Build a comprehensive lead scoring model with criteria, point values, MQL/SQL thresholds, and CRM setup notes. Works with ChatGPT, Claude, Gemini. Best for: Revenue operations, marketing ops, growth teams, HubSpot/Salesforce admins.
You are a revenue operations specialist. Build a lead scoring model for [company type] selling [product]. ## Scoring Framework ### Firmographic Scoring (Max 40 points) - Industry match: [point values by tier] - Company size: [points by range] - Revenue: [points by range] - Geography: [points by tier] - Technology stack signals: [points for having X tools] ### Demographic Scoring (Max 30 points) - Job title match: [points by seniority and function] - Decision-making authority: [points] - LinkedIn profile completeness: [points] ### Behavioral Scoring (Max 30 points) - Page visits: [point scale by page type] - Content downloads: [points by asset type] - Email engagement: [points for opens, clicks] - Free trial signup: [points] - Demo request: [points] - Pricing page visit: [points] - Return visits: [points] ### Negative Scoring (-points) - Competitor email domain: [-X] - Student domain: [-X] - No business email: [-X] - Unsubscribed from email: [-X] ## Thresholds - Cold Lead: 0-30 points - Marketing Qualified Lead (MQL): 31-60 points - Sales Qualified Lead (SQL): 61-80 points - Hot Lead / Immediate Outreach: 81-100 points ## CRM Implementation Notes - HubSpot / Salesforce setup instructions - Automation triggers at each threshold - Lead routing rules
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Replace company, product, and audience placeholders with your real data. For outbound prompts, include specific pain points and your unique value proposition. The AI output gives you a professional first draft — always personalise before sending.
ICP frameworks, prospecting lists with qualification criteria, outreach messaging templates, follow-up sequences, and objection handling scripts. Quality varies with the specificity of your inputs.
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