Repeat purchase rate: how it's actually calculated, and why buyers price it into an ecommerce multiple

What repeat purchase rate actually measures

A repeat purchase is a second order placed by a customer who has already ordered once before. Repeat purchase rate is the share of a store's total unique customer base, within a chosen window, who did that.

Klaviyo's glossary describes a customer-based ratio: identify people who purchased again, divide by the customer population for the chosen period, and express the result as a percentage.

That customer-based framing matters because it's easy to substitute a different, adjacent number.

Shopify's own reporting documentation defines a "returning customer" as "a customer who placed an order, and whose order history already includes at least one order" — see Shopify's customer reports help page.

That reporting definition can count someone whose first order predates the selected window. The stricter within-window formula below requires two orders inside the window. Neither definition should be silently substituted for the other: label the chosen method before comparing a dashboard with a buyer's reconstruction.

How to calculate repeat purchase rate correctly

The formula is simple. The execution is where sellers — and self-reported pitch decks — go wrong.

Repeat Purchase Rate = (Unique customers with 2+ orders in the window ÷ Unique total customers in the window) × 100

Three details determine whether the number means anything:

• Count customers, not orders. A customer who ordered four times in the window is still one repeat customer, not four repeat orders. Order-based ratios (total orders ÷ total customers) measure purchase frequency, a related but different number. - Fix the lookback window before comparing anything. A 90-day repeat rate and a 365-day repeat rate are not comparable, and a young store's window is mechanically shorter than an established one's.

State the window every time the figure is quoted. - Decide the cohort's start point. Some methods measure "first order this window, repeated within this window"; others use a rolling all-time customer base.

Google's own cohort exploration documentation frames this as grouping "users who share a common characteristic" — in this case, a first-purchase month — and then tracking how many of that same group transact again in each following period. That cohort structure is the cleanest way to see whether a repeat rate is improving, decaying, or an artifact of a recent acquisition spike diluting the denominator.

A store that just ran a large paid-acquisition push will show a temporarily depressed repeat rate — the denominator fills with brand-new customers who haven't had time to reorder yet. Read the trend by cohort month, not the trailing blended average.

Repeat purchase rate vs. retention rate vs. purchase frequency

These three get used interchangeably in casual conversation and shouldn't be.

• Repeat purchase rate answers: what share of customers ever came back? - Retention rate (or cohort retention) answers: of the customers active in period one, what share are still active in period two? It's period-over-period, not lifetime. - Purchase frequency answers: on average, how many orders does a customer place in the window?

This is an average, so a small number of high-frequency buyers can inflate it even if most customers never return — repeat purchase rate is the more honest lens on how widely loyalty is distributed.

This distinction matters for valuation because the metrics answer different questions. A buyer who treats retention and repeat ordering as interchangeable can misread a seller's numbers. For the acquisition-cost side, see LTV:CAC in website valuation. This article asks whether customers come back and whether the records establish that behavior.

Benchmark ranges by category

Repeat purchase rate is not a single target number — it is structurally different by what's being sold. BS&Co's analysis of 156,110 DTC customers over a 365-day lookback found an overall average of 18.8% — meaning roughly 81% of customers never placed a second order — with sharp variation by category:

The same study found that of customers who did repeat, 50.3% placed their second order within 30 days, and 77% reordered the same product rather than buying something new — evidence that most repeat behavior in DTC is replenishment, not cross-sell discovery.

Klaviyo frames a "good" blended repeat purchase rate as roughly 20% to 30%, while flagging the same category effect: rates run higher for "affordable or perishable goods" and lower for luxury or electronics. A single blended benchmark is less useful than the category-adjusted range.

Separate loyalty-program members from nonmembers when checking the benchmark. Program participation, discounts, and product mix can change the comparison. A loyalty program is not proof of profitable customer loyalty; inspect contribution margin alongside the ordering behavior.

Why buyers weight repeat purchase evidence in the multiple

A buyer underwriting an ecommerce acquisition is really underwriting one question: will this revenue exist in twelve months without the seller's specific effort or ad spend at the current level? Repeat purchase rate is direct evidence, because it describes customer behavior rather than a marketing claim.

