What Is B2B Intent Data? The 5 Types of Buyer Intent Data, Compared
A side-by-side table of the five buyer intent data types, the three that never get past the account, and a plain call on which ones a sales team should pay for.
Every intent data vendor sells the same promise: know who is in-market before your competitors do. The invoice usually arrives before anyone has checked which kind of signal was actually bought.
Most guides to B2B intent data stop at "first-party, second-party, third-party" and move on. That split tells you who collected a signal. It doesn't tell you what the signal can prove, which is the thing you're paying for. This guide sorts buyer intent data by what it actually observes, compares the five types side by side, and ends with a blunt view of which ones earn their price for a B2B sales team.
What Is B2B Intent Data?
B2B intent data is any observable signal that a company or a person at it is researching, evaluating or preparing to replace a product in your category. It answers when and why. Contact data tells you who exists, and your Ideal Customer Profile tells you who fits. Intent data tells you who is moving right now.
The distinction that matters is interest versus intent. Someone can be interested in your category for a year. Intent is the shift from passive awareness to active evaluation, and that shift usually happens long before anyone fills in a demo form. Most of the research is done by the time a buying committee talks to sales, which is the pattern the state of B2B software buying keeps finding.
If you want the sharper contrast with the "who" side of your data stack, the intent data vs. contact data explainer covers it.
The 5 Types of Buyer Intent Data at a Glance
The table sorts buyer intent data by what each signal observes, because that decides what it can and can't tell you.
| Type | What it observes | Grain | What it tells you | What it can't tell you | Cost to get it |
|---|---|---|---|---|---|
| First-party | Visits, downloads, product usage and email engagement on your own properties | Person, when known; otherwise account | Who in your orbit is warming up | Anyone who hasn't found you yet | Low. You already own it |
| Third-party co-op | Content consumption pooled across a network of publisher sites | Account | Which companies are reading more about a topic than usual | Who is reading, or why | High. Usually the biggest line in an intent budget |
| Review-site activity | Profile, pricing, comparison and category page views on a review platform | Account | Which companies are browsing your category or a competitor's page | Who is looking, or what's wrong where they are | Medium to high. Sold by the platform |
| Technographic | The software a company runs, and changes to it | Account | Fit, and which competitor to displace | Timing, on its own | Often bundled into contact databases |
| Review-derived | What a reviewer wrote about a product they use | Person when the reviewer is named; otherwise account | What's broken, in the buyer's own words, and when they said it | Whether they've started shopping | Free to read by hand; paid to monitor at scale |
Two things stand out. Three of the five types never get past the account, by design. And only one of them records anything the buyer actually said.
What Does First-Party Intent Data Tell You?
First-party intent data is your own record of what known prospects do on your website, in your product and in your emails. It's the most trustworthy signal you have, because you watched it happen. A pricing page visit from an open opportunity means exactly what it looks like.
The limit is reach. First-party data only sees people who already found you. You can tell when a known prospect gets hot. You can't see a competitor's customer getting fed up until they come looking for you, and by then they've usually built a shortlist.
Treat it as the baseline every other type gets checked against. It costs almost nothing beyond the analytics you already run, and it's the cheapest way to confirm whether an outside signal was real.
Third-Party Co-op Intent Data: The Topic Surge
Third-party co-op data is what most people mean when they say "intent data." A network of publisher sites pools anonymized reading behavior, and the provider flags companies whose consumption of a topic jumps above their usual baseline. You get a list of accounts "surging" on, say, data warehousing.
It's broad, and it's the only type that sees research happening across the open web. That breadth comes with three costs. It's account-level, so you learn that a company is reading but not who or why. It's shared, so every vendor buying the same feed gets the same account names in the same week. And it rests on tracking that is getting harder to do: Safari's Intelligent Tracking Prevention blocks all third-party cookies by default, while Google dropped its plan to phase them out of Chrome and kept third-party cookies as a user choice.
Call it anonymous web traffic. It's useful for sizing which accounts are in the market at all, and weak as a reason to write to one specific person.
Review-Site Activity Data: Who's Browsing the Category
Review-site activity data is behavioral data a review platform collects on its own pages and sells back to vendors. It reports which companies viewed your profile, your pricing, a competitor's page or a comparison between the two, and it usually scores how actively each account is researching.
This is the closest the paid market gets to buying-stage evidence, because nobody reads a comparison page by accident. It's still an inference, though. The platform sees a page view from a company, not a sentence from a person. The G2 buyer intent breakdown walks through exactly what one platform's activity product reports and where its edges are.
Older guides, including the earlier version of this one, filed all review-platform data under "second-party." That label hides the split that matters: the browsing telemetry a platform sells, and the review text sitting on the same pages.
What Is Technographic Data Good For?
Technographic data tells you what software a company already runs, detected from its website code, job posts and other public traces. It answers fit and targeting questions: who uses the competitor you beat most often, who runs the stack your product plugs into, who is still on a tool you replace.
On its own it says nothing about timing. A company can run the same CRM for six years without a single complaint. Technographics get interesting when the stack changes, a new tool appears or an old one drops out, or when you pair them with a signal that does carry timing.
Most teams don't buy this as a standalone product. It often arrives bundled with a contact database, so check what you already have before you pay for it twice.
