How to Calculate Repeat Purchase Rate Without Misleading Yourself
The formula is easy.
The measurement is not.
A common repeat-purchase formula is:
Customers who placed two or more orders ÷ total unique customers × 100
Shopify uses a similar definition for returning-customer and repurchase measures, while BS&Co’s benchmark study defines a repeat customer as someone who placed two or more orders inside its 365-day measurement window. (shopify.com)
So why do published repeat-purchase rates vary so widely?
Because the formula is only one part of the measurement.
The denominator matters
Imagine:
10,000 customers acquired this year.
2,000 made a second purchase.
Repeat purchase rate:
20%.
Simple.
Now imagine instead you calculate:
repeat customers who purchased this month
divided by
all customers who purchased this month.
That is a different metric.
You may be measuring the composition of this month’s buyers, not the percentage of an acquisition cohort that ever progressed to purchase number two.
Both can be useful.
They should not be confused.
The time window matters
Suppose:
15% repeat within 90 days.
22% within 365 days.
Both figures are correct.
They answer different questions.
This is one reason the same category can appear to have different benchmarks across reports.
Shopify explicitly warns that retention benchmarks depend heavily on product type, purchase frequency and measurement period. (shopify.com)
Cohort repeat rate is often cleaner
For strategic analysis, I prefer cohort logic.
Example:
Customers first acquired January–March 2025:
50,000.
How many placed another order within:
- 30 days?
- 60 days?
- 90 days?
- 365 days?
Now every customer had the same opportunity window.
That reduces distortion.
Why recent customers can make the rate look artificially weak
Suppose you calculate a 365-day repeat rate today but include customers acquired last month.
They have not had 365 days to repeat.
The denominator includes customers who have not yet had a fair opportunity to qualify.
This is called right-censoring in time-based analysis.
You do not need the statistical terminology to avoid the mistake.
Just compare cohorts with equal observation windows.
Returning customer rate is not always the same thing
Shopify defines returning customer rate as the share of customers in a period who have purchased before. (shopify.com)
That can rise because a loyal minority is buying frequently even while most new customers never return.
A healthy returning-customer mix does not automatically prove your first-to-second purchase conversion is healthy.
Purchase frequency answers another question
Purchase frequency:
Total orders ÷ unique customers
A store can have:
low repeat customer rate
but relatively strong purchase frequency
if a small loyal group buys often.
That tells a different story.
Example
10,000 customers.
2,000 repeat customers.
Total orders:
15,000.
Repeat purchase rate:
20%.
Purchase frequency:
1.5 orders per customer.
Now another brand:
10,000 customers.
3,000 repeat customers.
Total orders:
13,000.
Repeat purchase rate:
30%.
Purchase frequency:
1.3.
Brand two has more customers reaching order number two.
Brand one has fewer repeat customers, but some of them buy much more frequently.
Neither metric alone tells the whole story.
Calculate progression by order number too
I would also measure:
Order 1 → 2
Order 2 → 3
Order 3 → 4
This is often more useful than one repeat-purchase percentage.
RetentionX explicitly recommends this purchase-funnel approach because a blended number can hide the stage where customers are actually being lost. (help.retentionx.com)
Segment the metric
Repeat purchase rate becomes more useful when sliced by:
- product,
- acquisition channel,
- offer,
- geography,
- subscription status,
- discount usage.
Suppose:
Organic search: 34%
Paid social: 17%
Referral: 41%
Now repeat purchase is telling you something about acquisition quality.
Use net revenue where economics matter
A repeat customer who repeatedly returns products or relies on deep discounts may create less value than gross sales suggest.
So alongside repeat rate, watch:
- refunds,
- contribution margin,
- discount rate,
- average order value.
The formula I would standardize
For website reporting:
365-day cohort repeat purchase rate = customers in the acquisition cohort who place a second order within 365 days ÷ all customers in that cohort × 100
Then separately report:
- 30-day rate,
- 90-day rate,
- purchase progression,
- purchase frequency.
Now the metric has a clear definition.
Frequently Asked Questions
What is repeat purchase rate?
The percentage of customers who make another purchase within a defined observation period.
What time window should I use?
Use a window that fits the category, but keep the definition consistent when comparing periods.
Is repeat purchase rate the same as retention rate?
Not necessarily. Retention can be defined in several ways, particularly in subscription and cohort analysis.
The important part is consistency
A mathematically perfect number that changes definition every quarter is useless.
Choose a clear method.
Document it.
Compare like with like.
Then use the metric to find where customer behaviour is changing.
Related reading
What Is a Good Repeat Purchase Rate?