---
title: "What is B2B intent data? Types, sources and uses"
description: "B2B intent data shows which companies are researching a topic or vendor. Here are the types, sources, limits and how sales teams use it."
canonical: https://www.leadsets.ai/answers/what-is-b2b-intent-data
updated: 2026-11-09
---

# What is B2B intent data?

B2B intent data is information about company behaviour that suggests an organisation is researching a product, service or problem. Examples include reading content on a topic, comparing vendors on review sites, visiting pricing pages or hiring for related roles. Sales and marketing teams use it alongside firmographic fit data to decide which accounts to contact first, when to reach out and what to say.

| Type | What it captures | Typical source | Usually tied to |
| --- | --- | --- | --- |
| First-party | Visits to your site, pricing page views, downloads, email and product engagement | Your analytics, marketing automation, CRM | Account or a known person |
| Second-party | Activity on another company's property, shared with you | Review sites, publishers, partners | Account |
| Third-party | Research on a topic across many sites, scored against a baseline | Data co-ops and publisher networks | Account |
| Event and hiring signals | Job posts, leadership changes, funding, technology changes | Careers pages, news, public filings | Account |

## What intent data actually measures

Intent data tries to answer one question: which companies are actively looking into something you sell, right now? Firmographic data (industry, headcount, location, technology) tells you whether an account *could* buy. Intent data adds a timing layer by showing whether it *seems to be* researching the problem.

The signals are behavioural. A company whose employees read several articles about warehouse forecasting this month, compare forecasting vendors on a review site and visit your pricing page twice is showing more intent than one that did none of those things. None of these actions proves a purchase is coming, but together they shift the odds.

Most intent data is reported at the **account level**, not the person level. Providers usually know that "someone at Parcelry" is researching a topic, not which individual. That is partly a technical limit and partly a privacy choice. For background on the wider category, see [what is B2B data](https://www.leadsets.ai/answers/what-is-b2b-data) and [what is sales intelligence](https://www.leadsets.ai/answers/what-is-sales-intelligence).

## The main types of intent data

**First-party intent** comes from your own properties: website visits, pricing and docs page views, webinar sign-ups, email clicks and, for product-led companies, product usage. It is the most reliable kind because you collect it directly and know exactly what it means. Its limit is reach: it only covers companies that already found you.

**Second-party intent** is another company's first-party data shared with you, typically by a review site or publisher. If a prospect compares vendors in your category on a review platform, that platform may sell the signal to vendors in the category.

**Third-party intent** is aggregated from many websites, often through data cooperatives or publisher networks. Content is tagged with topics, visits are resolved to companies, and the provider reports which accounts are researching which topics more than usual. It reaches companies that have never visited your site, at the cost of being further from the original behaviour.

**Event and hiring signals** are sometimes grouped with intent. A company posting a RevOps manager role, announcing funding, naming a new VP of Sales or adopting a new tool is not researching your product, but these events often predict a need. They are also public and easy to verify, which makes them a useful complement to topic-based intent.

## How intent scores are built

Third-party providers generally follow a similar pattern:

1. **Collect** page views and content consumption across a network of sites.
2. **Classify** content into a taxonomy of topics, such as "sales engagement" or "data warehousing".
3. **Resolve** activity to a company, commonly by matching IP addresses, domains or logged-in business emails.
4. **Compare** each company's recent activity on a topic with its own normal baseline.
5. **Score** the difference, often reported as a "surge" when activity rises well above baseline.

You then choose the topics that relate to your product and receive a list of surging accounts, usually refreshed weekly.

## Limits worth knowing

Intent data is useful, but it is a probability, not a fact.

- **Noise.** Students, analysts, journalists and competitors read the same content as buyers. A surge can come from one curious employee.
- **Company matching.** Remote and hybrid work makes IP-to-company resolution harder, so some activity is misattributed or missed.
- **Topic fit.** Broad topics produce long lists with weak signals; very narrow topics produce few matches.
- **No contact attached.** Knowing an account is surging does not tell you who to email. You still need the right people and working addresses, which is where [lead enrichment](https://www.leadsets.ai/answers/what-is-lead-enrichment) comes in.
- **Privacy.** Collection and use are shaped by laws such as GDPR and CCPA and by cookie consent rules. Ask any provider how data is collected and what legal basis it relies on.

