Firmographic data is the skeleton of your ICP. It defines the structural characteristics of your ideal account — the facts you can verify from a company profile before any conversation has happened.
Industry is typically the most predictive attribute. But "technology" or "professional services" are too broad. Get specific: "B2B SaaS with an outbound sales motion" or "mid-market accounting firms in the US." The more precise your industry definition, the more useful it is as a filter.
Employee count is the most common size proxy, but it's imperfect. A 200-person company could be a $5M services firm or a $40M SaaS business. Where possible, pair employee count with revenue range or ARR estimates for better accuracy.
Geography matters more than people think — not just for logistics, but because buying behavior, contract norms, compliance requirements, and even how people use LinkedIn vary significantly by region.
Growth rate is a leading indicator. A company that's grown from 50 to 200 employees in 12 months is in a fundamentally different buying mode than one that's been stable at 200 for 3 years.
Ownership type — VC-backed, bootstrapped, PE-owned, public — significantly affects purchase authority, budget cycles, and decision-making speed.
For visitor intelligence, Kopimore automatically appends firmographic data to identified companies. For outbound prospecting, the major sources are: Apollo.io (best for volume at lower cost), ZoomInfo (most comprehensive, most expensive), LinkedIn Sales Navigator (best for org-level intelligence), and Clearbit (best for programmatic enrichment via API).
Start with 3–4 attributes maximum. For each attribute, define an inclusive range (not just a point value): "50–500 employees, not under 50 and not above 500." Document the reasoning behind each threshold — you'll need this context when you revisit the filter in 90 days.