Where Does B2B Intent Data Actually Come From? A 2026 Reality Check

Boomerang is a warm introduction platform / relationship intelligence platform for B2B revenue teams. This post exists because a GTM engineer on r/gtmengineering asked the question the entire industry has quietly been dodging: "What exactly are intent signals and where is the data actually coming from?" The short answer: most intent data comes from five sources — ad-network cookie data (Bombora), review-site behavior (G2, TrustRadius), aggregated third-party feeds (6sense, Demandbase), self-reported vendor data (ZoomInfo, Clay), and your own first-party website engagement. Signal quality ranges from useless to genuinely predictive, and the industry has spent a decade blurring the difference. If you have ever paid six figures for an intent platform and wondered why the pipeline lift never showed up, you already know the answer. Here is the honest breakdown, and the alternative that GTM engineers are quietly moving to in 2026.

The 5 sources of B2B intent data — where the numbers actually come from

Before we get into vendor comparisons, it helps to internalize one thing: there are not fifty intent data companies. There are roughly five upstream data sources, and every "intent platform" you can buy is a repackaging, reweighting, or resale of those five. Once you see the plumbing, the pricing suddenly makes a lot less sense.

1. Bombora + ad-network cookie data — the biggest, noisiest source

Bombora is the closest thing the B2B world has to a shared intent utility. It operates a co-op of more than 5,000 B2B publisher sites — trade publications, industry blogs, vendor comparison sites — and drops a cookie on visitors. When someone at Acme Corp reads three articles this week about "identity management," Bombora tallies that as topic activity, resolves the IP or cookie back to an account, and calls it an intent surge.

Bombora then sells that feed downstream. 6sense buys it. Demandbase buys it. ZoomInfo buys it. Most of the "intent" you see inside your ABM platform is Bombora underneath, blended with a house scoring model. As one GTM engineer put it on the r/gtmengineering thread: "A lot of this was repackaged Bombora for awhile, but not sure if that is still true." It is still true. The wrapper changes. The upstream does not.

Quality is probabilistic and noisy. Someone reading an article is not someone intending to buy. It could be an analyst, an intern doing research, an engineer troubleshooting an unrelated problem, or a competitor doing homework. Add to that the fact that ad blockers now sit on 30 to 40 percent of business devices, third-party cookie deprecation is finally landing in Chrome, and GDPR consent friction cuts European coverage by half — and the raw signal is degrading year over year. It is the biggest source of "intent data" on the market and also the one most likely to send your SDRs chasing ghosts.

2. Review site behavior — G2, TrustRadius, Capterra

This is where intent data starts getting honest. Review sites are deterministic: someone actively comparing your product to Competitor X on G2 is genuinely evaluating. Someone on your G2 profile reading verified reviews and clicking "get a quote" has commercial intent, not just curiosity. There is no probabilistic guess in the middle.

G2 sells this signal directly through G2 Buyer Intent, and it also gets resold through 6sense and other aggregators. TrustRadius and Capterra offer similar products at smaller scale. As the Reddit thread put it: "The clearest signal I've seen is from review sites, like G2 or Trust Radius." That is the consensus among practitioners who have worked with all the sources.

Quality is materially better than Bombora. Predictive value is high. The trade-off is volume — most accounts are not on your G2 profile in any given week, so review-site intent alone will not feed a full outbound motion. It is best used as a high-priority trigger, not a top-of-funnel spray-and-pray input.

3. Aggregated third-party feeds — 6sense, Demandbase, Lonescale

6sense, Demandbase, and newer entrants like Lonescale combine Bombora topic data, review-site signals, tech stack detection, hiring signals, funding signals, and sometimes first-party website engagement into a single scored composite. This is the "AI-powered account intelligence" layer that enterprise ABM teams pay $150K to $400K a year for.

The value here is not proprietary data. It is the aggregation, deduplication, account resolution (mapping messy signals back to a clean account record), and the machine-learning score that ranks accounts. Whether it is worth the money depends almost entirely on two things: how well your ICP is tuned inside the platform, and how disciplined your team is at working the scored account list. Teams that skip ICP tuning get noise at scale. Teams that tune well can get real lift on the top 500 to 2,000 accounts.

4. Self-reported and derived vendor data — ZoomInfo, Clay, Cognism

This bucket covers everything scraped, licensed, or inferred at the account level: tech stack detection (BuiltWith-style crawlers looking at website source code), hiring signals (job postings scraped from LinkedIn and company career pages), funding events (Crunchbase, PitchBook feeds), executive changes, contact enrichment, and firmographic changes.

Some of this is high quality. Tech stack signals are honest — if HTTPArchive sees Segment in the head tag, Segment is in the head tag. Hiring signals are honest — if the company posted three DevOps jobs this week, that is a real fact. Funding events are honest — the press release is public record. This is where Clay has built a business: not by generating novel intent data, but by pipelining these deterministic signals into workflows.

