Build a lead scoring model

**Role:** You are a Sales Operations Manager building a lead scoring model for {company_name}.

**Context:**
Without lead scoring, sales teams spend equal time on every lead regardless of quality — wasting capacity on poor-fit prospects while letting high-intent, good-fit leads go cold. A good lead scoring model combines demographic fit (does this lead match our ICP?) with behavioral intent (are they showing buying signals?) to create a prioritized lead queue that maximizes conversion.

**Task:**
Build a two-dimensional lead scoring model: explicit scoring (firmographic/demographic fit) and implicit scoring (behavioral intent signals). Combine them into a matrix that produces a prioritized action tier for each lead.

**Input Available:**
- {company_name}: Company name
- {ideal_customer_profile}: ICP definition with priority attributes
- {disqualifying_criteria}: Characteristics that make a lead a poor fit (explicitly)
- {behavioral_signals}: Available behavioral data (page visits, content downloads, email opens, trial usage, demo requests, etc.)
- {conversion_data}: Historical data on which lead characteristics correlate with won deals
- {crm_and_marketing_tools}: What systems are available for tracking and scoring

**Output Format:**
1. Explicit scoring model:
   - Scoring dimensions: Company size | Industry | Role | Tech stack | Geography | Budget indicators
   - Points per attribute with rationale
   - Negative scores for disqualifying attributes
2. Implicit scoring model:
   - Behavioral signals and point values
   - Decay rules (actions older than X days lose points)
3. Combined score matrix: Explicit score band × Implicit score band = Lead tier
4. Lead tier definitions: A (immediate outreach) | B (outreach within 48 hours) | C (nurture) | D (disqualify)
5. Threshold recommendations: Score cutoffs for each tier
6. Implementation guide: How to configure in your CRM/marketing automation tool
7. Calibration plan: How to test and refine the model after 90 days

**Guardrails & Quality Control:**
- Do not rely solely on job title for lead scoring — a VP with no budget and no pain is not a good lead
- Disqualifying scores must automatically remove leads from active outreach — not just lower their score
- Decay rules are critical — a prospect who downloaded content 6 months ago has different intent than one who downloaded yesterday
- Validate score thresholds against historical data before deploying — set thresholds that maximize conversion, not just volume