After a customer sets up an account on the Stitch Fix website they are led through a comprehensive survey about their style tastes (refer to figure 1). The data from the survey is then fed through a recommendation engine which takes a first stab at picking out relevant items for the customer. The items are then forwarded to a human stylist who picks out exactly five items to be shipped in a box straight to the customer. Once the customer receives their unique box within three days they are charged a $20 styling fee and have to decide whether they want to keep the items in the box or return it back to Stitch Fix. Once this is done, customers are asked for feedback so Stitch Fix can continue to build on the dataset they have for each customer.<\/p>\n
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Figure One: Sample of survey question<\/a><\/p>\n <\/p>\n How has Stitch Fix performed?<\/strong><\/p>\n Stitch Fix has grown to over three million active users since its inception in 2011. There 2019 revenues have grown to about $1.5 billion, translating to $488 net revenue per active client \u2013 a 9% increase from 2018. The company also boasts a 63% match score, the estimated probability that a specific client will buy a specific item.<\/p>\n <\/p>\n How have they been so successful?<\/strong><\/p>\n The use of data helps in creating efficiency and effectiveness in picking out styles for clients, while the human stylists ensure that these styles are the right decisions. Having humans make the final decisions allows the company to add another layer of curation that the data may not have picked up on or add a little bit of surprise or spontaneity. This is especially true for such a personal experience like retail and especially if a client has a special request \u2013 i.e. what to wear for a specific event. Having the human touch augment machine learning helps to build trust in customer relationships.<\/p>\n <\/p>\n With each added interaction with customers, Stitch Fix gets better and better at predicting what their clients want. Their ability to continuously learn from customer feedback after delivering each box helps they develop a sustainable relationship with loyal customers. This also means that there will likely be low switching costs, given that Stitch Fix already has access to a large amount of customer data. This is evident in Stitch Fix\u2019s above average 6-month retention rate of around 30% vs. other similar fashion companies.<\/p>\n <\/p>\n The company has its own data science team of 115 individuals with its own Chief Algorithms Officer that reports directly to the CEO. This is important in signaling to the organization the important of data science in executing on their strategy. It is also important in making sure that all departments from operations to marketing interact with data science and utilize data to be more effective in how they conduct their business \u2013 i.e. to stock inventory more efficiently. It has also helped to foster innovation in the company, for example with the launch of hybrid designs, an in-house clothing brand conceived to fulfill a product gap in the market that was picked up using data analytics.<\/p>\n <\/p>\n <\/p>\n What are some challenges?<\/strong><\/p>\n Stitch Fix has a business model where it owns all of the inventory it pushes to its clients, meaning that any incorrect decisions can prove costly to the company. It is important that the company optimizes its inventory management to make sure they have enough of a particular clothing but also to make sure their clothes are turning over at a fast-enough rate. This risk is mitigated by continuously improving on their algorithms and making sure they can attract a large number of users to build up their data collection.<\/p>\n <\/p>\n Stitch Fix\u2019s competitive advantage comes from developing a robust recommendation algorithm that can effectively match users with their clothing preferences. But what is to stop Amazon or another large retailer from investing in the required technology to build their own system? Especially if these larger retailers will already have a customer base to leverage. To combat these players, Stitch Fix will need to strengthen their relationships with users perhaps through increasing their supplier inventory or introducing some sort of loyalty program.<\/p>\n <\/p>\n Word: 752<\/em><\/p>\n <\/p>\n Sources:<\/p>\n <\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Data analytics is at the backbone of Stitch Fix’s business model – a personalised online shopper for men and women. The company has experienced tremendous growth over the last few years. <\/p>\n","protected":false},"author":11720,"featured_media":12022,"comment_status":"open","ping_status":"closed","template":"","categories":[29,75],"class_list":["post-12021","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-big-data","category-fashion"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/competing-with-data-and-ai-challenge\/","yoast_head":"\n![\"\"](\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Screen-Shot-2563-04-20-at-15.57.59.png\")
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