Revenue Management and Policy Design in the Presence of Customer Multi-Item Shopping Behavior

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Revenue management (RM) has achieved major success in many multi-billion sectors, such as airline, hotel, and retail industries. The crucial element in successful RM is demand modeling. The importance of integrating customer choice into demand modeling has been well documented. However, most of the extant customer choice models assume that each customer purchases at most one product. This assumption largely deviates from reality. For example, customers often buy multiple items in the same product category, such as soft drinks and cereals. Online shoppers often purchase multiple items across different product categories, and in fact are often incentivized to do so to qualify for a promotional campaign or free shipping. Ignoring such behavior may result in prediction errors, rendering solutions to the operational problems and promotional policies less effective. To date, little work has been done in RM to model the multi-item purchase behavior despite its importance. The proposed research program seeks to bridge this gap and advance the modeling of the customer multi-item purchase behavior in the RM literature. We define "multi-item" as multiple units of items, some of which can be the same, within the same product category or across different categories. The items can be complementary, substitutable or independent of each other. That is, just as in real-world circumstances, customer choose their own bundle or basket of products. Our research is thus vastly different from and much more general than the bundling literature (where a customer chooses a pre-determined package of complementary products) and the basket shopping literature (where a customer picks a different product from multiple categories). This research program will actively leverage methodologies such as choice modeling, non-linear optimizations, game theoretic approaches, and emerging Big Data tools to address the following objectives: (1)Advance analytical models to capture customer multi-item purchase behaviors, as well as seek solutions to high-impact RM problems, such as assortment and price optimizations, using both analytical and data-driven approaches; and (2)Design optimal policies that incorporate such customer choice behaviors to help companies make more efficient and profitable operational decisions. Advances in RM analytics have the potential for significant economic impact, as even a moderate improvement of 1% in revenue as a result of advanced RM analytics could translate to additional sales of over CAD $5B in the Canadian retail industry. Therefore, the outcomes of this research are important for both RM researchers and industry practitioners. HQP in this project will gain knowledge of management science and advanced data analytics and skills in applying the knowledge to real-world problems, and get prepared for success in an academic or industry career where such skills are in high demand.