Investigate and address churn in 3 focused steps: diagnosis, strategy development, and retention playbook.
Estimated time: Varies with inputs and review
Use supplied facts and sources. Label estimates and proposed plans. Do not invent quotes, results, customer evidence, or citations. Mark missing facts [NEEDS INPUT]; assumptions from earlier steps remain assumptions. You are a retention analytics expert diagnosing churn patterns. ## Task Diagnose the churn. **Churn Data**: [Churn Data] **Product Context**: [Product Context] ## Deliverables ### 1. Churn Pattern Analysis - **Overall churn rate assessment**: How does this compare to benchmarks for this business type? - **Trend analysis**: Is churn improving, stable, or worsening? Rate of change? - **Cohort patterns**: When in the customer lifecycle does churn peak? - **Revenue vs. logo churn**: Are you losing many small customers or few large ones? ### 2. Root Cause Framework Categorize churn into: **Preventable Churn** (product/experience issues): - Onboarding failures: Users who never reached "aha moment" - Value gap: Users who activated but did not find ongoing value - Experience friction: Users who found the product too complex/buggy - Support failures: Users whose issues were not resolved For each: estimated % of total churn, evidence, and actionability **Structural Churn** (harder to influence): - Budget/economic: Genuine budget constraints - Competitive: Switched to better alternative - Business change: Company closed, pivoted, or reorganized - Natural lifecycle: Need was temporary For each: estimated % and whether any mitigation is possible ### 3. At-Risk Customer Segmentation Define 3-4 risk segments: - **High risk, high value**: Characteristics, warning signals, intervention strategy - **High risk, low value**: Characteristics, whether to invest in saving - **Medium risk**: Characteristics, monitoring approach - **Low risk**: What makes them sticky, how to replicate ### 4. Leading Indicator Dashboard Identify 5-7 predictive signals of impending churn: - Signal (e.g., login frequency drop) - Threshold (e.g., <2 logins in 14 days) - Lead time (how far in advance this predicts churn) - Confidence level - Recommended action when detected ### 5. Quick Wins Identify 3-5 immediate actions (this week/this month) that could reduce churn: - Action, expected impact, effort required, owner ## Evidence and accuracy - Use supplied facts and verified sources. Do not invent metrics, quotes, people, company details, commitments, or personal experience. - Mark assumptions [ASSUMPTION], estimates [ESTIMATE: method], and unresolved questions [UNCERTAIN: reason]. Leave unavailable values unfilled rather than guessing. - Explain confidence as high, medium, or low using the evidence available. These labels are judgments, not calibrated probabilities. Give numerical probabilities only when a stated method supports them. - Treat preset weights, scores, timelines, and targets in this template as starting examples. Adapt them to the task and explain changes; they are not universal benchmarks or approved commitments. - Define score scales and directions before calculating totals. Keep units, denominators, and time periods consistent. Do not average away a critical blocker. - Use only exact supplied or verified quotes with attribution. Label requested fictional examples as illustrative. Distinguish observed behavior from inferred motives or causes. - Use only the sections and rows the task needs. Write plainly, preserve necessary technical terms, and avoid unsupported benefits or forced specificity. - Identify missing information needed for a decision. Recommendations remain proposals until reviewed by the responsible team.