What Is Firmographic Data?

What is firmographic data?

Firmographic data is the set of attributes that describe an organisation rather than an individual: industry, headcount, revenue, location, age, ownership and funding. The word is a deliberate parallel to demographics. Demographics segment a consumer market by age, income and household; firmographics segment a business market by size, sector and geography.

The practical definition is narrower than the dictionary one. A firmographic field is an attribute a company has whether or not anybody is selling to it, and whether or not it is doing anything in particular this month. Headcount is firmographic. Industry is firmographic. "Downloaded a pricing guide last Tuesday" is not, that is behavioural. "Runs Snowflake" is not either, that is technographic. Keeping those three families separate is what stops a scoring model from double-counting the same signal.

Firmographics answer four questions that gate every B2B qualification decision before a rep ever picks up a phone. Is this company in a sector we sell to? Is it large enough to need what we sell and small enough to buy it without a twelve-month procurement cycle? Is it in a geography we can service and lawfully contact? And is it structured in a way that means the buyer we want actually sits here, rather than at a parent entity two countries away? A list that cannot answer those four is not a target list, it is a directory.

The reason the category is worth a page of its own is that firmographics are the cheapest data in B2B and the most load-bearing. Contact data decides whether you reach a person. Firmographic data decides whether reaching them was worth doing. Teams routinely spend an order of magnitude more on the former while leaving the latter half-populated, then wonder why their ICP score does not predict anything.

What are the standard firmographic data fields?

The standard set has six families: identity, industry, size, location, age and structure, and funding. Almost every provider ships some version of these, and the differences between vendors show up in coverage and freshness rather than in which fields exist.

Identity. Legal name, trading name, primary web domain, LinkedIn company URL, logo, and any aliases or former names. The domain is the field that matters most operationally, because it is the only one of the six that reliably works as a join key. Company names are ambiguous (there are thousands of businesses called Apex), and matching on them is the single largest source of wrong-company enrichment.

Industry classification. SIC and NAICS codes, plus the provider's own taxonomy and, increasingly, the LinkedIn industry label. These three rarely agree. NAICS is a US-centric government scheme designed for economic statistics, so it slices manufacturing finely and treats most of modern software as one code. LinkedIn's taxonomy is self-declared by the company. A provider's proprietary taxonomy is usually the most useful for GTM and the least portable between vendors.

Size. Employee headcount, headcount band, headcount growth over a trailing window, annual revenue, revenue band. Headcount is usually the most reliable size signal in B2B because it is observable from the outside. Revenue mostly is not, which is covered further down.

Location. Headquarters country, state or region, city, postal address, and the list of additional office locations. HQ country is the field that drives territory assignment and, in practice, compliance routing, because whether GDPR or CCPA governs an outreach depends on where the person is rather than where the company is incorporated.

Age and structure. Founding year, ownership type (public, private, private-equity-backed, non-profit, government), parent company, subsidiaries, number of locations, and whether the entity has been acquired. Structure is the family most teams skip and the one that most often explains a lost deal, because selling to a subsidiary of an account you already serve is a very different motion from a cold start.

Funding. Total raised, last round type and date, investors, and whether the company is bootstrapped. This family sits at the edge of the definition: it is stable and company-level like a firmographic, but it also functions as a timing signal, which is why it appears in both firmographic and signal-based playbooks.

On Cleanlist, an enriched company record comes back with up to 180 firmographic properties spanning those families, and the six that are exposed as structured Company Search filters are industry, employee count range, funding stage, location, company name and company domain.

Where does each firmographic field actually come from?

Each family has a different origin, and the origin is what predicts its reliability. There is no single register of company facts, so every provider assembles firmographics from a mixture of public filings, the company's own web presence, employment data and inference.

Identity and domain come from registries and from the web. Company registrations (SEC filings in the US, Companies House in the UK, provincial and state registries elsewhere) give legal names and incorporation details. Domains come from crawling and from DNS and WHOIS records. This family is the most accurate available because it is the most documented.

Industry classification comes from three separate places and inherits the flaws of each. Government codes come from what the company told a registrar, often years ago and often chosen for tax reasons. The LinkedIn label comes from what the company chose from a dropdown. Provider taxonomies come from a model reading the company's website, which is the most current source and the most likely to misread a holding page or a rebrand.

Headcount comes overwhelmingly from professional network profile counts: a provider counts the people who list the company as their current employer. That method is fast, current and systematically biased. It undercounts companies in sectors where staff do not maintain professional profiles (hospitality, field trades, manufacturing floor roles) and overcounts companies where former employees have not updated a profile. Filings-based headcount is more accurate and available for a small minority of companies.

