This article is written by John-David Klausner, GM International, Loop Returns
In today’s margin-sensitive climate, UK retailers are understandably scrutinising every operational lever, and returns are high on the list. With return rates in fashion now averaging 25 – 40%, according to the IMRG, it’s no surprise that more brands are introducing charges, limiting refunds, or tightening policies. But in doing so, many are unintentionally alienating a sub-group of customers who are, in fact, their most valuable.
The challenge is how retailers interpret return behaviour. Too often, we see decisions based on incomplete data: one customer, one order history, one store. But the reality is that consumer behaviour is far more complex. And context matters.
Let’s say a shopper has a 60% return rate with your brand. On paper, that looks like abuse. You might flag them as high-risk, disqualify them from free returns, or refuse refunds altogether. But zoom out, and a different story often emerges. Across a broader network of retail data, that same shopper may have a far lower average return rate and spend significantly more when they get the right fit or product the first time.
We’ve seen this repeatedly across the Loop ecosystem, where we process millions of returns each month. Take “John”, a fictional but representative example. John has a 60% return rate with one fashion brand. The retailer, operating on siloed data, assumes he’s gaming the system. But across the 5,000 brands using Loop, John’s overall return rate is just 15%. What’s more, when sizing is right the first time, his basket value and repeat spend are significantly higher.
The problem isn’t John. It’s the lack of visibility into his broader shopping behaviour.
This issue is particularly relevant in the UK, where return policies have recently become flashpoints in the fashion sector. ASOS, Boohoo, Zara, and others have introduced or expanded paid returns. For some, this has helped stem logistics costs and discourage serial returners. But for others, particularly independent and mid-size brands, there’s a risk of undermining loyalty — especially when decisions are based on a narrow view of customer value.
As the line between in-store and online continues to blur, and as cross-border shopping becomes more common, UK retailers need more nuance in how they interpret return behaviour. Blanket rules might simplify operations, but they also erase important distinctions between one-time bracketers and high-intent repeat customers trying to find the right size.
So how do we fix it?
It starts by shifting from reactive penalties to proactive interventions. There are several low-friction ways to retain valuable customers, all the while reducing return volume:
- Size-aware recommendations at the point of purchase, based on real historical fit feedback, not just generic size charts.
- Restock notification* for preferred sizes, tailored to individual return reasons (“too small”, “too short”).
- Instant Exchanges during the return process, offering immediate dispatch of a new item before the original is received – building confidence and preserving revenue.
- Automated sizing nudges: for instance, “Based on your history, you may prefer a Large in this style.”
This kind of feedback loop not only prevents future returns, it builds trust. Customers see that the brand remembers their preferences and is invested in getting it right.
Retailers often ask: ‘What’s the ROI of a better returns experience?’ From what we’ve seen, it’s not just about operational savings. It’s about keeping high-LTV customers engaged. In fact, our UK retail partners who prioritise exchanges over refunds see higher retention rates and fewer support tickets – outcomes that go far beyond return logistics.
Ultimately, treating every high-return customer as a problem to eliminate is short-sighted. The most valuable shoppers often experiment, explore sizing, and shop frequently, especially in categories like fashion, footwear, and activewear. They don’t need punishment. They need better tools.
Returns are one of the richest and least understood signals in ecommerce. The brands that thrive in 2026 and the years ahead will be those that move beyond siloed data and toward systems that learn, anticipate, and personalise. Because when you see the full picture, your best customers stop looking like risks and start looking like the assets they really are.