Stitch Fix is an apparel company run by co-founder and CEO, Katrina Lake. In just a span of 6 years, she’s been able to build the company with an annual revenue of a whopping $977 odd million. The company has been disrupting traditional retail and aims to bridge the gap of personalised shopping, that the former can’t accomplish.

Stitch Fix is more of a personalized stylist, rather than a traditional apparel company. It works in 3 basic steps:

1. Filling a Style Profile: Clients are prompted to fill out a style profile, where they share their style, price and size preferences.
2. Setting a Delivery Date: The clients set a delivery date as per their availability. Stitch Fix mixes and matches various clothes from their warehouses and comes up with the top 5 clothes that they feel would best suit the clients, based on the initial style profile, as well as years of experience in styling.
3. Keep or Send Back: The clothes reach the customer on the selected date and the customer can try on the clothes, keep whatever they like or send back what they don’t.

The aim of Stitch Fix is to bring a personal touch to clothes shopping. According to Lake, “There are millions and millions of products out there. You can look at eBay and Amazon. You can look at every product on the planet, but trying to figure out which one is best for you is really the challenge” and that’s the tear Stitch Fix aims to sew up.

In an interview with eMarketer, Julie Bornstein, COO of Stitch Fix said “Over a third of our customers now spend more than half of their apparel wallet share with Stitch Fix. They are replacing their former shopping habits with our service.” So what makes Stitch Fix stand out among its competitors?

How do they do it?

You see, Stitch Fix is not just any apparel company. It has created the perfect formula by blending human expertise with just the right amount of Data Science to enable it to serve its customers. When we’re talking about the kind of Data Science that Stitch Fix does, we’re talking about a relatively new and exciting term that’s on the rise - Full Stack Data Science.

Hello Full Stack Data Science!

For those of you who’ve heard of this before, cheers! I hope you’ve had the opportunity to experience its benefits. For those of you who haven’t heard of the term, Full Stack Data Science basically means a single data scientist does their own work, which is mining data, cleans it, writes an algorithm to model it and then visualizes the results, while also stepping into the shoes of an engineer, implementing the model, as well as a Project Manager, tracking the entire process and ensuring it’s on track.

Now while this might sound like a lot for one person to do, it’s quite possible and practical. It’s practical because of the fact that when these roles are performed by different individuals, they induce a lot of latency into the project. Moreover, a synchronization of priorities of each individual is close to impossible, thus creating differences within the team.

The Data (Science) team at Stitch Fix is broadly categorized based on what area they work on:

Because most of the team focuses on full stack, there are over 80 Data Scientists on board. That’s a lot of smart people in one company! On a serious note, although unique, this kind of team structure has been doing well for them, mainly because it gives each one the freedom to work independently.

Tech Treasure Trove

When you open up Stitch Fix’s tech toolbox, you won’t find Aladdin’s lamp glowing before you. Their magic lies in having a simple tech stack that works wonders when implemented the right way. They work with Ruby on Rails and Bootstrap for their web applications that are hosted on Heroku. Their data platform relies on a robust Postgres implementation. Among programming languages, we found Python, Go, Java and JavaScript also being used. For an ML Framework, we’re pretty sure they’re playing with TensorFlow.

But just working with these tools isn’t enough to get to the level they’re at. There’s something more under the hood. And believe it or not, it’s not some gigantic artificial intelligent system running on a zillion cores! Rather, it’s all about the smaller, simpler things in life. For example, if you have 3 different kinds of data and you need to find a relationship between them, instead of bringing in the big guns (read deep learning frameworks), a simple tensor decomposition using word vectors would do the deed quite well.

Advantages galore: Food for the algorithms

One of the main advantages Stitch Fix has, is that they have almost 5 years’ worth client data. This data is obtained from clients in several ways like through a Client Profile, After-Delivery Feedback, Pinterest photos, etc. All this data is put through algorithms that learn more about the likes and dislikes of clients. Some interesting algorithms that feed on this sumptuous data are on the likes of collaborative filtering recommenders to group clients based on their likes, mixed-effects modeling to learn about a client’s interests over time, neural networks to derive vector descriptions of the Pinterest images and to compare them with in-house designs, NLP to process customer feedback, Markov chain models to predict demand, among several others.

A human Touch: When science meets art

While the machines do all the calculations and come up with recommendations on what designs customers would appreciate, they still lack the human touch involved. Stitch Fix employs over 3000 stylists. Each client is assigned a stylist who knows the entire preference of the client at the glance of a custom-built interface. The stylist finalizes the selections from the inventory list also adding in a personal note that describes how the client can accessorize the purchased items for a particular occasion and how they can pair them with any other piece of clothing in their closet. This truly advocates “Humans are much better with the machines, and the machines are much better with the humans”. Cool, ain't it?

Data Platform

Apart from the Heroku platform, Stitch Fix seems to have internal SaaS platforms where the data scientists effectively carry out analysis, write algorithms and put them into production. The platforms exhibit properties like data distribution, parallelization, auto-scaling, failover, etc. This lets the data scientists focus on the science aspect while still enjoying the benefits of a scalable system.

The good, the bad and the ugly: Microservices, Monoliths and Scalability

Scalability is one of the most important aspects a new company needs to take into account before taking the plunge. Using a microservice architecture helps with this, by allowing small independent services/mini applications to run on their own. Stitch Fix uses this architecture to improve scalability although, their database is a monolith. They now are breaking the monolith database into microservices. This is a takeaway for all entrepreneurs just starting out with their app.

Data Driven Applications

Data-driven applications ensure that the right solutions are built for customers. If you’re a customer-centric organisation, there’s something you can learn from Stitch Fix. Data-Driven Apps seamlessly combine the operational and analytic capabilities of the organisation, thus breaking down the traditional silos.

TDD + CD = DevOps Simplified

Both Test Driven Development and Continuous Delivery go hand in hand and it’s always better to imbibe this culture right from the very start.

In the end, it’s really great to see such creative and technologically driven start-ups succeed and sail to the top. If you’re on the journey to building that dream startup of yours and you need resources for your team, here’s a few books you’ll want to pick up to get started with:

Hands-On Data Science and Python Machine Learning by Frank Kane

Data Science Algorithms in a Week by Dávid Natingga

Continuous Delivery and DevOps : A Quickstart Guide - Second Edition by Paul Swartout

Practical DevOps by Joakim Verona