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Analyze another fileIdentifies at-risk subscriptions from billing data using survival analysis and logistic regression. Segments churn by plan tier and subscription age, calculates MRR at risk, and produces a prioritized list of accounts to save. Works with Stripe exports or any subscription CSV with status, plan, amou
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Built for: Analyst, data scientist, business user
Typical data source: CSV with relevant columns
Identifies at-risk subscriptions from billing data using survival analysis and logistic regression. Segments churn by plan tier and subscription age, calculates MRR at risk, and produces a prioritized list of accounts to save. Works with Stripe exports or any subscription CSV with status, plan, amount, and tenure columns.
Overall churn rate, MRR at risk, and retention metrics
Churn rate and volume segmented by subscription plan
Revenue and churn metrics by plan tier
Kaplan-Meier survival curves by plan tier
Most important predictors of churn from logistic regression
Monthly recurring revenue at risk of churn by segment
Prioritized list of subscriptions most likely to churn
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
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