign-specific landing pages. Mutiny Personalise website experiences dynamically for target accounts. Real-time web personalisation for ABM visitors. Turtl Interactive and visually rich content for target accounts. Creating engaging, immersive content. Email & Direct Outreach Outreach Automate and optimise account-specific email sequences. Scaling personalised email sequences. Reply AI-powered email outreach with automated follow-ups. Targeting and nurturing ABM leads. Instantly High-volume, personalised cold email outreach. Reaching target accounts efficiently. Sendoso Direct mail campaigns and gifts for account engagement. Building meaningful account relationships. Reachdesk Automate gifting and direct mail as part of ABM strategies. Strengthening account-level engagement. Paid Media & Advertising Metadata. io Automates ad campaigns targeting specific accounts. Multi-channel ad delivery for ABM. Terminus Comprehensive ABM advertising and engagement platform. Running multi-channel ABM campaigns. RollWorks Targeted ads and sales insights for account activation. Scaling ABM campaigns across accounts. N. Rich Account-based native ad delivery. Hyper-targeted advertising for ABM programs. Sales Engagement lemlist Hyper-personalised cold email campaigns. Driving engagement through unique email content. clay Personalisation at scale through data-driven workflows. Automating outreach with enriched data. Warmly Generates personalised meeting prep and account insights. Tailored, data-driven account interactions. Usergems Tracks customer champions and provides relationship-based insights. Building deeper account connections. Default AI-native bundle of enrichment, signal capture, scheduling, and outbound in a single workflow - one of the new generation collapsing several ABM tools into one. Lean GTM teams consolidating their stack into fewer, AI-driven workflows. Best Reporting and Analytics Tools for ABM Category Tool Key Features Ideal For Analytics Looker Offers tailored metrics and data visualization for ABM campaigns. Monitoring and optimising ABM performance. Google Analytics Tracks website engagement and account activity. Measuring account behaviour and campaign performance. Mixpanel Provides insights into user behaviour and product engagement. Product-focused ABM campaigns and user insights. Segment Consolidates and integrates data for seamless analytics reporting. Unifying ABM data streams across platforms. Power BI Advanced reporting and analytics with customisable dashboards. Analysing cross-departmental ABM metrics. Tableau Visualises campaign data for actionable insights. Creating interactive ABM performance reports. Snowflake Scalable data platform for managing and analysing large datasets. Centralising ABM data for advanced analysis. Attribution Adobe Marketo Measure (formerly Bizible) Multi-touch B2B attribution. Bizible was retired by Adobe and the product now ships as Marketo Measure - same lineage, integrated with the wider Adobe Experience Cloud. Adobe / Marketo customers connecting marketing actions to revenue. Dreamdata B2B revenue attribution with complete pipeline visibility. Linking ABM efforts to revenue results. HockeyStack Attribution modelling for understanding campaign effectiveness. Evaluating the success of multi-channel campaigns. factors. ai Offers account-based attribution insights. Measuring the impact of ABM campaigns at the account level. Funnel Simplifies attribution reporting with data integrations. Reporting on ABM campaign ROI and engagement. Sales Forecasting Clari Provides pipeline health insights and revenue forecasting. Predicting ABM campaign outcomes and revenue. DOMO Combines business intelligence with pipeline management. Aligning sales metrics with ABM efforts. Engagement Tracking Heap Tracks user behaviour and product interactions with accounts. Analysing ABM engagement and optimisation. Pendo Monitors product usage and tracks user engagement. Understanding customer behaviour for upselling. Tools for Identifying Growth Opportunities Category Tool Key Features Ideal For Retention & Expansion Catalyst Monitors customer health and identifies at-risk accounts. Proactive churn prevention and customer retention. ChurnZero Tracks account engagement and predicts churn likelihood. Reducing churn and boosting upsell opportunities. Gainsight Provides comprehensive customer success metrics and playbooks. Driving customer retention and expansion strategies. Revenue Optimisation Revenue. io Combines sales enablement with analytics to optimise account strategies. Enhancing account ROI and revenue growth. Gong Analyses sales conversations to surface upsell and cross-sell opportunities. Optimising sales conversations for account growth. Amplitude Tracks account behaviour and identifies trends for upselling and retention. Understanding account journeys and product adoption. Customer Lifecycle Insights ActiveCampaign Automates account engagement and monitors lifecycle stages. Nurturing accounts with tailored engagement. Totango Customer success platform for managing lifecycle stages and metrics. Identifying expansion opportunities across accounts. InsightSquared Provides deep sales analytics and lifecycle tracking. Aligning sales strategies with account insights. Multi-Purpose Signal Intelligence Tools These tools sit across the find / act / measure split. They are listed together because they get compared in procurement, not because they do the same job. Worth being explicit: MarketSizer is a signal-led account prioritisation platform. It is not a contact database, not an outreach or sequencing tool, not a bulk export tool, and not a black-box intent score. Vendors like ZoomInfo, Apollo, Salesloft, and HubSpot do those jobs and do them well; MarketSizer sits on top, telling those tools which accounts are worth working this week and why. Tool Key Features Best For Category Coverage MarketSizer Signal-led account prioritisation. Surfaces accounts that are ICP-fit , in an active buying window (trial, renewal, switch), and historically winnable . Inspectable signals with timelines. Delivered inside the CRM, Chrome, and CSV workflows your team already uses. B2B SaaS vendors covering acquisition, winback, inbound qualification , and expansion against an observable signal Find (Fit + Timing + Context) Common Room Aggregates community, product, and 1P signals into a single account view. Successor to the original commsor positioning, now focused on signal intelligence and AI-driven prospecting workflows. Teams running community-led or PLG motions who need to surface in-product and community signals at the account level. Finding Accounts, Engagement Pocus Product-led sales platform that scores accounts on product-usage and intent signals, then routes the highest-priority ones to sales with recommended plays. PLG and hybrid-sales teams converting free users into paid pipeline. Finding Accounts, Engagement, Growth 6sense Predictive analytics, intent data, account prioritisation Teams needing end-to-end ABM execution and campaign optimisation. Finding Accounts, Engagement Demandbase Intent signals, account insights, and audience segmentation. Running targeted, multi-channel ABM campaigns. Finding Accounts, Engagement LinkedIn Sales Navigator Relationship mapping and actionable account insights. Building strong connections and uncovering decision-makers. Finding Accounts, Engagement Clari Pipeline forecasting and sales performance insights. Sales alignment, tracking success and opportunities Analytics, Growth ZoomInfo Data enrichment, firmographics, contact data Teams needing enriched data for targeting and prospecting. Finding Accounts, Engagement Apollo. io Prospecting, email outreach, intent signals Sales teams running efficient outbound ABM efforts. Finding Accounts, Engagement Bombora Third-party intent data for account targeting. Identifying accounts showing buying intent Finding Accounts, Engagement Salesloft Multi-channel sales engagement with robust analytics. Automating and personalising account interactions Engagement, Growth HubSpot CRM, marketing automation, email outreach, and analytics. Centralising ABM workflows across marketing and sales Engagement, Analytics, Growth Common Pitfalls when building an ABM tech stack The patterns that repeat across teams that end up with a stack that does not deliver: Tools chosen without a clear job: when nobody can name which question the tool answers (who, when, or what next), it gets used as a list builder by default. All-in-one platforms bought for one feature: paying enterprise pricing for a single use case while the other 80% of the platform sits idle. Sales, marketing, and CS working from different account lists: the highest-cost mistake. The same account showing as "hot" to marketing and "cold" to sales means one of them has the wrong signal. Strategy treated as a tooling problem: no stack rescues a target account list that was wrong before the procurement started. Tips for selecting the best ABM tools for your needs A clearer process makes the decision tractable. Name the question first. Is the tool answering who, when, or what next? If you cannot say, do not buy. Align to the CRM and workflow you already run. Tools that need a new process get adopted in week one and abandoned by week six. Make sales, marketing, and CS pick the account list together. Misalignment shows up as wasted credits later, not at signature. Avoid all-in-one bundles when you only need one slice. Three sharp tools beat one expensive one with eight half-built features. Check the integration list against your real stack, not the vendor's marquee logos. The connector you need is rarely the one on the homepage. Test scalability against your real account volume, not the demo dataset. Make adoption a procurement criterion. Track who opens the tool in week four, not who signed the contract in week one. A stack built this way ages better than one assembled from vendor pitches. Benefits of using the right ABM tools Higher conversion on a shorter list. Better targeting means fewer accounts worked, more of them won. Sales and marketing working the same list, with the same priority and the same evidence trail. Earlier visibility into churn and expansion, because the same signal layer that finds new buyers also flags existing customers who are evaluating alternatives. Compare individual tools with MarketSizer Side-by-side comparisons for the tools mentioned above: Apollo , ZoomInfo , 6sense , Clay , HG Insights , Cognism , Lusha , Amplemarket , Bombora , BuiltWith , Common Room , RB2B , Warmly . Full index at MarketSizer compared with other tools . FAQs What is the best ABM tool for small businesses? For small teams, Apollo. io and HubSpot are the strongest all-rounders - both bundle enrichment, outreach, and lightweight ABM features without enterprise pricing. If your motion is signal-led rather than volume-led, Common Room and MarketSizer offer the most leverage at a small-team price point because they replace several tools at once. Can I use general marketing tools for ABM? Yes, with customisation. CRMs like HubSpot or Salesforce, plus a marketing automation tool like Marketo, can run a basic ABM programme if you layer in account-level scoring, intent data, and sales alignment. The limitation is timing - generalist tools tell you which accounts fit your ICP, but rarely when they are actually in-market. How do I measure success with ABM tools? Look beyond engagement metrics. The metrics that matter are: pipeline created from target accounts, win rate on those accounts versus non-target, average deal size, and time-to-close. Engagement rates and account activation are useful leading indicators, but only if they correlate with downstream pipeline. Are ABM platforms worth the investment? For businesses focused on high-value accounts with longer sales cycles, yes - the deal-size lift typically justifies the spend within 12 months. For SMB-focused or transactional GTM motions, the ROI is harder to defend and you are usually better off with a lighter signal-intelligence layer on top of an existing CRM. What is the difference between ABM tools and CRM systems? ABM tools target accounts as the unit of work and surface the right ones to engage based on fit, intent, and timing. CRMs manage relationships and pipeline once those accounts are already in your funnel. They are complementary - the ABM tool feeds your CRM with prioritised accounts, and the CRM is where execution and tracking happen. How can I ensure team adoption of new ABM tools? Adoption fails when tools are bought for capability rather than workflow. Pick tools that route the right accounts into the systems your reps already live in (CRM, Slack, email) rather than asking them to log into yet another dashboard. Align Sales, Marketing, and CS on a shared definition of a Qualified Opportunity before rolling anything out. Final Thoughts: Build Your ABM Stack for Precision, Not Just Presence The job of an ABM stack is to answer three questions for every account: is it a fit, is now the right time, and what should the rep say next. The tools that do that well are the ones worth buying. The ones that look comprehensive but cannot answer those three questions are the ones that quietly stop getting opened. The shift worth making this year is from "signal intelligence" to Opportunity Intelligence. Signals tell you something is happening. A Qualified Opportunity tells you it is worth acting on, because the account fits your ICP, is in an observable buying window now, and is one you can win. The three-layer view is what separates a list from a priority. Related features in MarketSizer Opportunity Discovery - the ranked feed of in-market accounts that combines fit, intent, and timing into one queue. ICP Scoring - account-fit scored on observable behaviour (tech stack, vendor history, switching patterns), not just firmographics. Purchase Intent - real, evidence-based buying signals: trials, churn, renewals, competitive switches. Lead Qualification - score inbound and outbound leads