Reference no: EM133448709

San Francisco-based Stitch Fix embodies what founder and CEO Katrina Lake envisions as the future of apparel retailing. Lake observed retailers seeking organizational change only after an innovator entered the market, and she was determined to get ahead of the curve. In the Internet age, what would be the alternative to buying clothes in a store or from a catalog? Most retailers offer online shopping at a website that functions as a catalog. But information technology offers more than order taking; it also can gather and analyze massive quantities of data, so managers can use the analysis to improve decisions.

Lake decided to apply these capabilities to make retailing more like a personal shopping assistant. She created Stitch Fix as a retailer grounded in data science. The focus on data is apparent in its hiring of more than 80 data scientists, who work in a group reporting directly to the CEO. Typically, data analytics is a support function reporting to a technology or other support executive, but this organizational structure keeps the company's leaders focused on data as a driver of success.

Data collection begins when consumers sign up to receive Stitch Fix boxes. They complete a questionnaire about their size, tastes, desired price ranges, and clothing needs. Periodically, according to the schedule selected by the consumer, Stitch Fix sends out a box containing several items compatible with the consumer's questionnaire responses. Clothing purchases have an emotional component, so human stylists make the final selections in each box based on the data-driven recommendations, personal information that shoppers choose to share, and their own sense of style. For each box, the company charges a $20 styling fee, which is applied to reduce the price of any items purchased. The consumer decides which items to keep and returns the others along with feedback. The choices and feedback shape the next round of selections.

Stitch Fix also compiles data across millions of shoppers to find patterns. For example, men tend to overstate their height, so the software adjusts inseam lengths when selecting pants for male shoppers, and women over 40 often request sleeves, so Stitch Fix added more sleeved shirts to its inventory. While some adjustments involve human decision making, Stitch Fix also applies machine learning, in which software applies the results of past decisions-say, that a client tends to select certain colors of shirts or that a particular ratio of specific measurements will make a shirt appealing to men with large chest sizes-to adjust its recommendations.

This business model involves significant risks. The company must acquire a wide variety of inventory and then hope that consumers who receive selections they didn't make themselves will want to pay for those items. If consumers return most of them, the company has to pay for buying, storing, and shipping clothes without earning much from the expense. The primary quality control Stitch Fix uses is the application of data analytics to ensure that the choices the company makes are likely to appeal to its customers. When this works, Stitch Fix sells items fast enough that it can pay vendors with earnings from sales.

In addition, data analytics helps the company control costs. Having employed highly educated scientists to solve problems with data, Stitch Fix gives them latitude to apply their skills throughout the organization. Their initial charge was to write software for selecting clothes, but the group has also created programs for timing reorders of inventory, selecting which warehouses will fill particular orders, and arranging work flow in warehouses.

As of 2019, Stitch Fix had 3.4 million active clients (customers who received a shipment during the previous 12 months), revenues exceeding $1 billion, and an inventory drawn from hundreds of brands. It has expanded from women's apparel to include offerings for men and children. However, growth in the number of active clients has slowed, possibly due to the COVID-19 pandemic, and other retailers have observed Stitch Fix's initial success and are preparing to compete. The company has planned an entry into the United Kingdom, where it will hire local stylists who can recommend brands familiar to local consumers. Managers at Stitch Fix also have to figure out how to stay ahead of the curve in anticipating consumers' wishes. The better the company predicts what shoppers will order, the less it will spend on shipping and inventory of unwanted items. Even more importantly, it will retain more loyal customers to fuel future growth.

1. In general terms, according to the information provided, what measures of efficiency and effectiveness are important for controlling Stitch Fix's corporate performance?

2. How can Stitch Fix's organizational culture support the achievement of its goals?

3. How do you think the establishment of Stitch Fix might have created a need for organizational change at other clothing retailers?

4. How might their responses create a need for organizational change at Stitch Fix?