A Data-Driven and Real-time Approach to Personalized Bundle Recommendation and Pricing; from Theory to Practice

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A good pricing strategy plays a crucial role in any retailer's business process. This is largely due to the fact that pricing approaches are highly visible to both customers and competitors, and thus have an immediate and dramatic impact on the bottom line. The online market for retail goods has grown enormously over the past decade. The development of a sophisticated personalized product and price recommendation system can provide the necessary competitive edge for any Internet retailer, making the difference on the order of billions in profits. The challenge of the new retailing paradigm is to design models for bundling and personalized pricing that are able to extract value from the new streams of customer data that an e-tailer has access to. The goal of this research will be the development of a personalized bundling and pricing model that recommends a bundle of related products to a consumer, but at an incentivized price (lower than the sum of the prices of the individual items) during their online session. The innovation in this model will consist of balancing several key factors in terms of bundle and pricing offerings simultaneously, while developing a new approach to personalized demand estimation. The focal points of the work consist of four considerations: (i) developing meaningful personalization models (in terms of bundling of products but also in terms of pricing these products), (ii) developing inventory balancing methods that will balance the tradeoff between losing money due to future markdowns and at the same time facing potentially future inventory stock-outs, (iii) developing interesting structural results and insights on how personalization can tradeoff with bundling in terms of the pricing strategies. Finally, and very importantly, (iv) developing efficient solution approaches (theoretically but also computationally, the latter using actual industry data) that could impact practice. The goal will be to combine these distinct goals in the modeling: personalization, inventory balancing, pricing insights and efficiency/practicality. This research will design innovative models, analyzing them first from a theoretical as well as an applied standpoint but also in exploring the relationships between them and testing them first with real data. Furthermore, this research will build an integrated framework, models and solution techniques from machine learning, integer optimization, stochastic and robust optimization to key personalized bundle pricing problems that are accessible to all, in order to help academics and practitioners in the area of pricing.