What is Lead Scoring?
Lead scoring assigns numerical values to leads based on their characteristics (firmographic data, behavior, engagement) to prioritize sales follow-up. It bridges marketing and sales by ensuring reps focus on the most promising leads rather than treating all leads equally. An effective scoring model can increase sales productivity by 30-50% and improve conversion rates significantly.
The Formula
Lead Score = ฮฃ (Attribute Weight ร Attribute Value) Typically scored on a 0-100 scale with thresholds: Hot (80+), Warm (50-79), Cold (below 50)
Include both demographic fit (company size, industry, role) and behavioral signals (page visits, email engagement, content downloads).
Worked Example
A B2B SaaS company scores leads based on: company size (0-25 pts), role seniority (0-20 pts), website visits (0-20 pts), email engagement (0-15 pts), content downloads (0-20 pts).
- Lead A: 500+ employees (20), VP title (18), 8 page visits (14), opened 3 emails (10), downloaded whitepaper (15) = 77
- Lead B: 10 employees (5), Intern title (3), 1 page visit (2), 0 emails opened (0), no downloads (0) = 10
- Lead A is "Warm" (77) โ routed to sales for follow-up
- Lead B is "Cold" (10) โ stays in nurture sequence
๐ Lead A's score of 77 suggests high buying intent from a well-matched company. Sales should contact within 24 hours. Lead B needs further nurturing before sales engagement.
Why This Matters
Sales efficiency
Without scoring, sales reps waste 50%+ of their time on unqualified leads. Scoring ensures the best leads get immediate attention while poor-fit leads are nurtured or disqualified.
Marketing-sales alignment
A shared scoring model creates agreement between marketing and sales on what constitutes a "qualified lead," reducing the #1 source of tension between these teams.
Conversion rate improvement
Companies with lead scoring see 77% higher lead-generation ROI (Eloqua study). Reps calling hot leads convert 5-10x more often than those calling cold leads randomly.
Common Mistakes
โ Over-weighting demographic data
A VP at a Fortune 500 company who visited your pricing page once is less likely to buy than a manager at a 50-person company who attended your webinar, downloaded 3 resources, and visited pricing 5 times. Behavioral signals are stronger intent indicators.
โ Not implementing score decay
A lead who was active 6 months ago but has gone silent shouldn't keep their high score. Implement time-based decay that reduces scores for inactivity.
โ Setting it and forgetting it
Lead scoring models need quarterly calibration. Compare scored predictions against actual conversion data and adjust weights based on what actually predicts buying behavior.
Industry Benchmarks
| Category | Good | Average | Poor |
|---|---|---|---|
| Lead-to-Opportunity Rate | 15-25% | 8-15% | Below 5% |
| Sales Acceptance Rate | 85%+ | 60-85% | Below 50% |
| Scoring Model Accuracy | 80%+ predictive | 60-80% | Below 50% |
Source: Salesforce 2024 State of Sales Report
Benchmark data sourced from Salesforce 2024 State of Sales Report.