Intent data is information about which companies are researching a topic, product or problem right now, based on the content they read and the pages they visit. B2B sales and marketing teams use it to spot accounts that are likely to be in market before those accounts fill in a form. This guide explains the three types of intent data, gives concrete examples of each, walks through a scoring model you can copy, and covers the limits and the GDPR questions European teams need to answer.
What Intent Data Is, and What It Is Not
Most B2B purchases start with research. Before a company books a demo, people on the buying team read articles, compare vendors, visit pricing pages and download guides. Intent data captures that research activity and links it to the company doing it.
It is useful to be precise about what intent data tells you:
- It tells you that an account is paying more attention than usual to a topic related to what you sell, and roughly when that started.
- It does not tell you that the account will buy, who the decision maker is, what their budget is, or which vendor they prefer.
Demandbase puts it well in its own guide to intent data: intent is a signal for prioritization, not proof that an account is ready to buy. Treat it as a reason to look at an account more closely, and combine it with fit and your own data before acting.
The 3 Types of Intent Data
Intent data is usually grouped by who collects it. The table below compares the three types.
| Type | Who collects it | Examples | Strength | Limit |
|---|---|---|---|---|
| First-party | You, on channels you own | Website visits, pricing page views, content downloads, webinar attendance, email clicks, product usage | Most specific: the account is researching you | Only covers accounts that already found you |
| Second-party | A partner, shared directly with you | Activity on a review site or a publisher's site about your category or your competitors | Shows research on your category outside your site | Limited to that partner's audience |
| Third-party | A data provider, across many sites | A spike in an account's reading on topics such as "payroll software" across a network of B2B publishers | Finds accounts before they visit your site | Topic-level and noisier; you depend on the provider's methods |
First-party intent data
First-party intent data comes from your own website, marketing automation, CRM and product. It is the most reliable type because the activity is about your company specifically. A company that reads your pricing page three times in a week is telling you more than a company that read a general article about your category.
The catch is that most website visitors never fill in a form, so a lot of first-party intent stays anonymous. That is why first-party intent is usually combined with website visitor identification, which matches business visits to the companies behind them.
Second-party intent data
Second-party intent data is another organization's first-party data, shared with you through a direct partnership. Typical sources are review sites, which can share which companies compared vendors in your category, and publishers that report which companies read your sponsored content.
Third-party intent data
Third-party intent data is collected by specialist providers across a large number of websites. According to Bombora, one of the best-known providers, third-party sources include data cooperatives of B2B publishers, data bought directly from publishers, and bidstream data taken from programmatic ad auctions. Providers typically classify the content each company reads into topics, compare the volume with that company's normal baseline, and flag a "surge" when research on a topic rises above it. Choosing which topics to monitor works much like choosing buyer intent keywords for search.
Third-party data can show you accounts researching your category before they visit your website, across a far wider range of sites than a single partner covers. It is also the least specific: a surge on "data security" can come from a student project, an IT team fixing a problem, or a real buying process.
Intent Data Examples
Here are concrete signals for each type, from strongest to weakest. The stronger the signal, the more directly it points at a purchase.
| Signal | Type | What it suggests |
|---|---|---|
| Several people from one account view your pricing page in the same week | First-party | Active evaluation, often with a buying group |
| Repeat visits to integration or security pages | First-party | Checking technical fit, usually later in the process |
| An account compares you with a competitor on a review site | Second-party | Shortlisting vendors |
| A current customer reads your docs on a feature they do not use yet | First-party | Expansion opportunity |
| An account's research on two or more of your core topics surges at once | Third-party | Problem awareness, possibly early research |
| A single person reads one blog post | First-party | Weak on its own; watch for repeats |
For a longer list of what to watch for, see our guide to B2B buying signals.
How Intent Data Is Collected and Scored
Most providers follow the same basic process, whatever they call it:
- Collect activity: page views, content consumption and engagement, from your own channels or from a provider's network.
- Classify the content into topics, such as "visitor identification" or "ABM platforms".
- Resolve the activity to an account, usually through the visitor's IP address, a cookie or a known email address.
- Compare each account's activity with its own normal baseline, so that large companies that read a lot do not always look hot.
- Combine the result with firmographic fit and your CRM data to produce a priority score.
A Worked Example: Scoring Intent Data
The top-ranking guides on intent data describe scoring but rarely show the numbers, so here is a simple model you can copy and adjust. The weights are a starting point, not a standard. Review them after a quarter against the deals you actually won.
| Factor (max points) | Rule | Points |
|---|---|---|
| Fit (40) | Industry matches your ideal customer profile | 15 |
| Fit (40) | Company size is in your target range | 15 |
| Fit (40) | Located in a market you sell to | 10 |
| First-party intent (40) | Pricing page viewed in the last 14 days | 15 |
| First-party intent (40) | 3 or more sessions in the last 14 days | 10 |
| First-party intent (40) | 2 or more people from the account visited | 10 |
| First-party intent (40) | Case study or integration page viewed | 5 |
| Third-party intent (20) | Surge on 1 of your core topics | 10 |
| Third-party intent (20) | Surge on 2 or more of your core topics (replaces the 10) | 20 |
Halve any behavior points older than 30 days, so the score fades if the account goes quiet. Then route accounts by score: 70 or more goes to the account owner the same day, 40 to 69 goes into ads and nurture, and below 40 is monitored.
