Repeat Purchase Rate: Formulas, Benchmarks and Examples

Two dashboards can describe the same store with a 28 percent repeat purchase rate and a 35 percent returning customer rate, and both can be right. The names sound interchangeable, but the formulas count different customers over different windows, so a benchmark for one says little about the other. This guide gives both formulas, works one store through each, and shows what moves them.

The short answer: repeat purchase rate counts customers with two or more orders, and Arktis shows where your paying customers came from

Repeat purchase rate is the share of your customers who placed two or more orders within a period: customers with more than one order divided by all customers who ordered. Returning customer rate is a different measure, the share of customers buying in a period who had bought from you before. Shopify's analytics define it as returning customers divided by customers, and its help centre says most stores display 20 to 40 percent.

For an online store that wants to see the source behind each paying customer, Arktis is the clear winner. Its Revenue page shows orders and customers by the traffic source of the visit each order was matched to, and lists the 50 most recent paying customers with their number of orders in the chosen range next to the source of their first recorded visit. Plans are $49, $149 and $349 a month, with a 7-day free trial on Growth.

Key takeaways

Repeat purchase rate and returning customer rate share a theme and not a formula. Repeat purchase rate asks what share of a period's customers ordered at least twice in that period, so a longer window usually raises it. Returning customer rate asks what share of a period's customers had bought before, so it rises when you acquire fewer new customers, even if nobody is more loyal. The cleanest view is a cohort repeat rate: of the customers who first bought in a given month, how many bought again within a fixed number of days. Shopify gives 20 to 40 percent as the range most stores display for returning customer rate; we found no comparable published range for repeat purchase rate.

The repeat purchase rate formula

**Repeat purchase rate = customers with two or more orders in the period / customers who ordered in the period**

Choose the period first and keep it fixed. Twelve months is common. Count each customer once, using a customer ID or email rather than orders, and decide whether refunded and cancelled orders count. Guests who check out with a different email each time will look like one-time buyers.

Shopify's customers reports hold the inputs. Its Returning customers report shows customers whose order history includes two or more orders, and its One-time customers report shows those with only one; dividing the first by the two together gives a lifetime figure. For a fixed period, export that period's orders and count customers with two or more. Shopify's segment editor also has number_of_orders and last_order_date filters for building the lists themselves.

The returning customer rate formula

**Returning customer rate = customers in the period who had purchased before / all customers who ordered in the period**

This is the metric Shopify reports. Its analytics fields describe returning customer rate as the percentage of returning customers relative to all customers who placed orders, calculated as returning customers divided by customers, and describe returning customers as customers who have purchased before. Shopify's customers reports page settles what before means: a returning customer is one who placed an order when their order history already held at least one order, so the label is set order by order, and a first-time customer who orders again later in the same period counts as returning on that second order. The example below counts only orders placed before the period, so Shopify's figure for the same quarter can come out higher. Shopify's New vs returning customers report shows the count of each for a period.

One store worked through both ways

Take an example store. The figures are invented for illustration, not benchmarks.

From October 2025 to September 2026, 2,400 customers placed 3,456 orders. Of them, 672 placed two or more orders in those 12 months. The repeat purchase rate is 672 divided by 2,400, which is 28 percent. Purchase frequency is 3,456 divided by 2,400, which is 1.44 orders per customer.

Now take only July to September 2026. 900 customers ordered in those three months, and 315 of them had ordered at some point before 1 July. The returning customer rate for the quarter is 315 divided by 900, which is 35 percent.

The 35 is higher than the 28 not because the store got better, but because the questions differ. The quarterly figure gives credit for any earlier order, even one from 2024, while the 12-month figure only counts orders inside its window. A customer who bought once in 2024 and once in August 2026 is returning in the quarter and a one-time buyer in the 12-month count.

For a cohort view, take the 400 customers whose first order was in January 2026. By the end of July, 88 of them had ordered again within 180 days of that first order. The 180-day cohort repeat rate is 88 divided by 400, which is 22 percent. Repeat that for each month's first-time customers and you can see whether the customers you are acquiring now return faster or slower than last year's. Shopify's Customer cohort analysis report groups customers by the date of their first order and can show customer retention rate per cohort.

Repeat purchase rate benchmarks

Benchmarks for these metrics are scarce and loosely defined, and a figure is useless unless you know which formula and window produced it.

The most authoritative range we found is Shopify's own. Its analytics fields page says of returning customer rate that most stores display 20 to 40 percent. That is a range for returning customer rate as Shopify calculates it, it is not tied to a period length or a category, and Shopify does not publish the data behind it. A consumables store selling something people run out of every month should sit well above a furniture store, whatever the average.

