Repeat Purchase Rate – A Simplified Overview
Getting someone to buy from your Shopify store once is one thing.
Getting them to come back and buy again is another.
Repeat Purchase Rate measures the percentage of customers who make more than one purchase within a defined period. It helps answer a simple but important question:
How many of my customers actually come back?
For ecommerce brands, this metric can reveal whether customers are simply making one-off purchases or whether the business is building a customer base that continues to generate revenue.
A store can grow quickly by acquiring new customers, but if almost nobody returns, it may have to keep spending more on acquisition just to maintain that growth.
That is why Repeat Purchase Rate is an important customer retention and ecommerce metric to track alongside metrics such as Customer Acquisition Cost (CAC), Average Order Value (AOV), Customer Lifetime Value (CLV), and profit margin.
What Is Repeat Purchase Rate?
Repeat Purchase Rate (RPR) is the percentage of customers who purchase from your store more than once within a defined timeframe.
In simple terms:
Repeat Purchase Rate = How many customers came back to buy again
For example, imagine your Shopify store acquired 1,000 customers during a given period.
Within your chosen measurement window, 250 of those customers made another purchase.
Your Repeat Purchase Rate would be:
250 ÷ 1,000 × 100 = 25%
So, your Repeat Purchase Rate is 25%.
That means one out of every four customers in that group purchased from your store again.
Repeat Purchase Rate Formula
For example:
Total customers: 2,000
Customers who purchased more than once: 500
500 ÷ 2,000 × 100 = 25%
Repeat Purchase Rate = 25%
The important part is defining your measurement window.
For example, you could measure whether customers make another purchase within:
30 days
60 days
90 days
180 days
12 months
The right window depends heavily on what you sell.
A customer buying coffee or skincare may reasonably purchase again within a few weeks.
A customer buying furniture may not purchase another product for several months or even years.
So, comparing the two businesses using the same repeat-purchase window would not tell you much.
Repeat Purchase Rate = (Customers Who Purchased More Than Once ÷ Total Customers) × 100
Why Does Repeat Purchase Rate Matter?
Repeat Purchase Rate gives you insight into what happens after the first sale.
Acquiring a customer does not automatically mean you have created a valuable long-term customer.
Consider two Shopify stores.
Store A
1,000 customers
200 repeat customers
Repeat Purchase Rate = 20%
Store B
1,000 customers
400 repeat customers
Repeat Purchase Rate = 40%
Both stores acquired the same number of customers.
But Store B is getting significantly more customers to come back.
That can create a very different long-term growth model.
Higher repeat purchasing can contribute to:
Higher Customer Lifetime Value
More revenue from existing customers
Better customer retention
Greater purchase frequency
Less dependence on continuously acquiring new customers
More opportunities for email and retention marketing
More predictable future revenue
Shopify also identifies repeat customer behavior as an important retention signal and provides reports for new vs. returning customers, returning customers, and customer cohorts.
Repeat Purchase Rate vs. Returning Customer Rate
These terms sound almost identical, but they are not always measuring exactly the same thing.
This is one of the most important things to understand when analyzing Shopify customer data.
Repeat Purchase Rate
Generally asks:
What percentage of customers purchased more than once within my defined measurement window?
For example:
250 out of 1,000 customers purchased at least twice within 12 months.
RPR = 25%
Shopify Returning Customer Rate
Shopify’s reporting defines returning customer rate as the percentage of customers in the results who had purchased from the store before. Its formula is:
Returning Customers ÷ Customers
The distinction matters because a customer who purchased months or years ago can be classified as a returning customer when they place another order during the current reporting period.
So, don’t automatically treat Returning Customer Rate and Repeat Purchase Rate as interchangeable.
If you are measuring customer retention, always check:
Who is included in the denominator?
What timeframe is being measured?
Does the metric require a second purchase within that timeframe?
Is the analysis based on a customer cohort?
Consistency matters more than chasing a particular formula.
A Simple Repeat Purchase Rate Example
Imagine a Shopify store selling skincare products.
During January, the store acquires:
1,000 new customers
Over the next 90 days:
280 customers purchase again
The 90-day Repeat Purchase Rate is:
280 ÷ 1,000 × 100 = 28%
So:
90-Day Repeat Purchase Rate = 28%
This means 28% of the January customer cohort made another purchase within 90 days.
Now imagine that the same store changes its post-purchase strategy.
After a few months, a similar cohort produces:
350 repeat customers out of 1,000
350 ÷ 1,000 × 100 = 35%
The Repeat Purchase Rate increased from:
28% → 35%
That is a meaningful improvement because more customers are returning after their initial purchase.
What Is a Good Repeat Purchase Rate?
There is no universal “good” Repeat Purchase Rate for every ecommerce business.
This is because repeat purchasing behavior varies dramatically by product category.
A customer buying a consumable product has a natural reason to return.
A customer buying a durable product may not.
Recent ecommerce data illustrates this variation. Shopify cites research showing repeat purchase rates around 30–40% for consumables, roughly 12–17% for fashion, and around 10–15% for durable goods, although benchmarks vary depending on methodology and measurement window.
Another analysis of more than 156,000 DTC customers found an overall repeat purchase rate of 18.8%, again demonstrating how different benchmarks can be depending on the businesses and timeframe being analyzed.
So instead of asking:
“Is 25% a good Repeat Purchase Rate?”
A better question is:
“Is my Repeat Purchase Rate improving compared with my previous cohorts and similar businesses?”
