Reference no: EM133100332

Stitch Fix is an ebusiness that sends boxes, or "Fixes," of clothing, shoes, and accessories based on an individual's personal style and clothing preferences. This online personal styling service combines Netflix-style algorithms with human intuition and curation provided by more than 3,000 stylists who fine-tune each Fix.

When the company went public in November 2017, it raised $120 million and was valued at $1.46 billion. At 34, Katrina Lake, the CEO and founder, is the youngest female founder to ever lead an IPO and the first to stand at the Nasdaq podium while holding her toddler on her hip. Stitch Fix seeks to be that alternative to digital retail behemoth Amazon by offering its users a bespoke selection of garments as opposed to the quickest, fastest clothing. In 2017, it expanded its demographic, launching lines for plus sizes and a Premium Brands option that pulls garments from high-end lines such as Rag & Bone. In 2018, it rolled out its Style Shuffle game widely to give it more data on user preferences to make its service more effective. It also launched its Kids service and began the pro-cess of international expansion. Stitch Fix has developed its own internal sizing system for each brand and article of clothing, rather than using the industry standards. This enhances the customer experience, but Stitch Fix also uses the data to help brand partners improve their products, designs, and fit.

How Stitch Fix puts data to work:

Style Shuffle: A game, available on both Stitch Fix's site and app, asks customers to accept or reject an article of clothing with the prompt "Would you sport this one?" giving Stitch Fix more than a billion bits of feedback to improve its style algorithm. Style Shuffle added a ton of data into the company.

Latent Style: When working on Style Shuffle, data scientist Erin Boyle realized that people natu-rally sorted into fashion clusters. For example, women who like stripped shirts are more likely to like floral skirts. Boyle created the Latent Style algorithm to determine how much any client would like any particular piece of clothing. Latent Style also generates mood boards for each customer.

Hybrid Design: While watching designers create new clothing, the lead data scientists determine which styles to purchase with a process that closely resembles evolution in the scientific sense. Designers examine which clothing traits thrived in former seasons and then invent a new product based on past and current trends.

Questions

1. Why does Stitch Fix employ data scientists when it operates in the fashion apparel market?

2. Which ebusiness model is Stitch Fix using?

3. How do you mail clothes a customer will love without the customer ever getting measured or viewing the inventory?

4. How can you combine a unique set of algorithms and personalization to create a new kind of company?

5. Why are data and customer feedback critical to Stitch Fix's business model?