Fashion, Apparel and Activewear Benchmarks (2026): Repeat Purchase Rate, CLV and Loyalty

Short answer: The median Shopify clothing store had a 12-month repeat purchase rate of 16.2% in By the Numbers' benchmark of 1,326 Shopify stores, with the middle half of clothing stores between 11.4% and 23.0%. Measured as annual cohort retention, apparel retailers kept 31.7% of their prior-year buyers in 2024 in Bluecore's 2025 Customer Growth Benchmarks, against 27.8% for sports and hobbies and 22.2% for footwear. Those are two different metrics: use the first to judge a Shopify store's year, the second to judge how well you keep last year's customers.

Key figures at a glance

Fashion is four businesses with four purchase cycles

"Fashion" covers a $20 tee, a pair of running shoes, a set of leggings and a $600 dress. They are bought on different clocks, and the retention numbers split along the same lines.

Both datasets rank the subcategories the same way. Apparel sits above the all-retail retention line, activewear sits close to it, and footwear sits well below it. Here is what drives each.

Basics and everyday clothing. Wardrobe staples wear out, colours rotate and a customer who finds a fit that works tends to buy it again in another colour. That replacement pull is why apparel beats the retail average on annual retention. Bluecore credits apparel's strong active customer base to large product catalogs and many opportunities for complementary purchases and cross-selling.

Activewear. Sports apparel sits level with clothing on the 12-month measure. It has a replacement cycle (leggings, socks and sports bras wear out with training) plus a community and launch cycle that general apparel lacks; see how activewear brands use loyalty to drive repeat purchases. Its weakness shows up at BFCM, covered below, where sporting goods brands lean much more on first-time buyers than clothing brands do.

Footwear. Shoes last, and the right size and model is often a one-time discovery. Footwear has the lowest annual retention of the fashion verticals and the lowest 12-month repeat rate in the By the Numbers table. Bluecore's Black Friday 2024 benchmarks put it bluntly: footwear retailers have a "one-and-done problem", with nearly two-thirds of enterprise footwear buyers on Black Friday being new ones. The same Bluecore growth report found footwear had below average reactivation rates for lapsed buyers.

Luxury and high-end apparel. No trusted source publishes a luxury repeat purchase rate for direct-to-consumer brands. The closest signal is Black Friday: in Bluecore's 2025 Black Friday benchmarks, 17.46% of luxury retailers' sales came from repeat buyers, compared with the 27.19% overall average. Luxury buyers buy less often at much higher order values, so a luxury brand should judge itself on revenue per active customer and second-order rate within two years, not on a 12-month repeat rate built for basics.

The practical point: benchmark against your own subcategory. A footwear brand at a 15% 12-month repeat rate is in the top half of its peer group, while a basics brand at the same rate is below the clothing median.

Annual retention and repeat purchase rate are not the same number

Founders see the Bluecore figure in one place and the By the Numbers figure in another and assume one is wrong. Both are right. They count different people.

Source: Bluecore, 2025; By the Numbers, 2026; Klaviyo BFCM 2025 benchmarks.

The annual cohort figure is higher for a structural reason. Last year's buyers include customers who had already bought two, three or ten times, and those customers come back at high rates. The 12-month repeat rate is dominated by first-time customers acquired during the year, most of whom have not yet had time or reason to reorder. A brand that acquires heavily will see its 12-month repeat rate fall even while its cohort retention holds.

Store size matters too. In the same By the Numbers dataset, larger stores post higher median repeat rates than stores below $1m in revenue, and Bluecore's sample is mostly large retailers. If you are a $2m Shopify apparel brand, the By the Numbers clothing range is your closer peer set.

Seasons and drops set the clock for the second order

Fashion customers do not reorder on a replenishment timer. They come back when the season changes, when a new collection lands, or when a favourite sells out and restocks. The timing data shows it.

