Fashion Returns Start on the Product Page. Five Ways AI Can Cut Them at the Source

Many fashion returns start with the wrong size. SCAYLE’s Pascal Richter outlines five ways AI can cut them, from size recommendations to checkout logic.

HAMBURG, GERMANY, October 28, 2026 /EINPresswire.com/ — Expert Commentary | Press Information

Fashion brands have spent years speeding up the logistics behind returns. Meanwhile, shoppers keep turning their bedrooms into fitting rooms. Pascal Richter, Director Growth Continental Europe at SCAYLE, outlines five ways retailers can bring return rates down across the shopping and returns process.

Generous return policies were designed to build trust with online shoppers. They also keep an expensive cycle of reverse logistics running. Many shoppers order the same item in several sizes and send back what doesn’t fit. Each return ties up working capital, loses value, and adds transport emissions. Faster processing lowers the cost per package, but the packages keep coming.

More than half of senior executives at enterprise fashion brands (54%) say size and fit make fashion more complex than other retail sectors, and 38% point to high return rates. These are the findings in SCAYLE’s latest survey “Inside the Boardroom: Fashion Leaders on the Agentic AI Era.” Shoppers report the same problem. In DHL’s 2025 survey of 24,000 online shoppers across 24 countries, more than half said they had returned an item because it was the wrong size. In the UK alone, Retail Economics and ZigZag forecast that 19.5% of non-food goods purchased online would be returned in 2025, representing £25.1 billion (approximately $33.4 billion) in merchandise value.

The doubt behind a return starts much earlier: on the product page, in the cart, and at checkout.

1. Recommend sizes based on the brands shoppers already wear

A medium fits differently from one label and cut to the next, so static size charts only go so far. AI-based size engines combine anonymized purchase histories, return reasons, and garment measurements to predict how an item will fit a specific shopper. The recommendation builds on the brands and cuts a shopper already wears. Shown before the item goes into the cart, it removes much of the doubt behind multi-size orders.

2. Show products on bodies that look like the shopper’s

Product photos usually show a garment on one model. When shoppers can’t picture how an item will look on them, many order a second size to be safe. Generative AI can render catalog items on a wide range of body types, heights, and skin tones without extra photo shoots. Virtual try-on goes further and lets shoppers see outfits on an AI-generated likeness based on their own photos. In the SCAYLE survey, 45% of fashion executives say their organizations already use AI-generated models or visual content.

3. Catch bracketing in the cart

When the same item sits in the cart in several sizes, the store can flag it before payment. An AI assistant then compares the shopper’s previous fit preferences with the garment’s measurements and recommends the size most likely to fit. A small incentive, such as priority delivery or loyalty points, can encourage the shopper to remove the extra sizes. Every size left out of the order is one fewer item to ship, inspect, and restock.

4. Align checkout terms with return behavior

Machine learning can estimate the return risk of an order from return history and what’s in the cart, and stores can adjust payment and delivery terms accordingly. Customers who rarely return items keep pay-later options and free shipping as a perk. Frequent returners may be asked to pay upfront or cover part of the return shipping. In Germany, returns researcher Björn Asdecker of the University of Bamberg identifies pay-later payments as one possible reason for the country’s high return rates. Meanwhile, Asos has introduced a transparency feature in the UK that shows customers their personal return rate in its app and warns them when they approach thresholds that could trigger return fees.

5. Inspect returns with computer vision

Returns are often logged under broad reasons such as “doesn’t fit” or “didn’t like it,” which tell retail teams little. Computer vision at the returns station can scan incoming items and spot recurring defects, from faulty zippers and weak seams to sizing that’s off in a specific production batch. When problems pile up for a newly launched item, the store can add a fit note to the product page or pause the item until it’s fixed. Fewer customers receive the same faulty item, and fewer packages come back for the same reason.

How can AI reduce return rates in fashion?

AI can help prevent returns before checkout and use information from returned products to reduce future returns. Return policies can’t remove the uncertainty of buying clothes online. Size recommendations based on individual fit, product images that show garments on different bodies, and checkout terms that reflect return behavior can. Retailers that treat returns as part of the shopping experience protect their margin. The more uncertainty a shop removes before checkout, the less it loses to returns.

About Pascal Richter
Pascal Richter is Director Growth Continental Europe at SCAYLE. Previously, he was Co-Managing Director for Sales and Partnerships at the digital commerce agency Astound Digital. His expertise spans digital strategy and transformation, replatforming, and AI use cases in commerce.

About SCAYLE
SCAYLE is an enterprise commerce platform for global B2C brands and retailers. It enables businesses to scale internationally, manage complexity, and deliver customer experiences that drive conversion. SCAYLE combines deep retail DNA with native enterprise capabilities to handle complex omnichannel operations. It offers an extensive, modular feature set across PIM, Shop Management, Checkout, and OMS – unified in one intuitive user interface. Its scalable, API-first, and AI-ready architecture ensures flexibility for future-proof growth. Leading brands like Levi’s^®, The Container Store, Harrods, Manchester United, Snipes, and HMV trust SCAYLE as a true partner. SCAYLE is part of Zalando SE.
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Anja Schmidt
SCAYLE GmbH
anja.schmidt@scayle.com
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