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By Efe Berke Colaker, Founder at GetleadReviewed by the Getlead editorial team for accuracy. Last updated August 2026.
Every B2B database is organized around firmographics, and most segmentation arguments are really arguments about which of those fields anyone should trust.
The useful version is narrow: a handful of attributes that reliably separate accounts, plus honesty about the ones that do not.
The six attributes and what each is worth
Firmographic data is descriptive information about an organization rather than an individual, the company level equivalent of demographics.
Headcount is the workhorse. It correlates with budget, with process complexity and with who owns a decision, which is why a headcount band usually predicts fit better than a revenue estimate.
For example, a 40 person company and a 400 person company in the same industry are different buyers with different approval chains, while two 40 person companies in different industries often behave alike.
The fields that quietly mislead
Two fields carry most of the error in commercial databases, and both look authoritative in a spreadsheet.
- Revenue. Outside jurisdictions with public filings this is modelled or self reported, and estimates from different vendors on the same company routinely disagree by a wide margin.
- Industry codes. SIC, NAICS and vendor taxonomies classify the same company differently, and a software company selling to clinics may be filed under healthcare.
- Employee counts from profile pages. These lag reality and count profiles rather than people.
- Location. A registered address is not where the buyer sits, which matters for territory routing.
Treat these as filters that narrow a list, not as facts you state back to a prospect. Naming a company's revenue in a cold email is a fast way to be wrong in writing.
Building a segment that survives the database
A segment definition is only useful if it maps onto fields that actually exist and hold values on most records.
- Start with headcount band, since it is the most populated and most predictive field.
- Add industry as a broad group rather than a specific code, which avoids taxonomy disagreements.
- Add geography at the level you can serve, not the level you can filter.
- Check coverage: what share of records have every field you just required?
- Cut any filter that removes more than it clarifies.
For example, a four filter definition that leaves 300 accounts out of a 420 million record database is usually a data coverage problem rather than a tiny market.
Firmographics decay slower than contacts, but they do decay
Company attributes are more stable than people, which is why account records outlive contact records and why teams forget to refresh them at all.
Contact data decays at roughly 2.1% a month under the widely cited database decay model, and firmographics move more slowly, but headcount bands shift, companies get acquired and offices close.
Refresh the account layer on a schedule and record when each field was last confirmed, so a two year old headcount figure is visibly two years old rather than indistinguishable from yesterday's.
What firmographics cannot tell you
The most common failure is asking these fields to answer a question they do not contain.
Firmographics describe fit. They say nothing about whether an account is in market this quarter, who owns the budget, or whether the last vendor in this category left a bad taste.
That is what timing signals and contact level research are for, and layering them on a firmographic base is the ordinary shape of a working list.
Sources and method
First-party data (Getlead, 2026): the verification split of 43.4% confirmed valid, 23.9% invalid, 16.7% catch-all and 16.0% unknown comes from 383,368 addresses analyzed through live SMTP verification, and the 0.51% bounce rate comes from 34,973 tracked sends, aggregated and anonymized at campaign level. Full method in our cold email benchmark study.
External sources: notice duties when personal data is obtained from a third party are set out in Article 14 of the GDPR; US commercial email obligations come from the FTC CAN-SPAM compliance guide; contact decay of about 2.1% a month comes from the HubSpot database decay model.
Field reliability descriptions reflect our own experience with commercial B2B datasets and will vary by provider and market. Checked in August 2026.
Frequently asked questions
What is firmographic data?
Descriptive information about an organization rather than an individual: industry, headcount, revenue, location, corporate structure and company age. It is the company level equivalent of demographics and it forms the backbone of B2B segmentation.
Which firmographic field is most reliable?
Headcount. It comes from public profiles, correlates with budget and process complexity, and predicts fit better than revenue estimates. Company age is also reliable where it comes from registration records.
Why is revenue data unreliable?
Outside jurisdictions with public filings it is modelled or self reported, and estimates from different vendors on the same company routinely disagree by a wide margin. Use it to narrow a list, never to state a figure back to a prospect.
Why do industry codes disagree between providers?
SIC, NAICS and vendor taxonomies classify the same company differently. A software company selling to clinics may appear under healthcare in one dataset and software in another, so filter on broad industry groups rather than specific codes.
How often should firmographics be refreshed?
On a schedule, with a last confirmed date on each field. Company attributes move more slowly than contacts, which decay at about 2.1% a month, but headcount bands shift, companies get acquired and offices close.
Can firmographics tell me who is ready to buy?
No. They describe fit only. Whether an account is in market this quarter, who owns the budget and how the last vendor experience went are separate questions answered by timing signals and contact level research.