A high, well-documented repeat rate supports a few things a buyer cares about:

• Lower dependence on paid acquisition. Revenue from customers who already converted once is less exposed to a channel getting pricier or an ad account getting restricted.
• Some pricing power evidence. Customers who return, especially at full price, are choosing the product again — a soft signal, not proof, of brand strength.
• A base for forward revenue. Repeat behavior verified against a real cohort is a more credible input than trailing growth alone.

None of that converts into an automatic multiple premium. A high repeat rate built on a small, unrepresentative sample, or achieved only through unsustainable discounting, does not carry the same weight as one shown across full cohorts at full or near-full price.

Do not treat a self-reported repeat-rate percentage as an automatic adjustment to an RSW estimate. Customer-quality evidence supports the buyer's separate diligence judgment; this article does not claim that uploading a percentage changes the product's valuation or confidence calculation.

Improve repeat purchases without buying unprofitable loyalty

Start with the reason a customer would make a second purchase. Replenishable products have a natural purchase cycle; durable products may need accessories, service, or a genuinely useful new release. Sending more promotions cannot create a real need. Review product quality, delivery reliability, and customer satisfaction before deciding that email frequency is the problem.

A repeat buyer should contribute margin after discounts, shipping, support, and returns. Separate full-price repeat purchases from coupon-driven orders. Loyal customers can be economically valuable, but a customer who buys again only when each order loses money does not establish durable earnings. Customer lifetime value needs a margin calculation, not just a rising order count.

Test customer retention strategies against a comparable cohort. A useful post-purchase explanation, a replenishment reminder timed to usage, or a clearer returns process may encourage repeat purchases. Measure whether the change improves the overall customer experience and profitable repeat business. Do not treat every extra order as proof that retention efforts are working.

Record an initial purchase separately from the next purchase, and keep subscription renewals identifiable. The repeat customer rate does not distinguish voluntary repeat purchases from automatic billing unless the underlying export does. Buyers need to know which behavior they are underwriting.

Buyer checklist

Before treating a seller's repeat purchase rate as evidence, request:

• Raw order-level export with customer IDs, order dates, and order values - The exact formula and lookback window used to produce the quoted percentage - Cohort-by-month breakdown, not just a trailing blended figure - Whether the number includes or excludes subscription auto-reorders - Category and price-point context to judge the number against a comparable benchmark - Loyalty-program participation rate, if one exists

• Return and refund rates for repeat customers — a customer who reorders and returns everything is not a clean repeat sale

What is a good repeat purchase rate?

There is no single good number — it depends heavily on category. BS&Co's dataset puts consumables at 30–40%+, fashion around 12–17%, and durable goods around 10–15%. Klaviyo's general 20–30% rule of thumb only works after adjusting for category.

How do you calculate repeat purchase rate?

Divide the number of unique customers who placed two or more orders within a defined window by the total number of unique customers in that same window, then multiply by 100. Counting orders instead of unique customers, or mixing time windows, is the most common calculation error.

Is repeat purchase rate the same as customer retention rate?

No. Repeat purchase rate measures whether a customer ever came back; retention rate measures whether customers active in one period stay active in the next. They correlate, but the profit research often cited — Bain's findings via HBR — describes retention rate, not repeat purchase rate.

Does a high repeat purchase rate automatically raise a website's valuation multiple?

No. It's one input into how a buyer judges revenue durability, alongside margin, channel concentration, and how the rate was measured. A rate built on a small or unrepresentative sample carries less weight than one verified across full cohorts.

Continue through the RSW silos

Start with the ecommerce valuation calculator and the ecommerce business valuation guide, then compare Shopify store valuation. LTV:CAC in website valuation covers the acquisition-efficiency side of customer economics this article deliberately leaves out.

Related ecommerce operating-risk topics: tiktok shop as a sales channel, ecommerce returns and refund exposure, and supplier concentration risk.