For shoppers turning to their desktops and mobile phones to purchase apparel, the inability to try something on has made navigating size discrepancies all the more challenging, and returns more frequent.

In the U.S., returns now account for more than $351 billion in lost sales for retailers — an increase of more than $100 billion since 2015, when it was reported at $250.5 billion. Many of these returns can be attributed to the pervasive lack of size uniformity across retailers, rendering what one brand calls a size 4 to translate to a size 2 or 6 to its competitors. This size discrepancy continues to pave the way for machine learning and data-driven personalization tools, developed by internal data teams at companies like Stitch Fix and Tommy Hilfiger, or with the help of third-party algorithmic integrations like True Fit that can be embedded into e-commerce sites.

In an industry faced with mounting losses from returns, putting data science to the test has proved crucial to fighting fit disparity. Stitch Fix, for example, uses data to determine what clothing in its vast inventory catalog customers would be most likely to purchase, based on size as well as style and price. Others, like Rent The Runway, are turning to user-provided information by incorporating crowdsourced fit feedback onto the site.

“Our customers give us a lot of information about themselves — they’re happy to,” Hampton Catlin, senior director of engineering at Rent the Runway, said on the Glossy podcast earlier this year. “They review, rate, post pictures. It becomes a community feeling, especially with the subscription business.”

But clothing brands that weren’t built with such data capacities as Rent the Runway or Stitch Fix are hemorrhaging, and third-party vendors have popped up to capitalize on the desperate need to diminish return rates. According to James Lasson, head of marketing at True Fit, its brand partners experience an average of 5 percent increase in sales, thanks to higher conversion rates, and an average 35 percent reduction in returns.

Lasson said that since he joined the company in 2016, it has tripled in employee size and doubled in revenue. In addition to the 200 brands it counts as clients — including major department stores like Macy’s, Nordstrom and Bloomingdale’s and designer brands like Kate Spade and Michael Kors — it also has 65 million registered users who use the service across the e-commerce sites of these companies. In January, the company announced a $55 million Series C investment round, as investors increasingly see the opportunity to capitalize on the growing fit problem.

“Adapting to e-commerce — especially on mobile — is one thing that’s really driving our growth,” Lasson said. “We see the numbers increase year over year. There’s a lot of pressure with retailers, especially with the competition of Amazon, to reduce waste, reduce returns, to stay in business.”

True Fit operates by using a mix of self-reported consumer data — for example, height, weight and other measurements — paired with sales and return data provided by the brands. These data points work in tandem to give shoppers recommendations on sizing and fit based on the company and the shopper’s personal preferences, said Mike Woodward, head of analytics and insights at True Fit.

However, while True Fit works to bridge the information gap in size differences across retailers, it remains limited to the 200 brands it works with. Importantly, return rates actually rebounding will depend on brands’ abilities to make changes to sizing and fit based on data accumulated by True Fit.

Regardless of if, they use internal data teams of partner with companies like True Fit, Woodward said fast fashion and mass retailers are at a particular disadvantage in preventing loss due to their rapidly changing inventories and vast fit fluctuations.

“Even with a single retailer like H&M, just from style to style their sizes are so all over the place, and they know this, this is why they come to us to help them. The larger the retailer, the bigger the challenge,” Woodward said.

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