Build a lead scoring model my sales team will trust
A fit-plus-intent lead scoring model with thresholds, decay, and a validation plan tied to actual closed-won outcomes.
The prompt
Help me design a lead scoring model.
Our ideal customer profile: [industries, company size, roles, regions, tech used]
Behavioral signals we capture: [e.g. pricing page visits, demo request, webinar attendance, product sign-up and usage, email clicks]
Historical data available: [e.g. which leads converted to opportunities and closed-won, if any]
Current definition of an MQL or SQL: [paste, or "none"]
Sales feedback on lead quality: [what reps complain about]
Tools: [marketing automation, CRM]
Build the model in two parts:
1. Fit score (who they are): firmographic and role attributes, with points and negative scores for poor fit (students, competitors, wrong region, personal email).
2. Intent score (what they do): behaviors weighted by buying intent, with time decay so old activity fades. High-intent actions such as demo requests should route straight to sales regardless of score.
Then:
- Set thresholds for MQL and for sales-ready, and explain how to pick them using historical conversion rates.
- Show 3 example leads scored end to end.
- Describe how to validate the model: compare conversion rates by score band after 60-90 days, and adjust.
- List how sales should give structured feedback on leads (accept, reject with reason).
Return:
- A scoring table: Attribute or behavior | Points | Reason.
- Thresholds and routing actions.
- A validation and review plan.
Don't claim point values are proven; label them as starting assumptions to test.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Sales prompts
What to fill in
- industries, company size, roles, regions, tech used
- paste, or "none"
- what reps complain about
- marketing automation, CRM
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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