against the same evidence layer the rest of your stack runs on. Win Likelihood - historical win probability scored against 50M+ subscription outcomes in similar competitive scenarios. Recommended reading Best Purchase Intent Data Platforms for 2026 - the sister listicle, focused specifically on the purchase intent layer of the stack rather than the broader ABM platform space. Purchase Intent vs Intent Data - the definitional argument behind the evidence-vs-inference framing that runs through this listicle. How to Build a Signal-led GTM Motion - what to actually do with the stack once you have the right tools assembled. How to Implement Signal-led Prioritisation in Salesforce and HubSpot - the tactical CRM-side companion for the operating model. ## Blog: eDesk Partnered with MarketSizer to Gain a Competitive Edge How eDesk uses MarketSizer to identify in-market accounts and win more competitive deals. eDesk leads the way in eCommerce customer engagement , integrating seamlessly with over 400 platforms , including Amazon, eBay, Walmart, Shopify, and TikTok Shop. Competing with industry leaders such as Zendesk, Freshdesk, and Gorgias , eDesk sought a smarter way to understand its market, identify high-potential opportunities, and streamline its operations. That’s why eDesk partnered with MarketSizer to continuously transform raw data into actionable insights, automate time-consuming tasks, and uncover new opportunities for growth. The Challenge: Competing in a Fast-Moving Market Seeing the Full Picture eDesk needed clearer visibility into competitor trials, churned accounts, and market trends to stay proactive. Precision Targeting The ability to identify and prioritise high-purchase-intent customers across multiple countries. Scaling Smarter Addressing the specific needs of industries such as Automotive , Consumer Electronics , and Home & Garden . The Solution: Smarter Insights, Faster Decisions MarketSizer is providing a suite of ongoing, actionable solutions that enable eDesk to work smarter, engage more effectively, and scale with ease. 1. Competitive Visibility at Scale Tracking 63 competitors , including Zendesk, Freshdesk, and Gorgias, covering 80% of the market volume . Processing millions of active, trial, and churned subscriptions, helping eDesk refine its sales and marketing ABM strategies. 2. Real-Time Data That Works for You Enriching 100% of ICP-applicable CRM records with real-time subscription data, ensuring targeting is sharper and timing more precise. Delivering active competitor-trial intelligence that informs proactive re-engagement strategies for sales and support teams. 3. Smarter Customer Segmentation Categorising customers by lifecycle stage: New, Developing, Established, and Loyal . Prioritising leads with purchase intent scores: High, Medium, Low, or Not in Market . 4. Simplified Automation Supporting the automation of workflows across HubSpot, reducing manual effort and enabling faster sales and marketing responses. Providing ongoing support for campaigns such as the US Auto Parts initiative , using lifecycle and intent-driven insights to craft tailored messaging and timing. The Impact: Turning Insights into Action Sharper Competitive Insights Providing eDesk with a clearer understanding of where competitors like Zendesk, Freshdesk, and Gorgias are active. Real-Time, Actionable Data Delivering enriched, synchronised customer profiles for better decisions across 195 countries . Smarter Targeting Empowering eDesk to focus resources on high-value accounts, improving engagement and retention. Effortless Scalability Supporting the automation of workflows and tailored, multichannel campaigns across key eCommerce industries. eDesk’s Perspective "MarketSizer has redefined how we understand and act on our competitive landscape. With always-on intelligence and automation, we’ve replaced manual processes with precision, turning data into decisions faster than ever. This partnership has empowered us to focus on what matters: identifying opportunities and driving smarter global strategies. " - Brendan Hughes, CCO, eDesk Founder’s Perspective "Our partnership with eDesk demonstrates how actionable intelligence and automation can drive smarter decisions and real impact in competitive markets. " - Niall O'Gorman, Founder, MarketSizer About eDesk eDesk is a leading eCommerce customer service platform, enabling businesses to manage customer interactions seamlessly across over 400 platforms , including Amazon, eBay, Walmart, Shopify, and TikTok Shop. Recognised for its innovative tools, eDesk helps online retailers deliver exceptional customer support, improve productivity, and streamline operations in a competitive global market. Visit eDesk at www. edesk. com About MarketSizer MarketSizer provides B2B GTM teams with Opportunity Intelligence: identifying Qualified Opportunities based on ICP Fit, Purchase Intent, and Win Probability. Built on competitive subscription data, MarketSizer delivers live insights on competitor trials, renewal windows, and churn signals via CRM sync, a web copilot, and a standalone discovery app. The result: Less time on unqualified accounts and higher conversion on the opportunities that matter. Learn more at www. marketsizer. io Press Release Coordination For media inquiries and further information, please contact Beachhut PR , MarketSizer’s communications partner: Email : press@beachhutpr. com Phone : +353 1 555 1234 Website : www. beachhutpr. com FAQs What is Opportunity Intelligence for competitive SaaS selling? Opportunity Intelligence combines real-time subscription signals with ICP scoring and win probability to surface accounts worth pursuing right now. For competitive SaaS vendors like eDesk, this means knowing which accounts are trialling competitors, when renewals are approaching, and which scenarios your team has historically won, so GTM effort focuses on accounts where all three align. How does MarketSizer track competitor subscriptions? MarketSizer monitors software vendors in the customer support and live chat & other Martech categories using 70M+ subscription outcomes, with signals refreshed daily (max. 5-day lag). This gives vendors real-time visibility into competitor activity across their entire addressable market, without manual research. What results can a team expect from subscription intelligence? Based on MarketSizer's Opportunity Intelligence framework, teams typically see 30 - 50% less time spent on unqualified accounts, earlier identification of competitive threats and expansion windows, and improved engagement rates from better-timed, contextually informed outreach. See the live picture for yourself: the eDesk customer signals page shows the same win/loss and competitor-trial data this partnership runs on. Start free - 500 qualified opportunities, no credit card. ## Blog: 6 Strategies to Build High-Quality Target Account Lists Your TAL is only as good as the signals behind it. Here are six ways to build lists that convert. If your sales pipeline is full of unresponsive or unqualified leads, the problem might not be your pitch, it's your list. Building effective target account lists is one of the most overlooked growth levers in B2B sales. But what does it take to build a truly effective list? It's not just about collecting company names or industry data, it's about combining AI-powered tools, intent signals, and strategic alignment between your sales and marketing teams to turn prospects into loyal customers. In this guide, we'll reveal proven strategies to help you identify, segment, and engage high-value accounts, transforming your pipeline into a revenue-driving machine. This guide gives you 6 battle-tested strategies to build smarter, sharper, and more successful account lists with the help of intent data, ICP frameworks, sales intelligence, and multi-touch outreach. Let's get started. Strategy #1: Start with a Crystal-Clear Ideal-Customer Profile (ICP) A strong ICP is the foundation of every great B2B list. It defines who your solution is perfect for based on firmographics, behaviour, tech stack, and more - and once it is written down, ICP scoring is what keeps it honest against closed-won data instead of opinion. What is an Ideal Customer Profile? An Ideal Customer Profile is a detailed description of a company that would benefit the most from your offering. Unlike buyer personas, which focus on individual decision-makers, an ICP zeroes in on the ideal organisation to target. It includes attributes that make a company an ideal match, such as industry, company size, revenue, location, technology stack, and pain points that align with your solution. By using an ICP, your teams can filter out accounts that are less likely to convert, reducing wasted resources and focusing efforts on high-value prospects. This is crucial for businesses engaged in Account-Based Marketing (ABM), where a targeted, strategic approach is essential. Key Elements of a Successful ICP To build a high-quality ICP, it's essential to include several critical factors that define your ideal accounts. Here's a breakdown of the main components: Firmographics Industry : What industries are most likely to benefit from your product or service? For instance, a SaaS company offering project management tools may find success targeting tech, marketing, or consulting firms. Company Size and Revenue: Determine the ideal size of your target accounts by considering the number of employees, annual revenue, or market capitalisation. For example, if your solution is designed for large enterprises, focusing on SMBs may not yield high-value results. Technology Stack: Identify which tools and software your ideal accounts are already using. For instance, if your product integrates seamlessly with Salesforce, targeting companies that use it could increase conversion rates. Behavioural Indicators Behavioural data is a powerful predictor of intent and can help refine your ICP. Look for signals that indicate a strong likelihood to buy, such as: Past purchases or contracts with similar solutions. Engagement with your content, like whitepapers, webinars, or product demos. Recent hiring activity in relevant departments, indicating potential growth or expansion needs. Pro Tip: Use tools like Google Analytics, HubSpot, or LinkedIn Sales Navigator to capture and analyse these behavioural signals. Geography Defining the geographic scope of your ICP is crucial to avoid diluting your efforts. The target region should neither be too broad (like "APAC") nor too narrow (like a single ZIP code). Consider factors such as language, regulatory environments, and cultural nuances. For instance, a cybersecurity firm may prioritise countries with stringent data protection laws like the EU or Canada. Target Job Roles Understand who makes the buying decisions within your target accounts. This may vary depending on the industry and the size of the company. Focus on key decision-makers such as: Chief Information Officers (CIOs) for tech solutions. Chief Marketing Officers (CMOs) for marketing software. Directors of Procurement or Heads of Finance for cost-related solutions. How to Build an ICP in Practice Developing a clear and actionable ICP requires a structured approach. Here are some steps to guide the process: Analyse Your Best Customers Start by examining your existing customer base to identify the characteristics of your most successful accounts. This includes factors like industry, company size, technology use, and purchasing behaviour. Use your CRM data to analyse patterns. For example, if most of your top clients are mid-market tech firms, your ICP should reflect this. Conduct Customer Interviews Speak directly with existing customers to gain insights into why they chose your solution and the specific pain points it addresses. This qualitative data can reveal trends that quantitative data might miss. Ask questions such as: What was the trigger event that led you to seek a solution like ours? What specific problems were you trying to solve? What criteria did you use to evaluate your options? Combine External Data Sources Utilise external databases like ZoomInfo, Crunchbase, or LinkedIn to gather data on prospective companies that match your ICP criteria. Combine these insights with your internal CRM data to create a holistic view of your ideal customers. Refine and Validate Your ICP Regularly review and refine your ICP based on feedback from sales teams and performance data. If you notice that certain types of accounts are not converting as expected, adjust your ICP criteria accordingly. Continuously validate your ICP by comparing new leads against it to ensure that they align with your defined profile. Common Mistakes to Avoid When Defining Your ICP Being Too Vague: An overly broad ICP leads to targeting irrelevant accounts, which wastes time and resources. Be as specific as possible. Overlooking Behavioural Indicators: While firmographics are essential, behavioural indicators provide crucial insights into a company's readiness to buy. Don't rely solely on demographics. Failing to Update Your ICP Regularly: Market conditions, customer needs, and your offerings change over time. Regularly revisit and update your ICP to keep it relevant. 