Stitch Fix is a personalized styling service for clothing and accessories. Items are selected by a stylist from initial personalized recommendations. The client keeps the items they love, and returns the rest, while providing detailed feedback. We use machine learning and analytics to power every aspect of our business from personalized recommendations, to inventory management and demand modeling.

In this talk, I’ll focus on the use of machine learning within our inventory forecasting system, which relies on information and feedback captured throughout the user lifecycle to predict the flow of apparel and accessories between vendors, warehouses and clients. This problem domain has multiple metrics to measure success and for which to optimize, which makes it challenging. In highlighting the modular components of our forecasting framework, I will illustrate the architecture we have developed in-house.

While describing our use of Bayesian methods to predict which items a stylist will pick to send a client from a ranked list of personalized recommendations, I will give guidance on how this technique can be applied more generally to similar examples of beta binomial regressions. As this component is critical to our inventory forecast of quantity over time, we update the priors daily based on new evidence of stylists’ behavior. This detailed explanation of how machine learning has been applied to determining parameters of our physical process inventory models illustrates the blend of techniques used to accurately capture the variance in stock quantity over time by the inventory optimization team at Stitch Fix.