Predictive Churn Model with Early Warning Signals and Recovery Playbooks
Framework to identify customers at risk of cancellation through behavioral signals and execute preventive retention actions.
Build an early churn detection system that identifies at-risk customers 30–60 days before cancellation, enabling proactive retention efforts.
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Prompt objective
Build an early churn detection system that identifies at-risk customers 30–60 days before cancellation, enabling proactive retention efforts.
Real use case
FitClub App, a workout platform with 32,000 subscribers ($6/month), has a monthly churn rate of 7%. The team only discovers a customer is canceling when they request cancellation—there are no early warning signals. Each 1% reduction in churn represents $23,000/year in preserved revenue.
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Prompt
Develop a predictive churn model for [COMPANY NAME], a [SUBSCRIPTION/SaaS] service with [NUMBER] customers and [PERCENTAGE]% monthly churn.\\\\\\\\n\\\\\\\\n**Stage 1 — Identifying Churn Signals (Health Score):**\\\\\\\\nDefine a Customer Health Score (0–100) based on:\\\\\\\\n\\\\\\\\n| Signal | Weight | Healthy | Warning | Critical |\\\\\\\\n|--------|--------|---------|---------|----------|\\\\\\\\n| Usage frequency | 25% | [METRIC] | [METRIC] | [METRIC] |\\\\\\\\n| Engagement with key features | 20% | [METRIC] | [METRIC] | [METRIC] |\\\\\\\\n| Support ticket volume | 15% | 0 in 30d | 1–2 in 30d | 3+ in 30d |\\\\\\\\n| Individual NPS/CSAT | 15% | 9–10 / >4 | 7–8 / 3–4 | 0–6 / 1–2 |\\\\\\\\n| Payment delinquency | 15% | Current | 1 late payment | 2+ late payments |\\\\\\\\n| Communication engagement | 10% | Opens >50% | Opens 20–50% | Opens <20% |\\\\\\\\n\\\\\\\\nFormula: Health Score = weighted sum of indicators.\\\\\\\\n- Green (71–100): Healthy customer\\\\\\\\n- Yellow (41–70): Needs attention\\\\\\\\n- Red (0–40): High churn risk\\\\\\\\n\\\\\\\\n**Stage 2 — Alert Automation:**\\\\\\\\n- Integrate data from [PRODUCT TOOL + CRM + BILLING]\\\\\\\\n- Dashboard showing Health Score per customer (daily refresh)\\\\\\\\n- Automatic alert when Health Score drops to yellow (email to CSM)\\\\\\\\n- Urgent alert when it drops to red (Slack + automatic assignment)\\\\\\\\n- Weekly report: zone movement (how many moved out of green)\\\\\\\\n\\\\\\\\n**Stage 3 — Recovery Playbooks by Zone:**\\\\\\\\n\\\\\\\\n**Yellow Zone (Prevention):**\\\\\\\\n- Action 1: [CSM/automation] personalized check-in email\\\\\\\\n- Action 2: Invitation to webinar/free training\\\\\\\\n- Action 3: Showcase underutilized feature\\\\\\\\n- Timeline: actions on D1, D7, D14 after entering yellow zone\\\\\\\\n\\\\\\\\n**Red Zone (Rescue):**\\\\\\\\n- Action 1: Personal CSM call within 24 hours\\\\\\\\n- Action 2: Problem diagnosis with action plan\\\\\\\\n- Action 3: Retention offer (discount, extension, downgrade option)\\\\\\\\n- Action 4: Manager escalation if no response\\\\\\\\n- Timeline: actions on D1, D3, D7, D14\\\\\\\\n\\\\\\\\n**Stage 4 — Post-Churn Analysis:**\\\\\\\\n- Exit survey (mandatory, maximum 3 questions)\\\\\\\\n- Categorization of actual cancellation reasons\\\\\\\\n- Win-back campaign (D30, D60, D90 after cancellation)\\\\\\\\n- Cohort analysis: which customer cohorts have higher churn and why\\\\\\\\n\\\\\\\\n**Stage 5 — Program Metrics:**\\\\\\\\n- Health Score accuracy (% of reds who actually cancelled)\\\\\\\\n- Recovery rate by zone (% of yellows/reds who returned to green)\\\\\\\\n- Impact on monthly churn (before vs. after the program)\\\\\\\\n- Revenue saved (MRR preserved through retention actions)\\\\\\\\n- Cost per saved customer vs. new customer CAC\\\\\\\\n\\\\\\\\nMonthly recurring revenue (MRR): $[AMOUNT]. Current CAC: $[AMOUNT]. Average LTV: $[AMOUNT].
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