👉 Pro tip: Analyse your best customers and layer CRM insights with third-party tools like LinkedIn Sales Navigator or MarketSizer . Once your ICP is defined, take it further with an Opportunity Score: scoring accounts not just on ICP Fit, but also on Purchase Intent (are they in-market now? ) and Win Probability (can you realistically win this scenario? ). Accounts that score well across all three are Qualified Opportunities, the highest-value targets on any account list. Strategy #2: Use Intent Data to Identify Accounts Actively in Market Once you've established a clear Ideal Customer Profile (ICP), the next crucial strategy for building a high-quality target account list is using intent data. In today's competitive landscape, knowing when to reach out to a prospective account can be just as important as knowing who to reach out to. This is where intent data comes into play, it helps your sales and marketing teams identify which accounts are actively seeking solutions similar to what you offer, allowing you to prioritise high-intent prospects. What is Intent Data? Intent data is information collected about a company's online behaviour that indicates a potential interest in your products or services. It captures both explicit and implicit signals to help you gauge an account's readiness to buy. Explicit Signals: These are direct actions that demonstrate a clear intent to purchase. Examples include: Filling out a demo request form on your website. Downloading case studies, whitepapers, or e-books. Attending your webinars or virtual events. Implicit Signals: These are indirect actions that imply interest but aren't as overt. Examples include: Frequent visits to your pricing page or product pages. Searching for specific keywords related to your offerings. Engagement on social media, such as liking or sharing your content. Spending increased time on your website or interacting with your emails. By analysing these signals, you can identify which companies are in the consideration stage of the buying journey and ready for outreach. This allows you to focus your sales efforts on accounts with the highest potential for conversion. Why Timing is Critical in Sales In B2B sales, reaching out to a prospective account at the right moment is the single biggest lever on response rate. Even if you have the perfect solution for a company, contacting them when they aren't actively looking or ready to buy can result in missed opportunities. Intent data ensures that you're engaging with prospects at the right time, which increases the likelihood of capturing their interest. A study by Forrester found that 74% of B2B buyers conduct more than half of their research online before making a purchase decision. By the time they reach out to a vendor, they are already well-informed. Using intent data lets your sales team get ahead by engaging with these prospects before they officially raise their hands. How to Gather and Use Intent Data Effectively Here's a breakdown of how you can gather and utilise intent data to identify high-quality accounts: Track Website Behaviour Use analytics tools like Google Analytics, HubSpot, or Hotjar to monitor which pages prospects visit on your website and how long they stay. If multiple visitors from the same company are spending time on your product pages or blog content, that's a strong signal of interest. Look for patterns like multiple visits to pricing pages, product comparison charts, or demo sign-up pages. These behaviours suggest an account is actively considering solutions in your niche. Monitor Search Engine Activity Use platforms like Bombora or 6sense, which specialise in capturing intent data from search engines and external websites. These tools can show you which companies are searching for terms related to your products. By identifying accounts searching for specific industry keywords, you can reach out to them with tailored messaging that addresses their current challenges. Analyse Engagement on Content and Social Media Track interactions with your content across various channels. For instance: Email Click-Through Rates (CTR): High engagement with your email content, such as clicking on links or downloading resources, signals strong interest. Social Media Activity: Monitor LinkedIn or Twitter for companies engaging with your posts. Accounts that frequently interact with your content are often exploring solutions. Bring in Third-Party Data Providers Platforms like ZoomInfo, Demandbase, MarketSizer and LinkedIn Sales Navigator can provide insights into intent data beyond your website. These tools aggregate signals from multiple sources to highlight accounts showing strong buying intent. Pro Tip: Integrate these tools with your CRM to automate the process of identifying high-intent accounts and triggering alerts for your sales team. Using Intent Data to Prioritise Accounts Once you've gathered intent data, the next step is to prioritise which accounts to target. Not all signals are created equal, some are stronger indicators of purchase intent than others. Here's how to prioritise: Score Accounts Based on Intent Signals Assign a score to different intent activities (e. g. , visiting a pricing page might be worth more points than downloading a blog post). Use tools like HubSpot's lead scoring feature to automate the process. Accounts with the highest scores should be your top priority. Segment Accounts by Stage of Interest High Intent : Accounts showing active buying signals, e. g. competitor trials started, renewal windows open within 30 days, or multiple vendor evaluations underway. Prioritise these immediately. Moderate Intent : Accounts engaging with educational content like webinars, case studies, or pricing pages; interested but not yet fully in-market. Low Intent : Accounts browsing blog posts or social media content. Fit your ICP but not showing urgency signals yet. Not in Market : Recently renewed or stable. Keep warm through brand-building content. Not all accounts will be ready to buy immediately. Segment them based on where they are in the buying journey: High Intent: Accounts that have explicitly requested demos or pricing information. Moderate Intent: Accounts engaging with educational content like webinars or case studies. Low Intent: Accounts browsing blog posts or social media content. Create Tailored Outreach Campaigns Use the intent signals you've collected to craft personalised outreach messages. For instance: If an account has been researching a specific feature, emphasise that capability in your emails. For accounts that attended a recent webinar, follow up with related content or a case study. Strategy #3: Use Sales Intelligence to Surface Buying Triggers After defining a clear Ideal Customer Profile (ICP) and using intent data, the next strategy to enhance your target account list is utilising advanced sales intelligence tools. These tools provide invaluable insights into your target accounts, allowing your sales and marketing teams to engage with prospects more effectively. In today's data-driven world, having access to the latest, most accurate information is crucial to gaining a competitive edge. What Are Sales Intelligence Tools? Sales intelligence tools are platforms that gather, analyse, and provide insights about companies and their buying behaviour. These tools go beyond basic contact information, helping businesses understand their prospects' pain points, current solutions, and buying readiness. Used well, they turn a static list into opportunity discovery : a shortlist that reorders itself as accounts move. By using real-time data, you can refine your target account lists, identify opportunities, and prioritise outreach based on actionable insights. These tools can provide a wide range of data, such as: Company firmographics: Size, industry, revenue, and location. Technographic data: Information about the software and tools a company uses. Account activity: Monitoring the behaviour of target accounts across different channels. Competitive intelligence: Insights into an account's engagement with competitors, including contract renewals, active subscriptions, and churn data. Key Features to Look for in Sales Intelligence Tools When selecting sales intelligence tools, focus on platforms that offer the following features to enhance your targeting efforts: Real-Time Data and Alerts Tools like MarketSizer, ZoomInfo, LinkedIn Sales Navigator, and Demandbase provide real-time updates on target accounts. This includes data on funding rounds, company expansions, leadership changes, or new product launches. Set up alerts to notify your sales team when key accounts show buying signals, such as hiring relevant roles or increasing marketing spend. Technographic Analysis Understanding the technology stack used by a target account can reveal pain points and potential gaps your product can fill. For instance, if your solution integrates with Salesforce, prioritise accounts already using that platform. Tools like MarketSizer, BuiltWith or Datanyze can provide insights into the tools and software that a company relies on, helping tailor your outreach. Competitor Engagement and Churn Analysis Sales intelligence platforms can reveal whether a target account is using a competitor's solution, when their contract might be up for renewal, or if they have recently churned from a competitor's product. This data allows your team to time outreach perfectly, reaching out to accounts when they are most likely considering alternative solutions. Behavioural Tracking and Account Maturity Sales intelligence tools can classify target accounts based on their engagement with your brand over time. For instance: New Accounts: Accounts that recently engaged with your brand and are in the early stages of the buying journey. Developing Accounts: Prospects that have shown sustained interest over time but haven't yet converted. Loyal Accounts: Existing customers that may be ripe for upselling or cross-selling. Best Practices for Utilising Sales Intelligence Tools Integrate Sales Intelligence Tools with Your CRM Ensure that your CRM (like Salesforce or HubSpot) is integrated with your sales intelligence tools to streamline data flow. This allows your sales team to access account insights directly within their workflow. Automated alerts and reports can be set up to notify your team of changes in target accounts, ensuring timely follow-ups. Regularly Clean and Update Your Data Sales intelligence data is only as good as its accuracy. Make it a practice to regularly clean your CRM of outdated information and update account details based on the latest intelligence. Train Your Team on the Insights Provide training to your sales and marketing teams on how to interpret sales intelligence data and incorporate it into their strategies. This ensures they can fully utilise the tools at their disposal and adjust their approach based on real-time insights. Strategy #4: Segment Your Accounts for Personalised Outreach Once you've developed a clear Ideal Customer Profile (ICP) and identified high-intent accounts, the next step is to segment your target account list. Segmentation is critical because it allows your sales and marketing teams to focus their efforts on specific groups of accounts with tailored strategies, improving engagement and conversion rates. Why Account Segmentation Matters Rather than treating all prospects the same, segmentation helps you: Personalise outreach for different groups, increasing the relevance of your messaging. Prioritise resources toward high-value accounts, optimising the efforts of your sales team. Boost engagement rates by targeting prospects based on their unique needs and behaviours. Key Strategies for Effective Account Segmentation Here's how you can segment your accounts to maximise the effectiveness of your outreach: Firmographic Segmentation Focus on core characteristics like industry, company size, revenue, and geography. Tailor your messaging based on these elements to resonate better with each segment. For example, highlight regulatory compliance for financial firms or emphasise scalability for tech companies. Technographic Segmentation Analyse the technology stack used by accounts to identify opportunities where your product fits best. Tools like MarketSizer or BuiltWith can help pinpoint which platforms your target accounts use. Example: If your product integrates seamlessly with Microsoft Dynamics, focus your efforts on companies already using it. Behavioural Segmentation Group accounts based on their engagement with your brand, such as website visits, content downloads, or social media interactions. Prioritise accounts showing strong buying signals (e. g. , frequent visits to your pricing page) to increase conversion rates. Intent-Based Segmentation Use intent data to identify accounts that are actively searching for solutions similar to yours. Segment them into tiers based on their readiness to buy. Here are some example tiers: High Intent : Accounts showing live subscription signals, e. g. competitor trials detected, renewal windows opening, or vendor evaluation patterns. Act immediately. Moderate Intent : Accounts engaging with product content, webinars, or whitepapers. Nurture with targeted, specific content. Low Intent : Accounts that fit your ICP but haven't shown in-market signals. Keep warm with brand content and track for signal change. Not in Market : Recently renewed or stable. Investment in this segment should focus on long-term awareness, not near-term conversion. Implementing Segmentation in Your CRM To streamline your efforts, make sure your CRM system is optimised for segmentation: Tag and filter accounts based on your chosen criteria (e. g. , industry, technology stack). Automate workflows to assign accounts to the right segments based on their behaviour and engagement levels. Best Practices for Successful Segmentation Start Simple and Scale: Begin with broad categories like industry or company size, then refine further as you gather more data. Collaborate Across Teams: Ensure both sales and marketing teams are aligned on segmentation criteria to avoid miscommunication. Regularly Update Segments: Markets and customer needs change; regularly review and adjust your segments to stay relevant. Strategy #5: Align Sales & Marketing to Avoid Wasted Play Once you have segmented your target accounts, the next essential step is ensuring alignment between your marketing and sales teams. Without this collaboration, even the most precise targeting efforts can fall short. Studies show that misalignment between sales and marketing can cost companies up to 10% of their revenue. However, when these teams work together seamlessly, it can drive significant growth by improving engagement and closing high-value deals faster. Why Alignment is Key for Target Account Success Sales and marketing alignment is more than just generating leads or running campaigns. It's about creating a cohesive strategy that ensures both teams are working toward the same objectives: Increased Conversion Rates: Targeted accounts receive consistent, personalised messaging, resulting in better engagement. Faster Sales Cycles: A unified approach helps sales teams act on qualified leads faster, reducing the time to close. Efficient Use of Resources: Collaboration ensures that both teams are focusing on accounts with the highest potential value. 