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lead-scoring

conversionv1.0.0

Design and implement lead scoring models. Qualify leads based on behavior, demographics, and engagement.

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Lead Scoring

Scoring Model Design

Two-Axis Model

Score leads on two independent axes:

  1. Fit Score (0-100): How well they match your ICP (demographics)
  2. Engagement Score (0-100): How actively they interact (behavior)

Combine: Total Score = (Fit × 0.4) + (Engagement × 0.6)

Fit Score (Demographics)

SignalPointsExample
Company size matches ICP+2050-500 employees
Industry match+15SaaS, fintech
Job title/seniority+20VP+, Director, C-level
Budget range confirmed+15>$50K ARR potential
Geography match+10Target market
Tech stack match+10Uses compatible tools
Revenue range match+10$5M-$50M ARR

Engagement Score (Behavior)

SignalPointsDecay
Pricing page visit+20-5/week
Demo request+30None
Free trial signup+25-5/week inactive
Case study download+10-3/week
Blog post read+2-1/week
Email open+1-1/week
Email click+5-2/week
Webinar attended+15-3/week
Multiple sessions (3+)+10-2/week
Returned after 30d absence+15-5/week

Score Decay

Apply weekly decay to prevent stale high scores. A lead who visited pricing 3 months ago isn't hot anymore.

Thresholds

ScoreClassificationAction
0-30Cold leadNurture sequence
31-50Warm leadTargeted content
51-70MQLMarketing-qualified, alert SDR
71-85SQLSales-qualified, direct outreach
86-100HotImmediate sales attention

Qualification Frameworks

Details: references/scoring-models.md

References

  • references/scoring-models.md — BANT, CHAMP, MEDDIC frameworks with implementation guides
  • references/signal-weights.md — Calibrating signal weights with historical data