Revenue is the weakest field in the set. Public companies report it. Everyone else is estimated, usually by modelling from headcount and sector, which means the revenue field for most private companies is a transformation of the headcount field rather than an independent observation. Treat it as a band, never as a number.

Location comes from registrations, from the company website's contact page, and from job posting locations. Office lists in particular are frequently reconstructed from where a company advertises roles, which is why a firm that hires remotely can appear to have offices it does not have.

Funding comes from press releases, regulatory filings such as Form D, and the crowd-maintained funding databases. It is well covered for venture-backed technology companies in North America and Western Europe and thin nearly everywhere else, which is a coverage bias worth knowing before a funding filter becomes the spine of a segment.

Cleanlist resolves these live rather than holding an index. The company data layer behind Company Search is supplied by Crustdata, whose own site describes a real-time API carrying hiring, funding and company event signals, and a flat file it describes as "a fully refreshed dataset monthly, unified from 11 sources" (crustdata.com, read 2026-09-01).

How stale is firmographic data, and which fields decay fastest?

Firmographic data decays far more slowly than contact data, and within firmographics the decay rate varies by an order of magnitude between fields. 6sense puts company data decay at roughly 15% a year. For comparison, Cognism puts overall B2B contact data decay at about 22.5% a year and email address decay at about 25%, SparkDBI puts phone number decay at about 18%, and LinkedIn's Economic Graph puts annual job changes at about 10.9%. The gap between 15% and 25% is the whole reason firmographics and contacts deserve different refresh schedules.

Ranked from most durable to most volatile, and this ordering is the one that should drive your refresh budget:

Never changes. Founding year. Once resolved, it is resolved.

Changes rarely, and loudly when it does. Legal name, primary domain, headquarters country, ownership type. A rebrand or an acquisition changes several of these at once, and it is usually announced, which is why event-driven refresh beats calendar-driven refresh for this family.

Changes on a quarterly scale. Headcount, headcount band, office list, industry classification as a provider's model re-reads a changed website. A headcount figure six months old is directionally fine for banding and unusable for growth scoring.

Changes unpredictably and matters immediately. Funding stage and last round date. A Series B announced yesterday invalidates yesterday's segment, and this is the one firmographic field whose staleness costs you a timing advantage rather than an accuracy point.

Was never fresh. Revenue for private companies. It is a modelled estimate, so it does not decay so much as remain approximate, and re-pulling it more often does not make it more true.

The practical schedule that falls out of this: reverify contacts monthly if you run daily outbound, refresh firmographics quarterly, and treat funding as an event subscription rather than a field you refresh. A team spending equally on both is overspending on the durable half.

One structural point that is easy to miss. A provider that owns and licenses a static index ships you its age at the moment of query, so an index refreshed annually contains a full year of closed offices, changed headcounts, acquired entities and dead domains on the day you buy it. A provider that resolves at request time has no index to age. Cleanlist is in the second category, which is a genuine advantage on freshness and a genuine limitation if what you want is a bulk file to load into a warehouse.

How is firmographic data different from technographic, demographic and intent data?

They differ in what they describe and in how long they stay true, and mixing them is the most common modelling error in ICP scoring. Firmographic data describes what a company is. Technographic data describes what it runs. Demographic and contact data describe the people inside it. Intent data describes what it appears to be doing right now.

Firmographic attributes (industry, headcount, revenue band, location, funding) are structural. They change on a quarterly-to-annual scale and they describe fit. Fit is the question of whether this company should ever buy from you.

Technographic attributes (CRM in use, cloud provider, marketing stack, payment processor) describe compatibility and displacement opportunity. They are sourced by scanning a company's public web footprint, job posting requirements and DNS records, which means coverage is excellent for anything with a visible web fingerprint and poor for anything running behind the firewall.

Demographic attributes in a B2B context describe the person: title, seniority, department, management level, tenure, location. They decay fastest of all four because people change jobs, and they answer who to reach rather than whether to reach anyone.

Intent signals (content consumption, review-site activity, hiring surges, search behaviour) describe timing. They are the shortest-lived and the noisiest, and they are worthless without accurate contact data underneath them, because knowing an account is in-market does nothing if the person you reach has left.

The reason to keep them separate in a scoring model is that they answer different questions and should therefore be weighted differently and refreshed differently. A model that adds a technographic point to a firmographic score and calls the total "fit" produces a number that moves when a company swaps a marketing tool, which is not a change in fit at all.