Repeat Purchase Rate by Product Type
Your product’s natural repurchase cycle has a major impact on RPR.
Consumables
Examples:
Coffee
Supplements
Skincare
Pet food
Food and beverages
These products are used up, creating a natural reason to reorder.
A customer who loves a product may return every few weeks or months.
Fashion
Customers may return frequently, but they do not necessarily need the same product again.
Repeat purchasing can depend on:
New collections
Seasonal products
Brand loyalty
Discounts
Product variety
Durable Goods
Examples:
Furniture
Large appliances
Home equipment
A customer might love the product but have no reason to buy another one soon.
A low short-term Repeat Purchase Rate is therefore not automatically a sign of poor customer experience.
The purchase cycle matters.
What Can Affect Repeat Purchase Rate?
1. Product Quality
The simplest factor is often the product itself.
If customers are satisfied with what they purchased, they are more likely to consider buying again.
If the product does not meet expectations, no amount of email marketing can completely solve the problem.
2. Customer Experience
The experience surrounding the purchase also matters.
This can include:
Shipping speed
Packaging
Customer support
Returns
Communication
Website experience
Post-purchase service
A good product combined with a poor customer experience can still produce low repeat purchasing.
3. Purchase Frequency
Some products naturally have a higher purchase frequency than others.
For example:
Coffee → every few weeks
Skincare → every 1–3 months
Clothing → several months
Furniture → potentially years
Your Repeat Purchase Rate should therefore be analyzed according to the expected repurchase cycle.
4. Post-Purchase Marketing
The relationship does not end when the order is delivered.
Brands can use:
Email marketing
SMS
Product recommendations
Replenishment reminders
Loyalty programs
Cross-selling
Personalized offers
to encourage customers to return.
The goal is not simply to send more promotions.
The goal is to give customers a relevant reason to purchase again.
5. Product Assortment
A customer may want to buy from your brand again but have nothing else they need.
Expanding your product range can create more opportunities for repeat purchases.
For example:
A customer initially buys a cleanser.
Later, they may purchase:
Moisturizer
Serum
Sunscreen
Toner
The first purchase can become the starting point for a larger customer relationship.
Don’t Optimize Repeat Purchase Rate at the Expense of Profit
A higher Repeat Purchase Rate is not automatically better.
Imagine you run a promotion:
“50% OFF YOUR SECOND ORDER!”
Your repeat purchases increase dramatically.
Great?
Maybe.
Now imagine those repeat orders generate almost no contribution margin.
You successfully increased your Repeat Purchase Rate—but potentially made your business less profitable.
This is why RPR should be analyzed alongside:
Net Profit
Profit Margin
Contribution Margin
Average Order Value
Customer Acquisition Cost
Customer Lifetime Value
Purchase Frequency
For example:
Before promotion
Repeat Purchase Rate: 22%
Repeat-order Contribution Margin: 35%
After promotion
Repeat Purchase Rate: 32%
Repeat-order Contribution Margin: 12%
The RPR looks much better.
But the economics may actually be worse.
The goal isn’t simply:
Get customers to buy again.
The better goal is:
Get valuable customers to buy again profitably.
How to Increase Repeat Purchase Rate
1. Improve the First-Purchase Experience
Before trying to create the second purchase, make the first purchase worth repeating.
Look at:
Product quality
Delivery experience
Packaging
Customer support
Returns
Product education
2. Identify the Typical Repurchase Window
Find out how long customers usually take to purchase again.
For example:
First order → 45 days → second order
That 45-day period gives you a useful starting point for retention campaigns.
You can then test reminders, recommendations, or offers around that point.
3. Use Product Recommendations
Recommend products that naturally follow the customer’s first purchase.
For example:
First purchase: Running shoes
Possible second purchase: Running socks
Possible third purchase: Running accessories
The goal is to make the next purchase relevant rather than simply promotional.
4. Segment Customers
Not every customer should receive the same message.
You can segment customers based on:
Number of orders
Last purchase date
Total spend
Product purchased
Purchase frequency
Customer lifetime value
This is where customer segmentation and RFM analysis can become particularly useful.
A customer who purchased yesterday should not receive the same message as someone who has not purchased in 180 days.
5. Measure Cohorts
Don’t only look at one store-wide Repeat Purchase Rate.
Break customers into cohorts based on when they made their first purchase.
For example:
| Customer Cohort | Customers | Repeat Customers | 90-Day RPR |
|---|---|---|---|
| January | 1,000 | 280 | 28% |
| February | 1,200 | 360 | 30% |
| March | 1,500 | 525 | 35% |
| April | 1,400 | 560 | 40% |
This tells you something much more useful than a single store-wide number.
In this example, each newer cohort is generating more repeat customers.
That could indicate improvements in:
Product experience
Customer targeting
Post-purchase marketing
Product assortment
Retention strategy
Shopify’s Customer cohort analysis is specifically designed to group customers based on their first order and analyze subsequent customer behavior.
How to Track Repeat Purchase Rate in Shopify
Shopify provides several customer reports that can help you analyze repeat purchasing.
In Shopify admin, go to:
Analytics → Reports → Customers
Shopify provides reports including:
New customers over time
New vs. returning customers
Returning customers
One-time customers
Customer cohort analysis
The Returning customers report includes customers whose order history contains two or more orders, while Customer cohort analysis lets you analyze customer behavior based on when they first purchased.
For deeper analysis, you can combine customer behavior with your store’s revenue, COGS, advertising, and other costs to understand whether repeat purchasing is actually contributing to profitable growth.
