Repeat customer rate: how to count it and how to read it
Everyone wants more repeat customers. Few shops agree on how to count them, which is why the benchmarks you read online rarely match your own number.
- 2ways to count repeat customers, with different answers
- 31%of orders from returning customers in the worked example below
- 12 monthsis the window that makes the two methods comparable
Two ways to count, two different answers
The customer method asks: of everyone who bought in the window, how many bought at least twice? The order method asks: of all orders in the window, how many came from someone who had bought before? The second is always higher, because one loyal customer adds many orders but only one customer.
A worked example: one shop, the same twelve months, two methods
Example shop, last twelve months
- Formula
- Customers with 2+ orders divided by all customers
- Formula
- Orders from returning customers divided by all orders
How the share moves from month to month
Tap a bar for the number
Show as a table
| Month | Example shop |
|---|---|
| Apr | 27% |
| May | 29% |
| Jun | 31% |
| Jul | 33% |
| Aug | 30% |
| Sep | 32% |
The jump in July follows a reorder reminder mail that went out for the first time in June. One month is not a trend; three in a row are.
Pick a window and keep it
A 30-day window makes every shop look like it has no repeat customers. A lifetime window makes an old shop look loyal for free. Twelve months is long enough for a second order and short enough to move when you do something. A shop younger than two years can compare quarters instead, with the same method, and accept that the first comparisons will be noisy.
Compare this year's twelve months with last year's, by orders, on your own data. That is the only benchmark that answers whether what you did worked.
How to pull the numbers from your shop
Neither method needs a plugin. Export the last twelve months of orders with the customer's email, then count in a spreadsheet. Two of the three platforms also have a report that does half the work.
What each platform gives you
Reports and exports
- Where
- Orders › Orders, filtered to twelve months, then Export.
- Where
- Analytics › Orders, then Download.
- Where
- Reports › Customer Orders.
- Put the email in one column and lowercase it, so the same person with different capitalisation counts once.
- Count orders per email. Customers with two or more orders divided by all customers is the customer method.
- Mark every order whose email already placed an earlier order, inside the window or before it. Those orders divided by all orders is the order method.
- Write the method and the window next to the result.
WooCommerce's report hands you the order method and OpenCart's hands you the customer method, which is one more reason two shop owners quote different numbers for the same thing. What the OpenCart reports count and what WooCommerce Analytics includes cover the details.
What moves the number
- A reminder mail for consumables, sent a little before the product runs out.
- A reason to come back in the parcel: a code for the next order, or a note on what pairs with what they bought.
- An account offered after the order, not demanded before it. Guests who had a good first order come back; people who were forced to register often do not finish the first one.
Measure the same way as the weekly abandoned-carts check: one rule, one window, the number written down with its method, and the change read against your own earlier months.
Questions
- Do guest orders break the count?
- Only if the same person checks out as a guest with different emails. Match on email, lowercased, and most of it resolves.
- Should I count by revenue instead of orders?
- Count orders first, then look at revenue. Returning customers often have a different average order, so a 30% share of orders can be 40% of revenue, and the revenue share is the one that matters when you decide how much to spend on ads.
- What about shops that sell things people buy once?
- A mattress shop will never have a high repeat share, and that is fine. Compare against your own earlier years, and work on referrals rather than reminders.
- How far back should the earlier-order lookup go?
- As far as your data goes. Someone who bought three years ago and returns is a returning customer, even though the window for the rate itself is twelve months.
About the author: Mantas Lukošius
Mantas has worked in ecommerce for more than 15 years, almost all of it on PrestaShop: from his own first shop to business stores and large clients. He builds ManteIQ. Ecommerce and the data behind it are still what he enjoys most.
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