The 3,900 stylists who translate these signals into clothing choices are mostly part-time employees who work from home, clustered in smaller cities like Sacramento and Dallas, so they can join periodic in-person meetings. As they put together Fixes, they scroll through algorithm-generated clothing suggestions, each with a “match score” (the percentage chance a particular client will buy the item), and glance at what’s been sent in the past to avoid anything too similar.
Stitch Fix has found that the most important attribute isn’t style or price, but fit. “We ‘re better off sending you a dress that’s $68—that you’re going to love, that fits you great—than something that’s discounted because it’s not working,” Lake tells me. “There’s no price for a bad dress.” That is to say, if a stylist recommends a Calvin Klein shift dress that you never would have considered, but it fits uncannily well, you may just shell out $88 to keep it, as I did.
Still, using algorithms as the North Star to identify people’s style and find them the right clothes to match is difficult. The data science is getting better, but it’s far from perfect. “A client who’s 60 years old, who lives outside of Minneapolis,” Lake says, “is going to get a totally different Fix than a client who is 24 years old and lives in Manhattan.” But I didn’t entirely see that play out in my own Fixes, nor have some of my Bay Area friends, who feel they keep receiving iterations of the same cardigans and basic layering shirts—i.e., suburban-mom wear. Stitch Fix is ideal for people who hate shopping, don’t have a great idea of what looks good on them, and for whom clothes from the fashion median are just fine. Which is a lot of women, and arguably even more men. It’s the place to find a leather jacket, a floral maxi dress, a button-down shirt, and jeans. (Unless you’re willing to pay for Stitch Fix’s premium designer labels, with prices in the $100 to $600 range.)
Although Stitch Fix works with more than 1,000 brands, it also designs its own clothing in-house to fill out holes in the marketplace and ensure consistent inventory. The company’s data and merchandise teams combine popular attributes from past items to create pieces that will have predictably high profit margins and buy rates. Stitch Fix does not disclose the percentage of its own labels that make up any given Fix, but in 2017 revealed that it was about 20%. Lake insists that Stitch Fix is not trying to become a vertically integrated fashion house, making all its own clothes. “There are so many great vendors out there,” she says.