What intent data actually is
Intent data is a record of research activity tied to a company, not a person. When someone at a business reads about a product category on an industry site, downloads a comparison guide, or searches a relevant term, that activity can be logged, matched to the company's IP range or device ID, and rolled up into an account-level signal. Sales and marketing teams use that signal to guess which accounts are actively evaluating a purchase before anyone from that account has filled out a form.
The idea is not new. What has changed is scale. Providers now track behavior across thousands of publisher sites and roll it into taxonomies covering tens of thousands of B2B topics. Bombora, one of the larger third-party providers, says its Data Co-op spans more than 5,000 sites and captures signals from close to 4.7 million unique business domains a month, with roughly 86% of that data shared exclusively with Bombora (Bombora, 2026, vendor disclosure).
Why it exists
Buyers do most of their research before a seller ever hears from them. Gartner's most recent B2B sales survey, based on 645 buyers surveyed in August and September 2025, found that 67% of B2B buyers now prefer a rep-free purchasing experience, up from 61% a year earlier (Gartner, 2026). That leaves sales teams working from a shrinking window of visibility into what accounts are actually doing.
Intent data was built to close that gap by surfacing accounts that show research behavior before they raise their hand through a form fill or an inbound call.
How the approaches differ
Providers split roughly into three models.
Co-op or crawled third-party data. Bombora and similar providers pool anonymized content-consumption data from a network of publisher sites, then flag accounts whose activity on a topic spikes above their own historical baseline (Bombora, 2026). This data is broad and anonymous. It tells a team an account is researching a topic, not who at the account is doing it or how far along they are.
Predictive, first-party-blended intent. 6sense and Demandbase layer third-party signals on top of a company's own CRM history, engagement data, and closed-deal patterns, then use machine learning to score accounts by ICP fit and estimated buying stage (6sense, 2026, vendor disclosure). This model is more specific to a company's own definition of a good account, but it depends on having enough historical CRM data to train against.
Bundled sales intelligence. ZoomInfo and similar platforms combine intent signals with verified contact and firmographic data so a rep can move from "this account is spiking" straight to a name, title, and email address in one workflow (ZoomInfo, 2026, vendor disclosure).
None of these is a purchase signal. All of them are a research signal, and the distance between the two is where most of the disappointment with intent data comes from.
What to check before buying
A buying evaluation should ask four questions rather than take a vendor's coverage claims at face value.
How is the signal matched to a company? Anonymous browsing data has to be reverse-matched to a business, usually through IP resolution. Ask how the provider handles remote and mobile traffic, since that is where matching breaks down most often.
How old is a signal by the time it reaches a rep? A topic spike from three weeks ago describes a moving target. Ask for the latency between the research activity and the alert landing in the CRM or sales engagement tool.
Does the taxonomy match your category? A topic like "cloud security" can span a dozen unrelated buying motions. A narrow, well-curated topic list will out-perform a broad one even if the broad one shows more total volume.
Can a rep see why an account scored the way it did? Predictive models that return a score with no visible reasoning are hard for an SDR to act on with any judgment. A model that shows which signals drove the score is easier to trust and easier to challenge.
What commonly goes wrong
The most common failure is treating a research spike as a buying signal without qualifying it first. A spike in activity on a topic can come from a competitor doing market research, a student, a consultant, or an employee with no purchase authority reading for unrelated reasons. The signal shows that someone at the company is reading about the topic. It does not show who, or why.
The second common failure is routing every flagged account straight into an SDR's queue without any filter for fit. A high volume of low-fit accounts trains reps to ignore the alerts entirely, which defeats the purpose of buying the data in the first place.
The workflow that holds up in practice pairs the intent signal with a fit score and routes only the overlap into active outreach, with the reasoning behind the flag visible to the rep who has to act on it. Intent data narrows where to look first. It does not replace the judgment of the person making the call.
Sources
- Bombora, "Our Data" - https://bombora.com/our-data/
- Bombora, "One-of-a-kind B2B Data Co-op" - https://bombora.com/co-op/
- Bombora, "What is Company Surge?" - https://surfing.bombora.com/knowledge/what-is-company-surge
- Gartner, "Gartner Sales Survey Finds 67% of B2B Buyers Prefer a Rep-Free Experience" - https://www.gartner.com/en/newsroom/press-releases/2026-03-09-gartner-sales-survey-finds-67-percent-of-b2b-buyers-prefer-a-rep-free-experience
- 6sense, "AI Account Scoring & Buying Stage Detection" - https://6sense.com/6ai/
- 6sense, "Understanding Intent Data" - https://6sense.com/platform/intent-data/what-is-intent-data/
- ZoomInfo, "Bombora Review: Features and Insights" - https://pipeline.zoominfo.com/sales/bombora-review