Now apply it to three example accounts:
| Account | What happened | Fit | First-party | Third-party | Score | Action |
|---|---|---|---|---|---|---|
| A: 400-person retailer in your core market | Two people viewed pricing, four sessions this week, surge on two core topics | 40 | 35 | 20 | 95 | Sales calls today |
| B: 20-person firm in your target industry, outside your markets | Surge on one core topic, no website visits | 15 | 0 | 10 | 25 | Monitor |
| C: 600-person logistics firm in your core market | One person read two case studies, no surge | 40 | 5 | 0 | 45 | Retargeting ads and nurture |
Two lessons stand out. First, third-party intent on its own rarely justifies a sales call: account B is researching, but it is too small and outside your markets. Second, first-party intent from a well-fitting account is usually the strongest signal you have, which is why account A beats account C by a wide margin even though both fit equally well.
How to Use Intent Data
- Prioritize accounts for sales. Give reps a short daily list of high-scoring accounts instead of a long list of cold ones, with the pages and topics that triggered the score.
- Time outreach. Contact an account while its research is active. Reference the problem, not the tracking: "Teams in logistics often ask us about X" works better than "I saw you on our pricing page".
- Run account-based ads. Send accounts in the 40 to 69 range to LinkedIn or display audiences so they see relevant content while they research. See our guide to account-based marketing.
- Personalize content. Match emails and landing pages to the topics an account is researching.
- Protect and grow customers. Watch for customers researching competitors or reading about features they do not use yet. Our post on intent data for customer success covers this in detail.
- Refine your ideal customer profile. Compare the industries and sizes of high-intent accounts with your ideal customer profile. If a segment keeps showing up, test it.
Limits of Intent Data
- It is account-level. Most intent data tells you a company is researching, not which person. You still need to find the buying group.
- It produces false positives. Research is not buying. Expect some high-intent accounts to go nowhere, and judge the data on pipeline over a quarter, not on single accounts.
- Topics are only as good as their definitions. Choose topics that are specific to the problem you solve. Broad topics such as "marketing" surge for almost everyone.
- Coverage varies. Smaller companies and remote teams on home networks leave fewer signals, so they score lower even when they are buying.
- Signals go stale. Research activity loses value quickly, so act fast and let scores decay.
Intent Data and GDPR
For teams selling in Europe, intent data raises two practical questions: is the data personal data, and what is the legal basis for using it?
IP addresses are central to most intent data, and the Court of Justice of the EU ruled in Breyer (C-582/14, 19 October 2016) that a dynamic IP address can be personal data for a website operator that has the legal means to identify the visitor. In practice, this means you should treat visitor tracking as processing of personal data, even when your goal is to identify companies.
On the legal basis, Recital 47 of the GDPR states that processing personal data for direct marketing purposes may be regarded as carried out for a legitimate interest. Many B2B teams rely on that basis for account-level intent data, supported by a legitimate interest assessment, a clear privacy notice and an easy way to object. Person-level tracking of anonymous visitors usually needs consent. Cookie rules in your country may also require consent for the tracking script itself, so check with your legal team.
When you evaluate providers, ask where their data comes from, whether the original sites collected it with a valid legal basis, and whether they can give you a data processing agreement. Our post on Albacross and GDPR explains how we approach this, and our guide to comparing intent data providers lists the questions to ask.
How Albacross Helps
Albacross turns website visits and intent signals into a ranked list of accounts. Identify shows which companies visit your website and what they look at. Signal combines website behavior, firmographic fit, CRM history and third-party intent into a single priority score, and sends scored accounts to your CRM, Slack, Teams or ad audiences. You can also bring your own intent data and custom scoring rules.
See how Signal scores accounts.
Frequently Asked Questions
What is intent data in simple terms?
Intent data shows which companies are researching a topic related to what you sell. It is based on the content they read and the pages they visit, on your website or across other websites.
What are examples of intent data?
Common examples are repeat visits to your pricing page, several people from one company viewing your website in the same week, vendor comparisons on review sites, and a rise in a company's reading about topics in your category across B2B publishers.
What is the difference between first-party and third-party intent data?
First-party intent data comes from your own channels, such as your website, emails and product, so it shows interest in your company specifically. Third-party intent data is collected by a provider across many other websites, so it can show research before an account visits you, but it is less specific.
Is intent data accurate?
It is directional rather than exact. Intent data reliably shows changes in research activity, but research does not always lead to a purchase. Combine it with fit and first-party signals, and measure it against pipeline over a quarter.
Is intent data GDPR compliant?
It can be. Under GDPR, IP-based tracking is usually treated as processing personal data. Many B2B teams use legitimate interest for account-level intent data, with a privacy notice and an opt-out, while person-level tracking of anonymous visitors usually needs consent. Check your provider's data sources and get a data processing agreement.
What is the difference between intent data and buying signals?
Intent data is one source of buying signals. Buying signals also include events such as funding rounds, new hires in a relevant role, technology changes and direct requests for a demo. See our guide to B2B buying signals.
Can small teams use intent data?
Yes. Start with first-party intent from your own website, which you already have. Identify which companies visit your key pages, score them with a simple model like the one above, and add third-party data later if you need to find accounts earlier.
Further Reading
Marcus Svensson
Head of Growth
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