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Repeat Purchase Rate: Definition, Formula & Benchmark

Monthly repeat rate hides the trend. Cohorts show whether each new group of buyers comes back faster.

Data Analytics Stack Retail Analytics Team 5 min read
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Repeat purchase rate is the percentage of customers who place at least two orders within a defined period. Calculate it by dividing the customers who ordered at least twice by the total number who ordered in that period, then multiplying by 100. For Shopify and DTC brands, it shows whether first purchases lead to repeat sales, and how much the business leans on acquiring new customers.

What is Repeat Purchase Rate?

A 15% repeat purchase rate means 15% of the customers in the measured group came back for at least a second order. At 40%, far more of them did. That difference shapes revenue stability and how hard acquisition has to work, though what counts as normal depends on the product category and the window you measure.

The definition matters as much as the result. The customer based version counts customers who placed two or more orders, not the repeat orders themselves, so before comparing yourself to a published figure, check which customer group it covers and over what period.

Window length alone can change the picture dramatically. One cohort analysis found that the median share of first time customers who placed a second order within 90 days was 11.4%. This shows why the measurement window matters: customers need time to return, and buying patterns differ by product category.

Repeat Purchase Rate Formula

  • Repeat Purchase Rate = (Customers with 2+ Orders ÷ Customers Who Purchased in the Period) × 100

For example, 1,400 of 5,000 customers who ordered during a 12 month period also placed at least one more order in that same period:

  • (1,400 ÷ 5,000) × 100 = 28% repeat purchase rate

Use identical start and end dates for both counts. If you want to know whether each customer returned within 12 months of their own first purchase, that is a cohort based measure and belongs in a separate calculation.

It is worth running both the whole base number and a cohort view. A cohort groups customers by when they first bought, so comparing March against September tells you whether newer customers behave differently, and often exposes gaps between marketing channels.

Shopify/DTC Benchmark

Repeat purchase rate varies by product category and measurement period. Across 1,326 Shopify stores, a 2026 benchmark study found a median 12 month repeat purchase rate of 18.1%, with the top 25% of stores above 28.2%. Useful as a starting point, but not a target for every business.

Repeat Purchase Rate by Industry

Industry  Median 12-month repeat purchase rate
Health food and supplements 30.9%
Clothing 16.2%
Sports apparel 16.6%
Decor and furniture 11.8%
All Shopify stores in the study 18.1%

Product type helps explain the differences between categories. Consumables get reordered when they run out; furniture and other durable goods simply are not bought often. Compare yourself with similar businesses over the same 12 month period, then watch whether your own cohorts improve over time, a newer cohort returning at a higher rate than an older one is the signal worth acting on.

Why is Repeat Purchase Rate Important?

Repeat purchase rate shows how much revenue is likely to come from customers who return after their first order. Those purchases reduce reliance on acquisition, though retention is not free either, customer service, email campaigns and discounts all carry a cost.

It can also point to problems with the product or the experience around it. For something people use regularly, a low repeat rate may mean customers were not satisfied, the replenishment timing is off, or nobody followed up. Not every product is built for frequent repurchase, so read it against what your category supports.

The decision this metric should support is whether to focus more on acquisition, retention or the product experience.

Improve Repeat Purchase Rate with Customer Analytics

Most brands can calculate a blended repeat purchase rate. Fewer can see whether newer customer cohorts are returning more often than earlier ones. We connect customer behavior, repeat purchases and retention trends to help you make better growth decisions.

We’re a managed Customer Analytics partner, not a dashboard tool you have to build yourself, and not a one off consulting project. Explore our Customer Analytics service to see how we help brands understand customer behavior and lifetime value.

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Frequently Asked Questions

Divide the number of customers who placed two or more orders by total customers in the same window, then multiply by 100. Of 5,000 customers with 1,400 returning, the rate is 28%. Use the customer based version when comparing against published benchmarks.

There is no single good rate for every Shopify store. Consumables and subscription businesses usually see more repeat purchases than apparel or home goods brands. Compare against relevant category benchmarks using the same measurement window, and track whether your own cohorts improve over time.

A 12-month window works well for annual benchmarking. For retention decisions, track cohorts over shorter periods that match your product’s buying cycle. One Shopify store benchmark found the median share of first time customers placing a second order within 90 days was 11.4%. Your own data will show when buyers return and when follow-up messages may help.

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