Lead Scoring Model Prompt
Prompt
Design a lead scoring model. What we sell: [describe] ICP: [firmographics and persona] Data we have: [fields collected — title, company size, pages visited, emails opened, content downloaded] CRM/marketing tool: [what will implement scoring] Please design: 1. Demographic fit scoring (firmographic attributes) 2. Behavioural scoring (actions and their point values) 3. Negative scoring (signals that reduce score) 4. MQL threshold: at what score is a lead marketing-qualified? 5. Score decay: how should scores decrease if a lead goes inactive?
Why it works
Separating demographic fit scoring from behavioural scoring reflects the two dimensions that actually predict sales-readiness — who someone is and what they've done. Including negative scoring explicitly prevents the model from ignoring disqualification signals like competitor job titles or unsubscribes. Asking for a handoff threshold forces the team to pre-define the MQL/SQL boundary rather than debating it deal-by-deal.
Watch out for
Lead scoring models are only as good as the data fields you actually collect and trust. The AI will design a logical scoring framework but point values require calibration against your historical win data — the first version will be a hypothesis, not a validated model. Plan to review and adjust the scoring after 90 days of operation based on whether MQL-to-SQL conversion rates improve.
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