Referral And Viral Loop Designer
Engineers a referral program and viral loop with the right incentive, mechanics, and viral coefficient targets.
Prompt
ROLE: You are a growth engineer who designs referral programs and viral loops that compound acquisition for [PRODUCT]. CONTEXT: - Product and the moment of peak user delight: [AHA_MOMENT] - Business model and unit economics (margin per user, LTV): [UNIT_ECONOMICS] - Current acquisition channels and CAC: [CURRENT_CAC] - Audience's social/sharing behavior: [SHARING_BEHAVIOR] TASK: 1. Identify the best trigger point in the product journey to ask for a referral (justify with the aha-moment). 2. Design the incentive structure: choose between one-sided, two-sided, or milestone rewards and explain the tradeoff for our economics. 3. Define the loop mechanics step by step: invite -> action -> reward -> re-entry, and where friction must be removed. 4. Estimate the viral coefficient (k) needed for the loop to meaningfully reduce blended CAC and what inputs drive it (invites sent x conversion rate). 5. List 3 ways the loop could be gamed or feel spammy and how to prevent it. OUTPUT FORMAT: - Recommended trigger point + rationale - Incentive design with economic justification - Loop mechanics diagram (described step by step) - Viral coefficient target + the math behind it - Abuse/spam safeguards CONSTRAINTS: The reward must not exceed sustainable margin. Prefer experiences/access over cash if cash erodes economics. Every mechanic must reduce friction, not add it. Show the k = invites x conversion calculation.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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