Tractable Markdown Optimization for an E-tailer

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The research goal of this award is to design operational models to provide decision support for markdown pricing by e-tailers. The models will include strategic customer behavior in the presence of business rules, and will have the potential to be applied operationally. Existing models for MDO with strategic customers make strong assumptions about customer behavior that are difficult to estimate or validate with the data that can practically be collected. Our research focuses on pricing models that can be estimated with customer visit data. Such data is already being collected by e-tailers through user logins and cookies. Such information would not be practical to collect for brick-and-mortar stores, the traditional context for the MDO problem. The research plan is to first try to understand the impact of limited strategic (i.e. returning but myopic) customers on the optimal prices for an e-tailer in a single-item setting. Next, we will build upon the foundation such MDO models by additionally considering business rules - these are practically important hard constraints that retailers impose on the sequence of prices. Finally, we will generalize our models to the case of multiple items. We will analyze these models first from a theoretical standpoint but also will exploit the relationships between them and test them in practice using real data. This research will employ methodologies from a variety of fields with the long term goal to deepen our understanding on issues in dynamic pricing as they relate to the retail industry and beyond. We will develop an integrated framework, models and methods for the application of stochastic and robust optimization to key pricing problems.If successful this research will fill a gap between theory and practice in the existing research that will transform the pricing processes of e-tailers. It will empower them to benefit from a better understanding of strategic customer behavior. This is vital because e-commerce an increasing segment of retail business. Further, we believe that the applications of this research go beyond the field of pricing. We will share our integrated framework, models and methods, to help both academics and practitioners. From an educational perspective, the results of this project will serve as components in teaching modules at MIT. These include modules in core courses for which the PI already has shared responsibility. This project lends itself ideally to mentoring undergraduate and graduate students in research on tractable practice-based optimization.