Read the chart from the right. Of the clothing customers who place a second order within their first year, roughly 42% do so after day 90. Footwear shows the same pattern. A retention programme that stops working a customer after a 60 or 90 day post-purchase flow gives up on almost half of the second orders it could have influenced.

Revenue is seasonal as well. Lulus, a US online womenswear brand, recognised 23%, 29%, 26% and 22% of its fiscal 2025 net revenue in the first through fourth quarters, according to its annual report. Its second quarter, the spring and summer occasion season for dresses, is its biggest. Your own curve will differ, but every fashion brand has one, and the second order clusters around the next peak.

Three things follow for measurement and planning:

1. Use a 12-month window, not 90 days, as your fashion headline. Track the 90-day rate as an early signal, but judge the brand on day 365.
2. Map second orders to your drop calendar. Group first-time customers by first-order month and look at which collection or season their second order came from. A customer acquired in November often reorders in March, not December.
3. Give lapsed customers a reason tied to the calendar. Early access to a drop, a restock alert for a sold-out size, or a members-first window on the new season gives a dormant customer a reason to return that a generic discount does not. Our guides to time to second purchase and the second-purchase problem cover the analysis.

Returns and fit: the retention cost that cohort reports miss

Returns hit fashion harder than most categories, and the main reason is fit. A customer who orders two sizes and sends one back still counts as a buyer in every retention report, but only part of that order is revenue.

Retailers expect 15.8% of all 2025 sales to be returned, according to the NRF and Happy Returns 2025 Retail Returns Landscape, with an estimated 19.3% of online sales returned. Those are all-category figures. No trusted source publishes a current apparel-specific online return rate, but fashion sits at the top of the range: fit, colour and feel can only be judged on the body.

Returns also shape whether a customer comes back. In the same NRF survey, about 71% of consumers said they are less likely to shop with a retailer again after a poor returns experience, up from 67% in 2024. The report also found 76% of consumers are more likely to choose a return option that provides an instant refund or exchange.

Public filings show how large the gap between orders and kept revenue can get. Revolve, an online fashion retailer with a liberal return policy, reported a $299 average order value for 2025, measured before returns, in its fourth quarter 2025 results. Multiplying that order value by its total orders, also counted before returns, gives roughly $2.83 billion of gross demand against $1.23 billion of net sales (Mage calculation). Revolve does not publish a return rate and the gap includes more than returns, but it shows why a fashion brand has to measure CLV on kept revenue, not on orders placed.

What this means for retention:

• A returned second order is not a retained customer. Shopify counts a customer as returning on order placement. Strip fully refunded orders before you calculate repeat rate, or your number will flatter you.
• The return is a retention moment. A refund ends the relationship with the money back in the customer's account. An exchange or store credit keeps the money and the customer in your store. Offering instant store credit on return, with a small bonus for choosing it over a refund, is one of the few retention levers that pays for itself on the same day.
• Fit data is retention data. Brands that capture size and fit outcomes from the first order can steer the second order to the right size and cut the return that would otherwise break the relationship.

The Store Credit Playbook covers how to replace discounts with store credit to protect margin and drive repeat orders, including on returns.

BFCM: apparel leans on loyal customers, activewear and footwear lean on new ones

Black Friday shows each subcategory's customer mix in one weekend.

Source: Klaviyo BFCM 2025 benchmarks; Bluecore Black Friday 2025 benchmarks; Bluecore Black Friday 2024 benchmarks.

Apparel's BFCM weekend is more than half existing customers. A deep site-wide discount mostly subsidises people who were going to buy from you anyway. Give existing customers early access, bonus rewards or a members-only drop, and keep the sharpest acquisition offer for new visitors.

Activewear and footwear face the opposite problem. They acquire heavily at BFCM, and their base retention is lower, so the January to March follow-up decides whether the weekend pays back. The BFCM second-purchase playbook covers that follow-up, and the Black Friday customer retention benchmarks show what happens to BFCM buyers after the sale.

