Stress-test your unit economics under three growth scenarios
Unit economics that look healthy at current scale often break at 3x. This prompt forces a structured stress test before you hire or raise, not after.
I want to stress-test the unit economics for {{COMPANY_NAME}}. Here are our current metrics: CAC: {{CAC}}, LTV: {{LTV}}, Gross Margin: {{GROSS_MARGIN}}, Average payback period: {{PAYBACK_PERIOD}}, Monthly churn rate: {{CHURN_RATE}}.
1. Calculate the LTV:CAC ratio and payback period from the numbers I've provided. If my numbers are internally inconsistent (e.g., the LTV doesn't match the churn and ACV math), flag the discrepancy before continuing.
2. Run three scenarios — Conservative (churn increases 30%), Base (current metrics hold), and Aggressive (CAC increases 40% due to channel saturation). For each scenario, state the resulting LTV:CAC ratio and payback period, and give a one-sentence verdict on whether the business is healthy, borderline, or in trouble.
3. Identify the single variable in my current metrics that has the highest leverage — meaning a 10% improvement in it produces the largest gain in overall unit economics. Explain why.
4. Identify the most common way businesses in a {{BUSINESS_MODEL}} model erode their unit economics at scale (e.g., rising CAC as easy channels saturate, falling NRR as enterprise deals add support cost). Flag whether any of these risks are visible in my current numbers.
5. Recommend one operational change I could make in the next 90 days to reduce the biggest risk you identified.
Do not provide investment advice. This analysis is for internal planning only. {{COMPANY_NAME}}{{CAC}}{{LTV}}{{GROSS_MARGIN}}{{PAYBACK_PERIOD}}{{CHURN_RATE}}{{BUSINESS_MODEL}}
How to use this prompt
- Copy the prompt above (Copy button on the top-right).
- Replace each
{{VAR}}with your own value. Variables:{{COMPANY_NAME}}{{CAC}}{{LTV}}{{GROSS_MARGIN}}{{PAYBACK_PERIOD}}{{CHURN_RATE}}{{BUSINESS_MODEL}}. - Paste it into one of the recommended tools below.
- Iterate: tighten constraints in the prompt if the output is generic.
Why this prompt is structured this way
The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.
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