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<meta property="og:title" content="Stitch Fix Algorithms Tour" />
<meta property="og:description" content="How data science is woven into the fabric of Stitch Fix." />
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<title>Stitch Fix Algorithms Tour</title>
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Engineering
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Algorithms
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Algorithms Tour
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<a href="http://algorithms-tour.stitchfix.com/" class="global-nav__item current">
Tour
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Careers
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Engineering
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Algorithms
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Algorithms Tour
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Tour
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Careers
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Blog
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<h1 class="intro-title-main">Algorithms Tour</h1>
<p class="intro-title-sub">How data science is woven into the fabric of Stitch Fix</p>
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<p style="margin-bottom: 0px">
At Stitch Fix, we’re transforming the way people find what they love. Our clients want the perfect clothes for their individual preferences—yet without the burden of search or having to keep up with current trends. Our merchandise is curated from the market and augmented with our own designs to fill in the gaps. It’s kept current and extremely vast and diverse—ensuring something for everyone. Rich data on both sides of this 'market' enables Stitch Fix to be a matchmaker, connecting clients with styles they love (and never would’ve found on their own).
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<div id="topics">
<a href="#warehouse-assignment" class="topic" id="warehouse-assignment-topic">
Warehouse Assignment
</a>
<a href="#recommendation-systems" class="topic" id="recommendation-systems-topic">
Recommendation Systems
</a>
<a href="#matchmaking" class="topic" id="matchmaking-topic">
Matchmaking
</a>
<a href="#human-computation" class="topic" id="human-computation-topic">
Human Computation
</a>
<a href="#logistics-optimization" class="topic" id="logistics-optimization-topic">
Logistics Optimization
</a>
<a href="#state-machines" class="topic" id="state-machines-topic">
State Machines
</a>
<a href="#demand-modeling" class="topic" id="demand-modeling-topic">
Demand Modeling
</a>
<a href="#inventory-management" class="topic" id="inventory-management-topic">
Inventory Management
</a>
<a href="#new-style-development" class="topic" id="new-style-development-topic">
New Style Development
</a>
<a href="#data-platform" class="topic" id="data-platform-topic">
Data Platform
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<section class="step" style="margin-top: 0px; margin-bottom: 0px">
<p></p>
</section>
<section class="step" style="margin-top: 250px;">
<p>
Our business model enables unprecedented data science, not only in recommendation systems, but also in human computation, resource management, inventory management, algorithmic fashion design and many other areas. Experimentation and algorithm development is deeply engrained in everything that Stitch Fix does. We’ll describe a few examples in detail as you scroll along.
</p>
</section>
<section class="step" style="margin-top: 150px;">
<p>
So what does the data look like? In addition to the rich feedback data we get from our clients, we also receive a great deal of upfront data on both our clothing and our clients. Our buyers and designers capture dimension and style details, and our clients fill out a profile upon signup that’s calibrated to get us the most useful data with the least client effort.
</p>
</section>
<section id="client-experience-part-1" class="step" style="margin: 80px 0">
<p>
Let's first walk through the filling of a shipment request to see a few of the many algorithms that play a role in that process, before zooming out to view the bigger picture.
</p>
</section>
<h3>Client Experience Part 1: Profile & Request</h3>
<section id="warehouse-assignment" class="step">
<p>
As noted, when a client first signs up, he or she fills out a Style Profile (it can be updated at any time). Then, scheduling a delivery is easy: an algorithm is used to populate a calendar from which she selects a delivery date.
</p>
</section>
<h3>Warehouse Assignment</h3>
<section class="step" style="margin-bottom: 100px">
<p>
The shipment request is processed by an algorithm that assigns it to a warehouse. This algorithm calculates a cost function for each warehouse based on a combination of its location relative to the client and how well the inventories in the different warehouses match the client's needs.
</p>
</section>
<section class="step" style="margin-bottom: 100px">
<p>
This set of cost calculations is carried out for each client to produce a cost matrix.
</p>
</section>
<section class="step" style="margin-bottom: 100px">
<p>
The assignment of clients to warehouses is then a binary optimization problem.
</p>
</section>
<section class="step" style="margin-bottom: 300px">
<p>
And the global optimum includes this particular client's warehouse assignment.
