Lead Scoring Model
The model that says who's hot and who's not.
Workflow
Step 1Metric Def
ObjectiveDefine what scores.
InputLead data, ICP
OutputScoring metrics
Key activities
Define behavior and fit signals
Decision points
What predicts purchase?
Tools: Metrics frameworkRoles: RevOps · marketing
Success: Metrics defined
Quality gate: Go: predictive. Loop back: guessed.
Why it matters
- A calibrated score routes the right leads fast and protects reps from garbage. It's the brain of lead management.
Best practices
- Design from outcomes (closed-won), not activity.
- Weight behavior and fit deliberately.
- Test and calibrate against results.
- Keep it explainable.
Common mistakes
- Scoring engagement, not intent.
- Weights set by opinion, never tested.
- A model that never gets recalibrated.
Key questions
- Does a high score predict a sale?
- What would make the model more accurate?
Agents that drive this
QualifierHygiene
Tools we use · alternates in [ ]
HubSpot[ Salesforce, Pipedrive, Attio ]Supabase[ Postgres, Firebase ]