Dynamic Pricing in the Presence of Participation-Dependent Social Learning

For Internet-based services, users' quality of service (QoS) depends on not only the available resource (capacity) but also the number of users who use the resource simultaneously (e.g., congestion effect). When a new Internet-based service provider first enters the market, there can be uncertainties regarding both the capacity and congestion, and hence the uncertainty of QoS. In this paper, we consider a participation-dependent social learning over the QoS through users' online reviews, where the QoS changes with the number of review participants. We study how such a learning process affects the provider's dynamic pricing strategy. With a simple two-period model, we analyze the strategic interactions between the provider and the users, and characterize the provider's optimal two-period dynamic pricing policy. Our results show that when the capacity is small or the users' prior QoS belief is high, the provider will choose a higher introductory price in the first period (than the price in the second period). This is in sharp contrast with the common practice of setting a lower introductory price to attract users (when congestion is not an issue). Furthermore, the learning process is beneficial to the provider with a large capacity.