You get:
- leads with high scores but no budget (wasted time)
- leads with low scores that are ready to buy (missed deals)
- no alignment between marketing and sales on what’s a good lead
- sales reps ignoring leads because they don’t trust the score
- no way to prioritize follow-up
But lead scoring is not random.
It is a system for prioritizing your limited sales time.
- Demographic: role, seniority, company size
- Firmographic: industry, revenue, location
- Behavioral: website visits, content downloads, email opens
- Engagement: meetings attended, demos requested, replies
Without lead scoring, you treat all leads the same.
This framework forces AI to build a data-driven lead scoring system.
Assume the role of a sales operations specialist who builds lead scoring systems. Your task is to create a lead scoring criteria. Generate: 1. DEMOGRAPHIC SCORING (points) - Job title / role - Seniority level - Department 2. FIRMOGRAPHIC SCORING (points) - Company size (employees) - Industry - Revenue range - Geography 3. BEHAVIORAL SCORING (points) - Website visits (pricing page, product page) - Content downloads - Email opens/clicks - Demo requests 4. ENGAGEMENT SCORING (points) - Sales accepted (converted from MQL) - Meeting attendance - Reply to outreach 5. SCORING THRESHOLDS - Hot lead (score X+): sales ready - Warm lead (score Y-Z): nurture - Cold lead (score below Y): recycle or discard 6. SCORE DECAY RULES - How quickly scores decrease without engagement INPUTS: Your Ideal Customer Profile (ICP): [DESCRIBE] Typical Buyer Role: [INSERT] Typical Company Size: [INSERT] Key Behavioral Signals (what indicates interest): [LIST] Marketing Qualified Lead (MQL) Criteria (current): [DESCRIBE OR "NONE"] Sales Accepted Lead (SAL) Criteria: [DESCRIBE OR "NONE"] RULES: - Demographic: role and seniority are highest weight - Firmographic: company size and industry fit - Behavioral: page visits and content downloads - Engagement: replies and meetings are highest weight - Hot leads go to sales immediately - Warm leads stay in marketing nurture - Cold leads are recycled or removed - Scores decay over time (30-90 days without engagement)
- Demographic and firmographic criteria define fit (can they buy?).
- Behavioral and engagement criteria define interest (do they want to buy?).
- Hot leads (high fit + high interest) go to sales immediately.
- Warm leads (high fit + low interest) stay in marketing nurture.
- Cold leads (low fit) are recycled or discarded.
- Scores decay over time (leads get cold without engagement).
Your Ideal Customer Profile (ICP): B2B SaaS companies, 50-500 employees, $10M-100M revenue, VP of Sales or Sales Ops buyer
Typical Buyer Role: VP of Sales, Sales Operations Director
Typical Company Size: 50-500 employees
Key Behavioral Signals: Pricing page visit, demo request, case study download, email reply
Marketing Qualified Lead (MQL) Criteria: Content download + email open
Sales Accepted Lead (SAL) Criteria: Budget identified + authority confirmed
This framework improves outcomes by forcing:
- demographic scoring (fit)
- firmographic scoring (company fit)
- behavioral scoring (interest)
- engagement scoring (intent)
- scoring thresholds (prioritization)
Great lead scoring doesn’t just assign points — it prioritizes your limited sales time on the leads most likely to close.
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