Lead Scoring Framework Builder
Build an ICP-based lead scoring model that actually predicts conversion. Stop arguing about MQL definitions with a data-driven framework.
Why Most Lead Scoring Fails
Most lead scoring models are built backwards. Teams assign arbitrary points to actions (downloaded whitepaper = 10 points!) without validating against actual conversion data.
The result: Sales gets "MQLs" that don't convert, loses trust in marketing, and the model becomes ignored.
The Two-Axis Scoring Model
Effective lead scoring has two independent components:
| Axis | What It Measures | Max Points |
|---|---|---|
| ICP Fit Score | How well does this lead match your ideal customer profile? | 50 points |
| Engagement Score | How actively is this lead engaging with your content/product? | 50 points |
Why two axes? A perfect-fit company with no engagement isn't ready for sales. A highly engaged lead from a bad-fit company will waste sales time. You need both to be high.
ICP Fit Scoring (50 points max)
Score these firmographic and demographic attributes:
| Attribute | Ideal Match | Points |
|---|---|---|
| Company Size | Your sweet spot (e.g., 200-2000 employees) | +15 |
| Adjacent (e.g., 50-199 or 2001-5000) | +8 | |
| Outside range | 0 | |
| Industry | Primary target industry | +15 |
| Secondary industry | +8 | |
| Non-target | 0 | |
| Job Title/Seniority | Decision maker (VP+) | +10 |
| Influencer (Manager/Director) | +6 | |
| Individual contributor | +2 | |
| Geography | Primary market | +5 |
| Secondary market | +2 | |
| Tech Stack | Uses complementary tech | +5 |
Engagement Scoring (50 points max)
Score behavioral signals by intent strength:
| Action | Intent Level | Points |
|---|---|---|
| Demo/Trial Request | High | +25 |
| Pricing Page Visit | High | +15 |
| Case Study Download | High | +10 |
| Product Page (2+ visits) | Medium | +8 |
| Webinar Attendance | Medium | +8 |
| Whitepaper/Guide Download | Medium | +5 |
| Blog Visit (3+ articles) | Low | +3 |
| Email Click | Low | +2 |
| Email Open | Low | +1 |
Score Decay: The Missing Piece
Engagement scores should decay over time. A demo request from 6 months ago isn't as valuable as one from last week.
Recommended Decay Schedule
| Time Since Action | Score Retention |
|---|---|
| 0-30 days | 100% |
| 31-60 days | 75% |
| 61-90 days | 50% |
| 90+ days | 25% |
Implementation: Most MAPs support score decay. In HubSpot, use a workflow that reduces engagement score by 25% every 30 days if no new activity.
MQL Threshold Calibration
Your MQL threshold should be calibrated against actual conversion data, not arbitrary numbers.
Calibration Process
- Apply your scoring model retroactively to the last 6-12 months of leads
- Segment by score ranges (0-25, 26-50, 51-75, 76-100)
- Calculate conversion rate to SQL and closed-won for each segment
- Set MQL threshold where conversion rate becomes acceptable to sales