Where Cleanlist sits in that grid is worth stating plainly. It ships structured firmographic filters and structured demographic filters on people (seniority, department, management level, title, function). It ships no technographic filter and no intent data of any kind. What it offers instead of a technographic field is an AI column that researches a company's public footprint at the moment you ask and writes back what it finds, which is research output rather than a verified structured field you can filter a search by. If a technology install base or an intent feed is the spine of your targeting, buy a provider that sells one.

How do teams use firmographic data for segmentation and territory design?

Segmentation is the primary job firmographic data does, and in practice it means cutting a total addressable market into a serviceable one along three axes: industry, size and geography. Those three carry the most weight because they are the three that actually define whether a company can be sold to, serviced and lawfully contacted.

Market sizing. Counting the companies that match an industry-plus-size-plus-geography filter is how a serviceable addressable market gets a number instead of an adjective. This is also where a free search meter changes behaviour: if every count costs credits, teams size the market once and then defend the estimate. Company Search on Cleanlist costs 0 credits regardless of how many queries you run, so re-cutting the market ten different ways to find out which cut is real costs nothing.

Territory design. HQ location plus headcount band assigns accounts to reps, and the two together balance territories by potential rather than by count. Splitting on geography alone gives one rep a hundred enterprise accounts and another a hundred sole traders.

Segment-specific messaging. Industry drives what the first line of an email says, and company size drives which problem it says. The same product sells to a 30-person company on "you have nobody doing this" and to a 3,000-person company on "you have four teams doing this differently", and only firmographics tell you which sentence to send.

Exclusion lists. The underrated use. Firmographic filters are more valuable for what they remove than what they add: current customers, companies below a viable size, sectors you cannot service, geographies you are not licensed in, and competitors. Every one of those is a firmographic filter, and building them once saves more rep hours than any inclusion filter.

The mechanics matter here. On Cleanlist, filters combine with AND across keys and OR within a key, so listing several industries widens a search while adding a headcount band narrows it. One trap that costs people entire afternoons: enum values are matched literally, so a funding stage filter takes the lowercase snake_case form (series_b matches, Series B returns nothing), and an invalid enum returns zero rows silently rather than raising an error. If a count collapses to zero right after somebody edited a filter, check the casing before you conclude the segment is empty.

How does firmographic data feed ICP scoring and lead routing?

Firmographic attributes are the fit half of a lead score, and fit is the half that should gate routing while behaviour decides sequencing. An ICP score built on firmographics answers whether a company belongs in your pipeline at all; an engagement score built on behaviour answers which of the qualifying companies to call first. Collapsing the two into one number is why so many scoring models rank an unqualified company that opened three emails above a perfect-fit account that has not visited the site.

Building the fit half is a four-step job. First, derive the profile from won deals rather than from a whiteboard: pull the firmographics of your last fifty closed-won accounts and your last fifty closed-lost, and look at which fields separate them. It is frequently not the field the exec team assumed. Second, weight the fields by how strongly they separate, not by how important they feel. Third, set the score to gate routing: above the threshold goes to a rep, below it goes to nurture or nowhere. Fourth, re-derive quarterly, because an ICP that is not re-derived is a snapshot of the market you sold to two years ago.

Routing rules then hang off the same fields directly. Headcount band decides SMB versus mid-market versus enterprise ownership. HQ country decides territory and which privacy regime governs the outreach. Industry decides which specialist takes the account. Funding stage decides urgency. None of these need a model, they need populated fields, which is the actual reason most routing rules misfire: the rule is fine and the field is blank.

The honest limit on any scoring layer is that it is a filter over data you already hold, so its accuracy is capped by the data underneath it. Scoring a database whose headcount field is 60% populated produces a confident number for the 60% and silence for the rest, and the rest is usually where the new logos are.

On Cleanlist the sequence is: search for nothing, enrich for a credit, score for five. Company Search and People Search both cost 0 credits, a company record is 1 credit, a verified work email is 1, a direct dial is 10, both on the same contact is 11, and an AI qualification is 5 credits per person and writes the agent's reasoning next to the number rather than only the number. A row the agent cannot answer is not charged.

Why do two providers report different headcount and revenue for the same company?

Because they are measuring different things and calling them the same name. This is the single most common surprise in a firmographic evaluation, and it is a methodology difference rather than one vendor being wrong.

Headcount disagreements usually trace to the counting method. A provider counting current professional network profiles is measuring people who claim the company as an employer. A provider reading filings is measuring full-time equivalents on a payroll at a reporting date. A provider modelling from web presence is measuring an inference. For a company with contractors, a recent acquisition, or a workforce that does not maintain profiles, these three produce genuinely different and individually defensible numbers.

