Return rate calculator
What share of orders came back.
Fill in the fields and the result will appear here automatically.
Return rate measures returned orders within a selected order cohort. Returns must belong to the orders in the denominator: this month’s return events may concern earlier purchases. Count each returned order once, regardless of the number of items returned.
How it works
Formula and logic
Return rate = returned orders from the cohort ÷ all orders in the cohort × 100. The complement is 100% minus the return rate. Both counts are whole, the denominator is positive, and returns range from zero to the cohort size. The form does not match orders to returns automatically.
Example
45 returned orders from a cohort of 900 give 5%, with a 95% complement. Zero of 900 gives 0%; 900 of 900 gives 100%. Another item return from the same order does not increase the order count.
Fields and units
- Returned orders — unitless
- Total orders — unitless
How to use
- — Choose an order cohort, such as all orders dispatched in a month.
- — Enter returned orders from that cohort and its whole total; count each order once.
- — Read the return rate and its complement to 100%; allow for the cohort’s return window.
Method and limitations
- Calculation method
- Formula and logic
- Limitation
- Counts unique orders under your policy, without importing histories or matching returns automatically. The complement to 100% does not prove the return window has closed.
FAQ
What counts as a return?
Choose one rule, for example an order with at least one returned item. Count each order once and use the matching cohort in the denominator. Pre-dispatch cancellations and individual returned items are different measures.
Why can returns not exceed orders?
Unique returned orders within one cohort cannot exceed its size. An excess may mean mixed cohorts, dates or duplicate return events; the form cannot identify the cause.
Is a high return rate always bad?
There is no universal benchmark. Compare like categories, channels and observation windows. A high rate may fit a particular sales model, but assess its revenue and cost effects separately.
How does this affect unit economics?
Returns cut revenue and add logistics costs, so contribution margin should be recalculated on kept orders.