If you’ve heard “intent data” thrown around in every demand gen conversation for the past two years but still aren’t sure exactly how to turn it into pipeline, you’re not alone.

Most B2B marketers understand the premise: someone out there is researching a problem you solve. Intent data tells you who that is. But the gap between that concept and a working campaign is where most teams get stuck.

This playbook closes that gap. We’ll cover what B2B intent data actually is, where it comes from, how to score it, and — most importantly — how to activate it in campaigns that reach the right individuals at the right moment.

What Is B2B Intent Data?

Intent data is information that signals a buyer’s interest in a particular topic, product category, or problem. It’s behavioral data — captured from what people read, research, download, and engage with across the web.

In practical terms, intent data tells you which companies (and ideally which individuals) are actively researching topics related to what you sell — before they fill out a form on your website.

There are three types:

First-party intent data is behavior on your own properties — page visits, content downloads, email clicks, demo requests. You own it, it’s high-signal, and it reflects people already engaging with your brand.

Second-party intent data is first-party data shared by a partner — for example, a media publisher sharing which of their readers consumed content in your category.

Third-party intent data comes from data aggregators that track content consumption across thousands of external sites. Bombora is the most well-known source. This data tells you who’s researching your topic category across the broader web, not just on your site.

The most effective intent programs use all three — with third-party data for net-new account discovery, second-party for category awareness, and first-party for late-stage conversion.

Where Intent Data Comes From (and Why the Source Matters)

Not all intent data is equal, and the source determines how much you should trust it.

Content co-op networks (like Bombora’s Business Spend Index) aggregate data from publishers who share anonymous behavioral signals. When someone reads three articles about “ABM platforms” in a two-week window, that surge gets attributed to their company.

Review site engagement (G2, TrustRadius, Capterra) is high-signal intent — someone evaluating vendors in your category. This is mid-to-late funnel.

Search data captures keyword behavior at the individual level, typically through partnerships with data providers or search platforms. This is highly precise but limited in scale.

Event and webinar engagement is often overlooked as intent data, but attendance at a competitor’s webinar or an industry event is a strong buying signal.

The key limitation of most third-party intent data: it’s company-level, not individual-level. You know that Acme Corp is researching your category — but not which person at Acme Corp is doing the research. That’s a meaningful gap when you’re trying to reach a specific buyer.

Building Your Intent Scoring Model

Intent data without a scoring framework creates noise, not signal. The goal of intent scoring is to prioritize which accounts and individuals deserve your attention right now.

Step 1: Define your intent tiers

Most effective models use three tiers:

Tier	Signal strength	Action
Tier 1 – High intent	Active research, competitor comparison, review site visits	Immediate outreach, high-touch ABM
Tier 2 – Medium intent	Topic surge, industry content engagement	Nurture sequence, targeted ads
Tier 3 – Passive interest	Light topic engagement, newsletter sign-up	Brand awareness, retargeting

Step 2: Weight your signals

Not all intent signals are equal. Assign point values based on signal strength and recency:

Visited your pricing page: 25 points
Downloaded a comparison guide: 20 points
Third-party intent surge (Tier 1 topic): 15 points
Attended competitor webinar: 20 points
Opened three consecutive emails: 10 points
LinkedIn ad engagement: 5 points

Signals decay over time. A 90-day-old intent spike is much less valuable than one from last week. Build time decay into your model — halve the score for signals older than 30 days, remove them after 90.

Step 3: Layer in fit scoring

Intent data tells you who’s in-market. It doesn’t tell you whether they’re a good fit for your product. Combine intent scores with ICP fit scores (firmographic match, company size, industry, tech stack) to get a composite priority score.

High intent + strong fit = work immediately.
High intent + weak fit = pass to a lighter nurture track.
Low intent + strong fit = build awareness over time.

Activating Intent Data in Campaigns

Capturing intent signals is step one. Getting them into campaigns fast enough to matter is where most teams struggle.

