How Stitch Fix Relies on Data Science to Build the Perfectly Personalized eCommerce Experience

Style is a very personal part of what makes someone who they are. The way you dress is a reflection of who you are or who you want to be, and what speaks to you may be totally foreign to the next person. Knowing all of that, it’s understandable if you believe that something as personal and experience-driven as style could never be boiled down to data points or plugged into an algorithm. But… you’d be wrong.

At Stitch Fix, a combination of human stylists, powerful A.I., and behind-the-scenes technology has created a winning model that delivers a personalized online shopping and styling experience straight to clients’ homes. A powerful data science team is one of the key reasons that Stitch Fix has been able to launch its valuation into the billions. Stephanie Yee is the VP of Data Science at Stitch Fix, and on this episode of Up Next in Commerce, she explains all the ways that data and technology are being put to use to create the best customer experience possible.

Stephanie describes how technology like GPT-3 is going to finally make seemingly unimportant data consumable to a consumer audience, and she explains how an event like COVID-19 can impact your backend models and what to do to adapt in that situation. Plus, she gives tips on how any ecommerce operation can go about building a data science team, and the soft skills to focus on when hiring talent. 

Main Takeaways:

• Asking the Right Questions: The most important skill a data scientist can have has nothing to do with technical prowess. It’s about having the ability to frame a problem and then ask and answer the right questions. Encourage your team or new candidates to pump the brakes and reevaluate the “why” behind the question they are trying to answer or the problem they are trying to solve. 
• Making The Indecipherable Easily Digestible: With the shifting demographics, and older generations now becoming more comfortable shopping online, tools need to be created to ingest and answer long-form questions in a way the consumer connects with. Technology like GPT-3, which is the most advanced language model to date, has the ability to do just this. Tune in to hear how! 
• The Quick Change: Deploying algorithms and A.I. in conjunction with human resources/industry experts is critical for organizations to be able to adapt to big changes in a market. COVID-19 had a drastic impact on models that were trained on pre-COVID data. Should you scrap your current model and start over? Or build on what you have? Stephanie says a little bit of both.

For an in-depth look at this episode, check out the full transcript below. Quotes have been edited for clarity and length.

---

Up Next in Commerce is brought to you by Salesforce Commerce Cloud. Respond quickly to changing customer needs with flexible Ecommerce connected to marketing, sales, and service. Deliver intelligent commerce experiences your customers can trust, across every channel. Together, we’re ready for what’s next in commerce. Learn more at salesforce.com/commerce

---

Transcript:

Stephanie Postles:

Welcome back to Up Next in Commerce. This is your host, Stephanie Postles, co-founder of mission.org. Today on the show, we have Stephanie Yee, the VP of data science at Stitch Fix. Stephanie, welcome.

Stephanie Yee:

Thank you. I'm excited to be here.

Stephanie Postles:

Me, too. I know it's going to be a good interview when there's two Stephanies, but I'm slightly worried about how the transcript will look. Like who's saying what? Who sounds smart? I'll just take all your quotes and pretend they're mine.

Stephanie Yee:

Perfect.

Stephanie Postles:

So tell me a little bit how long have you been at Stitch Fix for?

Stephanie Yee:

I've been at Stitch Fix for almost four years. Yeah, four years in January.

Stephanie Postles:

Well, tell me a little bit what does the role of the VP of data science look like day-to-day?

Stephanie Yee:

Yeah. If I have to think about it, being the VP of data science, it really comes down to maximizing the value that the data science itself and the team can bring to the company, like how do we really get the full promise of an algorithm's approach to things? I think as you guys probably know, Stitch Fix is really thinking about how do we help people find what they love and how do we use data science and human expertise to do that? So the types of things that I think about in service of that are things like what are new opportunities that we haven't really discovered yet? And that's been pretty exciting over the last four years.

Stephanie Yee:

I think another area that I think about a lot is like what's the right almost interface between data science and data scientists and the business partners. So this is if we have data scientists working with the design team, or the product team, or the marketing team, or even executives, what's the place where data scientists can contribute the most? And also, just being really intellectually honest, like what's the place where it makes sense for others to take over? And then obviously, the last part of my job is to really create an environment where the team can be motivated and fulfilled in doing things that bring out the best to each of them.

Stephanie Postles:

That's great. So it would be great to dive a bit more into Stitch Fix. I know what it is because I'm a customer, but I think a lot of people may not know exactly what it is or all the things that go on behind the scenes to get the pretty box on your door. So could you explain what it looks like, what is Stitch Fix from a high level, for anyone who doesn't know, and then what goes on behind the scenes to create the company that it is?

Stephanie Yee:

Yeah, so Stitch Fix is a personal styling company. And at the core, we use both data science and real stylists and their expertise to help people find what they love. If you think about unpacking that, it's really about understanding... or from a data science perspective, it's really about understanding a client's needs, as well as being able to set the stylist up for success. The core of Stitch Fix, the way that it shows up is in a box of one or more items and clients are able to try it on, they're able to send back what they don't like and really just keep what they really love.

Stephanie Postles:

Tell me how do you go about making sure that you give the customer the exact outfits they would like or refine that process to where maybe the second or third time you've nailed it? Because for me, at least when I am getting the outfits, I'm like, "The first time, maybe like one thing was off or something," but then after that, it's like, "Okay, now, this stylist knows me, or this algorithm knows me." So how do you refine that behind the scenes?

Stephanie Yee:

Yeah. I think that that's a great question. I think a lot of it... I mean, as a data scientist, like I always think about the data that we collect and what's available, and this comes both from what clients tell us as well as what we're able to infer, so a really interesting example of this, and this is where you had mentioned like, "Okay, there might be one item off at first and the algorithm really learns over time," we really think about things in terms of the ability to say like, "Okay, what data do we have now?" And with the stylist, the stylist is incredibly important throughout the client's life cycle. With the stylist, like what's the right thing to be sending right now? And in response to feedback like, "Oh, that item that didn't really work out for whatever reason," we're able to respond to that.

Stephanie Yee:

I think a really interesting example of the approach that Stitch Fix takes, or rather one of the interesting things about Stitch Fix is that we're thinking about this and we're thinking about a purchase experience in terms of soft goods. So if you think about the way that ecommerce really started off, or at least as I recollect it, it was like comparison shopping sites where you were looking at like how many megapixels do you want in your digital camera. And a camera, those are very easy to compare because it's like, "Oh, it is three or it is four." Whereas with what I think of as soft goods, there's so many different variants on like a V-neck top that it's almost a little bit overwhelming.

Stephanie Yee:

And then on top of that, a lot of the typical searching and filtering is not really going to get people there, just because what might be a great top, even if it's the same aesthetic, what may might be a great top for you might be not as great for me or vice versa, just because it's like, "Oh, you know what? I really need things that are machine washable, or I have very narrow shoulders or something like that." So Stitch Fix is really trying to distill a lot of these things that are ultimately very difficult to categorize into what we would call a latent space, but really to say like, "Okay, we have something like style." Style is not what lunch table did you sit at in high school, it's really a form of self-expression. And because people are so different, we need to be able to use data science to quantify where people are on a spectrum versus