Firmographic enrichment is the process of appending company-level attributes, employee count, industry, annual revenue, headquarters location, ownership type, and growth signals, to a lead record so you can tell which accounts match your ideal customer profile. Where contact enrichment describes a person, firmographic enrichment describes the business that person works for. It is the layer that turns a raw list of company names into a list you can filter down to "the accounts worth contacting." This guide explains the fields, how accurate each one really is, the difference from technographic data, and how to run firmographic enrichment inside a spreadsheet using scraping plus AI.
What is firmographic enrichment?
Firmographic enrichment takes a thin company record, often just a name or a domain, and fills in the descriptive fields that define a business. The word "firmographics" is the company-level equivalent of demographics: instead of age and income for a person, you get headcount and revenue for a firm. Enrichment is the act of populating those fields automatically from a data source rather than researching each account by hand.
The reason it sits at the front of every outbound workflow is simple. You cannot target what you cannot see. If your ideal customer profile is "SaaS companies, 50 to 200 employees, North America, post Series A," then employee count, industry, geography, and funding stage are the exact fields you need before you can decide whether a row is even worth a contact lookup. Firmographics qualify the account; contact data comes after.
What fields count as firmographic data?
Firmographic attributes describe the shape of a company. The common ones are:
- Industry classification. Often expressed as a NAICS or SIC code, or a vendor-specific taxonomy like "B2B SaaS" or "Logistics."
- Employee count. Usually given as a band (11 to 50, 51 to 200) because exact headcount drifts daily.
- Annual revenue. A figure or a band, almost always estimated for private companies.
- Headquarters location. Country, state or region, and city.
- Ownership type. Public, private, subsidiary, non-profit, or government.
- Year founded and growth stage, from bootstrapped through seed and Series A to D and public.
- Growth signals. Recent funding rounds, hiring velocity, new office openings, or layoffs.
These are the inputs to account scoring. A filter like "private SaaS firms, 50 to 200 staff, raised a Series A or B in the last 18 months" is built entirely out of firmographic fields.
How is firmographic enrichment different from technographic and contact enrichment?
The three enrichment types answer different questions, and knowing which you need stops you paying for fields you will not use.
Firmographic data answers "what shape is this company": its size, industry, revenue, and geography. Technographic data answers "what tools does it run": the CRM, analytics, cloud, or security stack it uses, which matters when your product complements or replaces a specific tool. Contact data answers "who do I reach inside it": the person's title, seniority, work email, and phone.
The practical order is firmographics first to qualify the account, then contact data only for the accounts that pass. Spending on contact lookups for companies that were never a fit is the most common way to burn an enrichment budget on rows you will never email.
How accurate is firmographic data?
Accuracy varies sharply by field, and treating every field as equally trustworthy is a mistake. Industry classification at the broad level is generally reliable. Employee count is accurate within a band but often off by 10 to 30 percent on the precise number, which is why most providers and ICP filters work in bands rather than exact figures. Revenue is the hardest: for private companies it is almost always estimated rather than reported, and estimates for the same company can vary by three to five times between providers.
The takeaway for outbound is to segment on the fields that hold up, industry, size band, geography, ownership, and treat revenue as a soft signal rather than a hard gate. A useful citable rule: firmographic accuracy is usually measured as the percentage of returned values within plus or minus 20 percent of an authoritative source, and revenue is the field most likely to fall outside that range.
Where does firmographic data come from?
Two broad sources. The first is licensed databases: large providers maintain company records compiled from filings, surveys, and partnerships, and sell access by credit or seat. These are strong on coverage and on fields you cannot read off a website, like estimated revenue across millions of private firms.
The second source is the open web, and this is the one most teams underuse. A surprising amount of firmographic data is sitting in plain sight: a company's about page states its industry and often its size, the careers page reveals hiring velocity and team count, press releases announce funding rounds, and the footer gives the headquarters city. For company-level enrichment, scraping these pages and extracting the fields with AI covers a large share of what a licensed database would return, at a fraction of the cost.
How to do firmographic enrichment in Google Sheets
You can run a credible firmographic pass without a separate data platform. The mechanic is two steps chained together, and it runs from a sidebar where your list already lives.
- Scrape the source. Web scraping in Google Sheets pulls the raw text of a company's about, product, and careers pages into a context column.
- Extract the fields with AI. An AI extraction prompt reads that text and writes structured columns back: "From this page text, return the industry, employee range, and headquarters city, one per column. If the page does not say, return
Unknown."
Because the AI reads real scraped text rather than guessing from a company name, the output is grounded in something you can verify. Every field lands in its own column, so you can spot-check a value instead of trusting a black-box credit. The full pipeline, including scoring and email verification, is laid out in the lead enrichment in Google Sheets guide, and ReplyLabs ships an ICP Enrichment template that chains scrape, extract, score, and verify in one sheet.
The one habit that keeps output clean: tell the model what to do with a missing input. Instructing it to return Unknown when the page does not state a field stops it inventing a revenue figure, which is exactly the field you should not trust in the first place.
What does firmographic enrichment cost?
Standalone platforms price on credits, and the effective cost per enriched lead typically lands between roughly $0.14 and $0.67 depending on plan, often with a charge even on failed lookups and a monthly seat fee on top. A scrape-and-extract approach in a sheet prices per operation and only on success: scrape from $0.005 per URL, AI extraction at the provider's raw cost times 1.25 plus a small base fee per succeeded row, so a 1,000-row firmographic pass lands in low single-digit dollars. Failed or blank rows cost nothing. To model a specific run, use the AI cost calculator, and for the head-to-head against the best-known credit platform see how to enrich leads without Clay.
Common questions
What is the difference between firmographics and demographics?
Demographics describe people (age, income, role); firmographics describe companies (size, industry, revenue, location). Firmographic enrichment is the company-level version of demographic enrichment, used to segment B2B accounts rather than individual consumers.
Is firmographic data accurate enough to target on?
Industry, size band, geography, and ownership are reliable enough to filter on. Exact employee count is often off by 10 to 30 percent, and revenue for private companies is usually estimated and can vary three to five times between providers, so treat revenue as a soft signal rather than a hard cut.
Can I get firmographic data without buying a database?
For company-level fields that appear on the open web, yes. Scraping a company's about, careers, and press pages and extracting the fields with AI covers most firmographic enrichment. Licensed databases still win for estimated revenue at scale and for fields you cannot read off a website.
What is the difference between firmographic and technographic enrichment?
Firmographic enrichment describes the company (size, industry, revenue, location). Technographic enrichment describes the technology stack it runs (CRM, analytics, cloud). Use firmographics to qualify account fit and technographics when your product targets users of a specific tool.
Do I pay for companies that fail to enrich?
In ReplyLabs, no. Every step charges only for rows that return a result, so failed or blank lookups cost nothing. Many credit-based platforms do charge for failed lookups, which is a common hidden cost. New accounts also start with a free $20 credit.