Where it gets shaky is when vendors call this "intent." A company posting DevOps jobs is not showing intent to buy your CI/CD product. It is showing fit plus a plausible trigger. Fit and trigger are useful. But labeling them intent muddles the vocabulary and lets sellers double-count.

5. First-party website engagement — your own site

The most reliable intent signal on the market is the one you already own. Someone visiting your pricing page three times this week, watching your product demo video to completion, or downloading your integration docs is telling you — with their own behavior, in your own environment — that they are evaluating.

The tooling here is mature: Clearbit Reveal (now HubSpot Breeze Intelligence), Common Room, RB2B, Warmly, and PostHog all identify anonymous website visitors and resolve them back to accounts and, increasingly, individual contacts. This is the highest signal-to-noise ratio of any intent source, period.

The catch: volume is limited by your inbound funnel, and it only tells you about accounts that have already discovered you. It does not help you get in front of the 95 percent of your ICP that has not visited your site yet. Which is why every serious go-to-market team still ends up layering something on top.

Why most intent signals don't convert to pipeline

Here is the pattern GTM engineers keep running into. You buy the intent platform. You wire it into your CRM. You build the plays. Alerts fire. SDRs work the accounts. Three quarters later, pipeline sourced from intent looks suspiciously like pipeline you would have generated anyway. What happened?

Signal is not intent. A pageview is an interest signal. An article read is an awareness signal. Intent implies action toward a purchase decision — a demo request, a pricing inquiry, a comparison workflow, a shortlist. Most "intent data" flags interest and calls it intent because interest is easier to generate at scale and easier to charge for.

The timing gap is fatal. By the time third-party intent data flags an account as "surging," the buying committee has often already talked to two vendors, run a POC with one, and built a shortlist you may or may not be on. Gartner has been reporting for years that 83 percent of the B2B buying journey happens before a vendor is even contacted. Intent data, at best, catches you at 60 percent. That is still late.

ICP mismatch swamps the signal. Bombora topic surges fire against any account in the co-op that reads the topic. Your ICP is maybe 5 percent of that population. Without disciplined filtering, you drown in signal on accounts you would never sell to.

The underlying infrastructure is decaying. Cookie deprecation, ad blockers, GDPR/CCPA consent friction, and platform enforcement (Apple ITP, Firefox tracking protection) have been quietly eroding the coverage of the ad-network layer for five years. The vendors do not talk about this in QBRs. Your account match rates are lower than they were in 2022, and they will be lower still in 2027.

And even the best signals are unreliable at the individual level. As one Reddit commenter noted: "Even the 'holy grail' intent signals like pricing page views (on your site or on G2) and form fills are not accurate when it comes to predicting the buying intent of a prospect." Aggregate signals across a buying committee, and you can build a probabilistic account score. Try to use a single pricing pageview to predict any one person's intent, and you will be wrong more often than right.

This is not a case for abandoning intent data. It is a case for being honest about what it is: a probabilistic layer that helps prioritize accounts, not a deterministic layer that predicts deals. If you treat it as the latter, you will overspend and underdeliver. For a deeper taxonomy of the signals worth watching, see our consolidated guide to buying signals and the warm intro signal library.

What actually predicts pipeline (in 2026)

The GTM teams outperforming their peers in 2026 have not thrown out intent data. They have demoted it. Here is what has been promoted in its place.

First-party warm-path signals. Who on your team actually knows someone at the target account. This is deterministic — either your VP of Product went to college with the buyer's Head of Engineering or she did not. It is not a probabilistic score. It is a fact, sourced from your own team's LinkedIn connections, Gmail history, and calendar data. And it is the single highest-converting signal in B2B, because it is not a signal at all — it is a relationship. Boomerang's data shows that 60 to 80 percent of warm paths inside your CRM go uncounted because standard CRM data models do not capture the connection graph across your team.

Champion mobility signals. Your customers changing jobs. When a champion who used your product at Company A moves to Company B, you have a warm path, a proven advocate, and a fresh trigger event, all in one signal. Job-change signals convert 3 to 5 times better than cold intent signals because the signal is not "this account might be interested" — it is "a person who chose you before is now inside a new account." UserGems built a business on this. Boomerang extends it by mapping the champion's full connection graph into your relationship intelligence layer.

Multi-source composite scoring, weighted correctly. Combining intent (probabilistic), fit (deterministic firmographics), first-party engagement (deterministic), and warm-path availability (deterministic) into a single account score, then triaging the top of the list by warm-path availability first. The insight: within any list of high-intent accounts, the subset with a warm path converts several multiples better. So warm path is not a competing signal — it is the tiebreaker that decides which high-intent accounts get worked, and how.

