Lead Scoring Models
Detailed scoring templates, example models by business type, and calibration guidance.
Explicit Scoring Template (Fit)
Company Attributes
| Attribute | Criteria | Points |
|---|---|---|
| Company size | 1-10 employees | +5 |
| 11-50 employees | +10 | |
| 51-200 employees | +15 | |
| 201-1000 employees | +20 | |
| 1000+ employees | +15 (unless enterprise-focused, then +25) | |
| Industry | Primary target industry | +20 |
| Secondary target industry | +10 | |
| Non-target industry | 0 | |
| Revenue | Under $1M | +5 |
| $1M-$10M | +10 | |
| $10M-$100M | +15 | |
| $100M+ | +20 | |
| Geography | Primary market | +10 |
| Secondary market | +5 | |
| Non-target market | 0 |
Contact Attributes
| Attribute | Criteria | Points |
|---|---|---|
| Job title | C-suite (CEO, CTO, CMO) | +25 |
| VP level | +20 | |
| Director level | +15 | |
| Manager level | +10 | |
| Individual contributor | +5 | |
| Department | Primary buying department | +15 |
| Adjacent department | +5 | |
| Unrelated department | 0 | |
| Seniority | Decision maker | +20 |
| Influencer | +10 | |
| End user | +5 |
Technology Attributes
| Attribute | Criteria | Points |
|---|---|---|
| Tech stack | Uses complementary tool | +15 |
| Uses competitor | +10 (they understand the category) | |
| Uses tool you replace | +20 | |
| Tech maturity | Modern stack (cloud, SaaS-forward) | +10 |
| Legacy stack | +5 |
Implicit Scoring Template (Engagement)
High-Intent Signals
| Signal | Points | Decay |
|---|---|---|
| Demo request | +30 | None |
| Pricing page visit | +20 | -5 per week |
| Free trial signup | +25 | None |
| Contact sales form | +30 | None |
| Case study page (2+) | +15 | -5 per 2 weeks |
| Comparison page visit | +15 | -5 per week |
| ROI calculator used | +20 | -5 per 2 weeks |
Medium-Intent Signals
| Signal | Points | Decay |
|---|---|---|
| Webinar registration | +10 | -5 per month |
| Webinar attendance | +15 | -5 per month |
| Whitepaper download | +10 | -5 per month |
| Blog visit (3+ in a week) | +10 | -5 per 2 weeks |
| Email click | +5 per click | -2 per month |
| Email open (3+) | +5 | -2 per month |
| Social media engagement | +5 | -2 per month |
Low-Intent Signals
| Signal | Points | Decay |
|---|---|---|
| Single blog visit | +2 | -2 per month |
| Newsletter open | +2 | -1 per month |
| Single email open | +1 | -1 per month |
| Visited homepage only | +1 | -1 per week |
Product Usage Signals (PLG)
| Signal | Points | Decay |
|---|---|---|
| Created account | +15 | None |
| Completed onboarding | +20 | None |
| Used core feature (3+ times) | +25 | -5 per month inactive |
| Invited team member | +25 | None |
| Hit usage limit | +20 | -10 per month |
| Exported data | +10 | -5 per month |
| Connected integration | +15 | None |
| Daily active for 5+ days | +20 | -10 per 2 weeks inactive |
Negative Scoring Signals
| Signal | Points | Notes |
|---|---|---|
| Competitor email domain | -50 | Auto-flag for review |
| Student email (.edu) | -30 | May still be valid in some cases |
| Personal email (gmail, yahoo) | -10 | Less relevant for B2B; adjust for SMB |
| Unsubscribe from emails | -20 | Reduce engagement score |
| Bounce (hard) | -50 | Remove from scoring |
| Spam complaint | -100 | Remove from all sequences |
| Job title: Student/Intern | -25 | Low buying authority |
| Job title: Consultant | -10 | May be evaluating for client |
| No website visit in 90 days | -15 | Score decay |
| Invalid phone number | -10 | Data quality signal |
| Careers page visitor only | -30 | Likely a job seeker |
Example Scoring Models
Model 1: PLG SaaS (ACV $500-$5K)
Weight: 30% fit / 70% engagement (heavily favor product usage)
Fit criteria:
- Company size 10-500: +15
- Target industry: +10
- Manager+ role: +10
- Uses complementary tool: +10
Engagement criteria:
- Created free account: +15
- Completed onboarding: +20
- Used core feature 3+ times: +25
- Invited team member: +25
- Hit usage limit: +20
- Pricing page visit: +15
Negative:
- Personal email: -10
- No login in 14 days: -15
- Competitor domain: -50
MQL threshold: 60 points Recalibration: Monthly (fast feedback loop with high volume)
Model 2: Enterprise Sales-Led (ACV $50K+)
Weight: 60% fit / 40% engagement (fit is critical at this ACV)
Fit criteria:
- Company size 500+: +20
- Revenue $50M+: +15
- Target industry: +15
- VP+ title: +20
- Decision maker confirmed: +15
- Uses competitor: +10
Engagement criteria:
- Demo request: +30
- Multiple stakeholders engaged: +20
- Attended executive webinar: +15
- Downloaded ROI guide: +10
- Visited pricing page 2+: +15
Negative:
- Company too small (<100): -30
- Individual contributor only: -15
- Competitor domain: -50
MQL threshold: 75 points Recalibration: Quarterly (longer sales cycles, smaller sample size)
Model 3: Mid-Market Hybrid (ACV $5K-$25K)
Weight: 50% fit / 50% engagement (balanced approach)
Fit criteria:
- Company size 50-1000: +15
- Target industry: +10
- Manager-VP title: +15
- Target geography: +10
- Uses complementary tool: +10
Engagement criteria:
- Demo request or trial signup: +25
- Pricing page visit: +15
- Case study download: +10
- Webinar attendance: +10
- Email engagement (3+ clicks): +10
- Blog visits (5+ pages): +10
Negative:
- Personal email: -10
- No engagement in 30 days: -10
- Competitor domain: -50
- Student/intern title: -25
MQL threshold: 65 points Recalibration: Quarterly
Threshold Calibration
Setting the Initial Threshold
- Pull closed-won data from the last 6-12 months
- Retroactively score each deal using your new model
- Find the natural breakpoint — what score separated wins from losses?
- Set threshold just below where 80% of closed-won deals would have scored
- Validate against closed-lost — if many closed-lost score above threshold, tighten criteria
Calibration Cadence
| Business Type | Recalibration Frequency | Why |
|---|---|---|
| PLG / High volume | Monthly | Fast feedback loop, lots of data |
| Mid-market | Quarterly | Moderate cycle length |
| Enterprise | Quarterly to semi-annually | Long cycles, small sample size |
Calibration Steps
- Pull MQL-to-closed data for the calibration period
- Compare scored MQLs vs. actual outcomes:
- High score + closed-won = correctly scored
- High score + closed-lost = possible false positive (tighten)
- Low score + closed-won = possible false negative (loosen)
- Adjust weights based on which attributes actually correlated with wins
- Adjust threshold if MQL volume is too high (raise) or too low (lower)
- Document changes and communicate to sales team
Warning Signs Your Model Needs Recalibration
- MQL-to-SQL acceptance rate drops below 30%
- Sales consistently rejects MQLs as "not ready"
- High-scoring leads don't convert; low-scoring leads do
- MQL volume spikes without corresponding revenue
- New product/market changes since last calibration