5 Ways to Align Sales and Marketing Check out our guide on LinkedIn for 5 strategies to align your sales and marketing teams ! Special Tip: Use Account-Based Marketing (ABM) to Align Teams Effectively One of the most powerful ways to align your sales and marketing teams is by adopting an Account-Based Marketing (ABM) approach. ABM focuses both teams on a pre-defined list of high-value accounts, ensuring that resources are directed where they matter most. Here's how it can help: Unified Strategy: With ABM, both teams collaborate from the start to develop a comprehensive plan for engaging key accounts, including shared messaging, personalised content, and coordinated touchpoints. Clear Ownership and Accountability: By agreeing upfront on which accounts to prioritise, marketing can drive awareness while sales focuses on closing deals, ensuring that each team knows their role in the process. Measurable Results: ABM allows you to track joint performance metrics like account engagement and revenue impact, making it easier to demonstrate the value of alignment efforts to stakeholders. How to start: To kickstart your ABM strategy, co-create target account plans with both sales and marketing teams, identifying specific pain points and crafting personalised outreach campaigns tailored to each account. This joint effort will foster collaboration and accelerate conversions. Strategy #6: Build Multi-Touch, Multi-Channel Outreach Campaigns With your sales and marketing teams aligned and a segmented target account list in place, the next crucial step is executing a multi-touch outreach strategy. This approach involves reaching out to your target accounts through multiple channels and touchpoints to maximise engagement. A single email or phone call is no longer enough to break through the noise, today's decision-makers require a more persistent, yet personalised, approach to capture their attention. What is a Multi-Touch Outreach Strategy? A multi-touch strategy is the practice of using a combination of channels (like email, LinkedIn, phone calls, and targeted ads) to connect with prospects at various stages of their buyer journey. It's not just about quantity but strategically orchestrating these touchpoints to create meaningful interactions that move prospects closer to making a purchase. The key is to balance persistence with personalisation. You want to stay top of mind without coming across as pushy or impersonal. Why Multi-Touch Outreach is Effective Modern B2B buyers are inundated with outreach from countless vendors. According to industry research, it can take 8 to 12 touchpoints to effectively engage a prospect and initiate meaningful conversations. Here's why a multi-touch approach is critical: Increased Visibility: Engaging prospects across different platforms helps ensure your message isn't missed. Enhanced Personalisation: Using various channels allows you to tailor your messaging to the context of each interaction, making your outreach more relevant. Building Trust Over Time: Consistent, well-timed touchpoints help build credibility, especially when your outreach offers value through helpful content or insights. Key Components of a Successful Multi-Touch Outreach Strategy Use Multiple Channels Use a mix of channels to engage your target accounts. Here's a breakdown of effective options: Email: Ideal for sharing resources like case studies, whitepapers, or product updates. Keep emails concise and personalised to the recipient's interests. LinkedIn: A great platform for connecting with decision-makers, sharing relevant content, and engaging in conversations. Focus on building relationships rather than going straight for the pitch. Phone Calls: Useful for follow-ups and adding a personal touch. A well-timed call can be effective after a prospect has engaged with your content or shown interest. Targeted Ads: Use platforms like LinkedIn Ads or Google Display Network to keep your brand top of mind for high-value accounts. Pro Tip: Tailor your approach based on the preferred communication channel of your target accounts. For example, some industries may respond better to LinkedIn outreach, while others may prefer phone calls. Craft Personalised Touchpoints Based on Engagement Data Use the insights gathered from your CRM and intent data to inform your outreach strategy. For instance: If an account has downloaded a specific whitepaper, follow up with an email offering a related case study. If a prospect frequently visits your pricing page, it may be a good time for a direct outreach call or a personalised demo invite. Apply automation tools like Outreach. io or Salesloft to streamline multi-touch campaigns while still allowing for personalisation. Create Sequences and Cadences Design structured sequences for different segments of your target accounts. For example: Top-tier accounts may receive a high-touch sequence that includes personalised emails, phone calls, and LinkedIn connections over a month-long period. Lower-priority accounts might go through an automated sequence of emails and targeted ads to gauge their interest before investing more personalised effort. Cadence Example: Day 1: Send a personalised email introducing your solution. Day 3: Connect on LinkedIn and share a relevant article or case study. Day 5: Follow up with a phone call to discuss challenges the prospect may be facing. Day 10: Send a targeted email based on their specific interests or behaviours. Offer Value at Every Touchpoint Each interaction should be meaningful and provide value to the prospect. This could be in the form of industry insights, solutions to common challenges, or exclusive invitations to webinars and events. Avoid hard sells in early touchpoints. Instead, focus on building trust by demonstrating your expertise and understanding of the prospect's pain points. Track, Measure, and Optimise Continuously track the performance of your multi-touch outreach efforts using analytics tools. Key metrics to monitor include: Open and response rates for emails. Engagement metrics on LinkedIn and other social channels. Call-to-meeting conversion rates for phone outreach. Use A/B testing to refine your messaging and determine which channels are most effective for different segments. Best Practices for Multi-Touch Outreach Be Consistent but Flexible While consistency is crucial, be prepared to adjust your cadence based on the prospect's engagement. If an account responds positively to an initial touchpoint, shift your approach to more personalised follow-ups. Automate Without Losing the Human Touch Tools like HubSpot, Outreach. io, or Pardot can help automate your outreach while still allowing for personalisation. Automate the routine parts of your outreach but always include personalised elements to stand out. Test and Iterate Regularly review the performance of your outreach sequences and adjust based on what's working. Experiment with different touchpoint intervals, channels, and messaging to find the optimal mix for your audience. Common Pitfalls to Avoid Overwhelming Prospects: Too many touchpoints in a short period can feel spammy and drive prospects away. Focus on quality over quantity. Using Generic Messaging: Avoid sending the same message to all prospects. Tailor your outreach to the unique needs and pain points of each account. Neglecting Data: Not using engagement data to adjust your approach can result in missed opportunities. Continuously analyse data to refine your strategy. Bonus: How to Measure Success (and Improve) A successful target account strategy requires more than just generating leads. It's about consistently engaging the right accounts, converting them into customers, and maximising their lifetime value. By measuring the impact of your strategy, you can: Optimise your approach based on real data rather than assumptions. Identify bottlenecks in your outreach process and address them promptly. Ensure alignment between sales and marketing teams to achieve shared goals. Prove the ROI of your account-based marketing (ABM) initiatives to stakeholders. Key Metrics to Track for Success To measure your target account strategy effectively, focus on a mix of quantitative and qualitative metrics. Here are some of the most critical metrics to monitor: Account Engagement Score This metric tracks how actively a target account is engaging with your brand across channels (emails, social media, website, etc. ). High engagement scores indicate strong interest, while low scores may require adjustments in your outreach strategy. Tools like HubSpot, Terminus, and Demandbase can help you monitor engagement scores and identify which accounts are showing the most interest. Conversion Rates by Account Segment Track how many of your target accounts are converting at each stage of the sales funnel, from initial engagement to closed deals. Segment conversion rates by industry, company size, or other ICP attributes to understand which segments are performing best. If conversion rates are low in a specific segment, consider adjusting your messaging or outreach tactics. Pipeline Velocity Measure how quickly accounts move through your sales pipeline. Faster pipeline velocity indicates that your outreach is effectively engaging the right prospects and pushing them toward a purchase decision. Regularly review the average time it takes to convert accounts and look for stages where deals are stalling. Return on Investment (ROI) for ABM Campaigns Calculate the ROI of your account-based marketing efforts to ensure that your resources are being used effectively. This involves comparing the revenue generated from targeted accounts against the costs of your marketing and sales efforts. ROI can be improved by continuously refining your ICP, optimising touchpoints, and reducing customer acquisition costs. Customer Lifetime Value (CLV) CLV measures the total revenue you expect to generate from a customer over the course of their relationship with your company. A successful target account strategy should not only focus on converting accounts but also on retaining them for long-term value. Focus on accounts with high CLV potential, as these will deliver the greatest ROI. Win Rate and Deal Size The win rate (percentage of closed deals compared to opportunities) and average deal size are key indicators of how effective your strategy is. An increase in these metrics suggests that your targeting efforts are attracting high-quality accounts. Analyse which segments or industries have the highest win rates to refine your ICP and prioritise similar accounts. Continuous Monitoring and Optimisation of Your Account List While tracking metrics is essential, it's equally important to regularly review and optimise your account lists. The market is always evolving, and your strategies should be too. Here's how to keep your account list fresh and effective: Review and Update Your ICP As you gather data on account performance, refine your Ideal Customer Profile to better reflect the characteristics of your highest-value accounts. Adjust your targeting criteria to focus on segments with the highest conversion rates and CLV. Use Feedback from Sales Teams Your sales team interacts with prospects daily and can provide valuable insights into what's working and what's not. Use their feedback to adjust your outreach strategies, messaging, and touchpoints. Schedule regular debriefs with sales reps to discuss account performance and identify areas for improvement. Use Intent Data to Reassess Priorities Continuously monitor intent data to identify changes in your target accounts' behaviour. Accounts that were previously unengaged may show renewed interest, while others may drop off your radar. Re-prioritise your target list based on the latest data to focus on accounts that are actively showing buying signals. Automate Reporting for Real-Time Insights Use automation tools to generate reports on key metrics and account performance. This enables you to quickly identify trends, adjust your strategy, and act on new opportunities. Tools like Salesforce, Marketo, or LinkedIn Sales Navigator can help automate the collection and reporting of data, saving your team time and effort. Common Pitfalls to Avoid Relying Solely on Top-of-Funnel Metrics: Don't just focus on engagement rates. Track metrics that cover the entire customer journey, from initial contact to closed deals and beyond. Ignoring the Feedback Loop: Failing to incorporate insights from sales into your strategy can lead to missed opportunities. Always keep the lines of communication open between departments. Not Updating Your Strategy Regularly: The business landscape changes quickly. Failing to update your ICP, account lists, or outreach tactics can leave you targeting outdated or irrelevant accounts. Final Thoughts: Quality Over Quantity Wins Every Time In today's competitive B2B landscape, success hinges on precision and focus. By implementing the strategies we've outlined, starting with a well-defined Ideal Customer Profile (ICP), using intent data and applying advanced sales intelligence tools, segmenting your target accounts, aligning your sales and marketing teams, and executing a multi-touch outreach strategy, you can transform your approach to customer acquisition. Building high-quality target account lists is not just about gathering names and contact information; it's about targeting the right accounts at the right time with the right message. With data-driven insights and a well-coordinated strategy, you can drive better engagement, increase conversion rates, and maximise your return