6sense’s 2025 Buyer Experience Report contains a finding that deserves more attention than it gets: the vendor a B2B buyer contacts first wins the deal approximately 80% of the time. Not the vendor with the best demo. Not the one with the lowest price. The one that was there first.
This statistic sits alongside two others from the same report. 83% of buyers define their purchase requirements before they ever speak with a sales representative. And 80% of first contact is now buyer-initiated — the buyer reaches out to the vendor, not the other way around.
Read together, these three numbers describe a purchasing process that is largely concluded before any sales conversation takes place. The buyer researches independently, builds a shortlist, forms a preference, and then contacts the preferred vendor to confirm what they’ve already decided. The sales call is a formality dressed as a discovery session.
For the selling organisation, this raises an uncomfortable question. If the outcome is substantially determined by whether you were present during the buyer’s research phase — not the evaluation phase, not the negotiation phase, but the quiet period when the shortlist was being formed — then the most important capability a sales team can have is not persuasion. It is detection. Knowing which companies are entering a buying cycle, and knowing it early enough to be visible before the shortlist closes.
This is the problem that signal-based identification attempts to solve: distinguishing the small number of accounts in your market that are actively moving toward a purchase from the much larger number that are not — and doing so with enough lead time to matter.
The B2B Buying Process — Where Seller Influence Actually Occurs
Source: 6sense 2025 Buyer Experience Report[3]
The Problem with Static Targeting
Most B2B sales organisations still identify target accounts through static criteria: industry, company size, geography, technology stack, maybe a firmographic scoring model. The output is a list — 500 accounts, 2,000 accounts, sometimes more — ranked by fit.
Fit is necessary. It's not sufficient.
A list built on fit alone tells you which companies could buy your product. It tells you nothing about which companies are about to. And the difference between those two states — potential buyer versus active buyer — is where most of the waste in B2B sales lives.
Consider the economics. A sales development team working a list of 500 ICP-fit accounts with no signal data can expect, at current industry benchmarks, something in the range of a 3–4% reply rate on cold outreach. That's 15–20 replies from 500 contacts. Half will be negative. The team generates maybe 8–10 meetings, of which 2–3 will advance to pipeline. The cost per qualified meeting, when you factor in tooling, salaries, and infrastructure, runs $800–$2,000 depending on the organisation.
Now consider the same team working a list of 50 accounts — filtered from the same 500 — that are showing active buying signals. Research from Common Room and UserGems suggests that signal-stacked outreach (two or more buying indicators on the same account) produces substantial conversion improvements — UserGems reports 2.5x higher conversion for competitor accounts and 3x when referencing an existing customer, with these effects compounding when signals are layered together.[5][6] Reply rates climb from the cold baseline of 3–4% to 15–25%, with heavily stacked outreach reaching as high as 25–40%. The same team generates more meetings from 50 accounts than it did from 500.
The math isn't subtle. But it requires a capability that most teams don't have: the ability to distinguish active buyers from passive ones, in near-real-time, across their entire addressable market.
| Metric | Static List (500 accounts) | Signal-Filtered (50 accounts) |
|---|---|---|
| Reply rate | 3–4% | 15–25% |
| Replies | 15–20 | 8–13 |
| Positive replies | 8–10 | 6–10 |
| Meetings booked | 8–10 | 6–10 |
| Cost per qualified meeting | $800–$2,000 | $150–$400 |
| Accounts contacted per meeting | ~55 | ~6 |
Three Layers of Purchase Readiness
Not every buying signal carries the same weight. A company hiring a CRO is not the same signal as a company browsing a G2 category page — both indicate something, but the specificity, the urgency, and the reliability differ substantially.
In practice, purchase readiness indicators fall into three layers, and the distinction between them matters for how you prioritise accounts and calibrate your response.
Structural Readiness
Structural readiness means the company has the organisational conditions in place to buy: they have budget (from a funding round, a fiscal year reset, or a reallocation), they have a decision-maker (a recently hired executive with mandate to change things), and they have a need (a regulatory deadline, a scaling challenge, a competitive gap).
These signals are high-reliability but not always high-urgency. A company that just raised a Series B has structural readiness — they have capital, they're hiring, they'll need tools. But the purchasing decisions might still be 60–90 days away. Structural readiness tells you the company is capable of buying. It doesn't tell you they're buying this week.
