Person:

Ferreira, Kristine

Assortment rotation – the retailing practice of changing the assortment of products offered to customers – has recently been used as a competitive advantage for both brick-and-mortar and online retailers. Fast-fashion retailers have differentiated themselves by rotating their assortment multiple times throughout a standard selling season. Interestingly, the entire online ash sales industry was created using this idea as a cornerstone of its business strategy. In this paper we identify and investigate a new reason why frequent assortment rotations can be valuable to a retailer, particularly for products where consumers typically purchase multiple products in a given category during a selling season. Namely, by distributing its seasonal catalog of products over multiple assortments rotated throughout the season – as opposed to selling all products in a single assortment – the retailer effectively conceals a portion of its full product catalog from consumers. This injects uncertainty into the consumer's relative product valuations since she is unable to observe the entire catalog of products that the retailer will sell that season. Rationally-acting consumers may respond to this additional uncertainty by purchasing more products, thereby generating additional sales for the retailer. We refer to this phenomenon as the value of concealment. A negative value of concealment is possible and represents the event that rationally-acting consumers respond to the additional uncertainty by purchasing fewer products. We develop a consumer choice model and finite-horizon stochastic dynamic program to study when the value of concealment is positive or negative. We show that when consumers are myopic, the value of concealment is always positive. In contrast, we show that when consumers are strategic, the value of concealment is context-dependent; we present insights and discuss intuition regarding which product categories likely lead to positive vs. negative values of concealment.

We consider a price-based network revenue management problem where a retailer aims to maximize revenue from multiple products with limited inventory over a finite selling season. As common in practice, we assume the demand function contains unknown parameters, which must be learned from sales data. In the presence of these unknown demand parameters, the retailer faces a tradeoff commonly referred to as the exploration-exploitation tradeoff. Towards the beginning of the selling season, the retailer may offer several different prices to try to learn demand at each price (“exploration” objective). Over time, the retailer can use this knowledge to set a price that maximizes revenue throughout the remainder of the selling season (“exploitation” objective). We propose a class of dynamic pricing algorithms that builds upon the simple yet powerful machine learning technique known as Thompson sampling to address the challenge of balancing the exploration-exploitation tradeoff under the presence of inventory constraints. Our algorithms prove to have both strong theoretical performance guarantees as well as promising numerical performance results when compared to other algorithms developed for similar settings. Moreover, we show how our algorithms can be extended for use in general multi-armed bandit problems with resource constraints, with applications in other revenue management settings and beyond.

We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts on designer apparel and accessories. One of the retailer's main challenges is pricing and predicting demand for products that it has never sold before, which account for the majority of sales and revenue. To tackle this challenge, we use machine learning techniques to estimate historical lost sales and predict future demand of new products. The nonparametric structure of our demand prediction model, along with the dependence of a product's demand on the price of competing products, pose new challenges on translating the demand forecasts into a pricing policy. We develop an algorithm to efficiently solve the subsequent multi-product price optimization that incorporates reference price effects, and we create and implement this algorithm into a pricing decision support tool for Rue La La's daily use. We conduct a field experiment and find that sales do not decrease due to implementing tool recommended price increases for medium and high price point products. Finally, we estimate an increase in revenue of the test group by approximately 9.7% with an associated 90% confidence interval of (2.3%, 17.8%).