Stitch Fix is diving deep into data for a more tailored experience
In just eight years, online retailer Stitch Fix has created a flourishing business. More than 3.2 million shoppers use its service annually to buy merchandise from jeans to wool sweaters to bracelets.
Unlike with conventional online retailers, customers subscribe to Stitch Fix to receive boxes of apparel and accessories, or “fixes,” as often as they want. When signing up, clients answer a long list of questions about the kind of clothes they like and their body type—information that the company’s algorithms and human stylists use to choose which items to send. Customers keep and pay for what they like, and send the rest back.
Now Stitch Fix CEO Katrina Lake is laying the groundwork for her company’s next chapter. She wants to tap Stitch Fix’s data-crunching prowess to even more accurately predict what shoppers want to buy and keep, and to drum up more business between so-called fixes.
“We are trying to figure out how we can use personalization to deliver more parts of your closet so that you can use those items for all occasions,” Lake tells Fortune.
For Wall Street, Stitch Fix’s push for new revenue sources can’t come soon enough. As of mid-October, its shares were down 30% from their 2019 high, owing both to the rising cost of attracting and retaining customers and to rivals’ copying its personalized approach to e-commerce.
The ongoing upheaval in the retail industry makes Stitch Fix’s latest push that much more challenging.
One crucial test for the company is Shop Your Looks, a feature that suggests additional items to customers to complement what Stitch Fix sends them in their fixes. For example, clients who keep a jacket sent to them may later receive a suggestion via email that they buy a pair of sunglasses to go with it.
The hope is that Shop Your Looks will prompt an impulse buy between boxes and get customers to visit Stitch Fix more often. Lake recognizes the risk of making Stitch Fix just another online retailer that bombards shoppers with an exhausting list of “suggested items.”
That means the e-tailer shows at most 30 to 40 suggested items online—a lot of choice, but not the endless scroll shoppers see in Amazon’s or eBay’s search results. So far, 60% of people who have bought an item using this feature have bought more than one.
In terms of its business, Stitch Fix is getting mixed results. In the 12 months ended Aug. 3, its revenue soared 29% to $1.58 billion compared with the preceding year. During that period, the company had a profit of $36.9 million. But that profit was down 18% from the previous year, as Stitch Fix spent heavily to build out new services.
Stitch Fix must show nervous investors that it can continue to attract new customers and sell more to existing ones, all while grappling with the apparel industry’s rampant discounting of overstocked clothing.
As KeyBanc Capital Markets analyst Ed Yruma points out, Stitch Fix also faces “long-term concerns” related to Amazon. The self-proclaimed Everything Store’s overall apparel business is growing rapidly, and in July it debuted its own personal shopping service—putting Stitch Fix directly in its crosshairs.
As if that’s not enough, Stitch Fix has a serious rival in Nordstrom’s Trunk Club, a slightly higher-end bespoke shopping service. Meanwhile, Instagram and Pinterest have both made their services friendlier to online retailers, adding a new wrinkle to what is already a complex retail environment.
That means Stitch Fix must keep improving the accuracy of its technology. An army of some 3,000 human stylists uses what the algorithm spits out to help decide what to include in customer fixes.
Style Shuffle, a feature added last year that shows customers prospective products one at a time and lets them vote on each, is part of the company’s effort to improve its data crunching. The information collected through the tool—some 3 billion ratings have been submitted—helps make customer suggestions more accurate.
Meanwhile, Stitch Fix is also working on expanding its clothing selection, which is heavy on smaller brands. Big-name clothing makers have gradually come on board, including New Balance and Madewell. Part of the pitch is that Stitch Fix can share data with them about what customers like. That kind of information also helps Stitch Fix more accurately predict demand for its own clothing brands, an increasingly crucial part of its business.
For her part, Lake doesn’t see any choice but to focus on data.
“If somebody is not receiving things that they love, they’re going to stop [using Stitch Fix],” she says. “We live and die by our ability to personalize for people. That is our lifeblood.”
My Own Personal Shopper
Stitch Fix’s CEO shows what it’s like to let algorithms and human stylists choose your wardrobe. By Michal Lev-Ram
“Do you want a ripped denim?” asks Katrina Lake, CEO of online styling service Stitch Fix, her computer cursor hovering over an image of faded blue jeans.
I’ve never actually owned a pair of pants with premade holes, so I’m unsure how to answer. Lucky for me, the software Lake is running on her MacBook has already spit out an educated guess on my behalf: There’s a 74% chance that I’ll like this particular garment. I tell her yes, and the CEO clicks on the image, adding it to my “fix” (the personalized box of five items Stitch Fix sends to its clients). We move on to outerwear.
“Ooh, this one is good for San Francisco weather,” she says, pointing to a black jacket. Apparently, it belongs in my closet—I have a 62% chance of keeping it, according to Stitch Fix’s software.
It’s not just algorithms that pick clothes for customers at Stitch Fix; it’s also art. The company’s human stylists have a say when creating fixes for customers. Today, Lake is giving me a behind-the-scenes look at the process—and using my real-life Stitch Fix profile to put together a real-life fix for me.
Here’s how it works: Before a fix is started, Stitch Fix’s technology pairs a customer with a stylist, taking into account variables like location and fashion preferences (we’ve skipped that step because Lake has been designated as my stylist for this demo). Then the selected stylist accesses the client’s account to review a preselected assortment of clothes that the system’s algorithm has deemed to be in line with that shopper’s taste.
A lot of data feeds into this computerized curation, including a customer’s profile (I’ve told Stitch Fix not to send me “critter” prints, for example) and purchase history (I may say that I want bold colors but tend to keep black tops). The stylist still has say over the final selection and can override the system’s suggestions.
“It helps the stylist thoughtfully make the right choices,” Lake says of the technology, adding that stylists can send shoppers an item with a low score if the shopper specifically asks for it.
I see this play out in real time when I ask Lake to find me some boots. When she clicks into the category, though, the highest-ranked boots are listed as having only a 4% likelihood of ending up in my closet. “We’ve sent you 11 pairs of shoes, and you’ve only kept two,” Lake says. (We decide to skip the boots.)
A few days later, my fix arrived on my doorstep, along with a note from the CEO. “Just for fun, here are our predictions on what you’ll like!” wrote Lake, noting for each item the statistical probability that I will. As it turned out, the combination of Lake’s eye and her company’s algorithms was a winner: The three garments I kept all happened to have the highest likelihood of my keeping them—and, yes, I’m now the proud owner of ripped denim.
A version of this article appears in the November 2019 issue of Fortune with the headline “Stitch Fix Thinks Outside the Box.”
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