Lead Scoring
The discipline of ranking prospects by likelihood-to-buy before spending outbound effort on them — produces a prioritized call sheet instead of a flat list.
Lead scoring takes the underlying signals about a prospect — for an agency, things like website presence, review count, ad-pixel presence, hiring activity — and reduces them to a single number that sorts the call sheet. The agency owner's Friday-afternoon question becomes 'who do I call first?' instead of 'who am I going to skip?'
Two scoring philosophies exist. Heuristic scoring uses fixed weights derived from human judgment ('25% review count, 20% no website, 15% page speed...') and works well at the start of an agency's data journey when there are no closed-won outcomes to train against. Statistical scoring uses logistic regression or a tree model trained on actual close-rate data and outperforms heuristic once the agency has 50+ closed-won outcomes per vertical.
Most local-services agencies live on heuristic scoring for their first two years, then migrate as they accumulate enough outcome data to train against. The discipline matters more than the algorithm — applying the same score to every prospect is what makes the call sheet repeatable.
Related terms
- Opportunity ScoreA 0-100 composite ranking how marketing-ready a local business is, derived from review volume, web presence, ad activity, and demographic context.
- Agency ProspectingThe full process by which a marketing agency finds, qualifies, and converts local businesses into paying clients — distinct from B2B SaaS sales prospecting in tooling, signals, and conversion expectations.
- Close Probability ScoreA statistical estimate of how likely a specific prospect is to close into a paying client, trained on the agency's historical outcome data — the long-run replacement for heuristic lead scoring.
Run a free scan in your real market
Three scans, no card. See the score, line type, DNC, and CRM-ready export in action.