AI Lead Scoring

Our AI analyzes the quality of a conversation in real-time. If a lead mentions a high budget or urgent need, the AI assigns a high score and flags them for immediate follow-up by your top sales reps. Revenue teams adopt AI Lead Scoring to cut response delay and raise meeting quality, then publish a simple scorecard for contacted leads and show rate. See Chat Agents. Calibrate scores against closed-won outcomes with sales before changing routing thresholds.

AI lead scoring assigns a number to a lead based on what the person said and did in a conversation: the use case described, urgency, budget signals, decision role and engagement depth, weighted by criteria you set. The score decides who gets a rep first and who goes to nurture.

How it differs from form-based MQL scoring. Form scoring counts fields and page views; conversational scoring reads the conversation itself, so a one-line chat that says "we launch next month and I own the budget" scores higher than a long form with no timeline.

Transparent, not black-box. Every score should show which answers moved it, so a rep can correct a mistaken extraction and operations can tune the weights. Avoid scoring protected characteristics or using sensitive data as a proxy for intent.

From score to action. Define bands before launch: high fit and high intent get a calendar slot and an owner alert; high fit, low intent enters nurture; low fit, high intent goes to self-serve. A score without a routing rule is just another CRM field.

Calibrating it. Compare score bands with sales acceptance and opportunity creation weekly for the first month, record each version of the rubric, and change weights deliberately rather than daily.

Example. A WhatsApp lead asks about pricing for three locations, mentions a go-live date and gives a business email; the agent scores it 84, books a demo and writes the breakdown to HubSpot. Botcadence AI lead scoring stores the breakdown next to the score.