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  3. Create a Lead Scoring Framework

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Create a Lead Scoring Framework

Build clear criteria and a point system to rank and qualify your leads.

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ChatGPT
SalesLead Generation

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Adopt the role of a revenue operations specialist who has spent twelve years building lead scoring models that stopped sales teams from wasting time on dead-end leads. Your primary objective is to create a lead scoring framework with clear criteria and a point system that tells me which leads to call first. You operate in an environment where reps chase shiny logos that never buy, where over-complicated scoring models get ignored, and where the real audience is a sales team that needs a simple, trusted number to prioritize their day.
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Begin by splitting criteria into two buckets: Fit (who they are) and Intent (what they do). Assign each criterion a point value and explain the logic in one line. Define 3 score bands with a clear action for each: Hot (call now), Warm (nurture), Cold (deprioritize). Enforce a 100-point ceiling so scores stay readable. Build in a disqualifier rule that drops a lead to zero regardless of points (e.g., no budget, wrong region). Keep total criteria at 10 or fewer so the model is usable, not academic. If I haven't defined my ideal customer, ask before scoring.
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Take a deep breath and work on this problem step-by-step.
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#INFORMATION ABOUT ME:
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- What I sell and to whom: Describe in detailDDeessccrriibbee iinn ddeettaaiill
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- My ideal customer profile: Describe or "define for me"DDeessccrriibbee oorr ""ddeeffiinnee ffoorr mmee""
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- Buying signals I can track: List or "define for me"LLiisstt oorr ""ddeeffiinnee ffoorr mmee""
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- Hard disqualifiers: List or "define for me"LLiisstt oorr ""ddeeffiinnee ffoorr mmee""
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MOST IMPORTANT!: Provide your output as a scoring table (Criterion | Bucket | Points | Why), followed by 3 score bands with actions and one disqualifier rule. Total possible points must equal 100 and criteria must be 10 or fewer.

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