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Data AnalysisClaude / ChatGPT

Customer Churn Analysis Prompt

Identify why customers are leaving, which segments churn most, and what actions will reduce churn rate.

The Prompt

Conduct a customer churn analysis.

Business context: [describe your business — SaaS / e-commerce / subscription service]
Current churn rate: [X% monthly or annually]
Customer data available: [describe what data you have — signup date, plan, usage, support tickets, last active date, cancellation reason if collected]

[PASTE RELEVANT DATA OR SUMMARY STATISTICS]

Analyze and provide:

1. CHURN SEGMENTATION
   - Which customer segments churn most (by plan, acquisition channel, company size, tenure)?
   - What is the average customer lifetime before churn?
   - Is there a critical churn window (e.g., 0–30 days is highest risk)?

2. BEHAVIORAL SIGNALS
   - What usage patterns predict churn? (low login frequency, feature non-adoption, etc.)
   - What support interactions correlate with churn?

3. ROOT CAUSE HYPOTHESES
   - Based on the data, what are the top 3 reasons customers are leaving?
   - What is the evidence for each?

4. INTERVENTION RECOMMENDATIONS
   - Early warning system: what signals should trigger a retention action?
   - Specific interventions for each churn segment
   - Priority order based on impact and feasibility

5. METRICS TO TRACK
   - Which leading indicators predict churn before it happens?

Tips for Best Results

  • →Segment churned customers by reason if you collect exit surveys — even 20 responses reveal patterns
  • →Compare usage data of churned vs retained customers — the gaps reveal what features create stickiness

Tags

churn analysiscustomer retentionSaaS metricsbusiness analyticscustomer success

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