Pricing and Matching in the Sharing Economy

Abstract

Sharing economy platforms use crowdsourced suppliers to provide customers with services or goods. Their decision making often revolves around pricing and matching. Platforms like Uber charge the customers a price for using the services or goods and offer the crowdsourced suppliers a wage or pay for providing the services or goods. First, we study how the platform could optimally set the price and the wage for a single service or product in different market conditions, and investigate the performance of the fixed commission contract which uses a fixed commission percentage across all market conditions. Second, even with determined pricing decisions, the platform also faces the task of matching customers with suppliers. We consider a stochastic, dynamic model with multiple demand types to be matched with multiple supply types over a planning horizon. We characterize the optimal matching policy by determining the priorities of the demand-supply pairs, under a sufficient condition on the reward structure. Then, the results are applied to two cases with more specific reward structures; namely, the horizontal reward structure and the vertical reward structure, to better characterize the optimal policy. Finally, we study the joint pricing and matching decision by a platform for a single service or product and take into account suppliers’ and customers’ forward-looking behavior. We propose a simple heuristic policy: fixed price and wage plus waiting compensation, in conjunction with the greedy matching policy on a first-come-first-served basis. This heuristic policy induces forward-looking suppliers and customers to behave myopically and is shown to be asymptotically optimal.