What is a good repeat purchase rate for ecommerce?

There is no well-sourced benchmark. The two figures in circulation are 30%, which traces through a 2023 third-party guide to Statista with no methodology, and 18.8% from one agency own client portfolio in February 2026. Use your own cohorts instead.

How do I measure returning customers in Shopify?

Shopify exposes first order date, number of orders, amount spent and cohort dimensions in its analytics data, so repeat rate at 90, 180 and 365 days can be built without exporting anything.

Is it true that 65% of revenue comes from returning customers?

Nobody appears to know. That figure is repeated constantly and we could not trace it to any dated, sized, ecommerce-specific study. Measure your own revenue share by order index rather than assuming it.

Why is my returning customer rate falling?

It may be retention, or it may be identity fragmentation. Guest checkout and multiple email addresses split one person into several customer records, and that bias grows as the store ages. Check whether guest checkout share changed before concluding retention got worse.

First‑time and returning buyers, and why the split matters

Last reviewed 15 August 2026

Short answer

The split between first-time and returning buyers tells you whether growth is being bought or is compounding. It is also the benchmark with the worst public sourcing in ecommerce: the most repeated figures trace to nothing dated or sized. Measure your own cohorts rather than aiming at a number from a blog.

What the split actually tells you

Revenue tells you what happened. The new versus returning split tells you what kind of business produced it.

A store where revenue is flat and returning share is rising is quietly getting healthier: acquisition is doing less work each month and the customer base is carrying more. A store where revenue is rising and returning share is falling is buying its growth, and will keep having to.

This is the only one of the four core numbers that leads rather than lags. Retention problems show up in the cohort months before they show up in revenue, because the customers who will not come back have not yet failed to come back.

The benchmark problem, stated plainly

There is no Baymard-caliber running benchmark for repeat purchase rate. We looked properly, and this is what exists.

Shopify own blog cites 30% as an ecommerce customer retention rate. That figure is not Shopify data: the article attributes it to a third-party benchmarking guide from 2023, whose own table is attributed onward to Statista with no methodology given. It is a citation three hops from anything measurable.

A DTC agency published a figure of 18.8% average repeat purchase rate in February 2026, across 156,110 customers with a 365-day lookback. That is a real number with a real sample, and it is explicitly the agency own client portfolio rather than an independent study. Usable as a labelled data point, not as an industry standard.

That is the entire credible field. Anyone presenting a confident repeat-rate benchmark is presenting one of these two numbers with the caveats removed.

The two numbers we will not repeat

Almost every article on retention contains both of these, usually in the first paragraph.

That around 65% of ecommerce revenue comes from returning customers. That acquiring a new customer costs five to twenty-five times more than retaining an existing one.

Neither traces to a primary source. The second appears to descend from general retention research from the 1990s and 2000s, restated so many times that the original scope, which was not ecommerce, has fallen away entirely. We found no dated, sized, ecommerce-specific study behind either.

They may still be roughly true. They are not evidence, and a strategy justified by them is justified by nothing.

What to measure instead

Your own cohorts answer the question these benchmarks cannot, because they are about your customers rather than an average of somebody else.

Shopify exposes the fields needed for this directly, including first order date, number of orders, amount spent, cohort dimensions and RFM grouping, so this does not require exporting anything.

Repeat rate at fixed horizons: what share of a month cohort bought again within 90, 180 and 365 days
Revenue share by order index: how much of this month revenue came from second and later orders
Time to second order: the single most predictive number for whether a cohort will ever repeat
Cohort curves side by side: whether customers acquired this quarter behave like last quarter

Where this is genuinely hard

Identity is the unfixable part. Guest checkout, a second email address, a work address and a home address, a name typed differently: each of these turns one person into two customers, and every one of them makes your returning share look worse than it is.

This bias is not constant either. It gets worse as you grow, because more of your orders come from people who bought long enough ago to have changed something. A returning-customer share that drifts down over two years may be measuring your own data hygiene.

Seam reads the customer data Shopify computed and inherits all of that. It can tell you the split moved. It cannot tell you whether the split moved or your customers changed email addresses, and neither can any other tool reading the same records.

Common questions

What is a good repeat purchase rate for ecommerce?: There is no well-sourced benchmark. The two figures in circulation are 30%, which traces through a 2023 third-party guide to Statista with no methodology, and 18.8% from one agency own client portfolio in February 2026. Use your own cohorts instead.
How do I measure returning customers in Shopify?: Shopify exposes first order date, number of orders, amount spent and cohort dimensions in its analytics data, so repeat rate at 90, 180 and 365 days can be built without exporting anything.
Is it true that 65% of revenue comes from returning customers?: Nobody appears to know. That figure is repeated constantly and we could not trace it to any dated, sized, ecommerce-specific study. Measure your own revenue share by order index rather than assuming it.
Why is my returning customer rate falling?: It may be retention, or it may be identity fragmentation. Guest checkout and multiple email addresses split one person into several customer records, and that bias grows as the store ages. Check whether guest checkout share changed before concluding retention got worse.

Sources

one email. the thing that changed.

Seam reads your store every day and sends one paragraph about what actually moved. No dashboard to open.

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