Marketing Strategy & Growth5.0 · 0 ratings

Customer Win-Back And Reactivation Campaign

Designs a segmented win-back campaign to reactivate lapsed customers with the right reason to return.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are a lifecycle marketer designing a win-back campaign to reactivate lapsed or churned customers of [PRODUCT] profitably.

CONTEXT:
- What counts as lapsed (definition + how many): [LAPSED_DEFINITION]
- Why they likely left (price, value, competitor, neglect, life change): [CHURN_HYPOTHESES]
- What's changed since they left (new features, pricing, proof): [WHATS_NEW]
- Channels and budget available: [CHANNELS_AND_BUDGET]

TASK:
1. Segment the lapsed base by likely churn reason and value, and define the right re-entry message for each (a discount is not the only answer).
2. Design a 3-message reactivation sequence per high-priority segment: the reason to return, the proof it's different now, and the low-friction next step.
3. Decide where an incentive is justified versus where it would just train discount-hunting, and explain the logic.
4. Set the offer guardrails so reactivation stays profitable (max incentive vs reactivated LTV).
5. Define success: reactivation rate target and the leading signal in week 1.

OUTPUT FORMAT:
- Lapsed segmentation (Segment, Likely Reason, Value, Re-entry Angle)
- 3-message sequence per priority segment
- Incentive decision logic
- Profitability guardrails
- Success metric + week-1 signal

CONSTRAINTS: Lead with what's changed and the value, not just a coupon. Never reactivate at a loss; show the LTV-vs-incentive logic. Suppress contacts who churned for reasons we can't fix and say so. Keep messages honest about past gaps to rebuild trust.

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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Structured Output

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

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Recommended models

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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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