Upload your customer list, map the start date and churn column, and get your churn rate, a proper survival curve of customer lifetime, cohort-by-cohort churn, and a ranked list of what predicts cancellation. Free.
Free analyses run on up to 10,000 rows. Larger files are randomly sampled to that size — sign up to analyze your full dataset.
Analysing churn and customer lifetimes...
Sent to — churn rate and cohort breakdown, Kaplan-Meier survival curve with confidence band, churn drivers ranked by odds ratio, lifetime distributions, R code, and AI insights.
Analyze another fileEach customer's lifetime runs from their start date to their churn date (or to the latest date in the file if still active — a censored observation). The analysis computes the overall churn rate, groups customers into signup cohorts and compares churn across them, fits a Kaplan-Meier survival curve — the standard estimator that uses censored customers correctly instead of dropping them — to estimate the median lifetime, and fits a logistic regression of churn on the mapped driver columns to rank them by odds ratio with 95% confidence intervals.
Use it whenever you have customer-level data with a start date and any usable churn signal — subscriptions, memberships, contracts, repeat-purchase customers.
Not for event-level data (aggregate to one row per customer first), and not for comparing survival between predefined groups with formal tests — use the survival analysis tool for log-rank comparisons.
Built for: Founders, growth and retention teams, and analysts working subscription or repeat-purchase businesses
Typical data source: A customer export: one row per customer with signup date, cancellation status, and attributes like plan or region
One row per customer. For example, a subscription customer export:
Minimum 30 rows · Best with 200-10,000 customers with 1-8 driver columns
Standard-library analysis: how many customers churn, when they churn, and what predicts it. Map a start date and a churn indicator (a cancel-date column, a 0/1 flag, or yes/no text) and get the overall churn rate, churn by monthly signup cohort, a Kaplan-Meier survival curve of customer lifetime that counts still-active customers correctly, and — if you map candidate driver columns like plan or region — a logistic-regression ranking of churn drivers with odds ratios and confidence intervals.
Churn rate per signup cohort — whether the customers you acquired recently stick better or worse than older cohorts.
The share of customers still active after each number of days, with still-active customers counted correctly; where it crosses 50% is the median lifetime.
Every mapped driver on one odds-ratio scale with confidence intervals — above 1 raises churn odds, below 1 protects.
How long churned customers lasted versus how long active ones have been around — shows whether churn concentrates early.
The exact per-cohort counts and rates behind the cohort chart.
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
How bad is my churn, really?
Map when customers started and how you know they churned — a cancel date, a flag, or yes/no. You get the churn rate, a survival curve that handles still-active customers properly, and the median customer lifetime.
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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