</p>
</section>
<section id="recommendation-systems" class="step">
<p>
The shipment request is then routed to the Humans + Machines styling algorithm.
</p>
</section>
<h3>Intelligent Machines</h3>
<section class="step">
<p>
First, the machines perform a variety of algorithms to produce rank-ordered lists of the inventory.
</p>
</section>
<section class="step">
<p>
A filtering step removes styles from consideration that the client has received in a previous shipment or that have attributes which the client has asked to avoid.
</p>
</section>
<section class="step">
<p>
For each of the remaining styles, the machines then try to evaluate the relative likelihood that this particular client will love that particular style. This is a difficult problem, and we approach it in many different ways—only a few of which we’ll discuss here. But in general, note that we tag each item multiple times with match scores from different algorithms, and then rank them.
</p>
</section>
<section class="step">
<p>
In some ways, the problem is a classic collaborative filtering problem: given different clients' feedback on different styles, we must fill in the gaps in the (sparse) matrix to predict the result of sending a style to a client who has not yet received it. As such, we <i>do</i> use some standard collaborative filtering algorithms (e.g. those who have liked what you have liked have also liked ...).
</p>
</section>
<section class="step">
<p>
However, unlike most collaborative filtering problems, we have a lot of explicit data, both from clients' self-descriptions and from clothing attributes. This helps with the cold start problem and also allows for greater accuracy if we employ algorithms that consider this data.
</p>
</section>
<section class="step">
<p>
One such approach is mixed-effects modeling, which is particularly useful because of the longitudinal nature of our problem: it lets us learn (and track) our clients' preferences over time, both individually and as a whole.
</p>
</section>
<section class="step">
<p>
And in addition to the many explicit features available, there are some particularly pertinent latent (unstated) features of both clients and styles that we can infer from other data (structured and/or unstructured) and use to improve our performance.
</p>
</section>
<section class="step">
<p>
For example, a new client may tell us that she wears medium-sized blouses, but where exactly would her preference fall along the spectrum of smallish mediums to largish mediums? The same question also applies to particular styles of clothing in our inventory. (Note that in this illustration we’re treating fit as unidimensional for simplicity, but in fact at Stitch Fix we treat it as multidimensional.)
</p>
</section>
<section class="step">
<p>
With clients' fit feedback and purchase histories, we can learn where particular clients and styles fall along this spectrum. These latent features can then be used in our mixed-effects models and elsewhere.
</p>
</section>
<section class="step">
<p>
Moving our problem even further beyond classical collaborative filtering, we also have a lot of photographic and textual data to consider: inventory style photos, Pinterest boards, and the vast amount of written feedback and request notes we receive from clients.
</p>
</section>
<section class="step">
<p>
Sometimes it can be difficult to describe your style preferences in words, but you know it when you see it—so we have our machines look at photos of clothing that customers like (e.g. from Pinterest), and look for visually similar items in our inventory. We use trained neural networks to derive vector descriptions of 'pinned' images, and then compute a cosine similarity between these vectors and pre-computed vectors for each item in our inventory.
</p>
</section>
<section class="step">
<p>
Natural language processing is used to score items based on the client's request note and textual feedback from other clients about the same item.
</p>
</section>
<section id="matchmaking" class="step" style="margin-bottom: 100px">
<p>
All of these algorithm scores—and many others like them—are taken into account when ordering and presenting options for the human expert stylist to consider.
</p>
</section>
<h3>Matchmaking</h3>
<section class="step">
<p>
Once this machine ranking is complete, the shipment request gets routed to a human.
</p>
<p>
Humans are more heterogeneous than machines. The machines are all the same—just pick one. But human stylists are going to be better suited to some clients than to others. So we use algorithms to optimize this match.
</p>
</section>
<section class="step">
<p>
To do this, we first calculate a match score between each available stylist and each client who’s requested a shipment during the current period. This match score is a complex function of the history between that client and stylist (if any), and the affinities between the client's stated and latent style preferences and those of the stylist.
</p>
</section>
<section id="human-computation" class="step">
<p>
Subsequently, the stylist assignment optimization problem is similar to the warehouse assignment problem described above, except that (a) it need only consider those clients awaiting shipments, and (b) we must re-run the optimization problem much more frequently to account for the varying sizes of stylists' queues as they work.
</p>
</section>