What is Lead Scoring?
Lead scoring assigns a numeric value to each lead based on their likelihood to buy - so sales teams focus on the best prospects first.
Why lead scoring matters
Without scoring, reps contact leads in the order they arrive - which means wasting time on poor-fit prospects while high-intent leads go cold waiting. Lead scoring ensures your best prospects get immediate attention and your team's time goes where it has the most impact.
How traditional lead scoring works
Points are assigned for job title match, company size, industry fit, page views, email opens, content downloads, and demo requests. Total the points and you have a score. This works for basic filtering but misses nuance - a founder of a 10-person company might be a better fit than a VP at a 500-person wrong-industry company.
AI lead scoring
Claude evaluates each lead holistically: reading the company description, assessing product-market fit against your ICP, weighing intent signals together, and producing a score with a written rationale. This catches fits and disqualifications that a point system misses, and it improves as you give it examples of good and bad outcomes.
What data to score on
ICP fit (industry, company size, role, geography). Intent signals (pages visited, content consumed, form responses). Engagement (email open rate, reply rate, meeting attended). Company signals (recent funding, hiring in relevant roles, tech stack). The more data you can pass to the scoring model, the more accurate the output.
Building lead scoring with Make.com and Claude API
The typical setup: new lead arrives in CRM, Make triggers a scenario, pulls enrichment data from Apollo or Clearbit, sends it to Claude with a scoring prompt, and writes the score and rationale back to the CRM record. The rep sees the score and reason before making contact. Build time is typically 1-2 weeks.
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