A practical rule: treat intent as a way to *order* a list of accounts that already fit your ideal customer profile, not as a list on its own.

## How teams use intent data

Common uses include prioritising which accounts SDRs work this week, timing outreach to when interest is high, choosing message angles that match the topic being researched, targeting account-based ads and alerting account executives when an existing opportunity goes quiet but research continues.

A concrete example. Maya Chen runs revenue operations at Northwind Analytics. Her team has 400 accounts that fit their profile, far more than two SDRs can work well. Each Monday she:

1. Filters the 400 down to the accounts showing first-party visits or third-party surges on two relevant topics. This gives about 40.
2. Checks public hiring signals: Parcelry has just posted an operations analyst role and a RevOps manager role.
3. Finds the head of operations and the RevOps lead at each shortlisted account and gets verified work emails, so messages do not bounce. See [why email verification matters](https://www.leadsets.ai/answers/why-is-email-verification-important).
4. Writes outreach that references the visible, public signal (the new roles) rather than telling the prospect their browsing was tracked.

The intent data decides *where* to spend time; the contact data and the message decide whether it works.

## Where Leadsets fits

Leadsets does not sell third-party topic intent data. If topic surges are what you need, look at providers that publish an intent offering. Apollo says its intent data is powered by Bombora and lists 1 intent topic on its Free plan and 12 on Organization. ZoomInfo sells buyer-intent data, and its research notes describe intent as a separate SKU. Lusha says it offers 30+ buying signals, and Clay lists job-change signals on Launch and web-intent signals on Growth.

What Leadsets does cover is the event and action side. Its `scrape_jobs` tool turns public careers pages into open roles with titles and dates, and `scrape_company` turns a homepage into products, positioning and named customers ([web scraping](https://www.leadsets.ai/scrape)). An AI agent can then call `search_people`, `enrich_person` and `verify_email` to turn an account with a signal into named contacts with labelled emails. Scraping is an add-on on paid plans (packs from $9 for 1,000 units) and is not on Free. The free [GTM skills](https://www.leadsets.ai/skills) include a hiring signal finder that chains these steps.

## Questions and answers

### What is the difference between intent data and fit data?

Fit data, such as industry, headcount and technology, tells you whether a company matches your ideal customer profile. Intent data tells you whether that company appears to be researching a related topic right now. Most teams filter by fit first and then use intent to decide order and timing.

### What is first-party versus third-party intent data?

First-party intent comes from your own website, emails and product, so it is direct but only covers companies that already know you. Third-party intent is aggregated by a provider across many external sites and covers companies that have never visited you. It is further from the original behaviour and usually noisier.

### Is B2B intent data accurate?

It is a probability rather than a fact. Signals can come from non-buyers, and matching anonymous activity to the right company is imperfect, especially with remote work. It works well for ranking a list of accounts that already fit, and poorly as a standalone lead list.

### Are hiring signals a type of intent data?

They are often grouped with intent because a new role can predict a need, for example a company hiring SDRs may need sales tools. Unlike topic intent, job posts are public and easy to verify. Many teams combine both kinds of signal.

### How much does B2B intent data cost?

Pricing varies and is often not published. Apollo includes intent topics in its plans, listing 1 topic on Free and 12 on Organization, which it prices at $119 per seat per month billed annually with a 3-seat minimum. ZoomInfo does not publish platform prices, and its research notes describe intent as a separate SKU.

### Does Leadsets provide intent data?

Leadsets does not sell third-party topic intent data. It provides hiring signals from careers pages and structured company website profiles through scraping tools, plus people search, enrichment and verified work emails so an agent can act on a signal. Scraping is a paid add-on starting at $9 for 1,000 units.

## Sources (checked 2026-11-09)

- [Apollo pricing](https://www.apollo.io/pricing)
- [Apollo B2B data](https://www.apollo.io/product/b2b-data)
- [ZoomInfo pricing](https://www.zoominfo.com/pricing)
- [GTM.ai by ZoomInfo docs](https://docs.gtm.ai/)
- [Lusha data](https://www.lusha.com/data/)
- [Clay pricing](https://www.clay.com/pricing)

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