{"id":7742,"date":"2018-04-08T18:48:13","date_gmt":"2018-04-08T22:48:13","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/stitch-fix-a-marriage-of-art-and-science\/"},"modified":"2018-04-08T18:49:29","modified_gmt":"2018-04-08T22:49:29","slug":"stitch-fix-a-marriage-of-art-and-science","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/stitch-fix-a-marriage-of-art-and-science\/","title":{"rendered":"Stitch Fix: A Marriage of Art and Science"},"content":{"rendered":"

How does it work?<\/strong><\/span><\/p>\n

Founded in 2011 and IPO’d in 2017, Stitch Fix aims to marry the art of personal styling with the science of data analytics to deliver the most personalized and relevant styling suggestions for its 2.5mm+ active clients in a convenient fashion [1].<\/p>\n

A client who wishes to receive personalized styling must first answer a ten-minute online questionnaire, during which data is collected on the customer’s measurements, typical sizes in multiple apparel categories, body shape specifications, style and fit\u00a0preferences, and pricing constraints. Upon the completion of the style profile, the client then selects a date to receive his or her first box of apparel and accessories (also known as a “fix”). Meanwhile, the company’s proprietary algorithm matches the client to a Stitch Fix stylist, who then leverages the AI recommendations and chooses five items. These items are shipped free of charge to the client, who then decide to purchase or return any items (also for free). Stitch Fix charges a $20 styling fee per shipment, to be used towards any item that the client keeps. Should the client wish to keep the entirety of the “fix”, he or she will receive a 25% discount on the shipment [3]. A client may request the service at any time, or on a bi-monthly or quarterly subscription basis [2].<\/p>\n

Exhibit 1 – Illustrative Style Preference Question<\/strong><\/p>\n

\"\"<\/a><\/p>\n

Value Creation and Capture Through Data<\/u><\/strong><\/p>\n

Stitch Fix solicits the following types of data: client-generated data from the initial style profile and any subsequent adjustments, merchandise-level data (e.g. pattern and color of a dress), and positive and negative feedback from clients about the items received. Clients appear to be highly-motivated to provide data, as 85% of historical shipments have captured direct feedback [3]. As the company grows in the number of customers and the breadth and depth of inventory, the predictive power of its algorithm increases. Indeed, the company claims that there is a 63% probability of a specific client buying a specific item [1]. The high level of personalization also helps to engender customer loyalty and reduce churn. In 2017, the company reported a repeat rate of 86% [3], the average number of items purchased per fix grew 22% year-over-year [1], and the average client spent $489 annually with the service [1].<\/p>\n

Stitch Fix also uses data to inform product development decisions for both its 700 brands, which includes both its private label offerings and brand partners [3]. Data has also been utilized to drive category expansion decisions. Building upon its initial success with womenswear, Stitch Fix moved into the maternity and petite categories in 2015, menswear in 2016, and plus sizes in 2017 [2].<\/p>\n

Exhibit 2 – Data Captured by Stitch Fix<\/strong><\/p>\n

\"\"<\/a><\/p>\n

Challenges Ahead<\/strong><\/span><\/p>\n

The company currently employs 75+ data scientists and 3,500+ stylists [1]. Given the human touch required for each “fix,” scale is of\u00a0 concern.\u00a0While most stylists are part-time and work remotely, one can envision a scenario where tremendous human capital costs are incurred in order to service the growing customer base. Conversely, should the Stitch Fix algorithm become so sophisticated that stylists are no longer needed, does this erode the company’s original value proposition? Will customers embrace being completely styled by an algorithm? If managed poorly, the combination of man and machine may turn into a liability for Stitch Fix. Moreover, while Stitch Fix has amassed considerable data, it faces a formidable challenger in Amazon, which launched its Prime Wardrobe offering in 2017 to seemingly compete directly with the service.<\/p>\n

Source<\/strong><\/p>\n

[1] Stitch Fix Investor Presentation (March 2018): https:\/\/investors.stitchfix.com\/static-files\/a578e8ff-5685-41f2-b6d5-ed03d7ee0f42<\/p>\n

[2] Stitch Fix Company Fact Sheet (March 2018): https:\/\/investors.stitchfix.com\/static-files\/86696edb-107f-42de-9f04-899b3cd93363<\/p>\n

[3] Stitch Fix S-1:\u00a0https:\/\/www.sec.gov\/Archives\/edgar\/data\/1576942\/000119312517313629\/d400510ds1.htm<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

With a core competency in data analytics, Stitch Fix has ambitions to become the world's leading personal styling service.<\/p>\n","protected":false},"author":2100,"featured_media":7743,"comment_status":"open","ping_status":"closed","template":"","categories":[776,1265,814,75,372],"class_list":["post-7742","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-data-analytics","category-data-science","category-ecommerce","category-fashion","category-fashion-retail","hck-taxonomy-organization-stitch-fix","hck-taxonomy-industry-fashion","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/competing-with-data-challenge\/","yoast_head":"\nStitch Fix: A Marriage of Art and Science - Digital Innovation and Transformation<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-digit\/submission\/stitch-fix-a-marriage-of-art-and-science\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Stitch Fix: A Marriage of Art and Science - 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