Lead Scoring System

Define 3-5 fit criteria (e.g., company size, industry, role) and 3-5 intent signals (e.g., demo request, pricing page visit, email reply); assign point values (fit: 0-50, intent: 0-50).

Pull 20 recent leads from Attio; manually score each lead on fit and intent using your criteria; total score ranges from 0-100.

Create score tiers in a spreadsheet: Hot (80-100), Warm (50-79), Cold (0-49); categorize all 20 leads.

Set pass threshold: Hot leads must have >=2x meeting rate vs Cold leads within 1 week to validate scoring predicts engagement.

Reach out to all 20 leads with the same message/offer; track which leads respond and book meetings in Attio and PostHog.

Log lead_scored events in PostHog with properties for fit score, intent score, total score, and tier.

After 1 week, compute meeting rate by tier (Hot, Warm, Cold); if Hot leads have >=2x meeting rate vs Cold, scoring is predictive.

Analyze which fit criteria and intent signals most strongly correlate with meetings; consider adjusting point values.

Test whether calling Hot leads first yields faster pipeline generation than calling leads in random order.

If Hot leads convert >=2x better, document scoring criteria and point system, then proceed to Baseline; otherwise refine criteria or signals and retest.