Observable indicators: funding round closure, C-suite or VP-level hire, departmental scaling (5+ roles in a single function within 30 days), regulatory mandate approaching, post-acquisition integration underway.
Behavioural Readiness
Behavioural readiness means the company is actively researching solutions. They're visiting review sites, downloading comparison guides, attending webinars in your category, or engaging with competitor content. The buying process has begun — they're building a shortlist.
These signals are moderate-reliability (some are noisy, particularly third-party intent data) but high-urgency. A company showing a topic surge on Bombora is probably evaluating tools in that category right now. But the word "probably" does meaningful work in that sentence. Lift AI's research has suggested that third-party intent signals may be accurate less than 20% of the time.[8] First-party signals — visits to your own website, content downloads, pricing page views — are substantially more reliable, but they only capture companies that have already found you.
Observable indicators: first-party website engagement (pricing page, comparison pages, case studies), third-party intent surges, review site activity (G2 category browsing, comparison tool usage), social engagement with category content, search query patterns.
Transactional Readiness
Transactional readiness means the company is making active purchasing decisions. They've removed a competitor's tool. They're in contract renewal evaluation. They've issued an RFP. They're in the final stages — not researching, but selecting.
These signals are the highest-reliability and highest-urgency, but they're also the rarest and the hardest to detect. A company in transactional readiness has a buying window measured in days or weeks, not months. The deal is won or lost in this phase, and the first vendor with a relevant message has a disproportionate advantage.
Observable indicators: competitor tool removal (detectable through technology monitoring), RFP or RFI issuance, contract renewal window (estimable from prior purchasing patterns), "alternatives to [competitor]" search activity, churn language on review platforms.
Signal Convergence: Where the Predictive Power Lives
Individual signals are useful. Converging signals are where the real differentiation happens.
A company that just raised a Series B (structural) is worth monitoring. The same company whose new CRO is browsing sales engagement platforms on G2 (behavioural) is worth prioritising. That same company, now showing the removal of a competitor's tracking script from their website (transactional), is worth dropping everything for.
UserGems and Common Room have both published data on what happens when multiple signals stack on the same account.[5][6] The individual effects are well-documented — 2.5x conversion for competitor accounts, 3x when referencing existing customers, +45% from new-hire signals — and these multiply when layered together. Multiple sources report that reply rates climb from the 3–4% cold baseline to 25–40% for multi-signal stacked accounts, with deal cycles compressing by roughly 30%.
The compounding isn't additive — it's closer to multiplicative. Each additional signal doesn't just increase confidence that the company might buy. It narrows the window and sharpens the context. You know not just that they're in-market, but approximately when they'll decide, what they're replacing, and who's driving the evaluation. That level of specificity transforms outreach from interruption into relevance.
The practical challenge is that signal convergence requires monitoring multiple data source categories simultaneously — hiring data, funding data, technology data, intent data, review site data — and correlating them at the account level. Any single data source gives you a partial view. The converged view, where structural, behavioural, and transactional signals align on the same company, is where accounts move from "possible opportunity" to "active buying window confirmed."
Chart 4: Signal Convergence Matrix
The Regional Variable
Everything described above assumes reasonable signal coverage — that the data sources monitoring these indicators actually reach the market you're selling into. For teams targeting North America, this assumption holds. The signal infrastructure is deep: LinkedIn's coverage is near-universal, Bombora's publisher network captures intent data across thousands of B2B sites, ZoomInfo's database covers millions of contacts, and Crunchbase tracks funding at scale.
For teams selling into Southeast Asia, the assumption breaks down in specific, identifiable ways.
Hiring signals — among the most reliable structural indicators — depend on which job boards you're monitoring. In ASEAN markets, the dominant platforms are regional: JobStreet and JobsDB across multiple countries, Glints in Indonesia and Singapore, Kalibrr in the Philippines, TopCV in Vietnam. None of these are indexed by the major US signal platforms. A hiring surge at a Singapore-based SaaS company posting on JobStreet is invisible to a team relying on LinkedIn job alerts alone.
Funding signals are fragmented across DealStreetAsia, e27, MAGNiTT, and country-specific registries. A meaningful share of pre-Series A and bridge rounds in the region go unreported on the platforms that US-based tools monitor.
