A monopolist offers a product to a market of consumers with heterogeneous quality preferences. Although initially uninformed about the product quality, they learn by observing past purchase decisions and reviews of other consumers. Our goal is to analyze the social learning mechanism and its effect on the seller's pricing decision. This analysis borrows from the literature on social learning and on pricing and revenue management.Consumers follow a naive decision rule and, under some conditions, eventually learn the product's quality. Using mean-field approximation, the dynamics of this learning process are characterized for markets with high demand intensity. The relationship between the price and the speed of learning depends on the heterogeneity of quality preferences. Two pricing strategies are studied: a static price and a single price change. Properties of the optimal prices are derived. Numerical experiments suggest that pricing strategies that account for social learning may increase revenues considerably relative to strategies that do not.
This paper studies the strategic interaction between a monopolistic seller of an information product and a set of potential buyers that compete in a downstream market. The setting is motivated by information markets in which (i) sellers have the ability to offer information products of different qualities and (ii) the information product provides potential buyers not only with more precise information about the fundamentals, but also with a coordination device that can be used in their strategic interactions with their competitors. Our results illustrate that the nature and intensity of competition among the information provider’s customers play first-order roles in determining the information provider’s optimal strategy. We show that when the customers view their actions as strategic complements, the provider finds it optimal to offer the most accurate information at the provider’s disposal to all potential customers. In contrast, when buyers view their actions as strategic substitutes, the provider maximizes the provider’s profits by either (i) restricting the overall supply of the information product or (ii) distorting its content by offering a product of inferior quality. We also establish that the provider’s incentive to restrict the supply or quality of information provided to the downstream market intensifies in the presence of information leakage. The online appendix is available at https://doi.org/10.1287/mnsc.2018.3068 . This paper was accepted by Gad Allon, operations management.
Drivers on the Lyft ride-share platform do not always know where the areas of supply shortage are in real time. This lack of information hurts both riders trying to find a ride and drivers trying to determine how to maximize their earnings opportunities. Lyft’s Personal Power Zone (PPZ) product helps the company to maintain high levels of service on the platform by influencing the spatial distribution of drivers in real time via monetary incentives that encourage them to reposition their vehicles. The underlying system that powers the product has two main components: (1) a novel “escrow mechanism” that tracks available incentive budgets tied to locations within a city in real time, and (2) an algorithm that solves the stochastic driver-positioning problem to maximize short-run revenue from riders’ fares. The optimization problem is a multiagent dynamic program that is too complicated to solve optimally for our large-scale application. Our approach is to decompose it into two subproblems. The first determines the set of drivers to incentivize and where to incentivize them to position themselves. The second determines how to fund each incentive using the escrow budget. By formulating it as two convex programs, we are able to use commercial solvers that find the optimal solution in a matter of seconds. Rolled out to all 320 cities in which Lyft operates in a little more than a year, the system now generates millions of bonuses that incentivize hundreds of thousands of active drivers to optimally position themselves in anticipation of ride requests every week. Together, the PPZ product and its underlying algorithms represent a paradigm shift in how Lyft drivers drive and generate earnings on the platform. Its direct business impact has been a 0.5% increase in incremental bookings, amounting to tens of millions of dollars per year. In addition, the product has brought about significant improvements to the driver and rider experience on the platform. These include statistically significant reductions in pick-up times and ride cancellations. Finally, internal surveys reveal that the vast majority of drivers prefer PPZs over the legacy system.
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