Not all leads are equal. Lead scoring helps you focus on leads most likely to buy.
What Is Lead Scoring?
Assigning numerical values to leads based on characteristics and behaviors that indicate likelihood to purchase.
Why Score Leads?
- Focus sales time on best prospects
- Improve sales and marketing alignment
- Increase conversion rates
- Shorten sales cycles
- Better forecast pipeline
Lead Scoring Models
1. Demographic Scoring (Fit)
Score based on who they are:
| Factor | Points |
|--------|--------|
| Company size 50-200 | +10 |
| Company size 200+ | +20 |
| C-level title | +20 |
| Manager title | +10 |
| Target industry | +15 |
| Target geography | +10 |
2. Behavioral Scoring (Interest)
Score based on what they do:
| Action | Points |
|--------|--------|
| Visit pricing page | +15 |
| Download case study | +10 |
| Attend webinar | +15 |
| Request demo | +25 |
| Open 3+ emails | +5 |
| Visit 5+ pages | +10 |
3. Negative Scoring
Subtract for disqualifying factors:
| Factor | Points |
|--------|--------|
| Competitor email domain | -50 |
| Student email | -30 |
| Unsubscribed from email | -20 |
| Inactive 60+ days | -15 |
| Wrong industry | -20 |
Building Your Scoring Model
Step 1: Analyze Past Wins
Look at converted customers:
- What characteristics did they share?
- What content did they consume?
- How did they engage?
Step 2: Define MQL Threshold
Typically 50-100 points indicates marketing qualified.
Step 3: Start Simple
Begin with 5-10 criteria. Add complexity later.
Step 4: Align Sales and Marketing
Both teams must agree on definitions and thresholds.
Step 5: Test and Refine
Review monthly:
- Are MQLs converting?
- What scores do closers have?
- Adjust weights as needed
Lead Score Categories
| Score | Category | Action |
|-------|----------|--------|
| 0-25 | Cold | Continue nurturing |
| 26-50 | Warm | Increase engagement |
| 51-75 | MQL | Pass to sales |
| 76+ | Hot | Immediate follow-up |
Lead Scoring Tools
CRM with Built-In:
- HubSpot
- Salesforce
- Marketo
- Pardot
Standalone:
- Madkudu
- Infer
- Lattice
Common Scoring Mistakes
- Too complex - Start simple
- Not updating - Models decay
- Ignoring negative - Must disqualify
- No alignment - Sales rejects MQLs
- Set and forget - Review regularly
Advanced: Predictive Scoring
AI-based scoring using:
- Historical conversion data
- Pattern recognition
- Real-time signals
- Third-party data
More accurate but requires more data.
See our Lead Generation guide for full strategy.
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Related: Lead Generation Guide | B2B Lead Generation