Lead Scoring

Design, validate, and maintain a model that ranks leads by likelihood to become revenue. The practitioner standard splits scoring into two separate axes:

• Fit (can they buy): firmographic, demographic, technographic.
• Engagement (are they about to buy): behavioral, in-product.

This two-axis model appears under several names: Marketo's A-D x 1-4 grade-and-score grid, MadKudu's Customer Fit x Likelihood to Buy, and OpenView's product-qualified lead (PQL) in PLG.

Scoring fails far more often from organizational neglect - no sales sign-off, no recalibration, MQL count treated as the goal - than from bad math, so validation and governance are part of the design here, not an afterthought. The finished score is an input to assignment: routing, territories, and rep matching belong to mbfinotti/revops-skills@lead-routing. Defining the account-fit criteria this skill's fit axis weights - firmographic and technographic ICP tiers - belongs to mbfinotti/sales-skills@sales-icp-definition; this skill owns turning that definition, plus behavioral engagement, into a working score.

The capture-score-route-nurture mechanics are structurally identical for B2B and B2C/PLG. What genuinely differs between B2C/PLG and B2B:

• Dominant signal source is in-product behavior, not forms and content.
• No buying committee - one user's behavior can qualify.
• Cycles run in days, not quarters.

Because of these differences, decay, thresholds, rescoring frequency, and recalibration all run faster in B2C/PLG.