Upload a CSV with a duration, an event flag, and a group. Get Kaplan-Meier curves, median time-to-event with confidence intervals, a log-rank test, and Cox hazard ratios — with censoring handled properly. Free.
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Free analyses run on up to 10,000 rows. Larger files are randomly sampled to that size — sign up to analyze your full dataset.
Estimating survival curves...
Sent to — Kaplan-Meier curves per group, median time-to-event table, log-rank and Cox hazard ratios, event-timing histogram, R code, and AI insights.
Analyze another fileThe analysis builds a Kaplan-Meier survival curve for each group — the correct estimate of the fraction still event-free over time when some subjects are censored (still active at last observation). It reports each group's median time-to-event with a 95% confidence interval, runs a log-rank test of whether the curves genuinely differ, and fits a Cox proportional-hazards model to express each group's risk as a hazard ratio versus the largest group — 'this group reaches the event 1.8 times faster.'
Use it whenever the question is 'how long until X happens' and some subjects haven't had the event yet — churn, failure, relapse, conversion, time-to-hire.
Not for data where every subject's event time is fully observed and you only want mean differences (use a group comparison tool), and not for repeated events per subject or time-varying group membership.
Built for: Analysts, product and CX teams, reliability engineers, and researchers working with duration or retention data
Typical data source: Any spreadsheet or CSV with a duration column, an event/churned/failed flag, and a segment column
One duration column, one event flag, one group. For example, customer retention by plan:
Minimum 30 rows · Best with 100-50,000 rows and 1-6 groups
Standard-library analysis: how long until customers churn, machines fail, or patients relapse — with censoring handled properly. Kaplan-Meier survival curves per group, median time-to-event with confidence intervals, a log-rank test of whether the groups truly differ, and Cox hazard ratios that say how much faster one group reaches the event than another. Works on any duration data: map a time column, an event flag, and a group.
Each group's percentage still event-free over time — the curve that drops fastest is the group that churns or fails soonest.
Each group's median time-to-event with a confidence interval; 'not reached' means most of the group was still active at last observation.
The log-rank verdict on whether the curves truly differ, plus a hazard ratio per group saying how much faster it reaches the event than the reference.
When the observed events actually happened — whether risk concentrates early or spreads out over the whole timeline.
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
How long until my customers churn?
Map months-active as time, a churned yes/no flag as event, and the plan as group. You get retention curves per plan, median lifetime with confidence intervals, and a hazard ratio saying how much faster one plan's customers leave.
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
Run any analysis on your own data — validated R analyses, interactive reports, AI insights, and PDF export.
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