Intent data infers that someone at a company is researching your category, usually by tracking anonymous content consumption across a publisher network and resolving it back to a company. Buying signals are observable public events at a named company — a funding round, a hiring push, a new executive, a layoff. One is a probabilistic guess about interest; the other is a verifiable fact about circumstance. They are frequently sold as the same thing, and they are not.
What intent data actually is
A vendor aggregates content consumption across a large network of B2B publishers. When readers at a given company consume unusually much content about a topic relative to their baseline, the vendor flags a “surge” on that topic for that company. Bombora popularised the model and much of the industry resells or blends it.
Two things follow from how it is built:
- It is company-level, not person-level. You get “someone at Acme is reading about data warehousing”, not who. Some vendors offer person-level via their own logged-in properties, which is a much smaller pool.
- Resolution degrades with remote work. Matching an IP to a company works well for an organisation with its own office network and poorly for a distributed team browsing from home broadband.
Intent data is genuinely useful at scale, in a motion where marketing routes surging accounts to sales automatically and the cost is spread over thousands of accounts.
What buying signals actually are
Buying signals are events, not inferences. They are visible in public sources, and each one implies something concrete about budget or urgency:
| Signal | What it implies |
|---|---|
| Funding round closed | Budget exists and there is pressure to spend it on growth |
| Hiring into your function | Budget was approved and a problem is being staffed |
| New executive in the target function | Tooling gets re-evaluated in their first two quarters |
| New product, market, or office | New operational problems that need solving |
| Layoffs or a spending freeze | Nothing is getting bought this quarter |
Note the last row. Buying signals include negative ones, and intent data structurally cannot produce them — nobody browses their way into telling you they froze the budget.
The honest comparison
| Intent data | Buying signals | |
|---|---|---|
| Source | Inferred from anonymous browsing | Public events and announcements |
| Certainty | Probabilistic | Verifiable |
| Names a person | Rarely | Often, via the org chart |
| Detects negative conditions | No | Yes |
| Cost | Subscription, usually bundled with a data platform | Free from public sources; time is the cost |
| Best at | Prioritising thousands of accounts | Deciding on the account in front of you |
Neither is a substitute for the other, and neither tells you whether the company is a fit for what you sell. That part is still your job.
Where each one fails
Intent data fails when the company is small, distributed, or simply reading about a topic for reasons unrelated to buying — a competitor’s blog post did the rounds internally, someone wrote a conference talk, an analyst report landed. It also arrives without a name, so acting on it means guessing who to contact, which erodes much of the advantage.
Buying signals fail when the event is stale or misread. A funding round eighteen months ago is history, not a signal. A hiring push in a function unrelated to your product is noise. And a company can be in market with no visible public event at all — quiet, well-funded, and actively evaluating vendors without announcing anything. Signals will miss that account entirely.
Which one should a small team use?
Buying signals, almost always. Intent data’s economics assume volume: a subscription cost spread over thousands of accounts and an automated routing process to act on surges within days. A team working forty accounts a week does not have the volume to amortise it, and does not have the routing infrastructure to act fast enough for the data to still be fresh.
Buying signals are visible on the LinkedIn company page, the jobs tab, and recent news. The cost is the five to ten minutes per account it takes to gather them, which is exactly the cost that tooling removes.
How LinkedIntel handles this
LinkedIntel works on buying signals, not intent data. It reads the LinkedIn company page in front of you and scores what it finds — funding, hiring, leadership changes, financial momentum, and red flags — as IMMEDIATE, MODERATE, or COLD, in about ten seconds. It does not buy or resell intent feeds, and it will not tell you that someone at the company was reading about your category, because that is not something a public page can show.
If intent data is what you need, ZoomInfo and Cognism both bundle intent feeds and are the honest recommendation for that requirement.
Related reading
What are buying signals in B2B sales goes signal by signal through what to look for. The B2B account research checklist puts signals in the context of fit and risk, and what is sales intelligence covers the broader category both of these sit inside.