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Resolving Fashion’s Inconsistent Sizing Issue Through Data + AI

January 7, 2020

Size inconsistencies have become a major pain point in the fashion industry. Whether consumers shop in-store or online, these inconsistencies are apparent across all points of sale. 

When shopping in-store, consumers often must physically try-on multiple sizes, styles, and fits in the quest to find the ones that will appeal to their unique preferences. 

Online shopping poses even greater discrepancies, as consumers often need to decode generalized size charts, or physically measure themselves, in order to find items that will (hopefully) work.

It’s no secret that there’s a lack of size standardization across the fashion industry. There’s no universal size chart that all brands and retailers must abide by when manufacturing and creating clothes. However, when looking at the root of the problem, standardization isn’t the answer to this issue. The answer is personalization. 

Click here to read "It’s Not You. Clothing Sizes are Broken."

Where Fashion Meets Physics

True Fit’s Chief Analytics Officer, Christopher Moore, was recently featured in The Wall Street Journal and on NPR’s Air Talk to explain the scope of this problem and how data-driven technologies are reducing confusion and increasing consumer confidence in finding the best apparel and footwear for their unique preferences.

Moore has worked on projects related to cancer research and tracking global commodities, yet his current role helping consumers find clothing that they will love and keep has also presented a challenging problem to solve.

“It’s not about what size or style people should wear, it’s what they want to wear. Our focus is very much on understanding the consumer through datalooking at the things they’ve bought, the things they’ve returnedand what other consumers who are like them are also doing, so it’s really not about measurements or scans, it’s about understanding what people like and helping them find more of that,” said Moore.

Understanding Size and Fit Across Brands

Brands often create products based on their idea of their target consumer. Different brands have different consumers and want to appeal to the styles, sizes, and fit that they think will resonate best with those consumers.

“One of the struggles for consumers is that all of the brands are doing what they want to do to fit their niche in the market, which does make it hard on the consumers. That’s part of what we do is serve as a translation engine between all of the different sizes brands are putting out there, and just making a 1-to-1 recommendation for the consumer,” said Moore. 

In the age of the consumer, retailers have one shot to get it right and present a truly personalized experience. Those who do not deliver to the consumer’s expectations risk losing their loyalty to a competitor. So how can retailers ensure each and every shopper has a personalized shopping experience?

The Power of Personal Preference 

By implementing data-driven technologies, consumers no longer have to keep track of the best style, size, and fit for their preferences across brands. Rather, artificial intelligence and machine learning are used to develop an understanding of the consumer’s preferences and guide them to the styles, sizes, and fits where they will have the most success, wherever the consumer shops.

“We don’t focus on consumer’s measurements, what we’re really trying to understand is what are the properties of clothing and shoes that they actually like to wear,” said Moore.

The added layer of preference means consumers can receive recommendations for products that they are much more likely to love and ultimately keep. The power of data-driven technologies is they enable retailers to automatically personalize the shopping experience on a 1-to-1 basis for each and every consumer.

Want to learn more? Listen to the full interview with Air Talk.

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