Lead Fit Score
A lead fit score is a numeric grade that measures how closely a prospect matches your ideal customer profile based on firmographic and demographic attributes.
In depth
Fit scoring works by assigning weighted point values to attributes you collect, such as company size, industry, role, budget band, or geography. Each answer maps to a score contribution, and the sum places the lead into a tier like strong, moderate, or poor fit. Crucially, fit is about who the buyer is, not how engaged they are, which is why mature teams pair it with a separate behavioral or intent score rather than blending the two into one opaque number.
The common pitfall is over-weighting a single glamorous attribute, such as company headcount, while ignoring the qualifiers that actually predict revenue, like decision-making authority or use-case match. In a quiz-funnel workflow, every question you place in the flow can feed the fit calculation, so a respondent who picks "500+ employees" and "VP of Sales" silently accrues points. When the result page renders, the funnel routes high-fit leads to a calendar booking while low-fit leads receive self-serve resources, protecting your sales team's time.
Example in practice
Frequently asked questions
What is the difference between fit score and lead score?
Lead score is often an umbrella term that blends fit and engagement, while fit score isolates only how well the prospect matches your ideal customer profile. Keeping them separate lets you tell an interested-but-wrong-fit lead apart from a perfect-fit lead who is not yet engaged.
What data do I need to calculate a lead fit score?
You need firmographic and demographic attributes such as company size, industry, role, region, and use-case match. In a quiz funnel these are gathered directly from the questions a respondent answers, removing the need for third-party enrichment in many cases.
How often should I recalibrate my fit scoring model?
Review the weights every quarter or after any major shift in your ideal customer profile or pricing. Compare which scores correlated with closed-won deals and adjust the point values for attributes that proved more or less predictive.