How to Build a Lead Scoring Model with Enriched Data
TL;DR
Learn how to build effective lead scoring models using enriched data. Score leads based on firmographic, behavioral, and AI-enriched signals to prioritize sales outreach.
Table of Contents
What Is Lead Scoring?
Lead scoring assigns numerical values to leads based on how well they match your ideal customer profile and how engaged they are with your brand. High-scoring leads get prioritized for sales outreach, while low-scoring leads enter nurture campaigns.
Effective lead scoring requires rich data. With minimal information (just name and email), scoring is guesswork. With enriched data (company size, industry, tech stack, job title, engagement history), scoring becomes a reliable predictor of conversion potential.
Firmographic Scoring with Enriched Data
Firmographic scoring evaluates the lead's company against your ICP. Use enriched data to score: company size (employee count and revenue match), industry fit, geographic location, technology stack alignment, and funding stage.
Example scoring: Company size 50-500 employees (+20 points), SaaS industry (+15 points), uses Salesforce or HubSpot (+10 points), Series A-C funding (+10 points), US-based (+5 points). Maximum firmographic score: 60 points.
Enrichabl's AI enrichment can automatically assess ICP fit by analyzing each company against your scoring criteria. Create an AI column that scores companies 1-10 on ICP match and provides reasoning.
Behavioral Scoring
Behavioral scoring tracks how leads interact with your brand: website visits, email opens, content downloads, demo requests, and pricing page views. While behavioral data comes from your marketing automation platform, enrichment data helps interpret it.
A pricing page visit from a VP of Sales at a 200-person SaaS company (high firmographic score) should score much higher than the same visit from a student researcher (low firmographic score). Enriched data provides the context that makes behavioral scoring meaningful.
AI-Powered Scoring
AI enrichment enables a new category of scoring: AI-assessed fit. Instead of rigid point-based rules, use AI to evaluate leads holistically. An AI column prompt like "Score this lead 1-100 on likelihood to purchase a B2B lead enrichment tool based on their company size, industry, and role" produces nuanced scores that account for subtle signals.
AI scoring is particularly valuable for identifying leads that traditional rules might miss. A 20-person agency that specializes in outbound sales for B2B clients might score low on company size rules but high on AI fit assessment because the AI recognizes the strong use-case alignment.
Implementing Your Scoring Model
Start simple: firmographic score (0-60) + behavioral score (0-40) = total score (0-100). Leads scoring 70+ go to sales immediately. Leads scoring 40-70 enter nurture sequences. Leads below 40 receive low-touch marketing.
Refine continuously. Track which scores actually convert and adjust your weights. If industry is a strong predictor but company size isn't, increase industry points and decrease company size points. Review and adjust quarterly based on conversion data.
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Get Started FreeFrequently Asked Questions
How does enrichment improve lead scoring?
Enrichment provides the data that scoring models need: company size, industry, tech stack, job title, and more. Without enrichment, scoring relies on minimal self-reported data. With enrichment, scoring is based on comprehensive, verified data.
Can AI replace traditional lead scoring rules?
AI can supplement traditional rules with more nuanced assessment. Use AI scoring as an additional signal alongside firmographic and behavioral scores. AI is particularly good at identifying non-obvious fit patterns.
What is a good lead score threshold for sales handoff?
Most teams use 70/100 as the threshold for immediate sales outreach. Adjust based on your specific conversion data - if 70+ leads convert at 20%+ and 50-70 leads convert at under 5%, your threshold is well-calibrated.
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