| 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 | "2026-05-08T00:00:00.000Z" |
| 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
