Turning Fashion By The Numbers Into A Billion-Dollar Business

Since its founding in 2011, Stitch Fix has had a difficult challenge: how to ship customers clothing that matches both their size and taste preferences once a month, without the client ever having to be present for a measurement or inventory check.

The simple answer, is data (of course) but in Stitch Fix’s case there is a lot of data in play. There is the data customers input about themselves directly, the information Stitch Fix learns from what is kept and returned, data from reviews — and, for the last few years, data from Style Shuffle, which allows customers to rate a set of clothing images each day.

The ability to swipe left and right on outfits might seem like a small add-on — but three-quarters of Stitch Fix’s approximately 3 million shopper customer base have used it. All in, according to Stitch Fix, it has provided over a billion data points that has been able to then turn back on the problem of perfecting its personalized offerings for customers once a month.

Because, according to Stitch Fix CEO Katrina Lake, customers have too many options in many cases — and not a lot of good guide maps to move through them.

“Here are all these beautiful things,” says Lake told Fast Company.  “but the reality is only a subset of things are right for me.”

The Stitch Fix shopping experience melds the brand’s in-house labels with items from over 1,000 brands, many of which are pretty recognizable to the average shopper: pping.

Stitch Fix is also bringing innovation to suppliers — the brands it works with — including Kate Spade, Karl Lagerfeld Paris, Sam Edelman, John Varvatos, Toms and Rebecca Minkoff.

The brand went public a little over a year ago, and has grown up considerably from its original womenswear only focus: “Fixes” (the in-house term for the fashion boxes the firm serves up) are now available for men, children, plus sizes and for basics like underwear and socks. The firm will also be taking its first international steps into the U.K. later this year.

Its slow but steady pace has seen the firm achieve profitability, generating  $1.2 billion in sales in 2018. The company also reported its highest-ever rate of purchased items per Fix among female customers, meaning the algorithm is getting it right more often than it ever has. Good news for a firm nearly entirely driven by algorithmic instructions. From clothing suggestions, to stocking, to packing at the warehouse, to shipping — algorithms run the “how” for every aspect of the business, with an eye toward speed and efficiency.

Stitch Fix also makes customers move fast, as it requires them to decide what items to buy within three days.

The quick turnaround time, combined with its algorithmic buying, lets Stitch Fix turn over its inventory six times a year, instead of a department store’s four.

But the human touch is also critical when it comes to matching requests too subtle for artificial intelligence (AI). Those stylists are mostly work-from-home employees, who put together the consumer’s Fix by flipping through the algorithm’s suggestions and then matching them up against the user’s style and what items have already been sent in the past. What customer are looking for first and foremost, Lake said, is 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 said. “There’s no price for a bad dress.”

Moreover, she noted, because of the in-depth look the algorithm gets of the shopper, it can build what it sends around that fit, so that the boxes remain unique and tailored, so to speak, to the needs of the buyer.

“A client who’s 60 years old, who lives outside of Minneapolis,” Lake said, “is going to get a totally different Fix than a client who is 24 years old and lives in Manhattan.”

Some clients have debated that, noting that Stitch Fix favors a certain flavor of clothing and that it still lacks range in terms of the types of recommendations it makes. But Lake notes that those data tools are getting better, and the data sets are getting wider. Which means the matches it hopes to make going forward will be better — and will pull from an ever-widening range.

But since its inception, the brand has been committed to slow and steady growth — and to staying focused on refining and improving its product over time — and to meet the needs of their expanding client base.

“We did what we said we were going to do for [2018]: We committed to growth rates, revenue, and profitability, and we delivered on all of that,” Lake said. “As a public company, there’s a lot that’s outside of your control — and to some extent the stock price is one of those things in the short term — but it’s very much in our control long term.”