Revival of clothing companies in the era of Recommendation Engineering
Stitch Fix joined the Nasdaq in 2017. The company started in 2011 as an online subscription service for the personalized purchase of clothing for women, men, and children.
In 2018, it had revenues of $1.25 billion. Its basic philosophy is found in Recommendation Engineering. It uses customer data to guess what customers want to buy, who want to direct their lives, and suggests it. It uses the “Choice Architecture” philosophy, so its role is to advise them on how to make their appearance better.
This is its central architecture. The “no 2″ of the company, after the CEO, Katrina Lake, is Eric Colson, Chief Algorithm Officer – CAO, who was Vice President of Data Science & Engineering at Netflix. The CEO says: “Data science is our culture. The heart of our business. We build business algorithms around our customers and their needs. The Data Science department reports directly to me. ”
To create the different styles of each customer you need important data. The first thing they ask the customer when entering the service is detailed information about personal preferences, numbers, and money they want to spend. They use a kind of “game”, where the customer sees mixed clothes and accessories and swiping left or right can tell if he likes what he sees. Thus, it gives the Recommender important information about its key features.
The Recommender selects the best proposals from around 700 Brands. Then, these suggestions go to one of the 3,500 stylists available to the company for review. The stylist selects 5 products and sends them to the client every 15 days, month or quarter, depending on the client. The package includes personal tips for combining items. After receiving the package, customers evaluate each product on their personal account, on the site. Thus, the Recommender can constantly learn the needs of the client and evolve.
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