Intent data faces the deepest gap. Bombora's publisher network and 6sense's predictive models are trained predominantly on Western content consumption patterns. A buyer in Jakarta researching compliance tools on local-language sites does not register as an intent signal in these systems. The buyer is active. The signal is invisible.
This doesn't make signal-based identification impossible in Southeast Asia. It makes it dependent on regional signal infrastructure — aggregated coverage across local job boards, local funding databases, local regulatory registries, and local technology monitoring — that the global platforms haven't built.
Chart 5: Signal Source Coverage by Market and Readiness Layer
| Signal Layer | Indicator Type | North America | Western Europe | Southeast Asia |
|---|---|---|---|---|
| Structural | Hiring signals | LinkedIn, Indeed, ZipRecruiter — high coverage | LinkedIn, StepStone — medium-high | JobStreet, Glints, Kalibrr — fragmented, not indexed by global tools |
| Structural | Funding data | Crunchbase, PitchBook, SEC EDGAR — comprehensive | Crunchbase, Companies House — moderate | DealStreetAsia, e27, MAGNiTT — fragmented, incomplete |
| Structural | Regulatory triggers | Federal Register, SEC — centralised | EUR-Lex, national gazettes — moderate | Country-by-country: PDPC, Kominfo, NPC — local language, no aggregation |
| Behavioural | Intent data (3rd party) | Bombora, 6sense — deep publisher network | Moderate coverage | Near-zero — publisher networks don't cover regional B2B content |
| Behavioural | Review site activity | G2, Capterra, TrustRadius — comprehensive | G2, Capterra — moderate | Limited — adoption of review platforms is lower |
| Transactional | Tech stack monitoring | BuiltWith, Wappalyzer — global crawl coverage | Good coverage | Moderate — global crawlers deprioritise smaller regional sites |
| Transactional | Contract/renewal signals | Strong proxy signals available | Moderate | Weak — less public procurement data, fewer transparency norms |
From Identification to Action
Identifying in-market accounts is not the end of the process. It is the beginning of a different kind of sales motion — one where the outreach itself changes because the context is different.
When you know a company has just hired a CRO, is researching sales engagement tools on G2, and has removed a competitor's integration from their website, the outreach message is no longer a cold introduction. It's a response to a situation. The message can reference the specific context — not because you're being clever, but because the context is real and the buyer knows it.
Gartner's 2025 research found that 74% of B2B buying teams experience what they described as "unhealthy conflict" during the purchase process.[7] Buying committees of 6–10 stakeholders (sometimes 11–20 for enterprise deals) are navigating competing priorities, misaligned information, and decision fatigue. A seller who arrives with relevant context — who understands not just what the company is buying but why, and when the decision needs to be made — provides clarity that the buying committee often cannot generate internally.
This is the underappreciated function of signal-based selling. It's not just about reaching the buyer first. It's about arriving with enough situational understanding to be useful in a process that Gartner's own data suggests is breaking down under its own complexity.
The 5% of your market that is actively buying right now is identifiable. The signals are public, observable, and — when layered correctly — reliably predictive. The companies that build the infrastructure to detect them will systematically reach buyers during the window that determines the outcome.
The companies that don't will keep reaching out to the other 95% and wondering why nobody replies.
Each layer narrows the field and increases conversion probability. The smallest list produces the most pipeline.
References
- Dawes, J. — "Advertising Effectiveness and the 95-5 Rule: Most B2B Buyers Are Not in the Market Right Now" — Ehrenberg-Bass Institute — marketingscience.info
- LinkedIn B2B Institute — "The 95:5 Rule" — business.linkedin.com
- 6sense — "The B2B Buyer Experience Report for 2025" — 6sense.com
- Corporate Visions — "B2B Buying Behavior in 2026: 57 Stats and Five Hard Truths That Sales Can't Ignore" — corporatevisions.com
- UserGems — "Stacking Buying Signals is Key for Successful Outbound" — usergems.com
- Common Room — "Stack Buying Signals (Signal School: Part 3)" — commonroom.io
- Gartner — "74% of B2B Buyer Teams Demonstrate Unhealthy Conflict During the Decision Process" — gartner.com
- Lift AI — cited in Salesmotion's intent data provider research on third-party intent data accuracy — salesmotion.io