Find out who leaves, and when,
from a spreadsheet

Most subscription and repeat-purchase businesses can export a customer list with a signup date and a cancel date. That file is enough to measure churn, see how long customers stay, and compare which groups of customers leave soonest.

Three questions this answers

1. How fast are we losing customers, and how long do they stay?
2. Which plans, regions or other groups lose customers fastest?
3. Is churn worse for some signup cohorts than for others?

What your file needs

• One row per customer.
• A signup date (or an end date or how long they stayed).
• Whether they churned: a cancel date (blank means still active), a 0/1 flag, or yes/no. A last-purchase date is not enough on its own.
• Optional: plan, region or any other column you want to compare by.
• The free tool analyzes up to 10,000 rows; a larger file is randomly sampled to that size.

What you get back

• How many customers churned, and how many are still active.
• The share of customers still with you over time, with its likely range.
• The groups that churn most and least, with how sure we are.
• Churn by signup cohort, for cohorts of at least 10 customers.
• The method named and the R code.

A limit worth stating

The groups that churn most and least are an association in your data. The report shows how sure we are, but it cannot by itself say why a customer left.