on investment. However, the journey doesn't stop here. To stay ahead, it's crucial to continuously measure, optimise, and refine your approach based on real-time data and evolving market conditions. By using tools like MarketSizer and integrating the best practices covered in this guide, you'll be well-equipped to build stronger relationships with your target accounts and drive sustainable growth. A high-quality account list is not built once and forgotten. It is reviewed every week against the signals coming in, and the accounts at the top of the list this Monday are not the same as last Monday. Try MarketSizer if you want to see, for your own market, which accounts are in a competitor trial, approaching a renewal window, or showing churn signals right now - so the next account your team opens is one with an observable reason to act. Compare tools with MarketSizer Side-by-side comparisons for the platforms above: ZoomInfo , 6sense , BuiltWith , Bombora . Full index at MarketSizer compared with other tools . FAQs What is a target account list in B2B sales? A target account list is a curated set of companies that your sales and marketing teams have prioritised for outreach based on their likelihood to buy. High-quality target account lists go beyond firmographic filters, they incorporate intent signals, competitive context, and win probability to focus effort on accounts that are in-market now and winnable. The goal is quality over quantity: fewer, better-targeted accounts close at higher rates with less wasted effort. What makes a target account list "high quality"? A high-quality target account list combines three elements: ICP Fit (the accounts genuinely match the profile of your best customers), Purchase Intent (they are showing signals of active buying readiness, not just fitting a firmographic profile), and Win Probability (your historical win rate in similar competitive scenarios suggests you can win these deals). Accounts that meet all three criteria are Qualified Opportunities, the highest-priority targets on any account list. How often should you update a target account list? In SaaS, target account lists should be reviewed continuously, not quarterly. Buying readiness changes fast: an account that renewed three months ago is not the same priority as one with a renewal window opening in 30 days and a competitor trial in progress. Best practice is to use real-time subscription intelligence to automatically re-prioritise your list as signals change, rather than relying on periodic manual reviews. What is the difference between an ICP and a target account list? An Ideal Customer Profile (ICP) defines the type of account you want to win using firmographics, tech stack, use case, size. A target account list is the set of specific companies that fit your ICP and are ready to be pursued now. Your ICP is static (until you update it); your target account list should be dynamic, updated continuously as accounts move in and out of buying windows. What data sources are used to build target account lists? Strong target account lists combine: firmographic data (industry, size, location), technographic data (current tool stack), intent signals (behavioural indicators of buying readiness), and subscription intelligence (live data on competitor trials, renewals, and churn). Tools like MarketSizer, ZoomInfo, and LinkedIn Sales Navigator provide these layers, with MarketSizer specifically covering real-time subscription activity across multiple Martech categories. ## Blog: Startup Fundraising Guide: 6 Lessons Every Founder Should Know Hard-won fundraising lessons from the trenches. Fundraising is hard. Really hard. For most founders, it's an emotional rollercoaster filled with pitch fatigue, rejection, and pressure to prove your startup’s potential fast. And the statistics don't lie... 60% of startups fail between pre-seed and Series A funding stages ( SPD Load ) 35% of Series A startups fail before Series B ( SPD Load ) the average startup takes about 4 years to become profitable ( Earthweb ) But with the right mindset, clear goals, and structured execution, fundraising becomes not just survivable but strategic. Here are six lessons to help you navigate the chaos, avoid common traps, and fundraise with confidence. TL;DR - The Fundraising Survival Checklist 🎯 Get clear on your raise type, amount, and fit 📊 Know your risk profile and investor expectations 🔁 Treat fundraising like a structured pipeline 🔥 Create FOMO through traction, team, and vision ❌ Turn rejection into data and filter out fake “maybes” ❤️ Prioritise mental stamina, not just metrics Lesson #1: Get Clear on Your Funding Strategy Before stepping onto the fundraising roller coaster, clarity about your funding goals is paramount. Understand which type of investor or funding best aligns with your company, strategic partners, venture capitalists, angel investors, or crowdfunding. Consider the variety of funding instruments at your disposal. In ecosystems like Ireland, accelerators can offer crucial support via equity, CLNs, or SAFEs. Government grants, while sometimes non-dilutive, may require match funding or come with performance stipulations. Understanding these nuances is more than academic; it's essential for aligning your company's trajectory with investor expectations. Here are the two options most startups choose from: 1. Funding rounds through VCs, Angels, etc. Based on the stage of your startup, you can raise different types of funding from external investors like Venture Capitalists, Angel Investors or Strategic Partners Each funding round comes with its own characteristics, rising expectations, and implications the further you get in the journey Early Stage (Pre-Seed, Seed, Series A), Expansion Stage (Series B), Growth Stage (Series C), Late-Stage (Series D), Pre-IPO stage (Series E) 2. Startup Grants Non-repayable and repayable funds provided to early-stage startups to support their growth and development without giving up equity Can come from various sources: Government grants, state grants, private grants Con: Highly competitive, need to meet specific criteria and provide detailed progress reports Of course, many entrepreneurs "simply" bootstrap their business, meaning self-fund it with their own savings or asking friends and family to invest or even join a crowdfunding platform. Moreover, just because you want to raise money from a VC for example doesn't mean you're market opportunity is big enough for them. In fact, only 1% of startups actually raise VC capital 🫢 However, if you have the market opportunity and optimise for financial returns, only the above options will take you onto the fundraising journey where you're expected to pitch your business idea, showcase potential ROI and dive deep into the question "Why you? ". If you have clarity about the possible options and what you have to expect from each, you can navigate the twists and turns of each with more confidence and have a clear direction to your fundraising efforts. Lesson #2: Understand Your Risk Profile by Stage Despite common belief, the size of a funding round doesn't necessarily explain the funding stage . Early-stage companies can secure substantial amounts by showcasing market opportunities without bypassing Pre-Seed or Seed stages. For investors, funding stages illuminate your startup's risk profile; each stage presenting distinct challenges: Pre-Seed: Uncertainty reigns as your product is in development or non-existent. It's unclear for the investor if there will even be customers willing to buy your product. Funding sizes can vary a lot at this stage based on the market opportunity the investor sees, so anything from 50K to 1M is possible. Seed: At this stage, your startup has its first customers and you need to figure out how to best penetrate the market within budget. Typical funding sizes range from 1M to 4M. Series-A: At this point, you've shown that there's demand for your product and have a stable customer base. Now you'll need to show that your company can keep growing steadily and showcase consistent ROI. The money you can raise here goes up to 15M. Another point to consider is how much money you strive to raise in each funding round. If you raise too much money in early rounds, you give up valuable ownership that attracts investors in later rounds. Additionally, your startup is still evolving and you probably want to retain enough control to make strategic adjustments wherever you see fit, right? As your startup proves its worth, you’ll attract more customers and orders, which will naturally drive up your valuation. When you eventually seek funding for scaling, you’ll dilute fewer shares because your pre-money valuation will have risen. So you see, raising money through funding rounds requires strategic planning and managing one's own expectations realistically. That's why Lesson #3 is super important. Lesson #3: Treat Fundraising Like a Sales Process Once you've clarified your funding goals and understood your risk profile, it's time to make an actionable fundraising plan and implement dedicated processes in your day-to-day operations. Think of it like the sales or marketing department, now you have a fundraising department. A structured approach, borrowing tactics from sales and marketing, reduces stress by offering a clear schedule. Here's your step-by-step guide: 1. Build your investor pipeline Create a list of potential investors based on funding goals and stage, consider their industry expertise, portfolio companies and values Categorise those leads for importance 2. Get networking and warm up leads Establish connections, attend networking events in your city/country and be present on LinkedIn Gently warm up leads with non-committal pitches and apply for accelerator programs to get advice throughout the fundraising journey Apply for funding rounds / startup grants 3. Maintain consistent communication Follow-up with intent consistently, secure yes/no's and negotiate where appropriate (but also don't drag it out unnecessarily; if they don't want you, move on) Secure termsheets (Key Terms of a deal between a startup and its investors, read this resource on b2venture. vc for more information) Tip: Tools like Signal by NFX and OpenVC can help you identify aligned investors faster. Lesson #4: Build Leverage, Not Just a Pitch There are 305 million startups created globally each year and only about 40. 000 investors actively investing as of 2022. To stand out of the startup pool, you have to build leverage or, basically, create FOMO (fear of missing out) for investors. They should not want to miss out on the exciting journey your startup is on! The best founders don’t beg for capital. They create demand . Leverage is the secret sauce that makes investors eager to join the ride with you. Here are a few ways you can create it: Understand your market size: We can't emphasise this one enough; You need to know your market inside out by conducting thorough market research and presenting numbers like TAM, SAM, SOM convincingly. Investors are more likely to invest when they see a sizable market opportunity for a product or service. For SaaS founders, the strongest market size arguments combine top-down research with bottom-up evidence. Real subscription-level data, like the 70M+ records MarketSizer tracks across SaaS vendors in the Martech industry, can show investors not just the theoretical addressable market, but the actual volume of active, churned, and evaluating subscriptions right now. Evidence-based market sizing is increasingly expected at Seed and Series A. Have a working product or an MVP: A functional Minimum Viable Product (MVP) demonstrates that you're not just about ideas - you can execute them. It doesn't need to be the most beautiful version of your product yet, just focus on functionality so you can demonstrate how your product works. Well-defined growth stages: Think beyond year 1, what could your startup look like in 5 or even 10 years? How can your product serve more people over time, what functionalities can you add? Where do you want to expand to? Lay out your growth strategy clearly and think about scalability (this will be expected from you anyway in Series-A stage) Impressive Team: Last but not least, showcase that you have people with the right mix of expertise, experience, and passion. The employees you work with can make or break your young project. If you can showcase the buzz around your company, growing interest, and the potential of significant returns, investors are more likely to line up for a front-seat on your fundraising roller coaster. Lesson #5: Master the Art of "Yes", "No" and "Maybe" When you finally get to the point where you get that crucial "Yes" from an investor, it will set off a chain reaction, propelling your startup forwards and you start building momentum. However, before you get that first "yes", there will be a lot of rejections and "Maybe's". ❌ No = Feedback First of all, embrace the "No's". It wouldn't be a roller coaster without a descent before the next ascent. The key lies in viewing rejection not as a setback but as a redirection, an opportunity to refine your pitch and strategy. And it's certainly better than being ghosted! Whatever feedback you get, take it with grace and be respectful - you never know when your paths will cross again. Moreover, not every investor will share your vision, and that's perfectly okay! In fact, it's beneficial. You want to seek investors who resonate with your vision and align with your values as well as enthusiasm. 