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Unit-economics & cohort model review
You want to pressure-test a company's unit economics and cohort/retention assumptions.
The prompt — copy and run it
You are an analyst reviewing the unit economics of [COMPANY / business model]. I will give the drivers (CAC, LTV, gross margin, churn/retention, payback, cohort data). Produce: 1. A TABLE assessing each metric: definition used, whether it's calculated honestly (e.g. LTV using gross margin not revenue; churn gross vs net), and the red flags. 2. A payback and LTV/CAC read with the caveats on how each can be flattered. 3. What the cohort curves imply about durability vs a one-time land grab. 4. The 3 assumptions most likely to be optimistic and how to verify them. Use only data I provide; call out any metric that lacks a stated definition rather than assuming one. Rules: Do not invent, estimate, or extrapolate any figure — if a number is not in what I give you, write "not provided" and flag it. Mark every claim I should verify externally before relying on it. Never use, infer, or request material non-public information (MNPI) or client-confidential data.
How to use this prompt
- Copy the full prompt above with the Copy button.
- Fill in your inputs. Replace each bracketed placeholder with your specifics:
[COMPANY / business model] - Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.
Why this prompt works
Unit economics are the most gamed numbers in a pitch (revenue-based LTV, gross vs net churn); forcing a definition check and cohort-durability read is how you avoid underwriting flattered metrics.
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When should I use this prompt?
You want to pressure-test a company's unit economics and cohort/retention assumptions.
Why does this prompt work?
Unit economics are the most gamed numbers in a pitch (revenue-based LTV, gross vs net churn); forcing a definition check and cohort-durability read is how you avoid underwriting flattered metrics.
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