Intent Data vs Firmographic Data: What Each Tells You and How to Combine Them

B2B sales and marketing teams use many kinds of data to decide who to target and when. Two terms come up constantly: firmographic data and intent data. They answer different questions. Firmographics tell you whether a company could be a good customer. Intent data tries to tell you whether it's looking to buy right now. The best targeting uses both, plus a few other signal types.

Firmographic data: who the company is

Firmographics are to companies what demographics are to people: descriptive attributes that rarely change quickly. Common fields:

• industry and sub-industry,
• employee count,
• revenue or revenue band,
• headquarters and office locations,
• year founded,
• ownership type (public, private, PE-backed, VC-backed, family-owned),
• funding stage and total raised,
• business model (B2B, B2C, marketplace).

What it's good for: defining your total addressable market, building account lists that match your ideal customer profile and segmenting campaigns.

Limitations: it says nothing about timing. A company can be a perfect fit and have no interest in buying for years.

Where it comes from: company registries, filings, websites, LinkedIn, data vendors that aggregate and model these sources. Revenue for private companies is often estimated. See our guide on finding private company revenue.

Technographic data: what the company uses

Technographics describe the technology a company uses: CRM, marketing automation, cloud provider, analytics tools and so on.

What it's good for: targeting companies that use complementary tools (for integrations), competitors' tools (for displacement) or outdated systems ripe for replacement.

Where it comes from: website code scanning, job postings that mention tools, public case studies, review sites and surveys.

Limitations: back-office tools that don't leave web traces are hard to detect. Data can be out of date.

Intent data: what the company is researching

Intent data aims to detect when people at a company are actively researching a topic, which may signal a buying process.

First-party intent

Signals from your own channels:

• visits to your website, especially pricing and product pages,
• content downloads and webinar attendance,
• email engagement,
• product sign-ups and usage in product-led businesses,
• chat conversations.

First-party intent is usually the most reliable, because it's directly about your product. Its limitation is reach: it only covers companies already engaging with you.

Third-party intent

Signals collected by data providers from activity across other websites, such as publisher networks, review sites and content syndication. Providers analyse which companies are consuming content about particular topics more than usual, often called a "surge".

Limitations:

• it's usually at the company level, not the individual level,
• the link between reading content and buying is probabilistic,
• matching activity to companies relies on methods such as IP address mapping, which is less accurate with remote work,
• quality varies significantly between providers,
• privacy regulations affect how data can be collected and used.

Review site intent

Some software review platforms share signals when companies research product categories or compare vendors on their sites. These can be strong because they're closely tied to buying research.

Trigger data: what just changed

Triggers are events that create new needs:

• funding rounds. See funding as a sales trigger,
• new executive hires,
• job postings for relevant roles,
• acquisitions and mergers,
• office openings or expansion,
• product launches,
• regulatory changes affecting an industry.

Triggers are often public, verifiable and easy to reference in outreach. Unlike third-party intent, you can tell the prospect exactly why you're reaching out.

Side-by-side comparison

How to combine them

A practical model for prioritisation:

1. Fit first. Use firmographic and technographic data to build the list of accounts that could be good customers. Anything outside your ICP is excluded, however strong its intent signals.
2. Timing second. Within the fit list, use triggers and intent to identify who's most likely to buy now.
3. Engagement third. First-party intent tells you who's already aware of you and warm.

Using signals in messaging

Be thoughtful about how you use signals in outreach:

• Triggers: mention them directly. "Saw you're opening a Chicago office" is normal and relevant.
• Firmographics: use to tailor relevance. "Teams your size in logistics usually..."
• Third-party intent: don't mention it. "We noticed you've been researching X" feels intrusive. Instead, lead with the topic: "Many operations leaders are rethinking X this year..."
• First-party intent: use gently. Following up on a webinar they attended is fine. Mentioning which pages they viewed is usually not.

Evaluating data providers

1. Test against known accounts. Check accuracy on 50 companies you know well.
2. Ask about sources and methods. How is the data collected, matched and updated?
3. Check coverage in your target regions and segments.
4. Ask about compliance with privacy laws such as GDPR, CCPA and Canadian privacy law.
5. Run a pilot and measure whether signal-based accounts convert better than a control group.
6. Check integrations with your CRM and outreach tools.

Common mistakes

• Chasing intent signals from companies outside your ICP.
• Treating third-party intent scores as certain.
• Buying multiple overlapping data sources without measuring each one's contribution.
• Letting firmographic data go stale.
• Referencing tracking data in ways that make prospects uncomfortable.

A simple starting point

If you're starting from scratch, you don't need every data type. A practical, low-cost stack: a firmographic source to define your account list, public triggers such as funding rounds and executive hires to time outreach, and your own website and email engagement as first-party intent. Add third-party intent later, once you can measure whether it improves results. Our guide on building a B2B lead list walks through the setup.

Measuring whether signals actually help

Data vendors will tell you their signals improve conversion. The only way to know whether that's true for you is to test. A simple approach:

1. Pick a segment of accounts that all fit your ICP.
2. Split it into accounts that show a given signal (for example, a third-party intent surge or a funding round) and a comparable group that doesn't.
3. Run the same outreach to both groups over the same period.
4. Compare reply rates, meetings booked, opportunities created and, eventually, closed revenue.
5. Repeat for each signal type you pay for.

Results often differ by segment. A signal that works well for mid-market companies may add little for enterprise accounts, or vice versa. Let the data decide where to invest.

Building a signal-based scoring model

Once you've tested a few signals, combine them into a simple score. Keep it transparent so reps trust it:

These weights are illustrative. Start with your best guess, then adjust them quarterly based on which signals actually preceded closed deals. A simple model that reps understand and use beats a complex one they ignore.

Privacy considerations

Privacy rules increasingly affect how signal data can be collected and used, particularly website visitor identification and third-party tracking. Ask vendors how they comply with laws such as GDPR, US state privacy laws and Canadian privacy legislation, and make sure your own website's privacy policy and consent tools reflect how you use visitor data.

Bottom line

Use firmographics to decide who, signals to decide when, and engagement to decide how warm. Test every paid signal against your own results before scaling it.