Lead Scoring & Prioritization
Design lead scoring models with demographic, firmographic, and behavioral criteria to prioritize sales outreach by conversion likelihood.
Your SDRs treat every inbound lead the same — spending 30 minutes on a student downloading a whitepaper and 30 seconds on a VP who requested a demo. Without a scoring model, your best leads go cold while your team chases dead ends. Lead scoring turns gut feel into a repeatable system.
Who it's for: revenue operations leaders designing lead qualification frameworks, sales managers prioritizing pipeline for their teams, marketing operations managers aligning MQLs with sales-readiness criteria, demand gen teams optimizing lead handoff to sales, growth-stage startups building their first lead scoring model
Example
"Design a lead scoring model for our B2B SaaS product" → Complete scoring framework: demographic criteria (title, company size) with point values, behavioral signals (pricing page visits, demo requests) weighted by intent, firmographic fit scoring, MQL threshold definition, and SDR routing rules by score tier
CLAUDE.md Template
New here? 3-minute setup guide → | Already set up? Copy the template below.
npx ccplaybooks add recipe-lead-scoring-analysis# Lead Scoring & Prioritization Analysis
## Your Role
You are an expert marketing operations analyst. Your job is to design lead scoring models that accurately predict conversion likelihood and enable efficient sales prioritization.
## Core Principles
- Validate models against historical conversion data
- Behavioral signals outweigh demographic/firmographic
- Implement score decay for aging leads
- Maximum 10-15 scoring criteria for model simplicity
- Define clear MQL/SQL thresholds with action triggers
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What This Does
Builds lead scoring models that combine demographic, firmographic, and behavioral signals to rank leads by conversion likelihood. Helps sales teams focus on high-probability opportunities and stop chasing unqualified leads.
Quick Start
Step 1: Download the Template
Click Download above to get the CLAUDE.md file.
Step 2: Provide Historical Data
Gather data on past converted leads — company size, industry, engagement patterns, and deal outcomes.
Step 3: Start Using It
claude
Say: "Design a lead scoring model for our B2B SaaS product. Analyze our last 200 closed deals to identify which attributes predict conversion."
Scoring Model Components
| Component | Content |
|---|---|
| Firmographic Scoring | Company size, industry, geography, tech stack |
| Demographic Scoring | Title, seniority, department, decision authority |
| Behavioral Scoring | Website visits, content downloads, email engagement |
| Negative Scoring | Disqualification signals (competitors, students, wrong geo) |
| Threshold Definitions | MQL, SQL, and hot lead score boundaries |
| Routing Rules | Score-based assignment to SDR vs. AE |
Tips
- Validate against history: Test your model against past deals — does it correctly rank known converters?
- Behavioral signals matter most: What someone does predicts better than who they are
- Decay scores: Reduce scores for inactivity — a hot lead 6 months ago isn't hot today
- Keep it simple: 10-15 scoring criteria maximum. Complexity reduces trust
Commands
"Design a lead scoring model for [product/segment]"
"Analyze which lead attributes predict conversion"
"Set MQL and SQL thresholds based on historical data"
"Create lead routing rules by score tier"
Troubleshooting
Model doesn't predict well Say: "Analyze the last 50 conversions and 50 non-conversions. Which attributes differ?"
Too many MQLs, few convert Ask: "Raise the MQL threshold or add behavioral requirements to score qualification."
Sales doesn't trust the scores Specify: "Show the win rate at each score tier. Let data build confidence."