2026 research editionSee how 13 platforms were evaluated
100-point qualification rubric

First-Party Data Lead Qualification: A Reusable Scoring Framework

A transparent scoring model for fit, readiness, need, urgency, and data confidence.

A transparent 100-point rubric

A score should explain a routing decision, not hide it. This reusable model separates fit, need, readiness, urgency, and data confidence. Teams can change weights, but every rule should be visible and tied to a field that has a known origin.

DimensionWeightExample rule
Account fit25Target industry 10, size 10, geography 5
Role fit15Decision-maker 15, influencer 10, other 3
Declared need25High-cost problem 15, relevant use case 10
Readiness15Active evaluation 10, defined process 5
Urgency10Within 30 days 10, quarter 6, later 2
Data confidence10Verified identity 4, complete answers 3, explicit source and consent 3

Worked example: 82 points

A marketing operations lead at a target-size SaaS company scores 20 for account fit and 15 for role. The person declares a costly manual qualification problem for 22 points, is comparing tools now for 12, needs a solution this quarter for 6, and supplies a complete verified record for 7. Total: 82.

The record enters the high-fit segment, receives the relevant recommendation, starts a personalized sequence, and is offered a meeting. The score and component values remain visible to the recipient.

  • Cap each dimension so one answer cannot dominate.
  • Use first-party answers for need and urgency.
  • Apply negative points only for explicit disqualifiers.
  • Store component scores, not only the total.
  • Review thresholds against accepted and progressed leads.