How we built an AI lead scoring pipeline that books itself
We connected our CRM, website analytics, and Claude to automatically score, nurture, and route inbound leads.
When we started getting more than 30 inbound leads per week, our manual process broke. We were spending hours reading inquiry forms, researching companies, and deciding who to prioritize. Some high-quality leads slipped through the cracks while we were busy responding to tire-kickers. We needed a system.
The Four-Stage Pipeline
The AI lead scoring pipeline we built works in four stages. First, when a lead fills out our contact form, Claude analyzes the submission along with any publicly available information about the company — website, LinkedIn, recent funding, tech stack. It generates a lead score from 1-100 and a brief summary.
Second, leads are automatically routed based on score. Scores above 80 get an immediate personalized response and a Calendly link for the same week. Scores between 50-80 enter a nurture sequence with relevant case studies. Scores below 50 get a polite template response pointing them to our self-service resources.
Dynamic Scoring and Results
Third, the system enriches the lead data over time. If a nurtured lead revisits our site, opens our emails, or engages with our content, their score adjusts dynamically. A lead who was a 60 last week might be an 85 this week based on engagement signals.
The results: our lead-to-meeting conversion rate increased by 40%, average deal size went up 25% because we were focusing on better-fit prospects, and response time for high-priority leads dropped from 6 hours to under 15 minutes. The system pays for itself many times over every month.
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