Firmographics are the descriptive attributes of a company, such as industry, headcount, revenue, location and growth stage, used to segment B2B markets the way demographics segment consumer ones.
Firmographics is the set of company-level characteristics that define which organisations belong in a market segment. Where demographics describe a person by age, income and location, firmographics describe an organisation by size, sector, maturity and structure.
They are the foundation of B2B targeting because in B2B the buying unit is a company, not an individual. A VP of Engineering at a 3,000-person bank and a VP of Engineering at a 40-person startup share a job title and almost nothing else that matters commercially.
[table]
Variable | What it captures | Where it is available
Industry / vertical | The sector the company operates in | LinkedIn, most data providers, CRM
Company size | Headcount, usually in bands | LinkedIn, Clearbit, Apollo, ZoomInfo
Revenue | Annual revenue or ARR band | Data providers, rarely LinkedIn
Funding stage | Seed, Series A, Series B and onward | Crunchbase, PitchBook, data providers
Geography | HQ location and operating markets | LinkedIn, all providers
Technology stack | Software the company already runs | BuiltWith, HG Insights, Clearbit
Growth rate | Headcount or revenue trajectory | LinkedIn, data providers
Company type | Public, private, non-profit, agency | LinkedIn, data providers
[/table]
LinkedIn is the only major advertising platform that lets you target on most of these directly, which is the practical reason it dominates B2B paid social.
Three layers, often confused, each answering a different question.
Firmographics describe the company. Is this the kind of organisation that has our problem and can afford our solution?
Demographics describe the person inside it. Is this individual senior enough, in the right function, to influence the decision?
Technographics describe the company's software stack. Do they already run the tools that make our product relevant, or a competitor we can displace?
Effective B2B targeting stacks all three. Firmographics select the account, demographics select the people inside it, technographics sharpen the timing and the message.
They are what makes it possible to spend money on people who are not searching.
Search advertising needs intent. Somebody has to type something. That restricts you to the small share of the market currently in a buying window. Firmographic targeting removes the intent requirement: you can reach a Head of Security at a Series B fintech in the US who has never heard of your category, because LinkedIn knows the company size, the industry, the funding stage and the person's function.
That capability is the entire mechanism behind demand creation. It is also the reason B2B advertisers tolerate LinkedIn's CPMs. You are not paying for reach, you are paying for the ability to select an audience that no other platform can construct.
The second thing firmographics solve is disqualification. Excluding company sizes, industries and geographies that never close is often worth more than any bid adjustment, because it stops the algorithm from optimising toward cheap conversions from accounts that cannot buy.
Two failure modes.
The bands are too broad. An audience set to "software, 51 to 1,000 employees, United States" is several million members and describes nothing useful. It converts poorly, costs a lot, and produces demos from companies with no budget. Firmographic targeting earns its CPM through precision. A 30,000-member audience that matches the ICP exactly beats a two-million-member audience every time.
The ICP was never defined from closed-won data. Teams build firmographic filters from what the company aspires to sell to rather than from who actually buys. The corrective is to export the last 50 closed-won accounts, look at their real headcount, industry, funding stage and stack, and build the filters from that distribution. It frequently contradicts the deck.
Derive your firmographic filters from who actually closed, then keep the audience small enough to be specific.
Take the last 50 closed-won accounts. Record headcount, industry, funding stage, geography and whether they ran a particular tool. The distribution across those five variables is your real ICP, and it is usually narrower and stranger than the one in the pitch deck.
Build the LinkedIn audience from that distribution, apply job function and seniority on top, and set explicit exclusions for the segments that historically churn or never close. A tight audience with a specific message beats a broad one with a general message at every budget level.
Then revisit it every two quarters. Firmographic definitions drift as the product matures and the company moves upmarket, and a filter set built for last year's ICP quietly spends this year's budget on the wrong companies.
Company-level attributes used to segment B2B markets, including industry, headcount, revenue, funding stage, geography, technology stack and growth rate. They serve the same function in B2B that demographics serve in consumer marketing, describing the organisation rather than the individual.
Firmographics describe a company: its size, sector, revenue and maturity. Demographics describe a person: their age, seniority, function and location. B2B targeting needs both, since you are selecting the right people inside the right organisations.
Industry, company size by headcount band, company growth rate, geography, company type and named company lists, all of which can be combined with job function, seniority and title. Revenue and funding stage are not available natively and require an uploaded account list built from an external data source.
Export your last 50 closed-won accounts and record their headcount, industry, funding stage, geography and technology stack. The concentration in that data is your real profile. Building it from assumptions rather than from closed-won accounts is the most common source of wasted B2B ad spend.
Often more so. Excluding industries, company sizes and geographies that historically never close stops bidding algorithms from optimising toward cheap conversions from accounts with no budget, which is a slow and expensive way for an account to degrade.
Related: LinkedIn Matched Audiences · Buying Committee · Demand Capture
If your targeting is built on an ICP nobody has checked against closed-won data recently, that is usually where the wasted spend is hiding.
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