How AI is trying to solve the retail returns problem & more related news here

Press here; drag there; the draping is wrong. These are some of the examples of the feedback that a new generation of artificial intelligence applications could provide to a potential customer who tries on clothes before making a purchase and, in the process, reduces the chances of a product being returned to the store.

Fashion retailers are increasingly turning to AI to solve the problem of increasing product returns, a persistent drag on profitability and something many in the industry call their “silent killer.”

A growing number of AI startups have emerged offering virtual try-on technology, allowing potential customers to visualize fit and style before purchasing.

While technology companies have been trying to solve online adaptation problems since the 2010s, the rapid development of generative AI has finally made these applications good enough to significantly impact retailers’ bottom lines.

The US National Retail Federation estimated late last year that 15.8% of annual retail sales will recover by 2025, for a total of $849.9 billion. In the case of online sales, that figure jumped to 19.3%. Generation Z is driving this trend: Shoppers ages 18 to 30 averaged nearly eight online returns per person last year, the NRF found.

Most returned items never make it back on shelves, and it often costs the retailer more to process them than the value of the refund itself. It is a multi-billion dollar problem for the industry that is directly affecting companies’ margins.

“Figuring out how to proactively use returns and then how to minimize them can be a major driver of business and profitability,” Guggenheim senior managing director Simeon Siegel told CNBC.

While fit tech will never be as good as trying something on in person, it’s a great way to bridge the gap, Siegel said. “It’s going to continue to improve, I think that will continue to reduce returns.”

The main reason for returns and abandoned shopping carts is uncertainty about fit, Ed Voyce, founder and CEO of artificial intelligence startup Catches, told CNBC in an interview.

Catches has developed a platform that allows users to create a “digital twin” to try on clothes virtually with what it calls “mirror-like realism.” The app launched last month on luxury brand Amiri’s website for a select range of clothing.

Unlike other models that Voyce says “just look pretty,” the Catches platform incorporates the physics of fabric texture and how the material interacts with a moving body.

“The reason we created Catches was to take advantage of a kind of confluence of technologies that’s happening right now to effectively solve this problem,” says Voyce, who founded the startup backed by LVMH Antoine Arnault and built on Nvidia CUDA platform.

“The reason it can be solved now in terms of time is that you have to be able to run images for end users in the cloud, at a price cheap enough to make a [return on investment] for brands,” says Voyce.

“This technology has the potential to impact the entire industry and truly usher in the new wave of what end users expect.”

These AI tools are not only intended to reduce returns, but also help improve purchases.

While e-commerce has grown rapidly in recent years, with online shopping driving retail sales growth, ongoing US trade policy under President Donald Trump has slowed the sector that relies heavily on manufacturing in Southeast Asia. Across the price spectrum, retailers are struggling to maintain margins as costs rise and consumers become increasingly price sensitive amid inflationary pressures.

While returns are a significant drag on profit margins, they are also a critical factor in consumers’ purchasing decisions. NRF data shows that 82% of consumers consider free returns essential, but the cost of providing them is becoming unsustainable for many brands.

Retailers are now testing a combination of technology and policies to protect margins.

Strategies to reduce returns range from charging for return shipping to providing more detailed sizing information to incentivizing exchanges instead of refunds.

Zara, owned by Inditexwas one of the first to implement return fees for online orders, and while it was a controversial change for some customers, it helped the Spanish retailer protect its gross margin and discourage “bracketing,” the practice of buying multiple sizes to try on at home.

The retailer also launched a virtual try-on tool, “Zara try-on,” in December.

Meanwhile, ASOS It recently highlighted a marked improvement in profitability, driven in part by a 160 basis point reduction in its rate of return.

The online fast fashion player has been experimenting with virtual try-ons in partnership with deep tech startup AIUTA, allowing potential customers to see a garment on a variety of body types, heights and skin tones. ASOS, however, warns that the tool is designed to provide general guidance and that customers should still consult the size guides before purchasing.

buyMeanwhile, it has integrated startup Genlook’s AI virtual try-on app into its commerce platform, which it says “eliminates doubts about sizing, increases shopper confidence, and drives higher conversion rates while reducing costly returns.”

Tech giants like it Amazon, Adobeand Google They have also created virtual try-ons in various shapes and forms, partnering with major brands to implement the technology.

Starting April 30, Google’s virtual try-on technology will be accessible directly from product search results on Google platforms, according to the Google Labs website.

As for Catches, he projects that his app can generate a 10% increase in conversions and a 20 to 30x return on investment for brand partners. It focuses on luxury brands due to their higher price. The startup has not yet determined how much returns could decrease with the use of its platform, but is aiming for “massive reductions.”

“There are certainly companies that have made absolute profits; figuring out how to quantify them is more difficult,” Siegel says.

While the benefits are clear, the analyst warns that AI is not a magic wand. Beyond suitability, retailers are turning to AI for inventory management, customer targeting, and fraud prevention.

“These are all really interesting use cases, as long as companies don’t abandon who they are,” Siegel says.

“What you sell will always be more important than how you sell, so I think remembering that will help determine who gains, benefits, and is amplified by AI versus who is consumed by it.”