Cost of returns: which ones erode margin and which don't

Fashion retailers have spent years trying to reduce return rates. According to our benchmark data, around 40 UK fashion brands have actively pursued rate reduction through product data improvements, customer education, or sizing tools. Only two have seen meaningful results.

Return rates in fashion remain stubbornly resistant to change. The industry average sits between 25% and 40% depending on category, and that range has barely moved despite significant investment. This raises an uncomfortable possibility — return rate may not be the only metric to prioritize.

Bracketing myths vs. data

The term ‘bracketing’ — customers ordering multiple sizes with the intention of keeping one — tends to conjure images of shoppers ordering half a dozen versions of the same item, trying them all, and sending most back. It's a behavior many retailers instinctively want to discourage.

The data suggests a more nuanced picture. According to the brands we work with, most customers who bracket order two sizes, not six. The items are tried on briefly at home and returned in as-new condition, ready to be resold. Processed quickly, this creates a better outcome for inventory than a single-item order returned after three weeks.

Acquisition cost equation

The economics of bracketing start with customer acquisition. For most fashion retailers, the marketing cost of acquiring a new customer can often be larger than the marginal cost of shipping an additional item. This changes how your e-commerce team needs to evaluate bracketing.

Consider a first-time customer who orders a single item, finds it doesn't fit, and returns it. The retailer absorbs the return processing cost and loses the acquisition spend that brought that customer to the site in the first place. If the customer keeps nothing from their first order, they often conclude the brand simply isn't for them. Repeat purchase rates from that segment are typically very low.

Now consider the alternative where a first-time customer orders the same item in two sizes, tries both, keeps the one that fits, and returns the other. The return rate on that order is 50%, which looks problematic at the headline level, but the outcome is different. 

There’s a net sale, a recovered acquisition spend, and a customer who now has a piece that fits, with a high likelihood of becoming a long-term shopper. The incremental cost of sending a second item is comparatively small relative to the upside of a completed sale, a satisfied customer, and a higher chance of repeat purchase.

Keep rate, not return rate

Return rate as a metric doesn't distinguish between a return that still resulted in a sale and one that didn't. A 50% return rate on a two-item bracket order that produces a kept item and a repeat customer is fundamentally different from a 100% return rate on a single-item order that produces neither.

The more useful measures for retail leaders evaluating returns are keep rate — what share of first-time customers retained at least one item — and speed of recovery — how quickly and in what condition the items come back, and how quickly they can be resold at full price. These metrics capture commercial value in ways that return rate alone cannot.

Where the real problem sits

Customer-level segmentation reveals that the returns challenge is more concentrated than aggregate rates suggest. A small segment of customers, often around 1–2%, drives a disproportionate share of returns. These are customers who buy frequently and return almost everything. They are genuinely unprofitable.

The majority of customers who return occasionally, including those who bracket, remain profitable after accounting for returns. Customers who buy frequently and return occasionally can generate more profitable transactions over time than customers who buy once and keep everything.

Treating all returners the same, through blanket fees or restrictive policies, penalizes the profitable majority to address the behavior of a very small minority. Segmentation enables a more precise response.

​​Returns economics management

The brands in our benchmark that have made the most progress aren't focused on reducing return rates. They're focused on improving return economics through policy levers already within their control.

Exchange-first prompts convert potential refunds into new sales. When a customer initiates a return for fit reasons, offering an immediate exchange at no additional shipping cost keeps the relationship active. Data suggests exchange platforms can convert up to 30% of returns into new purchases, compared to single-digit percentages when customers must repurchase separately.

Dynamic return terms allow different customers to receive different conditions based on their history. Reliable customers, meaning those with consistent purchase behavior and low return rates, might receive extended return windows and free returns as a reward. 

Customers with high return histories might face shorter return windows or modest returns fees. The key is framing these as earned benefits rather than penalties, ideally integrated into loyalty programs where tiered rewards already exist.

These are policy-level changes that can be tested incrementally. They don't require tech transformation or large-scale operational overhaul. The retailers who have moved on them haven't reported the negative consequences many brands fear. What they have reported is improved cost recovery and faster inventory turns.

Net positive returns

The industry's focus on return rate as the headline metric has created a blind spot. Yes, some returns erode margin — late returns that miss the full-price selling window, serial returners who cost more to serve than they generate, wardrobing that degrades product quality. These deserve attention and targeted action.

Other returns recover acquisition spend, convert first-time visitors into long-term customers, and get stock back on the shelf quickly. Penalizing the second category to address the first comes at a cost that rarely shows up in return rate dashboards.

For retail leaders evaluating their returns strategy, the more productive question may not be ‘how do we reduce returns’, the goal that 40 major retailers have pursued with limited success, but ‘which returns are actually costing us money, and which ones are making us money’. 

This analysis is based on findings from the Return Economics 2026 report, a benchmark study of the UK's top 100 fashion retailers by Ingrid Delivery Intelligence Platform, Harper Concierge, and Limesharp agency.

How can Ingrid help your team?

Ingrid Platform makes delivery and returns a competitive advantage, not just a cost. We help retailers optimize delivery options at checkout, grow conversion and shipping revenue, and manage returns strategically, from data analytics to personalized exchange options.