Revenue disagreements are wider still, and for a structural reason: for the overwhelming majority of private companies, nobody knows. Vendors publish revenue anyway because buyers filter on it, and the number is almost always modelled from headcount and sector. That means filtering on revenue for private companies is frequently a laundered headcount filter with extra confidence attached. Filter on headcount directly and you get the same segmentation with an honest label.

Industry disagreements come from taxonomy mismatch rather than error. A company can be simultaneously correct as "Software Publishers" in NAICS, "Financial Services" on LinkedIn and "Fintech Infrastructure" in a vendor's taxonomy. When you migrate providers, the industry field does not map, and a segment that worked in one system quietly changes population in another.

What to do about it, in order. Pick one provider as the system of record for each field rather than blending them, because a blended headcount belongs to no methodology and is unreproducible. Prefer the field with the most direct observation (headcount over revenue, domain over name). Store the source and the date alongside the value so a disagreement is debuggable six months later. And test before you buy: take 200 to 500 companies you know well, run them through each candidate, and check the fields against reality yourself. Coverage claims are the easiest thing for a vendor to publish and the least predictive of whether the record is usable.

How do you evaluate a firmographic data provider?

Evaluate on refresh method, fill rate on your own list, and cost per usable record, in that order. Database size is the number vendors lead with and the one that predicts the least, because an index of 50 million companies refreshed once a year contains a year of decay at the moment you query it.

Refresh method first. Ask how a record gets updated and how often, and accept "not published" as an answer that tells you something. As of readings taken on 2026-09-01, Crustdata states its flat file is fully refreshed monthly from 11 sources and its API carries real-time hiring, funding and event signals; Diffbot prices refresh as an explicit metered action; Clay resolves at request time across its partner network; Coresignal and The Companies API publish no cadence on their pricing pages; ZoomInfo and Cognism publish none on the pages read.

Fill rate on your list, not theirs. Coverage is not uniform. A provider strong on North American venture-backed software is routinely thin on privately held European manufacturers, and the vendor's headline fill rate is an average over a population that is not yours. Run 200 to 500 of your own accounts through a trial and measure per-field fill: what percentage came back with a headcount, an industry, an HQ country. Then check a sample by hand, because a match is not an accuracy.

Cost per usable record. Compute it from the published credit rate and the per-record credit cost rather than from the plan price. Readings from 2026-09-01: Coresignal's $49 Mini plan carries 2,500 credits with a company record costing 10 to 20 credits, so roughly $0.20 to $0.39 each at that tier; Diffbot's free plan carries 10,000 credits a month against 25 credits per entity export, so about 400 free exports; The Companies API's $95 Startup plan carries 50,000 credits; ZoomInfo and Cognism publish no price at all, and ZoomInfo's own pricing explainer says so directly. Cleanlist charges 1 credit for a company enrichment and 0 for search, so on Starter at $79 for 1,500 credits a company record is about $0.05, on Pro at $229 for 5,000 about $0.046, and on Scale at $599 for 15,000 about $0.04, with 25% off annual billing. Re-check every one of these against the vendor's own page before you sign anything, because data pricing changes without notice.

Then the awkward questions. Where did the data come from, and can the vendor tell you per field? What is the deletion and opt-out process, and is it a real mechanism or an email address? Are the underlying sources disclosed as subprocessors in a document your privacy review can read? A provider that cannot answer those clearly is a provider whose compliance posture you are inheriting.

What does firmographic data cost?

Published rates ranged from free to roughly $0.39 per company record on the entry tiers of the vendors that publish a price at all, read on 2026-09-01, and two of the largest vendors publish nothing. That spread is real, and it means the sticker price is a weak signal on its own.

The pricing shapes you will meet are four. Credit-based per record, where you buy credits and spend them per lookup: the most predictable, and the easiest to model because you can compute a per-record rate. Per seat, where cost scales with headcount rather than usage: cheap for a two-person team and punishing at twenty. Annual contract with no published rate, the enterprise default, which costs a demo cycle to price. Free tiers, which exist mainly for evaluation: Diffbot's permanent free plan at 10,000 credits a month is roughly 400 entity exports, and ZoomInfo Lite offers 10 monthly credits with no card, which is an inspection allowance rather than a working plan.

The hidden costs are where budgets actually break, and there are four of them. A search or export meter, so that finding out how many companies match a filter costs money and teams stop exploring. A charge on a miss, so you pay for lookups that returned nothing. Per-seat charges layered on top of credits. And overage priced above the plan rate, which turns a busy month into a surprise.

