All Playbooks
Intermediate3-4 weeksMarketing
Designing a Lead Scoring Model
Prioritize leads based on fit and engagement signals
A practical guide to building a lead scoring model that helps sales focus on the highest-potential leads. Covers demographic scoring, behavioral scoring, and score decay.
Prerequisites
- Marketing automation platform (HubSpot, Marketo, etc.)
- CRM integration with marketing platform
- 6+ months of lead and conversion data
- Defined Ideal Customer Profile (ICP)
Outcomes
- Working lead score in marketing automation
- Clear MQL threshold definition
- Automated lead routing based on score
- Sales-accepted scoring criteria
Implementation Steps
1
Analyze Historical Conversions
1 weekUse data to identify what attributes and behaviors correlate with closed-won deals.
Tasks
- Export last 12 months of closed-won deals with lead source
- Identify top 5 demographic attributes of converted leads
- Map the content journey of converted leads
- Calculate conversion rates by lead source
- Interview 5 sales reps on what makes a 'good' lead
Deliverables
- Conversion analysis spreadsheet
- Top converting attributes list
- Content engagement patterns
Tips
- Look for non-obvious patterns (e.g., specific page visits)
- Compare converted leads to lost opportunities, not just all leads
2
Define Scoring Criteria
1 weekBuild the scoring model with demographic (fit) and behavioral (engagement) components.
Tasks
- Create demographic scoring rubric (title, company size, industry)
- Create behavioral scoring rubric (email opens, page visits, downloads)
- Assign point values to each criterion
- Define negative scoring criteria (competitor, student, etc.)
- Set score decay rules for inactivity
Deliverables
- Scoring matrix document
- Point value assignments
- Decay rules definition
Tips
- Weight behaviors more heavily than demographics (60/40)
- High-intent pages (pricing, demo) should score higher than blog
- Cap total points per category to prevent gaming
3
Set MQL Threshold
3-5 daysDefine the score threshold that triggers MQL status and sales handoff.
Tasks
- Apply scoring model retroactively to historical leads
- Identify score distribution of converted vs. non-converted
- Set initial MQL threshold based on data
- Define SLA for sales follow-up on MQLs
- Get sales leadership agreement on threshold
Deliverables
- Score distribution analysis
- MQL threshold documentation
- Sales SLA agreement
Tips
- Start conservative (higher threshold) and adjust down
- Threshold should balance volume with quality
4
Implement in Marketing Automation
1 weekBuild the scoring model in your marketing automation platform.
Tasks
- Configure demographic scoring rules
- Configure behavioral scoring rules
- Set up score decay automation
- Build MQL trigger workflow
- Create lead routing rules based on score
- Test with sample leads
Deliverables
- Working score in MAP
- MQL workflow active
- Lead routing automation
- QA test results
Tips
- Test extensively before going live
- Have a manual override process for edge cases
5
Monitor and Optimize
OngoingTrack scoring effectiveness and iterate based on conversion data.
Tasks
- Build MQL-to-SQL conversion dashboard
- Track score distribution weekly
- Review rejected MQLs with sales monthly
- Adjust weights based on conversion data
- Document changes in scoring changelog
Deliverables
- Scoring effectiveness dashboard
- Monthly review process
- Scoring changelog
Tips
- Plan for quarterly scoring model reviews
- Watch for score inflation over time
Common Mistakes to Avoid
- Over-weighting demographics vs. behavior
- Not implementing score decay
- Setting MQL threshold too low (volume over quality)
- Not getting sales buy-in before launch
- Set it and forget it - never reviewing the model
Success Metrics
- MQL-to-SQL conversion > 30%
- SQL-to-Opportunity conversion > 50%
- Sales acceptance rate > 80%
- Lead response time < 5 minutes for high scores