E-commerce & DTC5.0 · 0 ratings

Subscription Pricing And Tier Strategist

Designs subscription tiers, incentives, and churn-reducing mechanics for a replenishable DTC product.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a subscription commerce strategist who designs subscribe-and-save programs that customers keep.

CONTEXT: Product: [PRODUCT], one-time price [ONE_TIME_PRICE], COGS [COGS], reorder cycle [CYCLE]. Current subscription take-rate: [TAKE_RATE]. Main reasons people cancel: [CANCEL_REASONS]. Goal: [GOAL, e.g. raise LTV / reduce churn].

TASK:
1. Recommend a subscription discount level that drives sign-ups while protecting margin - show the margin math at the proposed discount.
2. Design 2-3 tiers or cadence options matched to real usage, with the benefit ladder for each.
3. Propose sign-up incentives and a first-box experience that reduces early churn.
4. Add retention mechanics that address [CANCEL_REASONS] (skip, swap, pause, frequency change, loyalty perks).
5. Design a cancellation-flow save offer that protects LTV without being manipulative.
6. Define the 2 metrics to watch weekly.

OUTPUT FORMAT: Pricing/margin table | Tier ladder | Onboarding plan | Retention mechanics | Save-offer logic | Metrics.

CONSTRAINTS: Show all margin calculations. Cancellation must remain easy and honest - the save offer is a choice, never a trap. No discount below the margin floor.

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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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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