Win-Back Strategy Segmented by Inactivity Reason
Framework for recovering inactive customers with differentiated campaigns based on the probable reason for inactivity.
Create win-back campaigns that identify and address different inactivity reasons (price, poor experience, forgetfulness, competitor) with specific approaches for each case.
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Prompt objective
Create win-back campaigns that identify and address different inactivity reasons (price, poor experience, forgetfulness, competitor) with specific approaches for each case.
Real use case
GymPass Local, a gym chain with 6 locations, has 4,200 former members who cancelled in the past 12 months. Their generic 'Come back with 30% off' attempt recovered only 2%. They want to understand why each person left to make targeted offers.
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Prompt
Develop a win-back strategy segmented by inactivity reason for [COMPANY NAME], operating in the [INDUSTRY] sector. Context: - Inactive/cancelled customers: [NUMBER] in the last [MONTHS] months - Average monthly spend when active: $[AMOUNT]/month - Known cancellation reasons: [LIST IF AVAILABLE] - Platform: [PLATFORM: RD Station/ActiveCampaign/HubSpot] - Incentive budget: $[AMOUNT]/month **1) Classification by probable reason:** Define criteria to automatically identify each group: **Group A — Price/Value:** - Signals: Cancelled after price increase, requested discount via support, compared competitors - Estimated %: [X]% - Strategy: Economical plan, value bundle, personalized ROI **Group B — Negative Experience:** - Signals: Open support ticket, detractor NPS score, negative review - Estimated %: [X]% - Strategy: Genuine apology, proof of changes, free trial period **Group C — Forgetfulness/Inertia:** - Signals: No declining engagement, simply stopped, no complaints - Estimated %: [X]% - Strategy: News and updates, FOMO, valuable content **Group D — Changed Need:** - Signals: Completed goal (e.g., graduated, lost weight), life stage change - Estimated %: [X]% - Strategy: New product/phase, referral program, alumni community **Group E — Switched to Competitor:** - Signals: Mentioned alternative, followed competitor on social - Estimated %: [X]% - Strategy: Honest comparison, exclusive feature, aggressive offer **2) Win-back sequence per group (4 emails each):** For each group, create 4 emails with: - Subject line (3 variations for A/B testing) - Full copy (header, body, CTA) - Specific tone for the reason - Scalable offer/incentive (email 1: no discount → email 4: maximum) - Timing between emails - Exit conditions (opened? clicked? converted?) **3) Reason survey (before the sequence):** - Email 0: 1-question survey (Why did you stop?) - 5 answer options mapped to Groups A-E - Automation: Response routes to specific sequence - Fallback: Non-responders enter Group C **4) Metrics and ROI:** - Expected recovery rate by group - Acquisition cost vs. reactivation cost - Estimated revenue recovered - Measurement period (90 days post-campaign) - Comparison: generic vs. segmented win-back **5) Post-recovery:** - Special onboarding for returning customers - Monitoring for repeat churn (90 days) - Feedback loop: update classification with real data
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