AI Lead Scoring
Move beyond rule-based scoring. How to build, calibrate and trust an AI lead score inside your CRM without breaking your existing pipeline.
Why rule-based scoring stopped working
Point-based models (job title = +10, opened email = +5) are brittle, biased toward what you already know, and completely blind to intent signals scattered across LinkedIn, changelogs, and hiring pages.
What an AI score actually looks at
A modern deal-health score blends: firmographic fit, technographic fit, buying-committee coverage, engagement recency, competitor presence, and negative signals like layoffs or leadership churn. Every input gets a confidence weight.
How to calibrate against your own closed-won data
Take your last 100 closed deals. Ask the model to score them cold. Plot the score against outcome. If closed-won clusters above 70 and closed-lost clusters below 40, you have a usable signal. If not, tighten the prompt or add features.
FAQ
How often should scores refresh?
On every meaningful event — new email reply, stage change, or weekly at minimum. Stale scores are worse than none.