Design and Analysis of Data-driven Pricing and Supply Chain Strategies for Online Multi-sided Platforms

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In recent years, there has been an emergence of a number of online platforms facilitating Peer-to-Peer (P2P) transactions between buyers and seller. This concept differs from its offline counterpart in two fundamental ways: (i) First in the offline space, it was nearly impossible to collect detail information regarding how the customers interact with the product and how they make their final purchase decision. On the other hand, the very presence of online interactions between consumers and retailers enabled supply chain companies to collect not only the final sales data but also the chain of interactions that take place before the final sales. This opportunity helps the firms devise strategies that are customized and adapted to finer details of consumer behaviors and attributes. (ii) Second, the interactions in the offline markets are typically one-sided. More specifically, on one side is there one seller who makes operational (i.e., procurement, inventory, transportation) and marketing (i.e., assortment, pricing, promotion etc.) and on the other side are customers who are making their purchase decisions from the choice set presented to them. On the other hand, in online platforms, the transactions are inherently multi-sided. For example, consider two-sided platforms such as Amazon, AliBaba and Airbnb. In these cases, the platform providers match suppliers, or owners on one side with the buyers, or consumers on the other side. Moreover, the platform providers play different roles varying from more passive market-mediation between consumers and suppliers to more active roles in which the platform providers make operational and marketing decisions on the behalf of platform participants. While some of the above issues have been addressed before in the supply chain literature, they have been done on a piecemeal basis without fully investigating the implications of marketplace innovations for the entire system. Building upon my current research on Supply Chain Management (SCM), in this 5-year integrative research program I would like to address this gap. In the short-term, I develop a modelling framework to capture customer interactions for two-sided markets. Using this framework, I intend to design and analyze efficient and optimal selling mechanisms that are incentive-compatible from the perspectives of customers and sellers. In the long-term, this proposal intends to develop data-driven computational techniques for online multi-sided markets. Online platforms are marketplace innovations that are transforming the economy in the near future. Success in sustainably harnessing the potential of these systems so as to serve the society most effectively would require a holistic approach that takes into account interdependencies between the various internal as well as external actors. This research will be among the first to develop such an integrated framework to help researchers analyze the optimal mechanisms for a sustainable growth.