| name | anti-icp |
| description | Generates an evidence-based Anti-Ideal Customer Profile to help avoid marketing, sales, or product investments in poor-fit audiences that seem attractive. |
| disable-model-invocation | true |
ANTI ICP Research Agent
Purpose: This prompt generates a clear, evidence-based Anti-Ideal Customer Profile (ANTI ICP) to help avoid marketing, sales, or product investments in audiences that seem attractive but are a poor fit. Use this to prevent churn, inefficiency, or wasted budget. All output should be grounded in factual reasoning and real behavior not stereotypes or assumptions.
Input Instructions: If the user does not input a product or service, use the most recent ICP in context as a mirror and reverse-engineer it for the ANTI ICP.
Input Fields
- Product or Service Description: [Insert product or service description here]
Your Tasks
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Identify groups with poor product-market fit by assessing:
- Behavioral mismatches
- Situational or contextual incompatibility
- Low urgency or low perceived value
- Evidence of churn or poor satisfaction from similar users
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Compare and contrast with the Ideal Customer Profile (ICP). Determine what factors distinguish these low-fit users from high-fit ones, and what signals lead to misalignment even when demographics appear similar.
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Explain why these groups are commonly but mistakenly targeted, and what signals lead marketers or product teams to pursue them in error.
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Create up to 2 Anti-Personas that include demographic and behavioral characteristics, their actual goals or constraints, and why the product is a mismatch (with factual justification).
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Develop an ANTI ICP Filter Checklist to help teams screen out poor-fit leads or segments before spending time or resources.
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Define Early Warning Signals (behavioral or demographic) that should trigger de-prioritization or qualification review.
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Propose a Validation Plan with 3 simple ways to verify low-fit status. Examples include landing page drop-off analysis, survey feedback or early churn triggers, and interview-based pattern recognition.
Output Format
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ANTI ICP Summary: Who is not a good fit for this product and what facts or patterns justify this disqualification?
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Anti-Persona(s): Short backstory and profile, what they want that your product doesn't provide, why they appear attractive (false positives), confidence level and supporting evidence.
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Mistargeting Traps: What assumptions or behaviors lead teams to target these segments? Where have similar companies gone wrong targeting this group?
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ANTI ICP Filter Checklist: Include yes/no conditions to eliminate low-fit customers early.
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Low-Fit Signal Detector: Observable traits, answers, or behaviors that predict low product satisfaction or value realization.
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Validation Plan: Three field tests or indicators that confirm a segment is a poor fit. Make the plan repeatable and low-lift.
Example Use Case
- Product: Multi-child stroller
- Top 3 Benefits:
- Efficient mobility for families with multiple small children
- Enhanced safety features for public outings
- Compact foldable design for storage and travel
- Application: Use the prior ICP data to derive anti-personas, such as customers with only one child, rural parents who drive everywhere, or budget-focused shoppers who won't pay a premium for safety features.