🤷 Maybe = Signal Check Yet, amidst the clear distinctions of "Yes" and "No", lies the ambiguous realm of "Maybe". Interpreting "Maybe" responses requires a nuanced understanding, distinguishing between cautious interest and polite deflection. Genuine curiosity, follow-up questions, and expressed engagement are signals of potential interest. On the other hand, vague or non-committal responses may indicate a lack of alignment. Entrepreneurs must proactively seek clarification, fostering transparent communication to discern genuine interest from indecision. It's this skill of navigating the grey areas that separates adept fundraisers from the rest. Here's a great overview from Mikkel Sundø on how to interpret what VCs say: Source: 5 tips on how to speak VC and raise capital, Mikkel Sundø on Medium Just remember, securing that initial "Yes" sparks the momentum, but it's your ability to embrace the "No's" and "Maybe's" and navigate the uncertainties that define your resilience as a founder. Lesson #6: Protect Your Energy Once you get on the emotional roller coaster of fundraising, stress is an inevitable companion. Not trying to sound dramatic but the success of your startup can turn your life around completely. Prioritising self-care in this scenario isn't a luxury, it's a strategic necessity. Picture self-care as the emotional seatbelt that keeps you secure during the twists and turns of the fundraising roller coaster. Adequate sleep, regular exercise, and moments of relaxation are not just indulgences but essential tools to equip yourself for the challenges ahead. Stress management is not about avoiding stress altogether, but about building the capacity to handle it effectively. Just as a roller coaster is designed to provide a thrilling yet safe experience, self-care ensures you can navigate the exhilarating journey of fundraising with resilience and poise. Here are some practical tips: Set realistic boundaries: Define clear boundaries between work and personal life. Allocate time for focused work and intentional breaks. This not only enhances productivity but also prevents burnout, a common challenge in the startup landscape. Seek support: Don't hesitate to lean on your support network. Whether it's talking to a mentor, sharing experiences with fellow founders, or seeking professional guidance, the emotional support of others can be a powerful antidote to stress. Celebrate small wins: Amid fundraising, celebrate small victories. Recognise and appreciate the progress you make, acknowledging that each step forward, no matter how small, contributes to the larger success of your venture. Find time for mindful moments: Incorporate short mindfulness exercises into your daily routine. Whether it's a few minutes of deep breathing or taking a walk outside, these moments can provide a mental reset, allowing you to approach challenges with clarity. Navigating the startup thrills Congratulations! You've now completed a crash course in surviving the emotional rollercoaster of fundraising. The startup journey is undeniably challenging, resembling a rollercoaster ride full of exhilarating highs and daunting lows. As founders, you're not merely passengers; you're the architects of this wild journey. In the startup world, where uncertainty and risk are constants, fundraising becomes a rite of passage. We've explored the critical lessons to equip you for the twists and turns, providing not just survival strategies but the tools for a triumphant ride. Are you a startup founder? The startup community thrives on shared experiences. As part of a startup myself, I'd love to hear the lessons you've learned and the tips that saw you through. Share your insights in the comments or connect with us on our LinkedIn page! 🚀 Sources used: https://medium. com/@mikkelsundoe/5-tips-on-how-to-speak-vc-and-raise-capital-5738f7bd3737 https://resources. b2venture. vc/fundraising Header picture adapted from: https://medium. com/@mikkelsundoe/5-tips-on-how-to-speak-vc-and-raise-capital-5738f7bd3737 FAQs How much should a B2B SaaS startup raise at pre-seed? Pre-seed funding typically ranges from €50K to €1M, depending on the team's track record, market opportunity, and how much product development is needed before customer validation. The goal is to fund enough runway to reach a meaningful proof point, usually first customers, a working MVP, or validated demand, without over-diluting early. Strategic investors like Delta Partners, Enterprise Ireland, and sector-specialist angels often participate at this stage. What do investors look for at pre-seed for a SaaS startup? At pre-seed, investors are primarily betting on the team and the market opportunity, not proven metrics. They want to see: a credible founding team with relevant experience, a clearly defined problem worth solving, evidence of customer demand (even informal), a believable path to product-market fit, and a market large enough to support the return they need. Having a working prototype or early design partnerships significantly strengthens the case. How do you build an investor pipeline for a startup fundraise? Treat it like a sales process: build a tiered list of target investors (warm intros first, cold outreach second), maintain consistent follow-up, and track each conversation in a CRM or spreadsheet. Use tools like Signal and OpenVC to identify investors with relevant portfolio experience. Aim for parallel conversations rather than sequential, creating momentum across multiple investors simultaneously generates the social proof and FOMO that moves term sheets forward. What is the difference between a "no" and a "maybe" from a VC? A genuine "no" is direct and gives a reason, often the most useful feedback you'll receive. A "maybe" is the danger zone: it can signal polite disinterest (a soft no), incomplete information, or genuine interest pending more data. To distinguish them, ask a direct follow-up question: "What would need to be true for this to be a yes? " Genuine interest leads to a specific answer. Continued vagueness is usually a soft no in disguise. ## Blog: How to Find Product-Market Fit (PMF): 6 Steps for Startups PMF is not a moment - it is a process. Here is how to navigate it. Finding Product-Market Fit (PMF), or Product-Segment-Fit as Anthony Pierri 🎸 framed it recently, can be make-or-break for startups. Whether you're launching a new product or entering a new market, finding PMF helps you validate demand, reduce churn, and build sustainable growth. However, according to a study by Startup Genome , startups often underestimate the time required to validate their market. In fact, startups typically need 2-3 times longer to validate their market than expected, leading to a risk of premature scaling or even business failure. This 6-step process brings clarity to the chaos helping you avoid premature scaling and build something people actually want. What exactly is Product-Market Fit? Product-Market Fit happens when your product solves a real, urgent problem for a clearly defined audience and people actively use, recommend, and pay for it. The crux of finding your PMF is identifying that pressing problem or need and creating an (innovative) solution, that consistently fills a gap within your competitive landscape. PMF is not static, so look at it like an ongoing task rather than a one-time goal. The Market & Your Target Audience Depending on the market you want to enter, you need to identify different market segments that feature diverse target audiences and have specific needs . For MarTech companies, this could be specific industries or business types that can best benefit from your technology. As this is a B2B market, the age or demographics of potential customers can vary a lot. Their unsatisfied needs, however, often overlap. The Product Your product needs to offer a unique solution, targeting the pain points identified in your market research. The answer to these pain points should be written down as your value hypothesis or proposition. It is "a short statement that describes the problems you want to solve and how you will do it differently than anyone else. It considers customer needs and wants, the uniqueness of your product, and the advantages of working with your company” (Source: aha. io ) After you've come up with the value your product can create, work over your feature set to make the vision come true and only then follow with the UX to make the product a pleasant experience. As an example, Dropbox doesn't just offer storage; it's a storage solution so seamlessly integrated into workflows that it appears as just another folder on your computer. Their comprehensive understanding of their market's needs and their product's unique value proposition led them to a robust PMF in their journey. Why is Product-Market-Fit important? Despite having a brilliant idea, compelling story, talented product team, and a fully functional product, without a viable market, a startup cannot thrive. PMF matters because it determines whether your product or service aligns with the needs and wants of your target customers. While you may think product-market fit is primarily a startup issue, it is just as important for established companies with successful product portfolios . When you hit PMF, you unlock: Higher retention and customer lifetime value (CLTV) Better conversion rates and word-of-mouth growth More effective GTM and product development strategies Increased investor confidence It's crucial for not just driving sales but for building effective marketing campaigns with appropriate messaging and feature focuses. It's also important for product development, ensuring you're not building a product that nobody wants to buy. How do I evaluate Product-Market-Fit? Generally, evaluating your Product-Market-Fit involves both objective and subjective elements. Quantitative Indicators Low churn rate (ideally <5% monthly) High Net Promoter Score (NPS) 40+ CAC:LTV ratio of 1:3 or better Consistent month-over-month growth in MRR Qualitative Signals On the subjective side, qualitative feedback obtained through surveys, reviews, and interviews can provide critical insights. Tools like Medallia or Qualtrics can help analyse such feedback to measure customer satisfaction. The two most popular methods are the Net Promoter Score (NPS) and the Product Market Fit survey by Sean Ellis . The 6-Step Journey to Product-Market-Fit If you search up how to evaluate PMF on Google, you'll find a lot of different approaches and resources. So, as previously promised, we've put together the following step-by-step process that takes all that information into account. 🤓 Step 1: Write down product ideas and concepts It makes no sense to randomly start developing a product if you can't show that someone will be interested in it. Validate if there's a problem worth solving by separating the problem from the solution and testing it via interviews with potential customers (or chat with your parents or your friends). 👉 The Problem-Solution-Fit Canvas by Daria Nepriakhina is a helpful tool for that purpose. The goal is to be where your customers are and solve a real problem, either by doing something familiar differently or introducing something new in a way they understand. Step 2: Identify your market size Market Validation or Market Sizing is not only a key component in any startup pitch but also for finding Product-Market-Fit. Deep-diving into market research and knowing your Total Addressable Market (TAM) Serviceable Addressable Market (SAM) Serviceable Obtainable Market (SOM) is vital. For B2B SaaS founders targeting the Martech sector, real market sizing goes beyond desktop research. MarketSizer's dataset based on competitive subscription intelligence gives founders and investors an evidence-based view of the actual serviceable market, active subscription volumes, and competitive landscape. Understanding who has already captured the market is as important as estimating how big it could be. Check out our Market Sizing Cheatsheet for more information! Step 3: Research your target customer Understanding your target customers is also important to achieve PMF or you'll risk building a product that doesn't fully resonate. Here are some popular ways to collect information about (potential) customers: Customer interviews Surveys Social listening Conducting a competitor analysis Collect feedback from these sources to influence future product iterations or to help design a new product. 👉 Build personas based on behaviour and priorities, not just job titles. Step 4: Build a Minimum Viable Product (MVP) A MVP is a basic yet functional version of your product that serves to validate the assumptions you've been making about your market and target audience. It's a low-risk way to test whether your product's core functionality resonates with potential users. Additionally, you can learn about your customers' needs and gather feedback to guide your product's future development. It de-risks the product development process by ensuring you don't invest heavily in building a product only to find out later that there's no market for it. But remember Reid Hoffman 's words: "If you are not embarrassed by the first version of your product, you've launched too late. " The essence of an MVP is to start small but think big , taking one step at a time towards achieving your vision. It's all about learning as much as possible about your customers and continually adapting your product to meet their needs. Step 5: Test, Test, Test for PMF (and learn fast) Once you've launched your MVP, that's when the real learning begins. Feedback from your MVP testing will help you understand if your product meets users' needs and how it can be improved. A potent strategy for gathering feedback is a Product-Market Fit Survey , also called the "Sean Ellis Test" where you ask users how disappointed they would be if they could no longer use your product. The responses will separate users into three groups: Very Disappointed, Somewhat Disappointed, and Not Disappointed. The ones who say they'd be "very disappointed" without your product are your golden users. They find real value in what you're offering and should shape your target audience. However, you should pay close attention to the "somewhat disappointed" group. Their feedback can be crucial to unearth improvements that could tip them into the "very disappointed" (or simply put, very happy with your product) category. Now, for those who say they wouldn't be disappointed at all, it may seem tough, but it's best to let them be. They're not your core audience, and chasing them could distract you from your intended product scope and the people you actually want to attract. Focus and depth are invaluable when creating a product people would truly miss. Step 6: Create your product roadmap Based on the insights gathered during MVP testing, you can begin to build your product roadmap, a strategic document that outlines the future development of your product. If you already have one, you'll just refine it. The purpose of it is to communicate the vision and direction of your product over time. It's an essential tool to help everyone - your team, investors, and even your customers - know where your product is headed and what the journey looks like. As Rahul Vohra, founder & CEO of Superhuman , wisely points out, "To increase your product/market fit score, spend half your time doubling down on what users already love and the other half on addressing what’s holding others back. " Remember, testing your MVP and building your roadmap isn't a one-and-done thing. They're iterative processes that help you better understand your users, refine your product, and navigate your product's journey. Your focus should always be on the users because after all, building a great product is a journey, not a destination. 