Pricing policy and queue-length disclosure in on-demand service platforms

Online service platforms, such as ride-hailing and freight exchanges, generate revenue and profits through commissions. To attract users and maximize profit while managing platform costs, these platforms strategically implement different pricing and queue-length disclosure policies. Dynamic pricing based on queue length can increase revenue but may also reduce user loyalty, leading to higher platform costs. The choice of queue-length disclosure policy influences customer balking behavior: revealing queue lengths may deter users from joining if they perceive the wait as too long, while not disclosing the queue length leads customers to decide probabilistically, driven by uncertainty about the wait time. We analyze a two-sided queueing model in which both customers and suppliers arrive randomly, queue separately, and engage in immediate matching at the front of the queue. We examine different pricing and queue-length disclosure policies to maximize the platform’s expected profit. Optimizing the underlying semi-Markov decision process requires solving a non-convex quadratically constrained quadratic program. Through uniformization, we derive and solve the optimality equations, and then compare the resulting optimal prices, profits, and throughput. Our findings indicate that pricing and queue-length disclosure policies are complementary. Specifically, dynamic pricing and visible queue lengths both increase expected profit, while static pricing and invisible queue lengths both increase throughput. These outcomes are driven by changes in the average transaction price under different policies. We identify unique thresholds that determine the preferred pricing and queue-length disclosure policies. The choice of pricing policy depends on the extra cost of implementing dynamic pricing compared to a static price, while the selection of queue-length disclosure policy depends on customer sensitivity to service delay.