Upload raw order or activity data, map a customer id and a date, and get a full cohort retention analysis — heatmap, retention curve, and whether newer cohorts are stickier. Free.
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Building cohorts and computing retention...
Sent to — retention heatmap, average retention curve, cohort sizes, cohort comparison table, R code, and AI insights.
Analyze another fileEvery customer is assigned to a cohort — the calendar month of their first activity. For each cohort, the analysis computes the percentage of its customers active exactly N months later (month 0 is always 100%). The heatmap shows the full cohort-by-month matrix, the curve averages cohorts into a typical lifecycle (counting only cohorts old enough to observe each month), and a first-half vs second-half comparison of month-1 retention reveals whether newer cohorts retain better or worse.
Use it whenever you have raw transactional or activity data and want to know if customers come back — and whether recent changes made them come back more.
Not for pre-aggregated data (it needs one row per event), and not for businesses with natural purchase cycles much longer than a month — a quarterly buyer looks churned at month 1.
Built for: Founders, growth and lifecycle marketers, and analysts tracking whether the customer base compounds or leaks
Typical data source: Order exports, event logs, or billing records with a customer id and a date
One row per order or activity event. For example, an order export:
Minimum 50 rows · Best with 500-100,000 rows spanning 6-24 months of activity
Standard-library analysis: how well do you keep your customers? From raw order or activity data (one row per order/event), it builds monthly acquisition cohorts, measures the percentage of each cohort still active 1, 2, 3... months after their first activity, and shows whether newer cohorts retain better or worse than older ones. Retention heatmap, average retention curve, cohort sizes, and a cohort-by-cohort comparison table.
Each row is an acquisition month, each column a month of age — read down a column to compare cohorts at the same age.
Your typical customer lifecycle: the height of the M1 bar is the single most important retention number.
How many new customers each month brought in — the volume feeding the retention engine.
Every cohort's size and month-1/3/6 retention in one table; blank cells are months the cohort hasn't reached yet.
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Are we keeping the customers we acquire?
Map a customer id and an order date. The heatmap shows every acquisition month's survival, the curve shows your typical customer lifecycle, and the trend tells you whether newer cohorts are stickier than older ones.
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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