A Discriminatory Mechanism for Real-time Pricing and Matching in On-demand Platforms Introduction

The rise and growth of on-demand platforms, which operate by leveraging independent sellers to serve requests from potential buyers through dynamic matching, have enabled new types transactions that provide important opportunities to enhance the economic and operational performance of underlying markets. Three critical features that distinguish on-demand platforms from traditional markets are real-time management of demand and supply, matching capabilities, and flexible payment schemes. First, on-demand platforms face opportunities to use real-time demand information when managing imbalances between demand and supply, which provides additional flexibility as compared to the traditional approaches to job scheduling and revenue management. Second, the management of demand and supply can be achieved through personalized matching between buyers and sellers, in contrast with most markets relying on first-come first-served operating procedures. For instance, a labor provider such as TaskRabbit can determine which seller to allocate to each request to satisfy objectives such as minimizing wait times, meeting individual requirements, or rewarding highly rated agents. Third, online payment capabilities permit the implementation of personalized pricing and revenue management schemes, in contrast with most markets relying on posted prices. For instance, surge pricing in transportation platforms provides capabilities to apply differentiated prices based on spatial-temporal characteristics of ride requests and driver supply. These platform characteristics enable the design and implementation of novel solutions to the long-standing economic and operational issues associated with the dynamic management of demand and supply imbalances in the presence of customer heterogeneity and information asymmetries. As most traditional markets, platforms face variable demand over time and capacity limitations that constrain their ability to serve buyer demand. Efficient platform management therefore involves demand-side pricing mechanisms (i.e., determining price levels for every buyer request with such objectives as maximizing revenues or market share) and supply-side allocation mechanisms (i.e., assigning sellers to buyer requests with such objectives as maximizing service levels and minimizing wait times). These problems can be significantly complicated by the heterogeneity of buyer preferences. Indeed, buyers typically exhibit differentiated willingness to pay for a good or a service, as well as differentiated willingness to wait if immediate service cannot be provided. However, this information is private to each individual buyer and unknown to the platform. Existing approaches to managing demand and supply typically rely on dynamic spatial-temporal pricing and revenue management [1, 2, 3]. However, these two paradigms rely exclusively on public information, and do not enable the buyers to reveal their preferences to the system, thus maintaining information asymmetries between the platform and the buyer pool. Ultimately, these asymmetries may result in lost revenue opportunities and mismatches between service offers and customers’ expectations. This paper proposes an original dynamic pricing and allocation mechanism in online platform systems that relies on the elicitation of buyer preferences in order to provide personalized pricing and service levels, while, at the same time, smoothing out the imbalances between demand and