Paid media activation

Upload your high-intent account list to LinkedIn Campaign Manager as a matched audience. Layer in job title targeting to reach the specific individuals most likely to be involved in the buying decision. Run thought leadership content to build familiarity while their research window is open.

For programmatic display, use individual-level B2B data to go beyond account-level targeting — serve ads directly to the VP of Procurement at a target account, not just anyone at that company who happens to visit a publisher site.

Sales activation

High-intent accounts should trigger a sales alert, not just an email sequence. Build a workflow where a Tier 1 intent spike creates a task in your CRM for the relevant SDR within 24 hours.

The window of active evaluation is short. Buyers who are actively researching often make a shortlist within two to four weeks. If your sales team doesn’t reach them during that window, a competitor will.

Email and nurture

For Tier 2 intent accounts, enroll them in a targeted nurture sequence that speaks directly to the topic they’ve been researching. If someone has been consuming content about data accuracy, your first email should lead with that angle — not a generic product pitch.

Personalization at this level requires knowing not just the account, but the individual. Generic account-level nurture (“we saw your company was researching X”) performs significantly worse than individual-level personalization.

The Individual-Level Problem in Intent Data

Most intent data operates at the account level. You know Acme Corp is researching “B2B data providers.” But Acme Corp has 200 employees. Who’s actually doing the research?

This is the central limitation of most intent programs — and why the combination of intent data with individual-level contact data is so powerful.

When you can match an account-level intent signal to the specific individuals most likely to be involved in the buying decision (VP of Marketing, Head of Demand Gen, Marketing Operations Manager), you can:

Serve ads to the right people, not the whole account
Personalize outreach with the correct name, title, and context
Suppress irrelevant contacts from paid campaigns to reduce waste
Attribute pipeline to the individuals who engaged, not just the company

This is the difference between knowing a building is on fire and knowing which floor. One lets you do something about it.

Common Intent Data Mistakes to Avoid

Treating intent as a lead. An intent signal is not a lead. It’s a signal worth acting on. Don’t route raw intent data to sales without validation and fit scoring — you’ll burn goodwill fast.

Ignoring signal decay. An intent surge from 60 days ago is cold. Build decay into your scoring model and deprioritize aged signals automatically.

Using account-level data for individual outreach. “Hi [First Name], I noticed your company has been researching B2B data…” is obvious and off-putting if the person didn’t actually do that research. Match intent to the right individuals before personalizing.

Not testing creative against intent context. A buyer in active evaluation mode responds to different messaging than one in early awareness. Segment your creative by intent tier and test accordingly.

Over-relying on a single intent source. No single provider has complete coverage. Layer multiple signals — first-party, third-party, and behavioral — for a more complete picture.

Putting It All Together: A Sample Intent Workflow

Here’s what an effective intent-to-pipeline workflow looks like in practice:

Daily intent feed pulls Tier 1 surges from your intent provider into your CRM or MAP
Fit score overlay filters for accounts that match your ICP
Individual-level match appends contact data for the specific personas most likely to be involved in the decision
Sales alert fires for accounts above a threshold composite score
Ad activation uploads the account + contact list to LinkedIn and programmatic platforms
Personalized email sequence launches with messaging matched to the intent topic
Signal tracking monitors whether the account engages with your content and adjusts their score in real time

The entire cycle — from intent signal to active campaign — should happen in 24 to 48 hours. Anything slower and you’re chasing a window that’s already closing.

Getting Started

Intent data is most powerful when it’s fast, individual-level, and connected directly to your campaign infrastructure.

If your current workflow involves downloading a CSV of intent accounts, manually enriching contact data, and uploading lists to each platform separately, you’re losing days — and deals — in the process.

The teams winning with intent data today aren’t working harder. They’re working with better data, matched to the right individuals, activated faster.

Hat Trick Data delivers individual-level B2B audience data matched to specific decision-makers — not just accounts. Custom audiences built in 2–4 hours. Talk to our team about adding individual-level precision to your intent programs.