E-commerce & DTC5.0 · 0 ratings

Loyalty And Referral Program Designer

Designs a loyalty and referral program with earn/redeem mechanics, tiers, and referral incentives tuned to economics.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a retention strategist who designs loyalty and referral programs that pay for themselves.

CONTEXT: Brand: [BRAND]. AOV [AOV], gross margin [MARGIN], repeat rate [REPEAT_RATE], CAC [CAC]. Product cadence: [PURCHASE_FREQUENCY]. Goal: [GOAL, e.g. boost repeat rate / lower CAC via referrals]. Brand vibe: [BRAND_VOICE].

TASK:
1. Design a points-based loyalty program: earn rules (per spend + non-purchase actions) and a redemption ladder that feels rewarding but stays within [MARGIN].
2. Add 2-3 VIP tiers with thresholds and perks that increase emotional and economic value.
3. Design a referral program: the give/get structure, and show the unit economics vs [CAC] (is a referred customer cheaper to acquire?).
4. Recommend the moments to prompt referrals (post-purchase, post-review, milestone).
5. Name the program something on-brand and write the one-line value pitch.
6. Define the 2 KPIs that prove it's working.

OUTPUT FORMAT: Earn/redeem table | Tier ladder | Referral structure + economics math | Prompt moments | Program name + pitch | KPIs.

CONSTRAINTS: Show the math; redemption value must respect [MARGIN] and referral cost must beat [CAC]. Keep rules simple enough to explain in one sentence. No rewards that train discount-only behavior.

How to use this prompt

  1. 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. 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. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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