B2B Intent Data: What It Predicts, Which Providers Pay Off

Intent data is worth paying for only when three things are already true: your unprioritised outbound converts at a defensible rate, you act on the first-party signal you already own, and someone can respond to a surge within days. What intent data predicts is narrow: that an account is reading more about a topic than it usually does. It does not predict revenue on its own. Revenue follows when that signal is acted on quickly, on accounts that already fit.

Providers stretch the term well past that, and the intent data definition states plainly what the signal establishes and what it never does. Teams that understand the narrow version get real value from it. Teams that expect it to hand them buyers get a five-figure annual contract and a dashboard nobody opens.

Is Intent Data Worth Paying For?

For most teams, not yet. Third-party intent is a timing and prioritisation layer: it tells you which accounts to work first, not who to write to or what to say. It pays when the rest of the motion already works and needs ordering, and it disappoints when it is bought to fix a motion that does not. A blunt readiness test, before any contract:

1. Your unprioritised outbound already converts at a defensible rate.
2. You have instrumented first-party signal and are acting on it.
3. Someone owns response inside 72 hours, with a sequence already written.
4. Your ICP is large enough that prioritisation is a real problem. Under a few hundred target accounts, just contact all of them.

If fewer than three are true, spend the money on list quality and message testing instead. The 7-layer GTM AI stack sets out the order these layers should be built in.

Does B2B intent data actually work? It works as far as its signal goes: on ICP-fit accounts, worked within days, it moves the order in which a team spends its time. It does not work as a list source, a targeting method or a substitute for an offer worth answering, and most disappointing programs asked it to be one of those.

What Intent Data Predicts, And What It Does Not

Intent data predicts one thing reliably: that an account has recently consumed more content about a topic than it usually does. Everything else, including which person is interested, whether budget exists and whether anyone is buying this quarter, is inference layered on top by you or by a vendor's model.

So what intent data predicts revenue? The signal closest to your own product predicts best: a person-level visit to your pricing page beats a review-site comparison view, which beats a category surge on a publisher network. The further the signal sits from you, the more it depends on fit and speed to become pipeline.

Marketing data provider is the umbrella term this market is shopped under, and it covers three purchases that behave differently. Audience and firmographic data tells you who exists, intent data tells you who has been reading, and identity resolution tells you who is already on your own site. Contact records are a fourth purchase with its own comparison in the data enrichment tools guide. The newer name for the motion is warm outbound, meaning outreach triggered by a signal rather than drawn from a static list, and the activation problems below are the same ones under the newer label.

First-Party Versus Third-Party Intent

Both spellings describe one purchase: third-party intent data, often written 3rd party intent data, is the account-level feed bought from a publisher co-op or a review site, and the table below is what separates it from the first-party signal you already own. Treating the two as one budget line is the first mistake.

First-party intent is your pricing page views, docs and changelog traffic, repeat visits, free-trial behaviour, email engagement, and views of your own review-site listing. It is stronger than anything you can buy, because the account is already looking at you. Most companies underinstrument it and then pay for third-party data to compensate. Platform-native intent signals, such as the buyer intent HubSpot surfaces from its own tracking, are first-party data under another name.

How Third-Party Intent Is Produced

Three mechanisms, and knowing which one you are buying tells you what the data can support.

Publisher co-ops. Bombora's Company Surge is the reference implementation. Bombora's homepage says its Company Surge intent data "reflects real research across 16B+ monthly events and 5M+ company domains", gathered through a co-op of publishers and resolved to companies. The output is a surge score rather than a raw volume. Bombora also lists featured partners that include Apollo, The Trade Desk, Reddit and Adobe, so the same signal can reach you through more than one vendor, and buying two can mean buying one signal twice.

Review and comparison sites. G2's seller site says its verified intent signals "show who's researching your product, category, or competitors in real time", and that the signals can be plugged into Salesforce, HubSpot and Marketo. Narrower coverage than a co-op, and much closer to purchase.

Predictive platforms. 6sense's pricing page puts third-party intent, web visitor identification and a predictive AI model with scores and dashboards inside its packages. You are buying the model and the orchestration, not just the signal.

What The Providers Publish About Price

Shortlists of buyer intent data tools mostly re-rank the same few purchases, so the useful question is not which vendor ranks highest but which mechanism you are buying and whether a platform you already pay for includes it.

Provider	What it sells	Price on its page
Bombora	Company Surge co-op intent	None; "Speak to an expert"
6sense	Three packages with intent inside	None; each card books a demo
G2 Buyer Intent	Activity on your G2 profile and category	None on its page; free 30-day look back
ZoomInfo	Intent beside its contact data	UNVERIFIED: page not readable

How much does enterprise-grade intent data cost per account, then? No provider above publishes a per-account or per-topic rate, so the honest answer is the quote you get, divided by the number of ICP-fit accounts you would actually act on. That second number is usually far smaller than the account volume the quote is sized on, which is the figure worth negotiating.

The chart below carries the annual ranges this guide has shown since it was published, which were reported secondhand rather than published by any of these vendors. None of them could be checked against a vendor page, so read them as UNVERIFIED orders of magnitude and get your own quote. If you already own a data platform, the add-on route is usually the first thing to price, and best ZoomInfo alternatives compares the underlying data layers.

Why Intent Data Disappoints

Every failed intent program we have seen shares the same causes, and none of them is data quality.

No activation motion exists. The account list arrives on the provider's schedule and lands in a spreadsheet. Intent data is perishable: a surge that is three weeks old is history. If there is no sequence ready to fire within days, the signal has no way of becoming pipeline.

That activation gap is exactly what sequencing software closes, and sales engagement platforms for SaaS teams compares the options built for fast, multi-channel follow-up.

Account-level signal, person-level outreach. The feed says Acme is researching your category. It does not say who. You still have to pick contacts, and if you pick the wrong five people you get the same reply rate as an unprioritised list. Intent narrows the account set; it does not write the list. Ordering an existing list so the surging accounts get worked first is what the signal supports; building the list from it is not, because the accounts a co-op reports are the accounts it can resolve rather than the accounts that fit.

Everyone gets the same feed. Your competitors buy the same co-op. Being first to a surging account is an operational advantage, not an informational one, and it is won by speed of response.

Surges are noisy. Job seekers, students, analysts, consultants and your own employees all generate content consumption from a corporate IP. Filter to ICP-fit accounts before you look at scores, never after.

It gets blamed for a bad offer. Prioritisation multiplies whatever your message already does. Doubling an illustrative 0.4 percent reply rate is still a bad campaign. Fix the motion first; 4 frameworks that fix broken lead generation covers the diagnosis.

An Activation Model That Works

Intent data pays off when each signal has a predefined action, an owner and a time limit. Write the table before you sign the contract.

Two rules make this work in practice. Never reference the surveillance itself: "I saw you were researching X" converts badly and reads as creepy. And keep the response window short enough that the signal is still true when the message lands. The channel matters less than the speed, which is why we run intent-triggered accounts through both email and LinkedIn outbound at the same time rather than one after the other.

Bottom Line

Third-party intent data is a timing and prioritisation layer, sold by quote, that returns nothing without an activation motion behind it. First-party signal is cheaper, sharper and almost always under-exploited. Build the motion, instrument your own traffic, then buy the co-op feed to widen the funnel.

If you want the motion built before the data spend, get a free campaign plan and we will show you what your current list and message produce first.

Pricing and features are taken from the vendors' own pages. Verify current terms with the vendor before relying on them.