05. For fixing retail one data point at a time
While working on a PhD in astrophysics, Chris Moody used supercomputers to simulate how galaxies crash into each other. For his first nonacademic job, he joined Square as a data scientist in 2013. About a year later, he started talking with some data-scientist friends who were employed at a startup called Stitch Fix, an upstart e-commerce service that delivered boxes of women’s fashion, known as “Fixes,” using a mix of algorithmic and human curation. ¶ Moody was mystified. “What on earth are you guys doing at a clothing company?” he recalls asking, admitting that his sartorial taste at the time hewed to “what costs less than ramen?” Their response, though, sent his brain firing. How do you mail customers clothes they’ll love, and that fit them perfectly, without the client ever getting measured or viewing the inventory? Soon he was pushing for a job. “When I was interviewing, I was like, Ooh, this is a place where I’m going to be continuously thinking about this stuff in the shower, going to bed, waking up in the morning.”
He joined in January 2015, and he’s still obsessed. Frustrated that the company only received feedback from customers on the five items mailed in each box, he designed a feature in 2017 called Style Shuffle, which allows customers to rate a set of clothing images each day. A sort of Tinder for clothes, it became available on Stitch Fix’s iOS app in March and has proven to be stickily addictive: It not only trains the company’s algorithm to understand holistically a client’s personal style, but it also draws customers back to the app and interests them in Stitch Fix’s inventory. More than 75% of Stitch Fix’s 2.9 interesting,” Moody tells me while wearing a decidedly unfrumpy Nehru-collared shirt.
