Firmographic data is the set of descriptive attributes that classify companies: industry, headcount, revenue, location, ownership, structure, age, and growth trajectory. It is the B2B equivalent of demographic data. Where demographics describe people, firmographics describe the organizations those people work for. Sales and marketing teams use firmographic data to define an ideal customer profile (ICP), segment a target market, and qualify accounts before spending rep time on them.

This guide covers the eight firmographic attributes worth filtering on, where each one actually comes from, how to combine them into an ICP you can act on, and the limitation every glossary entry skips: firmographic data tells you who fits, and nothing about when to reach out.

What is firmographic data?

Firmographic data is information that describes a company as an entity, independent of any person who works there. Industry, employee count, annual revenue, headquarters location, ownership type, and corporate structure are all firmographic attributes. You will also see the terms firmographics, firm demographics, and emporographics; they all mean the same thing.

The purpose of firmographic data is filtering. A B2B market contains millions of companies, and almost all of them are a bad fit for any given product. Firmographic attributes are the cheapest, most reliable way to shrink that universe to the segment worth pursuing: the right industries, the right size band, the right geography, the right budget capacity. Every ICP definition, territory plan, and account-based marketing program starts with firmographic filters, whether or not anyone uses the word.

Firmographic data examples: the eight attributes that matter

The eight firmographic attributes most teams filter on are industry, company size, annual revenue, location, ownership type, corporate structure, company age, and growth indicators. The table below maps each attribute to example values, where the data actually comes from, and the targeting decision it powers.

Two of these deserve a caveat. Revenue figures for private companies are almost always modeled estimates built from headcount, industry, and comparable benchmarks, not reported numbers, so treat a private company's revenue band as an approximation. And growth indicators are the one row that is not really static: hiring velocity and funding events change month to month, which is why they behave more like buying signals than like classic firmographics. More on that distinction below.

Firmographic vs demographic, technographic, and intent data

Firmographic data describes the company, demographic data describes the person, technographic data describes the company's tools, and intent data describes the company's research behavior. B2B targeting uses all four, and confusing them is the fastest way to build a filter that does not work.

The four types stack rather than compete. Firmographics define the universe of accounts worth caring about, demographics identify the people inside them, technographics refine fit for products with integration dependencies, and intent data attempts to rank who is active right now. A filter built only on firmographics finds companies that fit but may never buy; a feed built only on intent finds active companies that may not fit. You need the fit layer first.

Where firmographic data comes from

Firmographic data comes from four kinds of sources: government registries and filings, the company's own public footprint, professional networks, and commercial data providers that aggregate the first three. Which source a given field came from determines how much you should trust it.

Government sources (incorporation registries, SEC filings, tax records) are authoritative but slow-moving and thin for private companies. A company's own footprint (its website, careers page, and press releases) is current but unstructured; job postings in particular are a reliable, public way to read both headcount growth and technology choices. Professional networks supply the headcount numbers most providers display. Commercial providers stitch all of this together, standardize it into fields, and sell it, which is why two providers frequently disagree about the same company's size or revenue: they modeled it from different inputs at different times.

The practical takeaway: firmographic data is cheap and widely available, but it is assembled, not observed. Every aggregated field carries an update lag, and the lag is where lists quietly rot.

How to use firmographic data to build an ICP

The highest-value use of firmographic data is writing your ideal customer profile as a set of explicit, checkable filters instead of a vibe. An ICP written in firmographic terms can be handed to a data provider, a list-building tool, or a new SDR, and everyone retrieves the same companies.

A worked example. Say you sell compliance automation software and your best customers are mid-sized, regulated, US-based, and growing. Written as firmographic filters:

1. Industry: healthcare, financial services, or insurance (the regulated verticals where the pain is mandatory, not optional)
2. Company size: 51 to 500 employees (big enough to have a compliance burden, small enough to lack a dedicated team)
3. Revenue: $5M to $100M (budget exists, procurement is still fast)
4. Location: United States (where the regulations you cover apply)
5. Ownership: private, VC- or PE-backed (funded companies buy tooling; bootstrapped ones defer it)
6. Growth: headcount up over the trailing 6 months (growing companies hit compliance thresholds)

Each line is an attribute from the table above, and each has a reason attached. That last part matters: a filter you cannot justify from closed-won evidence is a guess wearing a spreadsheet costume. Beyond ICP definition, the same filters drive market segmentation, lead scoring (fit score = weighted match against these filters), lead routing (size band determines which team gets the account), and territory planning.

