With Stitch Fix, users don’t go shopping for their clothes. Professional stylists do the job for them and the personal shopping service ships the new clothes to their door.
That combo is behind the success at Stitch Fix, a San Francisco-based online subscription and shopping service founded in 2011.
“I think it’s the single most salient aspect of our company,” said Eric Colson, chief algorithms officer at Stitch Fix. “Our business is getting relevant things into the hands of our customers. This is the one thing we’re going to be best in the world at. We couldn’t do this with machines alone. We couldn’t do this with humans alone. We’re just trying to get them to combine their powers.”
Stitch Fix, a company with about 4,000 employees — 2,500 of them working as stylists — has amassed a following among busy women — and as of February, among men. That’s when the company launched a beta service for guys, with a full public launch scheduled for this fall.
Stitch Fix is aimed at people who either don’t enjoy shopping or simply don’t have the time to go to a brick-and-mortar store or cull through endless online pages of shirts, pants, sweaters and jackets.
Users start by filling out an online style profile. Do you like blousy or close-fitting tops? Favorite colors? Are you more urban hipster, Sex in the City chick or Bohemian? Do you prefer jeans over dresses?
Stitch Fix stylists, both human and machine, handpick a selection of five clothing items and accessories that fit each client’s taste, budget and lifestyle. Clients keep what they want and return the rest.
Colson, who was a vice president of data science and engineering at Netflix before joining Stitch Fix in 2012, noted that the company was using an algorithm for basic criteria filtering. If a client was a medium, it would filter out shirts that were a large or small. If she didn’t like the color yellow, those items would be excluded.
With his experience at Netflix, which bases much of its business on recommending movies and shows to users, Colson knew Stitch Fix could do more by using machine learning.
“A product manager at Netflix used to say if we were really bold, we wouldn’t present five or so recommendations. We’d present one and if we were going to do that, we should just play a recommendation when the user came online,” he said. ” Here is Stitch Fix being so bold as to say, ‘Don’t worry about picking out stuff. We’ll do that for you.’ That was exciting and bold. Could that be done? Is that possible?”
Later in 2012, the company got its first machine learning algorithm, which was designed to get smarter the more data it handled.
“We’ve been able to augment human judgment with machine algorithms,” said Colson. “We have to combine machines and expert humans. It turns out it works better than even I could have thought.”
Today, the company has hundreds of algorithms, like a styling algorithm that matches products to clients; an algorithm that matches stylists with clients; an algorithm that calculates how happy a customer is with the service; and one that figures out how much and what kind of inventory the company should buy.
Stitch Fix also has an algorithm that learns from images so it can check a client’s Pinterest pins and learn what styles she’s favoring even if the user has a hard time articulating it in an online form or in comments.
The company, according to Jeff Kagan, an independent industry analyst, is likely ahead of a trend where machine learning moves into the enterprise.
“I think this is just the beginning,” he said. “Many new business models will form with A.I. as the center of their universe….