Merchant Innovation

Stitch Fix Combines Big Data With High Fashion

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Regardless of gender, there’s no doubt that shopping for clothes presents one of the most burdensome processes to the average consumer. Between searching for the right sizes and keeping up with the latest trends, many shoppers can feel like they’ve wasted their cash, even when they end up with the clothing they originally wanted.

However, Stitch Fix and COO Julie Bornstein think that an infusion of Big Data in the curated fashion world can deliver a personal shopping experience, no matter the scale.

In an interview with Forbes, Bornstein explained how Stitch Fix users are initially prompted to fill out a lengthy questionnaire, including their fashion preferences, body measurements and ideal price ranges. Once that information is fed into an algorithm that generates a list of items that might appeal to a consumer’s taste, a Stitch Fix “stylist” then pours over the picks and selects five to send out to the shopper in a blend of high tech and high fashion.

“Most of retail is developed for people who love to shop, but a lot of people would rather do something else with their time or they don’t feel they’re great at it and there’s so much inefficiency in the [shopping] process,” Bornstein told Forbes.

Stitch Fix reportedly has about 50 data scientists and over 2,000 stylists working behind the scenes to bridge the gap between shoppers’ personal preferences and an algorithm’s ability to parse that into intelligible analysis. According to Bornstein, the startup, founded in 2011, has seen marked success — 80 percent of its first-time customers return for a second order within 90 days, and around a third spend 50 percent of their entire apparel budget with Stitch Fix.

Forbes also reported that the startup is on track to record more than $200 million in revenue by the end of 2015. With that kind of strong performance, Stitch Fix could usher in a new type of hybrid startup that delivers everything shoppers want, while cutting out the uncertainty retailers hate.

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