By name: lead-scoring displayName: Lead Scoring tagline: Score and prioritize leads based on engagement, firmographics, and behavioral signals. description: | Implements a lead scoring model that evaluates prospects based on demographic fit, firmographic data, behavioral engagement, and intent signals. Assigns numerical scores to prioritize sales outreach and route leads to the right team. Continuously refines scores based on conversion outcomes. department:
- Marketing
- Sales use_cases:
- Lead Qualification
- Sales Prioritization
- Marketing Automation tools_required:
- HubSpot MCP agents_compatible:
- Claude / Claude Code
- Cursor
- Windsurf
- ChatGPT
- Any MCP-compatible agent author: Webrix verified: true updatedAt: 2026-05-08 version: 1.0.0 exampleInput: | Create a lead scoring model for our B2B SaaS product. ICP: 50-500 employee tech companies in North America. exampleOutput: | Lead Scoring Model — B2B SaaS
SCORING CRITERIA (0-100) Firmographic (40 pts max) Company size 50-500: +15 Tech industry: +10 North America: +10 Revenue >$5M: +5
Behavioral (40 pts max) Visited pricing page: +10 Downloaded whitepaper: +8 Attended webinar: +8 Multiple site visits: +7 Opened 3+ emails: +7
Engagement (20 pts max) Requested demo: +15 Replied to outreach: +5
THRESHOLDS Hot (80-100): Route to SDR immediately Warm (50-79): Add to nurture sequence Cold (0-49): Continue marketing touches
Lead Scoring
Score and prioritize leads based on engagement, firmographics, and behavioral signals.
Integrations: HubSpot
When to Use
- The user wants to build or refine a lead scoring model
- Sales teams need to prioritize which leads to contact first
- The user mentions "lead scoring", "lead qualification", or "MQL"
Steps
Step 1: Define ICP Criteria
Establish firmographic and demographic criteria for ideal customers.
Step 2: Set Behavioral Signals
Define engagement actions and their point values.
Step 3: Configure Thresholds
Set score ranges for hot, warm, and cold lead categories.
Step 4: Test and Refine
Validate the model against historical conversion data.
Output
Deliver:
- Scoring model with criteria and point values
- Threshold definitions with routing rules
- Validation report against historical data