A B2B SaaS company implemented AI-powered lead scoring that identified high-probability prospects and dramatically improved sales team efficiency.
Sales team wasting time on low-quality leads. No way to prioritize thousands of monthly leads. Conversion rates were inconsistent. Needed data-driven approach to lead qualification.
We theorized that by training a machine learning model on historical conversion data and hundreds of data points, we could accurately predict which leads would convert and focus sales efforts accordingly.
Collected 3 years of historical lead and conversion data. Built predictive model using 200+ features (demographic, firmographic, behavioral). Integrated scoring into CRM with automated lead routing. Created tiered follow-up strategy based on scores. Implemented continuous model retraining. Built dashboards showing score accuracy and impact.
| Metric | Before | After | Change |
|---|---|---|---|
| Sales Rep Productivity | 8 meetings/wk | 19 meetings/wk | +138% |
| Lead-to-Customer Rate | 4.2% | 11.8% | +181% |
| Average Deal Size | $18K | $32K | +78% |
| Sales Cycle Length | 89 days | 61 days | ↓31% |
Growth trajectory showing +138% improvement in sales rep productivity
Duration
5 months
Launch Date
February 2024