💡 Tip : Split roadmap focus. 50% on what users love, 50% on what’s holding others back (Superhuman playbook). Scaling Product-Market-Fit After achieving PMF, the next challenge is scaling. It's about growing your user base while retaining, and in the best case scenario improving, the level of PMF you've already achieved. Why is scaling so critical though? Well, it's a cornerstone for a startup or company's sustainable growth and survival. It helps ensure your product continues to serve the needs of your expanding user base and adapts to ever-evolving market conditions. Get it right, and you're looking at increased revenue, happier customers, and a stronger competitive stance. The biggest mistake startups can make is to prematurely scale - spending significant amounts of money on growth before truly discovering and developing PMF. So, remember, solid PMF first, then scale. To scale your Product-Market Fit you should: Track your PMF score: As you grow, you'll encounter different types of users. Early adopters may be more forgiving of your product's shortcomings, but as you expand, users may become more demanding. Your PMF score will likely fluctuate as a result. Keeping an eye on it helps you stay on top of user needs and expectations. Optimise your resources and processes: Scaling isn't just about growing, it's also about becoming more efficient. Ensure you're utilising your resources effectively and have processes in place that can support your growth. Adopt a data-driven approach: Continuously check your PMF and analyse trends and patterns. This will help you identify opportunities, areas for improvement, and potential roadblocks. Keep iterating your product: As you gather more data and insights, continue refining your product to enhance the PMF. Stay open to feedback and ideas: PMF is ultimately a human experience, so great discussions with people, your team, customers, and industry peers, will propel your knowledge and success. Be flexible and willing to make changes based on the insights you gather. What's next? In the end, remember that achieving Product-Market Fit (PMF) means turning your users into your most passionate advocates. When users love your product so much they can't help but share their enthusiasm with others, they become your spontaneous salespeople, spreading the word far and wide. This kind of organic growth is the ultimate goal, a sign that you've truly hit a nerve in the market. Have you gone through finding product-market-fit for a startup or at an established company? What steps are you taking to ensure your product remains a steadfast companion to your target audience as they navigate their world? Share your experiences and lessons learned in the comments below! Got questions, or need advice? Feel free to contact us ! FAQs What is product-market fit (PMF)? Product-market fit is the point at which your product satisfies a genuine, urgent need for a clearly defined audience, evidenced by users actively adopting, retaining, and recommending it. It's not a one-time milestone but an ongoing state to maintain and measure. The most commonly used signal: if asked "how would you feel if you could no longer use this product? ", at least 40% of core users say "very disappointed" (the Sean Ellis benchmark). How do you measure product-market fit for a B2B SaaS startup? Key quantitative indicators include monthly churn below 5%, NPS of 40+, CAC:LTV ratio of 1:3 or better, and consistent MRR growth. Qualitatively, look for unsolicited referrals, organic word-of-mouth, and customers who become advocates without prompting. Combine the Sean Ellis PMF survey with retention cohort analysis: retention curves that flatten (rather than trending to zero) are a strong PMF signal. What is the difference between PMF and ICP? Your Ideal Customer Profile (ICP) defines the type of customer your product is built for, e. g. firmographics, use case, pain points. Product-market fit is the evidence that your product actually solves their problem well enough to retain and grow. You can have a well-defined ICP without having PMF, and finding PMF often requires iterating on your ICP based on who actually sticks around and gets the most value. ICP scoring makes that iteration measurable, and opportunity discovery tells you how many accounts the revised profile actually leaves you. When is it too early to scale a B2B SaaS startup? Scaling before PMF is the most common and expensive startup mistake. Signals that you're not ready to scale: high churn in early cohorts, acquisition driven by discounts or founder relationships rather than product value, inability to articulate clearly why customers stay, and NPS below 20. The Startup Genome project found companies typically need 2 - 3x longer to validate their market than expected, patience at this stage protects the capital needed to scale properly. ## Blog: MarketSizer set to revolutionise SaaS market intelligence with \u20ac1M pre-seed funding round Announcing our \u20ac1M pre-seed raise to build the future of subscription intelligence. Dublin-based MarketSizer provides cutting-edge data that helps companies understand market trends, and make smart decisions that drive growth and enhance their competitive edge. Founded just one year ago, MarketSizer has raised €1M in funding. The latest pre-seed funding round, led by venture capital firm Delta Partners, will be used to develop new platform features, accelerate additional job creation and support international expansion. MarketSizer improves client sales, boosts marketing efforts, retains customers, tracks competitor technologies and learns about customer preferences. Sales intelligence market projected to grow from $3. 8 billion in 2023 to $9. 2 billion by 2032, according Global Market Insights data from Q1 this year. Dublin, June 23rd: Market intelligence platform, MarketSizer, has today announced the closing of its pre-seed funding, totalling €1M. This round was led by venture capital firm Delta Partners with additional support from Enterprise Ireland and angel investors. The capital will be used to develop new features in the platform, accelerate additional job creation and help to support expansion into the UK and US market. Founded in 2023 by Niall O’Gorman, MarketSizer aggregates millions of data points to deliver real-time intelligence, helping businesses identify high-probability targets, optimise sales, improve marketing efforts and increase customer retention. MarketSizer is quickly positioning itself in the market intelligence space for SaaS companies, with future plans to expand into other sectors and markets. Niall O’Gorman, Founder and CEO at MarketSizer , commented: “Our pre-seed funding marks a significant milestone for MarketSizer, enabling us to expand our reach and enhance our platform. With over 25 years in the tech industry, I founded MarketSizer to address a critical gap in the SaaS sales process. Our mission is to turn vast datasets into actionable insights, helping sales teams target the right opportunities at the right time. The sales intelligence market is projected to grow from $3. 8 billion in 2023 to $9. 2 billion by 2032. MarketSizer aims to capture a substantial share of this expanding market. The future of business growth lies in making swift, data-driven decisions, and MarketSizer’s insights empower companies to stay ahead, adapt, and seize opportunities, defining the next generation of market intelligence. ” In MarketSizer, O'Gorman leverages decades of extensive experience in scaling ventures and first-hand knowledge of the challenges businesses face regarding market intelligence, having previously been one of the founders of ChannelSight. The platform has since evolved into a full Opportunity Intelligence layer for customer support and live chat SaaS GTM teams, with a scoring mechanism combining ICP Fit, Purchase Intent, and Win Probability at its core. Dermot Berkery, Partner at Delta Partners , commented: “In a market where leading SaaS companies are facing growth challenges, MarketSizer’s innovative approach to subscription intelligence is a standout. We believe in their vision and expertise to transform data-driven decision-making. We are excited to support their international growth and continued development. ” Anne Lanigan, Divisional Manager of Technology and Services at Enterprise Ireland , commented: “Pioneering Irish innovators are having a global impact as they develop world-class innovations and cutting-edge solutions, and MarketSizeris a great example. Enterprise Ireland is committed to supporting Irish companies like MarketSizer to start and scale, in order to achieve their global ambition. We are proud to support MarketSizer on this journey, including providing Pre Seed and Innovative HPSU funding. I would like to congratulate Niall and the team on this important milestone. ” For press enquiries, contact Beachhut PR: Stephen Mangan | +353 87 995 2800 | stephen@beachhutpr. comAaron Linnane | + 353 87 419 5919 | aaron@beachhutpr. com About MarkerSizer: MarketSizer is an Opportunity Intelligence platform for SaaS vendors in the Martech space. Built on competitive subscription intelligence, MarketSizer identifies Qualified Opportunities: accounts that are ICP-aligned, actively in-market, and winnable using a Opportunity Score across ICP Fit, Purchase Intent, and Win Probability. GTM teams use MarketSizer to spend 30 - 50% less time on unqualified accounts and act earlier on competitive threats, expansion signals, and renewal windows. For more information visit www. marketsizer. io About Delta Partners: Delta Partners is a venture capital firm that invests in early stage startups in Ireland. For over 30 years Delta Partners has been supporting entrepreneurs to build great businesses. Having invested in over 140 companies with exits generating €1. 8B,Delta has a track record of investment performance, with LPs including Enterprise Ireland, Bank of Ireland and Fexco, as well as the family offices of successful Irish technology entrepreneurs. Today, the Delta team are investing in early stage Irish companies across a range of sectors. For more information visit www. deltapartners. com Photo by Radu Alexandru Vasile - In the picture, from left to right: James Spalding (Dublin, Ireland), Svea Schüler (Hamburg, Germany), Niall O'Gorman (Dublin, Ireland), Razvan Vierasu (Ploieşti, Romania), and Yash Mulik (Dublin, Ireland). Not present on the day Benjamin Duval (Côte d'Azur, France) FAQs What does MarketSizer do? MarketSizer identifies Qualified Opportunities by combining ICP Fit, Purchase Intent, and Win Probability into a Opportunity Score, surfacing accounts where all three align simultaneously. Built on subscription intelligence across multiple Martech categories, signals are refreshed daily (max. lag 5-days) and delivered via CRM sync, a web copilot, or standalone web app. What is Opportunity Intelligence? Opportunity Intelligence is the practice of qualifying accounts not just on who they are (ICP fit), but on whether they're actively in-market right now (Purchase Intent) and whether you can realistically win them (Win Probability). It goes beyond intent data or lead scoring to give GTM teams a single answer: is this a Qualified Opportunity worth pursuing today? Who is MarketSizer built for? MarketSizer is purpose-built for GTM teams (sales, marketing, and customer success) at SaaS companies operating in the Martech industry. It's especially useful for vendors competing in a crowded market where knowing which accounts are evaluating competitors, approaching renewal, or showing expansion signals gives a measurable competitive advantage. Want to see what this looks like on your own market? Start free - 500 qualified opportunities, no credit card. # FAQ ### What does MarketSizer do? MarketSizer shows software vendors which companies are actively in-market to buy their product, qualified by ICP fit, purchase intent, and win probability. Instead of guessing who to target, you see which companies are trialling your competitors right now, which recently churned from incumbents and are evaluating alternatives, and which match your best customers' profiles in competitive scenarios where you historically win. MarketSizer qualifies every opportunity across three dimensions: ICP Fit (companies that match your best customers' tech stack and competitive profile), Purchase Intent (real signals from trials, churn, and renewals - not anonymous page views), and Win Probability (historical win likelihood based on subscription outcomes from 50M+ companies in similar competitive scenarios). ### How is MarketSizer different from other intent or sales intelligence tools? Most intent tools show you behavioural signals (website visits, content downloads, ad engagement). MarketSizer shows you subscription