What is a Lead Score?
A lead score is a numerical value assigned to each lead based on their likelihood to convert, calculated from both demographic fit (who they are) and behavioral engagement (what they've done). Higher scores indicate hotter leads ready for sales outreach; lower scores suggest leads needing more nurturing.
Lead scoring systems typically use a 0-100 point scale, with thresholds established for different actions. For example, leads scoring 60+ might become MQLs passed to sales, while those below 40 remain in marketing nurture sequences.
Why It Matters
Without lead scoring, sales teams waste time pursuing unqualified prospects while hot leads cool from lack of attention. Scoring creates triage, ensuring the hottest opportunities get immediate attention while less-qualified leads receive appropriate nurturing.
Lead scoring also improves marketing-sales alignment. When both teams agree on scoring criteria and thresholds, friction over lead quality disappears. Marketing optimizes for generating high-scoring leads; sales trusts leads handed off are worth pursuing.
Benchmarks
- MQL threshold: Typically 50-75 points on a 100-point scale
- Conversion improvement: Companies with lead scoring see 2-3x higher MQL-to-SQL conversion
- Scoring model components: Top models use 10-20 attributes across fit and behavior dimensions
- Recency factor: Engagement within past 7 days typically worth 2-3x engagement from past 30+ days
Best Practices
1. Score on two dimensions: fit and behavior - Fit scores capture demographic alignment (company size, industry, job title). Behavior scores reflect engagement (website visits, content downloads, email clicks). Both matter; high fit + low behavior needs nurturing, low fit + high behavior likely won't convert.
2. Start simple, then iterate - Don't build 100-point models immediately. Begin with 5-10 key attributes, measure results, and refine. Complex models that no one understands aren't used.
3. Align scoring with your ICP - Every scoring attribute should map to characteristics of your best customers. Analyze closed-won deals to identify what high-value prospects have in common.
4. Include negative scoring - Subtract points for disqualifying signals: competitor employment, wrong company size, lack of budget authority. Negative scoring prevents marketing from pursuing clearly poor fits.
5. Establish clear handoff thresholds - Define exactly what score triggers handoff to sales, and what happens at each threshold. Ensure sales understands and agrees with these thresholds.
Common Mistakes
- Building overly complex scoring models no one understands or maintains
- Scoring only on behavior while ignoring demographic fit
- Failing to update scoring models as the business evolves
- Not incorporating negative signals that indicate poor fit
- Setting thresholds too low, flooding sales with unqualified leads
Key Takeaways
- Lead scoring prioritizes sales outreach on the most promising prospects
- Two-dimensional scoring (fit + behavior) outperforms single-dimension models
- Simple, understood models beat complex, ignored ones
- Regular iteration keeps scoring aligned with market reality
- Scoring only creates value if sales trusts and uses the scores