What public apparel companies disclose

Listed fashion brands publish active customers and order value, which lets you see how differently subcategories monetise a customer in a year.

Source: Revolve Q4 2025 results; FIGS Q4 2025 results; a.k.a. Brands Q4 2025 results. "About" figures are net sales divided by active customers; all three define an active customer as one who purchased in the preceding 12 months.

Dividing revenue per active customer by AOV gives orders per customer per year. FIGS works out at about 1.8 orders per active customer and a.k.a. Brands at about 1.9. Revolve's customers place about 3.3 orders a year, but those orders are counted before returns, so try-on orders that go back are included. The lesson for a Shopify brand: an apparel customer worth keeping places roughly two orders a year, and the order value, not the frequency, separates premium from fast fashion.

For a Shopify reference point, the median clothing store's AOV was $113 in By the Numbers' AOV benchmarks, measured from July to October 2026.

What good looks like: tiers by subcategory

These tiers use the By the Numbers quartiles for Shopify stores as the cut-offs. They measure the 12-month repeat purchase rate, calculated on customers, not orders.

Source: Mage analysis of By the Numbers' quartiles for 1,326 Shopify stores, September 2025 to August 2026.

For annual cohort retention, the Bluecore averages set the floor. An apparel brand keeping fewer than three in ten of last year's buyers is below its vertical; for footwear, the line is about one in five. For luxury, track the share of first-time customers who place a second order within 24 months and the revenue per active customer, and compare each year with the last.

Worked example: moving a clothing brand to the top quartile

A Shopify clothing brand serves 15,000 customers a year at the category median AOV of $113 and a 16.2% 12-month repeat rate. That gives 2,430 repeat customers. Assume repeat customers place 2.4 orders on average, and the brand runs a 55% gross margin with an online return rate in line with the 19.3% all-category average.

Moving to the 23.0% top-quartile threshold gives 3,450 repeat customers, 1,020 more. Each places 1.4 extra orders, so the brand gains 1,428 orders and $161,364 of gross sales. After returns at 19.3%, $130,221 is kept. At a 55% margin, that is about $71,600 of extra gross profit a year, before the following year's compounding.

Now the returns lever. The brand's total orders come to about 20,850, or $2.36 million of gross sales, and about $455,000 of it comes back as returns. If a quarter of those refunds became exchanges or store credit, roughly $114,000 of revenue would stay in the business. That is close to the kept revenue from the repeat-rate improvement, for a change to the returns flow.

The two levers compound. Every return converted to store credit is a reason for a second order, which lifts the repeat rate. Run the numbers on your own store with the customer lifetime value calculator.

How to measure your fashion store in Shopify

The Shopify Help Center defines returning customer rate as returning customers divided by all customers who ordered in the period. Use it to watch your own trend, but compare like for like before you hold it against a benchmark:

• Fix the window. Compare a 12-month repeat rate with the By the Numbers figures and annual cohort retention with Bluecore. Never mix the two.
• Net out returns. Exclude orders that were fully refunded before counting a customer as repeat.
• Split by first product. A customer whose first order was footwear and one whose first order was a basic tee belong in different benchmark rows. Shopify customer segments filtered by first-order product type make this possible.
• Count exchanges carefully. Some returns tools create an exchange as a new order. If yours does, an exchange is not a second purchase.

Where Mage fits

Mage is a Shopify loyalty app, and its fashion loyalty program is built around the levers in this report: exchange credits that turn refunds into store credit, early access to drops gated behind spend-based VIP tiers, and collection-level point multipliers that pull customers into new arrivals. Mage connects with Loop Returns for returns flows, so a return can become an exchange credit instead of a refund. Set the target from the tier table above, then compare members with non-members on 12-month repeat rate and return rate to see whether the programme is moving them.

The The Store Credit Playbook covers replacing discounts with store credit to protect margin and drive repeat orders.