Dynamic Pricing with Forward Looking Social Learners: the Case of US Video Games Industry

Firms in durable good product markets face incentives to inter-temporally price discriminate, by setting high initial prices to sell to consumers with the highest willingness to pay, and cutting prices thereafter to appeal to those with lower willingness to pay. The profitability of such pricing policies is hampered by the fact that many experience goods are of uncertain quality to the consumers at first, who have incentive to resolve the uncertainty through social learning before making a purchase. In addition, forward-looking consumers wait for price drop, which further limits the effect of varying prices. I develop a framework to investigate empirically the optimal pricing over time of a firm selling a durable good product to such strategic consumers. Prices in the model are equilibrium outcomes of a game played between forward-looking consumers who strategically delay purchases to avail of better information and lower prices in the future, and a forward-looking firm that takes this consumer behavior into account in formulating its optimal pricing policy. The model outlines first, a dynamic model of demand incorporating forward-looking consumer behavior, and second, an algorithm to compute the optimal dynamic sequence of prices given these demand estimates. The model is solved using mathematical programming with equilibrium constraints (MPEC) method. I present an empirical application to the market for video games in the US. The results indicate that consumer forward-looking behavior has a significant effect on optimal pricing of games in the industry. Simulations reveal that the profit losses of ignoring forwardlooking behavior by consumers are large and economically significant, and suggest that market research that provides information regarding the extent of learning and discounting by consumers is valuable to video game firms.