Fashioning the Perfect Fit With AI: Stitch Fix’s Jeff Cooper

**Sam Ransbotham** (0:02)
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How can humans and generative AI work together to ensure we're dressing for success? Find out on today's episode.

**Jeff Cooper** (1:04)
I'm Jeff Cooper from Stitch Fix, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (1:09)
Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI.
I'm Sam Ransbotham, professor of analytics at Boston College. I'm also the AI and business strategy guest editor at MIT Sloan Management Review.

**Shervin Khodabandeh** (1:27)
And I'm Shervin Khodabandeh, senior partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

**Sam Ransbotham** (1:54)
Hi, everyone.
Today, Shervin and I are talking with Jeff Cooper, senior data science director at Stitch Fix. Jeff, thanks for taking the time to join us.

**Jeff Cooper** (2:02)
Thanks so much for inviting me. I'm really excited to be here.

**Sam Ransbotham** (2:04)
Let's get to some fashion basics here. What is Stitch Fix?

**Jeff Cooper** (2:08)
Stitch Fix is an online personal styling service. We serve more than 3 million clients, we call them, in women's, men's and kids.
And what we're trying to do is help people get dressed to offer the most convenient way to find clothes that you love, find clothes that you might have found yourself, and we can find a great way to put them together with other things and also find clothes that you might not have chosen for yourself and help you push the boundaries of your style. So we have this kind of unique art and science approach where when you sign up, you're matched with a stylist, one of our thousands of style experts, and then our stylists work together with our data science team and tools that we provide them to help find clothes for you. We send them to you, you keep what you like, you send back anything that you don't want for free.
We've been doing this for over a decade now. We just had our 13th birthday yesterday, actually, and we just passed 100 million fixes since pretty recently. So at this point, we have spent a lot of time thinking about how to serve our clients the best, how to blend great algorithms and data science with our style experts' intuition and understanding and so forth.
We really believe in this model as a way to help clients find what they need.

**Shervin Khodabandeh** (3:26)
What a great example of human and machine working together. Tell us more about how that actually happens.
What does the machine do? What does the human do? How do they work together?

**Jeff Cooper** (3:37)
We're really passionate about this model. We've been at it for a long time, really since the beginning.
As you can imagine, for any retailer, the idea of sending people things that they haven't specifically chosen for themselves and then they can return them for free feels a little risky, maybe.
And in order to do it well, as we think we do, you really have to know your customers incredibly well. So we have certain things that we think hard about doing. We have to have great learnings about our customers. We ask them many questions.
Our customers are interested in talking to us about their style. They're here to help be styled. And so we think really hard about all the ways that we gather feedback. When you try on an item, whether you keep it or return it, we ask for a lot of feedback. We get a lot of feedback. 85% of clients leave feedback on items. We ask for a lot of questions up front. We ask some questions as you go. And typically clients leave many requests as they go shipment by shipment.

**Sam Ransbotham** (4:36)
So you're learning a lot about your customers through these questions and requests.

**Jeff Cooper** (4:40)
Then you have the question of, okay, what do you do with it?