intelligence - which software companies actually use, when they trial alternatives, and historical switching behaviour. MarketSizer provides real subscription data (active tools, trials, churn events), transparent and inspectable signals with timelines, full competitive landscape and win probability, and daily detection, with new trial signals typically reaching your CRM within about 5 days. Other intent tools provide anonymous page views, black-box intent scoring, no competitive context, and lagging indicators. We are not a contact database, an outsourced SDR team, an outreach or sequencing tool, a bulk export tool, a black-box intent score, a generic enrichment database (like Clearbit or ZoomInfo), or a behavioural intent platform (like 6sense or Bombora). MarketSizer does not replace your sales team - it gives them better accounts to work: subscription intelligence that shows which companies are evaluating, which ones you can win, and when to engage. ### Who is MarketSizer for? MarketSizer is built for B2B software vendors whose buyers evaluate competitors before they buy. We are built first for customer support and live chat vendors - for example, Zendesk, Intercom, Freshdesk, LiveChat, Crisp, and Gorgias - and expanding across ITSM, CRM, marketing automation, and sales enablement. Specifically: Sales Teams (AEs who need to know which companies are actively evaluating, SDRs/BDRs who want to prospect into accounts that are in-market, sales leaders who need to prioritise pipeline based on win probability); Marketing Teams (demand gen running ABM campaigns, product marketers who need competitive intelligence, marketing ops enriching CRM data with purchase intent); Customer Success Teams (CS managers who need early warning when customers trial competitors, AMs looking for expansion opportunities, renewal teams who want to prevent churn before it happens). If you sell B2B SaaS and struggle to know which companies to target, when to reach out, and which deals you can actually win - MarketSizer is for you. ### How does MarketSizer calculate ICP Match? ICP Match is based on revealed market behaviour, not just firmographics. We look at: competitive context (which vendors they use, have evaluated, or churned from), tech stack patterns (do they use tools similar to your best customers?), historical overlap (do they match the profile of companies you have won before?), and structured attributes like size, industry, and geography (used for filtering, not as sole determinants). Example: a company might have the right size and industry (firmographic match), but if they are using deeply entrenched enterprise incumbents with long contracts, they are a poor ICP fit because they are not realistically winnable. ICP Match reflects reality - companies that look like your best customers AND are in scenarios you can actually win. ### Does MarketSizer work for inbound-only or PLG models? Yes. MarketSizer works for any go-to-market motion. For inbound/PLG teams: enrich inbound leads with competitive intelligence before your first call, know which competitors they are currently using or trialling, prioritise inbound leads based on ICP fit and win probability, and see if they have churned from a competitor recently (high intent). For outbound teams: build target lists of companies actively evaluating solutions, know when to reach out based on trial activity, and focus on competitive scenarios where you historically win. For hybrid models, use MarketSizer for both. MarketSizer works regardless of how prospects find you - it is about qualification, not lead generation. ### Will MarketSizer replace my existing intent tools? No. MarketSizer complements your existing stack. What MarketSizer adds that behavioural intent tools do not have: subscription intelligence (which tools companies actually use), competitive context (who they are evaluating, who they churned from), win probability (historical data on which scenarios you win), and real-time trial detection (not just 'someone visited your website'). Think of it this way - behavioural intent (6sense, Bombora): 'Someone at Company X is researching customer support software'. MarketSizer: 'Company X is actively trialling Intercom and Zendesk right now, and you historically win 65% of deals in this competitive scenario'. Best practice: use both. Behavioural intent shows interest. MarketSizer shows action and competitive context. ### Does MarketSizer support languages other than English? Currently the platform interface is in English, and we detect subscription signals globally regardless of language. Data coverage: we track companies worldwide - if they have a public web presence, we can detect it. We are actively expanding coverage and language support. If you have specific market needs, contact us and we will prioritise based on demand. ### How accurate is MarketSizer's data? MarketSizer shows evidence of what a company is running, not a confirmed contract. A detected product can reflect a live integration or other observable footprint, but on its own it does not prove a current contract, paid plan, renewal date or contract owner. We detect vendor installations through five detection methods across several sources, and the Opportunity Score then rates how promising each account is for you using ICP Match, Purchase Intent and Win Probability. We do not publish a single accuracy figure, because accuracy varies by vendor and by how a tool is deployed. Every score can be inspected back to the signals that drove it, and every insight includes timelines and supporting signals so you can verify before acting. ### Where does MarketSizer get its data from? MarketSizer is built on proprietary subscription intelligence combining multiple data sources and detection methods to identify which companies are in-market. Our data foundation includes: evidence-based subscription data (live and historical software usage across multiple Martech categories, 70M+ subscription outcomes from 50M+ companies analysed over 5+ years, more than 17M active subscriptions monitored today, real-time detection of trials, churn, and switching behaviour); competitive context and patterns (historical win/loss rates across similar competitive scenarios, incumbent displacement patterns and switching timelines, market-wide adoption and evaluation trends); and company intelligence (firmographic attributes for ICP matching, tech stack context relevant to your category, real-time verification of current environment). Unlike generic data providers, we do not just aggregate information - we qualify every opportunity across ICP fit, purchase intent, and win probability before you see it. ### How fresh is MarketSizer's data? How often does it update? When you search for a specific company, MarketSizer verifies their current environment in real-time and shows live and historical data (on-demand lookups). For bulk data: in-market signals are refreshed daily, with new trial detections typically reaching your CRM within about 5 days, and churn and renewal signals updated continuously. Trials are defined as new installation signals observed within the past 30 days. If a signal persists beyond 30 days, it is treated as an active subscription. When we say a company is 'trialling a competitor right now', that signal was typically detected within the past 5 days. ### What happens if I find inaccurate data? This is expected - no data platform is 100% perfect. When you find an inaccuracy: flag it in the platform (we have a feedback mechanism), we investigate and correct the detection rules if needed or explain why we think it is right, you get notified when the issue is resolved, and our models improve based on your feedback. Why this happens: legacy scripts can remain after tools are removed, agencies or shared infrastructure can create misleading signals, tools may be deployed only on subsets of pages, and timing effects can cause signals to appear before purchase or linger after. Our philosophy: treat MarketSizer insights as prioritisation signals, not absolute truth. Validate through outreach, then feed corrections back to us. ### What if MarketSizer doesn't cover my specific competitors? We monitor vendors across multiple Martech categories. If your competitor is not on the list, let us know. We continuously add new vendors based on customer requests, market share and relevance, and technical feasibility of detection. Most vendor additions take 2 to 4 weeks to implement and validate. Even if we do not monitor a specific niche competitor, we can still show you companies in similar tech stack scenarios and competitive contexts. ### How fast can we see value after implementation? Most teams see their first qualified opportunities within days of getting started. Week 1 (setup and first insights): quick setup process, get familiar with how MarketSizer qualifies accounts, install the Chrome extension and start seeing companies actively in-market in your category, and identify high-intent opportunities you can act on immediately. Weeks 2 to 4 (team adoption): sales team incorporates MarketSizer into their daily workflow, marketing builds campaigns around qualified target lists, and CS identifies early warning signals for at-risk accounts. Most common quick win: sales teams identify 10 to 20 high-intent opportunities in their first week and start booking meetings based on competitive intelligence they did not have before. You do not need months of setup or training. ### Can we try MarketSizer before committing? Yes, absolutely. No credit card required. Your organisation gets 2000 free credits to use over an 8-day trial, which starts when we set up your account. This lets you see which companies in your market are in-market right now, test the accuracy of our data against your own knowledge, run a campaign or sales sprint using MarketSizer insights, and validate that qualified opportunities convert better than your current lists. Want a deeper evaluation? Book a demo and we will show you a custom dataset for your specific competitive scenario. ### Does MarketSizer work with my CRM and sales tools? Yes. MarketSizer connects natively to the tools GTM teams run day to day. CRMs: HubSpot, Pipedrive, Zoho CRM. Outreach and email: Instantly, Brevo, Gmail. Meetings and team alerts: Google Calendar, Slack, Microsoft Teams. Data and developer: Google Sheets, CSV import/export, a public API and webhooks, plus n8n, Latenode and Zoho Flow for automation. Tools without a native connection - Apollo.io, Clay, Salesforce and others - pair with MarketSizer through CSV, a shared CRM, or the Chrome extension side panel. Important: you do not need to connect your CRM to use MarketSizer. The Chrome extension works as a standalone tool, and you can also use the MarketSizer web app independently. The full list is on the integrations page. ### How does MarketSizer help prevent churn? MarketSizer shows when your existing customers trial your competitors - typically 3 to 6 months before renewal conversations. Early warning signals: customer starts trialling a competitor, customer adds new tools to their stack in your category, customer shows switching behaviour patterns. Most CS teams find out about churn risk through declining usage metrics (lagging indicator), negative feedback in QBRs (often too late), or surprise non-renewal (way too late). With MarketSizer, you know immediately when a customer is evaluating alternatives, giving you 3 to 6 months to intervene, address concerns, and protect the relationship. Bonus: we also show expansion opportunities, where customers use competitors in other regions or product categories you could consolidate. ### Can MarketSizer help with expansion into existing accounts? Yes. MarketSizer shows where your existing customers use competitors in other regions, product categories, or teams. Example scenarios: customer uses your solution in North America but a competitor in EMEA; customer uses your chat tool but a competitor's ticketing system; customer's subsidiary in another region uses a different vendor. Your usage data only shows what they are doing with YOUR product - you do not know what competitors they are using elsewhere. With MarketSizer, you can bring data-driven expansion recommendations to QBRs, show evidence ('We see you are using Competitor XY in Region X. Let us talk consolidation'), and prioritise expansion plays based on competitive context. ### What's MarketSizer's company culture like? We are a small, focused team building for B2B SaaS GTM teams who are tired of guessing which accounts to pursue. What we believe: evidence over assumptions (every claim should be backed by data), quality over volume (100 qualified opportunities beat 10,000 unqualified leads), transparency (you should see the evidence behind every insight), and customer success equals our success (we win when you close more deals). How we work: fast iteration based on customer feedback, transparent about limitations, focused first on customer support and live chat while we expand into ITSM, CRM, marketing automation, and sales enablement, and aligned incentives (success-based pricing means we only win when you win). ### How do you handle data privacy and compliance? Company-level signals first: the core of MarketSizer is company-level subscription signals and business context. Contact-level data, meaning the people at an account and their business email and phone details, is enriched on demand from third-party business-contact providers listed on our subprocessor page, and is automatically anonymised 90 days after enrichment. CRM integration is optional and customer-controlled - you decide what data is connected and processed, and we never share your CRM data with third parties. Compliance: our policies and controls are designed to comply with EU and UK GDPR, SOC 2 Type II is in preparation, and detailed documentation on data handling, retention, and access controls is available during procurement. If you need a security review, we provide full documentation as part of your procurement process.