Why Repeat Purchase Rate matters
A higher rate usually points to more returning buyers. A lower rate can reflect weaker retention, a longer natural repurchase cycle, or a recent influx of first-time customers.
- Business question
- What share of our customers comes back for another purchase?
- Teams that use it
- E-commerce, growth, merchandising, marketing, and customer experience teams.
- Decisions it supports
- Lifecycle campaigns, loyalty programmes, assortment, replenishment reminders, and post-purchase experience improvements.
Repeat Purchase Rate formula
(Repeat Customers ÷ Total Customers) × 100
Formula components
- Repeat customers
- Distinct customers with at least two qualifying purchases under the documented rule.
- Total customers
- Distinct customers in the same population and observation window.
- Qualifying purchase
- A completed order after removing test, cancelled, or otherwise excluded transactions.
How to calculate Repeat Purchase Rate
- Choose a customer cohort and give every customer enough time to make a second purchase.
- Count distinct customers with at least one qualifying purchase.
- Count those with two or more qualifying purchases.
- Divide repeat customers by total customers and multiply by 100.
Repeat Purchase Rate example
An online retailer reviews a mature six-month customer cohort containing 2,600 buyers. Of them, 860 placed at least a second completed order.
- Repeat customers = 860.
- Total customers = 2,600.
- Repeat Purchase Rate = 860 ÷ 2,600 × 100 = 33.1%.
Repeat Purchase Rate is 33.1%.
Roughly one in three customers in this cohort purchased more than once during the observation window.
How to interpret the result
Compare customers with similar time to repurchase. A new cohort will almost always appear weaker than an older one simply because its members have had less time to return.
Typical rates vary with product life cycle, replenishment need, price, seasonality, channel, and the chosen window. A durable-goods seller should not use a grocery benchmark.
Common mistakes and limitations
- Unequal observation time
- Recent customers have fewer opportunities to repeat than older customers.
- Counting orders instead of customers
- The numerator is distinct repeat customers, not the number of repeat orders.
- Guest checkout duplicates
- One person can appear under several emails or identifiers.
- Ignoring the natural purchase cycle
- A short window can understate repeat behaviour for infrequent purchases.