The Equal Treatment Problem
Most sales teams treat every inbound lead the same way: add to CRM, send welcome email, schedule call. The problem? 80% of those leads were never going to buy — and the 20% that were ready got the same lukewarm treatment as everyone else.
What Changed Our Approach
We analyzed 18 months of closed deals and found clear patterns:
- Leads who visited pricing page 3+ times converted at 8x the average
- Leads from referrals converted at 5x the average
- Leads who engaged with case studies converted at 4x the average
- Company size 10-50 employees was our sweet spot
Building the Scoring Model
SCOUT uses a multi-signal scoring model that weighs:
- Behavioral signals (page visits, content downloads, email opens)
- Firmographic signals (company size, industry, location)
- Intent signals (pricing page visits, demo requests, competitor mentions)
- Engagement velocity (how quickly they move through the funnel)
The Results
After implementing AI-driven lead scoring:
- Overall conversion: 3.2% to 11.4%
- Sales cycle: 34 days to 19 days
- Revenue per rep: increased 47%
- Time spent on unqualified leads: decreased 68%
Key Takeaway
The goal is not to contact more leads. It is to contact the right leads at the right time with the right message. AI scoring makes that possible at scale.