{"id":32970,"date":"2018-11-13T16:22:01","date_gmt":"2018-11-13T21:22:01","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/stitch-fix-using-machine-learning-to-help-the-grinch\/"},"modified":"2018-11-13T16:22:01","modified_gmt":"2018-11-13T21:22:01","slug":"stitch-fix-using-machine-learning-to-help-the-grinch","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/stitch-fix-using-machine-learning-to-help-the-grinch\/","title":{"rendered":"Stitch Fix: Using Machine Learning to Help The Grinch"},"content":{"rendered":"

In the Jim Carrey version of The Grinch Who Stole Christmas<\/em>, he despairs, \u201cbut what will I wear?!\u201d when fronted with attending Christmas festivities. While she may have not had the green Dr. Seuss character in mind when creating Stitch Fix, founder Katrina Lake was essentially trying to solve that problem\u2014helping women figure out what to wear without breaking the bank. In other words, how could you make apparel personal stylists more scalable and affordable for consumers?<\/p>\n

According to Forrester, online sales will account for 17% of all US retail sales by 2022[1]<\/a>. As some major retailers are filing for bankruptcy (e.g., Sears, Toys R US), others are scrambling to figure out how technology, big data, and digital efforts can help them compete in a space that\u2019s increasingly online. The beauty of Stitch Fix is that Lake saw an opportunity to take a data-driven approach to solving some of the pain points seen in both brick and mortar and<\/em> traditional online apparel shopping. In other words, a proactive approach to serving customers based on industry-wide pain points, rather than a traditional retailer taking a reactive approach to navigating an evolving landscape.<\/p>\n

In an interview, Lake describes early tests with ~20 individuals, trying to figure out whether it was even feasible to make personalized recommendations without meeting in person.[2]<\/a> Initially, she started out making the recommendations to customers herself and including forms for users to share feedback. Even though they have been data-driven from the start, Lake needed to take a more rigorous approach to data science after amassing more users. Machine learning has been key to scalable recommendations for clients, and they have been able to implement a data feedback loop. They have up to 100 measurement points per garment, which is married up with the feedback they get from their 2+ million person user base to further enhance the recommendations they are able to make[3]<\/a>. This ever-increasing pool of data from different sources is critical for not only enabling the business model, but also for improving over time since \u201cresults improve as the amount of training data they\u2019re given increases.\u201d[4]<\/a><\/p>\n

While Stitch Fix was only founded in 2011, a longer term view of their trajectory shows they have since used machine learning to expand into additional offerings (Mens[5]<\/a>, Plus Size[6]<\/a>, Kids[7]<\/a>).They are now also using machine learning for more back-end functions like inventory management\u2014something their data scientists developed by their own volition[8]<\/a>. Interestingly, inventory management and inventory holding was part of the business model that investors saw as a risk for Lake early on.<\/p>\n

Moving forward, I would be concerned with how Stitch Fix maintains its competitive edge. If they aren\u2019t already, I would try to incorporate unstructured data sources to supplement their current data. Leveraging unstructured data such as social media (e.g., Instagram comments, blog posts) could be beneficial for improving offerings and customer loyalty[9]<\/a>, which is increasingly important with the rise of competitors such as Trunk Club. Stitch Fix has called out some objective comparisons with competitors on their website which shows they are actively thinking through their comparative value proposition.<\/p>\n

Figures from Stitch Fix website<\/em>[10]<\/a><\/p>\n

\"\"<\/p>\n

Two final questions I have are 1) is there transferability of their algorithms to non-apparel purchasing?, and 2) should they think about monetizing some of their technology for other retailers? One example for the first is Sephora claiming they have \u201ctrained a program that can measure [facial features] in real time\u201d [11]<\/a> to enable recommendations and virtual try-on. While the appeal of machine learning for beauty recommendations mirrors that of retail, Stitch Fix\u2019s model hinges on the ability to send clothes back (which you couldn\u2019t resell in cosmetics). This begs the question of whether there are other subsets of retail that are conducive to this model. And finally, I wonder if monetizing their inventory management machine learning algorithm would be beneficial or whether that point of differentiation will keep competitors from succeeding.<\/p>\n

 <\/p>\n

Word Count: 797<\/em><\/p>\n

Footnotes<\/span>:<\/p>\n

[1]<\/a> \u201cOnline Retail Forecast, 2017 to 2022 (US)\u201d, Forrester<\/em> (August 2017)<\/p>\n

[2]<\/a> \u201cStitch Fix: Katrina Lake,\u201d How I Built This with Guy Raz<\/em> (April 2018)<\/p>\n

[3]<\/a> \u201cStitch Fix\u2019s CEO on Selling Personal Style to the Mass Market,\u201d Katrina Lake, 性视界 Business Review<\/em> (May-June 2018)<\/p>\n

[4]<\/a> \u201cWhat\u2019s Driving the Machine Learning Explosion?\u201d, Erik Brynjolfsson & Andrew McAfee, 性视界 Business Review<\/em> (July 2017)<\/p>\n

[5]<\/a> \u201cStitch Fix launches Stitch Fix Men\u201d, PR Newswire<\/em> (September 2016)<\/p>\n

[6]<\/a> \u201cStitch Fix adds to its portfolio by launching Stitch Fix Plus\u201d, PR Newswire<\/em> (February 2017)<\/p>\n

[7]<\/a> \u201cStitch Fix launches Stitch Fix Kids\u201d, PR Newswire<\/em> (July 2018)<\/p>\n

[8]<\/a> \u201cStitch Fix\u2019s CEO on Selling Personal Style to the Mass Market,\u201d Katrina Lake, 性视界 Business Review<\/em> (May-June 2018)<\/p>\n

[9]<\/a> \u201cThe benefits of machine learning for retail\u201d, Cape Town: SyndiGate Media Inc<\/em>. (May 2017)<\/p>\n

[10]<\/a> https:\/\/www.stitchfix.com\/stitch-fix-vs-competitors<\/p>\n

[11]<\/a> \u201cHow Sephora is leveraging AR and AI to transform retail and help customers buy cosmetics,\u201d Alison Rayome, Tech Republic<\/em>: https:\/\/www.techrepublic.com\/article\/how-sephora-is-leveraging-ar-and-ai-to-transform-retail-and-help-customers-buy-cosmetics\/<\/p>\n","protected":false},"excerpt":{"rendered":"

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