Cleanlist's shape, stated so it can be compared like for like: Company Search costs 0 credits with no export meter and no cap on queries or results, a company enrichment costs 1 credit, a verified work email 1, a direct dial 10, both on the same contact 11, a validation 0.5 and an AI qualification 5. A lookup that returns nothing is not charged. Plans are Starter at $79 a month for 1,500 credits, Pro at $229 for 5,000 and Scale at $599 for 15,000, credits drawn from one shared team wallet rather than per seat, and annual billing takes 25% off. The Free plan includes 30 credits a month. A new workspace runs 14 days on Scale with 250 credits, 3 seats and no credit card, which is enough to run the fill-rate test described above on your own list before spending anything.

What firmographic data does Cleanlist return, and what does it not?

Cleanlist returns up to 180 firmographic properties on an enriched company record, resolved live across a pool of 25+ providers at the moment of the query rather than read out of an index Cleanlist owns. The descriptive set is what you would expect from the six families above: name, domain, industry, headcount, headquarters and office locations, founded year, ownership and funding history among the rest.

What is exposed as a structured, filterable field is a smaller set than what is returned, and the distinction matters when you are planning a workflow. Company Search filters on industry, employee count range, funding stage, location, company name and company domain. Filters combine with AND across keys and OR within a key. The search itself costs 0 credits however many queries you run, and you can pivot straight from a company result set into People Search across those domains, filtered by seniority, department, management level, job function or title, also at 0 credits, so both the account list and the buying committee are free to assemble and you spend only on the rows you decide to enrich.

What Cleanlist does not have, stated plainly because a page that only lists capabilities is a brochure:

No technographic dataset and no technology filter. You cannot search for companies by the software they run. An AI column can research a company's public footprint and write back what it finds, which is research output rather than a verified structured field.

No intent data of any kind. No topic surge feed, no anonymous website de-anonymisation, no predictive in-market model.

No org charts and no reporting lines. You can find the people and their seniority; you cannot read who reports to whom.

No revenue-range or headcount-growth filter on the public API. Those filters exist in the portal. The public API exposes funding stage and headcount band, not revenue. Any documentation or diagram that draws literal API filter chips has to reflect the API, not the portal.

No bulk file. Because records resolve at request time, there is no static company file to license and load into a warehouse. If warehouse-scale ingestion is the requirement, a bulk provider is the right purchase and Cleanlist is not.

No SOC 2 Type II or ISO 27001 certification. If your security review makes either a hard requirement, that is a real reason to buy elsewhere.

When is firmographic data the wrong thing to buy, and what does compliance require?

Firmographic data is the wrong purchase in three situations, and recognising them saves a wasted quarter. First, when your qualification problem is timing rather than fit: if you already know exactly which 400 accounts you want and the question is when to call them, you need signals, not attributes. Second, when your differentiator is technical compatibility: if the deal turns on whether they run a particular platform, a technographic provider answers that and a firmographic one does not. Third, when the segment you sell to is defined by something nobody publishes, such as an internal process or a regulatory exposure that no taxonomy encodes. In that last case the honest answer is that no dataset holds your ICP, and research beats filtering.

Firmographics are also the wrong first spend when your contact data is broken. An immaculately segmented account list whose emails bounce produces nothing, and the ordering that works is: fix deliverability, then segment, then score.

On compliance, firmographic data is meaningfully lower risk than contact data because most of it is not personal data at all. A company's headcount, industry and HQ are attributes of an organisation, and GDPR and CCPA govern personal data. That distinction has real limits. A sole trader's business is frequently identifiable with the individual. A named founder attached to a funding record is personal data. And the moment firmographics are joined to a named contact, which is the entire point of the workflow, the joined record is personal data and the whole regime applies.

What that means in practice for a B2B team. In the EU and UK, outreach to a named business contact typically relies on legitimate interest rather than consent, and legitimate interest is a documented assessment rather than an assumption, so write the Legitimate Interest Assessment down. Keep a record of where each field came from, because a subject access request asks exactly that. Provide a real opt-out in every message and honour it across the whole database rather than one campaign. Under CCPA, be ready to disclose sources and honour deletion. Under CAN-SPAM, include an unsubscribe and honour it within 10 business days. And ask any provider for its subprocessor disclosure before the contract rather than after, since that document is the first thing a data protection review will request and the providers behind a waterfall are part of your processing chain whether or not you chose them individually.