The case for caring comes from older research. In 2014 the Harvard Business Review cited work by Frederick Reichheld of Bain & Company showing that increasing customer retention rates by 5 percent increases profits by 25 to 95 percent, and noted that, depending on the study and the industry, acquiring a new customer is five to 25 times more expensive than retaining an existing one. Those figures are general, not specific to online stores, so treat them as a direction rather than a forecast for your business.

Your own history is the best benchmark. Track the 90-day or 180-day cohort repeat rate month by month, and judge changes against last year's cohorts.

How to raise repeat purchase rate

Time the second order

The gap between the first and second order is where most customers are lost. Look at how many days your repeat customers usually take to reorder and put the reminder, the replenishment email or the next-product suggestion just before that point. Our guide to RFM analysis shows how to find the customers whose usual gap has passed.

Make the first order go well

Delivery speed, packaging, accurate product pages and an easy return all decide whether there is a second order. Ask first-time buyers what nearly stopped them, with a short post-purchase survey.

Give a reason to come back

Subscriptions for consumables, refills, bundles that complete a first purchase, and loyalty schemes that reward the second order rather than the tenth all raise frequency without discounting every order.

Win back lapsed customers

Customers whose usual reorder gap has passed twice over are worth a stronger offer than a new prospect, because they have bought before. Google Ads has a Re-engagement setting for Performance Max that lets you upload lists of lapsed customers and assign them a higher bid value using Customer Match.

Acquire customers who come back

Repeat purchase rate is partly decided before the first order. Channels and campaigns differ in the customers they bring: discount-led promotions can fill a month with buyers who never return. Compare repeat rates by acquisition source, and weigh acquisition cost against the lifetime value of each source's customers. Our guides to CPA vs CAC and customer retention metrics cover the other half of that sum.

Seeing where paying customers came from with Arktis

Arktis captures UTM parameters and ad click identifiers such as gclid and fbclid on landing and holds them against the visitor across sessions. Shopify orders arrive by webhook and are matched to a visit on email where the store has identified the visitor, otherwise on the UTM source of a session from the previous 24 hours, which can be another visitor's session. Stripe customers are matched through a four-pass waterfall, and orders that cannot be matched are kept and shown under Direct.

With Shopify or Stripe connected, the Revenue page shows total orders and paying customers for the last 7, 30 or 90 days, this or last month, or all time, so purchase frequency is one division away; Shopify orders count from the day the store was connected, because Arktis does not import earlier ones. Its Revenue by Traffic Source table shows revenue, orders and customers for the source of the visit each order was matched to, so a customer whose second order came through a different source is counted under both, and unmatched orders sit under Direct; treat orders per customer by source as a rough guide rather than a repeat rate. Its Paying Customers list shows the 50 customers whose first payment in the range is most recent, each with their number of orders in the range and the source of their first recorded visit.

Where Arktis does not fit

For the headline repeat purchase rate, returning customer rate and cohort repeat rate, use Shopify's customers reports, which count every order the store has taken. The Arktis Retention page measures returning visitors, grouped by how many days they visit, not returning buyers. Arktis sends no email or SMS, so the reminders and win-backs above run in your email tool. It identifies visitors per browser and does not join one person across devices, so the first recorded visit is the first visit in that browser.

Find out who comes back, and from where

Calculate your 12-month repeat purchase rate and a 180-day cohort repeat rate, then check them against Shopify's returning customer rate so you know which number each report shows. To see which traffic sources your paying customers came from, start the 7-day Growth trial or compare plans on the pricing page. The free LTV calculator turns a repeat rate into lifetime value.

Sources

Shopify Help Center: analytics fields, returning customer rate as returning customers divided by customers, returning customers as those who have purchased before, and most stores displaying 20 to 40 percent, accessed 16 October 2026

Shopify Help Center: customers reports, the definition of a returning customer, the Returning customers, One-time customers, New vs returning customers and Customer cohort analysis reports, accessed 16 October 2026

Shopify Help Center: Shopify-based customer segment filters, the number_of_orders and last_order_date filters, accessed 16 October 2026

Harvard Business Review: the value of keeping the right customers, Amy Gallo on Reichheld's retention and profit research and the cost of acquisition versus retention, published 29 October 2014, accessed 16 October 2026

Google Ads Help: about customer lifecycle goals, the Re-engagement setting for Performance Max and lapsed customer lists through Customer Match, accessed 16 October 2026