Automated Lead Enrichment & Scoring
Increased qualified lead conversion by 40%
Client / Context
A growing B2B professional services firm with a 10-person sales team. They were receiving 200+ inbound leads per month but lacked the resources to properly research and prioritize them.
The Problem
Sales reps were spending 2-3 hours per day manually researching leads—looking up company information, checking LinkedIn, and trying to assess fit. Many leads went cold before they could be contacted. There was no consistent scoring methodology, so prioritization was based on gut feel.
Our Approach
We designed an n8n-based automation pipeline that would enrich incoming leads with company data, social profiles, and technographic information, then apply a scoring model based on the client's ideal customer profile. The goal was to reduce research time to near-zero and surface the best leads immediately.
The Solution
Built an n8n workflow triggered by new CRM entries. The workflow enriches leads using Clearbit and LinkedIn data, runs them through a custom scoring algorithm based on company size, industry, technology stack, and engagement signals, then updates the CRM with enriched data and score. High-score leads trigger immediate Slack notifications to the appropriate sales rep.
The Scoring Model
The lead scoring model considers multiple factors weighted by their correlation with historical conversion data:
Firmographic Signals (40% weight)
- Company size (employee count)
- Industry alignment
- Geographic location
- Revenue range
Technographic Signals (25% weight)
- Current technology stack
- Presence of complementary tools
- Technology sophistication indicators
Engagement Signals (35% weight)
- Content downloaded
- Pages visited
- Email engagement history
- Demo request vs. general inquiry
Scores are recalculated as new engagement data comes in, so a lead's priority can change based on their behavior.
Ongoing Optimization
The scoring model is reviewed quarterly based on actual conversion data. We've made three rounds of adjustments since initial deployment, each time improving prediction accuracy.
Results
- 40% increase in qualified lead conversion rate
- 90% reduction in manual research time (from 2.5 hours to 15 minutes per day)
- Average response time to high-value leads dropped from 4 hours to 12 minutes
- Sales team capacity effectively increased by 25%
- ROI payback period of 6 weeks
Stack Used
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