What is firmographic segmentation?

Firmographic segmentation is dividing a market or account list into groups by firmographic attributes, so each group can get different messaging, pricing, or sales coverage. It is the B2B version of demographic segmentation in consumer marketing.

Concrete examples of firmographic segments:

• US software companies with 200 to 500 employees (mid-market SaaS motion)
• Logistics firms in Western Europe under $20M revenue (SMB, single-region play)
• PE-backed healthcare providers that acquired another company in the past year (roll-up integration pitch)

Segmentation earns its keep when the segments actually get treated differently. If every segment receives the same sequence from the same team, you have labels, not segments.

The limits of firmographic data

Firmographic data has two structural limits: it decays faster than most teams assume, and it says nothing about timing. Both follow from what firmographic data is, a snapshot of facts that change.

On decay: as of mid-2026, industry benchmarks put B2B data decay around 2.1% per month, compounding to roughly 22.5% per year, and in high-turnover segments like tech startups the annual figure can reach 70%. Companies raise, hire, shrink, move, merge, and rebrand continuously, and every one of those events invalidates a field in your export. CRM owners feel this directly: 76% of businesses say less than half of their CRM data is accurate and complete. A firmographic list is most accurate the day you pull it and degrades every day after.

On timing: firmographics measure fit, and fit is nearly constant. The 200-person Texas healthcare software company fits your ICP this quarter, fit last quarter, and will fit next quarter. Nothing in its industry code or size band tells you that this month it hired a new VP of Operations, lost its biggest integration partner, or posted six compliance job openings. Those events, the buying signals, are what separate an account worth calling today from one worth calling eventually. Firmographic filters answer "who belongs on the list." They cannot answer "who on the list is ready," and treating a fit score like a timing score is the most common way firmographic targeting disappoints.

From firmographic filters to live signals

The fix for both limits is the same: stop treating your target market as a list you pull, and start treating it as a set of accounts you monitor. The firmographic ICP defines the set; monitoring catches the changes and the timing the snapshot misses.

This is the model Hunch is built on. You describe both your ICP and your signals in plain English, for example "US healthcare software companies between 50 and 500 employees that posted a compliance job opening in the last 60 days," and Hunch finds every company matching it now, then monitors your accounts daily for new matches, with per-account evidence and sources you can read yourself. Contacts come included: every plan has unlimited contact data with verified emails, so a firing signal arrives with the people to act on it. Pricing is public and per-account, $0.75 per monitored account per month, so a 500-account ICP costs $375 a month to watch rather than a five-figure platform contract.

Firmographic data builds the map. Signals tell you where the movement is. Teams that combine the two stop debating list quality and start arguing about which rep gets the account that lit up this morning, which is a better argument to have.

Frequently asked questions

What is an example of firmographic data?

A company's industry, employee count, annual revenue, headquarters location, ownership type, and founding year are all examples of firmographic data. A concrete record might read: healthcare software company, 250 employees, roughly $30M annual revenue, headquartered in Austin, Texas, privately held, founded 2014.

What is the difference between firmographic and demographic data?

Demographic data describes individual people: age, job title, seniority, location. Firmographic data describes companies: industry, size, revenue, structure. B2B targeting uses both, in sequence: firmographics select which companies to pursue, and demographics select which people inside those companies to contact.

What is another word for firmographics?

Firmographics are also called firm demographics, company demographics, business demographics, and occasionally emporographics. All of these terms refer to the same thing: the descriptive attributes used to classify and segment organizations.

What are the four main types of B2B data?

The four types B2B teams use most are firmographic data (company attributes like industry and size), demographic data (person attributes like title and seniority), technographic data (the software and hardware a company uses), and intent data (signals about a company's research behavior). Most targeting stacks layer at least two of them, starting with firmographics for fit.

How often should firmographic data be refreshed?

Fast-changing fields like headcount, funding stage, and growth indicators deserve monthly or continuous refresh, while slow fields like industry and location can be verified quarterly. B2B data decays at roughly 2% per month on average, so any list older than a quarter should be treated as unverified. Continuous account monitoring replaces batch refreshes entirely by catching changes as they happen.