Review-Derived Signal: What the Buyer Actually Wrote
Review-derived signal is the content of a review: a dated, attributed account of what a real user thinks of a product they use, in their own words. It's the fifth type of B2B intent data, and it's the only type in the table that is read rather than inferred.
Compare what each type gives you. A co-op feed says a company is reading about integration tools. A review might say the Slack integration breaks every time the team adds a workspace and ops has stopped trusting it, and it comes with the reviewer's role, company and the date they wrote it. You can answer that second one directly.
The useful frustration isn't confined to one-star reviews, either. We analyzed 29,677 G2 reviews as of August 2026 and found 41.8% of five-star G2 reviews contain a pain point. A star filter would throw most of that signal away.
Review-derived signal is pre-intent, and it helps to say so plainly: it shows frustration, not a purchase decision. Across the corpus we monitor, roughly 1 in 13 negative reviews mention switching or an alternative outright (571 of 7,419). Most frustrated customers haven't started shopping when they write, and that's exactly why it's worth catching. You reach them before the shortlist exists, which is the idea behind Frustration-Led Growth.
The tradeoff is volume. There are far fewer reviews than page views, so this type works as a precise, low-volume layer, not a replacement for broad coverage. One way to act on it without reading competitor reviews by hand is Reechee's Opportunity Alerts, which flag new reviews of the competitors you watch whenever they contain an actionable pain point, five-star reviews included, with the reviewer's company attached and contact details available to unlock. Reviews are picked up after the platform publishes them, so expect alerts on a short delay rather than a live feed.
Which Types of B2B Intent Data Are Worth Paying For?
Pay for the type that answers the question your team gets wrong most often. Start with first-party data, which you already own. Add review-derived signal next if you sell against named competitors, because it's cheap to start and it carries context. Buy co-op or review-site activity data once account coverage, not message quality, is the bottleneck.
A rough guide by situation:
- You're a small team selling into a crowded category. First-party plus review-derived. You can't afford to chase every surging account, and the specific pain in a review gives a two-person team a reason to write that a topic score never will.
- You run account-based marketing across thousands of accounts. Co-op data earns its price here, because breadth is the job. Feed it into your ABM program and treat it as a ranking input rather than a reason to call.
- You live or die on competitive displacement. Technographics to find who runs the competitor, review-derived signal to find who's unhappy with it. Review-site activity helps if your competitors' pages see real traffic.
- You can't tell which intent spend is working. Stop adding feeds. Check which signals your first-party data confirmed in the last quarter and cut the rest.
Whatever you buy, the test is the same: does it change what a rep says, or only whom they call? The five plays for using intent data show what each looks like once the signal lands.
How Should Sales Teams Layer Intent Signals?
Layer types that cover each other's blind spots. One signal from an account is noise. Account-level activity plus a person-level reason plus a first-party confirmation is something a rep can act on today.
In practice that changes four decisions. The first is order: SDRs work the accounts showing activity before anything on the static list, instead of an arbitrary list on an arbitrary Tuesday. The second is who to contact. Account-level data names the company, but multithreading means knowing which two or three people to reach, and only person-level signals narrow that down.
The third is the first message. The buyer persona gets you the problems people in that role usually have. A review gets you this person's actual one. The fourth is what the rest of the team gets out of it: recurring complaints about a competitor turn into talk tracks and sales enablement material instead of dying in one rep's inbox.
Review platforms aren't the only place people complain in public. Community threads and social posts carry earlier, noisier signal, and the social listening for SaaS guide covers that layer.
The pattern holds in reverse, too. What frustrates your competitors' customers is a preview of what could frustrate yours, which is why teams also use the same signal to reduce churn by learning from competitors.
FAQ
What is the difference between intent data and buyer intent data?
In practice, nothing. "Buyer intent data" and "B2B intent data" are used interchangeably for signals that a company is researching or evaluating a purchase. The term that matters is the grain: ask whether a product reports accounts or people before you buy, because the label won't tell you.
Is buyer intent data worth it for small sales teams?
Yes, if you pick the right type. Small teams gain the most from precision because they can't afford wasted cycles. Start with first-party data and review-derived signal, which are cheap and specific, before paying for broad account-level feeds built for large ABM programs.
Can you get B2B intent data for free?
Partly. Your own analytics, public reviews on platforms like G2 and Capterra, community threads and search trends are all free to read. What you pay for is scale: aggregation, account matching, contact enrichment and alerts, so nobody has to check why some SaaS companies get more reviews than others one listing at a time.
Where do buyer intent keywords fit?
Search terms are a form of first-party intent data when they land on your site. The words a prospect types into a search bar tell you what stage they're in. The guide to buyer intent keywords breaks down the four types and how sales teams use them.
Key Takeaways
Sort intent data by what it observes, not who collected it. The first-party, second-party, third-party split hides the difference between an inferred page view and a sentence a buyer wrote.
Three of the five types stop at the account. They tell you a company is moving. Only first-party data on known contacts and review-derived signal reach the person.
First-party data is the baseline. It's the cheapest type and the one every outside signal should be checked against.
Review-derived signal is pre-intent. It shows frustration before the shortlist forms, which makes it early and specific, and also lower in volume than activity data.
Buy for the question you keep getting wrong. Breadth problems call for co-op or review-site activity data. Message problems call for review-derived signal.
Ready to see how review-based intent surfaces earlier buying signals your team might be missing? See how Reechee works - 30-day free trial included.