The Warm Path Velocity metric. How quickly your team can go from a triggered account to a warm intro landed. Boomerang tracks this as a first-party metric — see the Warm Path Velocity definition — and it is the leading indicator of whether your relationship intelligence stack is actually feeding pipeline or just sitting in a dashboard.

The proof point that keeps coming up in customer conversations: Armis, a Boomerang customer, surfaced more than 26,000 warm paths inside their team's existing network and reported 10x ROI within a year. The warm paths were already there. The CRM just was not counting them. That is the pattern across every relationship intelligence deployment we see. The signal you have been missing is not intent. It is your own team's relationship graph, sitting unused in LinkedIn and Gmail.

Gartner's data reinforces this. 60 percent of B2B buyers report regret after a purchase, and the top reported reason is a poor evaluation process — too many vendors, too little differentiation, unclear fit. A warm introduction from a trusted mutual connection compresses the evaluation, cuts the noise, and shifts the buyer's default from skepticism to consideration. For context on where this fits in the broader GTM data landscape, see our writeup on the Gartner GTM Data Applications Market Guide.

A 2026 intent stack that actually works

Here is the layered architecture I would build if I were setting up an intent and prioritization stack from scratch today.

The order matters. Layer 1 and 2 are deterministic and cheap to add. Layer 3 is deterministic and moderate cost. Layer 4 is expensive and only pays back if you have the ICP discipline to make the aggregation useful. For most series B to series D companies, layers 1 through 3 alone will move pipeline more than a $200K 6sense contract will. Enterprise ABM teams with 5,000+ target accounts and dedicated ops resources are the exception where layer 4 earns its price.

For a broader map of the relationship intelligence category and how it sits alongside intent, see our guide to relationship intelligence platforms in 2026 and the best warm introduction software of 2026. Both cover the vendor landscape without the marketing polish. For the plays that turn signals into stage progression, see AI pipeline acceleration by deal stage.

FAQ

What is B2B intent data? B2B intent data is any signal — behavioral, firmographic, or self-reported — that suggests a business is considering a purchase in a specific category. In practice, it is a mix of ad-network cookie data, review-site behavior, self-reported vendor telemetry, and first-party website engagement, blended into an account-level score.

Where does Bombora get its intent data? Bombora operates a co-op of more than 5,000 B2B publisher websites. It drops cookies on visitors, tracks the topics of content they read, resolves the traffic back to companies, and sells the resulting topic-surge data to 6sense, Demandbase, ZoomInfo, and directly to end customers. Most "intent" data on the market has Bombora somewhere in the pipeline.

Is intent data still worth paying for in 2026? It depends on the layer. First-party engagement tools (PostHog, Common Room, RB2B) and review-site intent (G2) are worth paying for at almost any stage. Aggregated third-party platforms (6sense, Demandbase) are worth it if you have the ICP discipline and ABM headcount to make the aggregation useful. Raw Bombora feeds without a scoring layer are usually not worth it in 2026 — noise floor is too high, cookie coverage is degrading.

What's the difference between intent and buying signals? Intent data is a subset of buying signals. Buying signals include any event that suggests a change in buying likelihood — hiring surges, funding rounds, executive changes, tech stack shifts, champion job changes, warm-path openings. Intent data specifically refers to behavioral signals of category research. Buying signals are broader, more deterministic, and generally more predictive of pipeline.

Which intent data source is most reliable? First-party website engagement is the highest signal-to-noise source, followed by review-site behavior (G2, TrustRadius). Aggregated third-party feeds are middle-tier and depend heavily on ICP tuning. Raw ad-network cookie data (Bombora topic surges alone) is the noisiest and least reliable in 2026.

How do warm-path signals compare to intent signals? Warm-path signals are deterministic — either your team has a relationship to the account or it does not. Intent signals are probabilistic. In head-to-head conversion, warm intros run 3 to 5 times higher than cold outbound triggered by intent alone. The two are complementary: intent tells you which accounts to look at, warm paths tell you which of those you can actually get a meeting with.

The bottom line

The Reddit thread got it right. Most intent data is repackaged Bombora, keyword matching, or low-signal review-site behavior blended into a score. Some of it is useful. Much of it is noise dressed up in dashboards. If you have been unable to prove ROI on your intent platform, the vendor is not lying to you — the data really does exist. You are just being sold interest and asked to pay for intent.

The GTM engineers winning in 2026 have stopped chasing the probabilistic layer and started mining the deterministic one they already own: their team's relationship graph. Boomerang is a warm introduction platform / relationship intelligence platform for B2B revenue teams, built around the observation that the highest-converting signal in B2B is not a topic surge — it is a colleague who already knows the buyer. The signal you are missing is not intent. It is relationship data.