Build a Lead Scoring Model Using Enrichment Data
Stop treating every lead the same. Use enrichment data to build a scoring model that surfaces your best prospects.
Not all leads are equal. A VP of Sales at a 200-person SaaS company is worth more than an intern at a 5-person agency. Lead scoring helps you prioritize — but only if you have the data to score on.
Why most lead scoring fails
Most scoring models rely on behavioral data alone: page views, email opens, form fills. The problem? A curious student browsing your pricing page scores higher than a perfect-fit buyer who visited once.
You need firmographic and demographic scoring alongside behavioral signals.
Enrichment-powered scoring dimensions
Contact-level signals
| Signal | High score | Medium | Low |
|--------|-----------|--------|-----|
| Seniority | VP, C-level | Director, Manager | IC, Intern |
| Department | Sales, Marketing, Ops | Product, Engineering | HR, Legal |
| Confidence | 90%+ | 70-89% | <70% |
Company-level signals
| Signal | High score | Medium | Low |
|--------|-----------|--------|-----|
| Headcount | 50-500 (ICP sweet spot) | 500-2000 | <10 or 2000+ |
| Industry | SaaS, Fintech | E-commerce | Government, Education |
A simple scoring formula
score = seniority_score (0-30) + department_score (0-20) + headcount_score (0-25) + industry_score (0-15) + confidence_score (0-10)- 80-100 → Hot lead, route to AE immediately
- 50-79 → Warm lead, enter SDR sequence
- <50 → Nurture via content and re-score in 30 days
Implementation
- Enrich every new lead via Leads Pro
- Score using the enriched fields
- Store the score in your CRM
- Trigger routing rules based on score thresholds
Iterate
Review closed-won deals monthly. Update score weights based on which enrichment signals actually predict conversion. Your scoring model should improve every quarter.
Ready to enrich your leads?
Start with search, batch workflows, and enrichment in one workspace. Plans start at $9/month.
View pricing