Optimal and equilibrium pricing metrics for digital goods

Flooded by a large number of variables found by modern business intelligence applications, pricing managers are perplexed by the task of selecting variables for price discrimination. However, relevant literature remains scarce. This dissertation attempts to investigate how sellers should determine the optimal or equilibrium combination of pricing metrics in a monopoly or duopoly industry. In the second chapter, I develop a model that closely resembles linear regression and probit regression to solve the pricing metrics selection problem. The criterion found is similar to the selection of independent variables for linear regression; it is revenue-maximizing to select the variable that best reduces the residual variance of buyer's willingness-to-pay. In the third chapter, I investigate the metrics selection problem by a general linear duopoly demand system. This model suggests that the value of information embedded in pricing metrics depends on two factors: (1) The explanatory power of product demands: equivalently, metrics that best reduce residual variance of demands are good candidates. (2) The price and demand coefficients: specifically, this study shows that when these two products are substitutes and the pricing metric affects two demand curves in the sauce direction (different directions), duopoly sellers should adopt that pricing metric simultaneously (unilaterally) in the equilibrium. When two products are complements, these effects are reversed. In the last chapter, a two-product monopoly model is examined in a setup that matches the second paper. The optimal metrics selection is qualitatively similar to the equilibrium metrics selection in the second paper. This model shows that it is more profitable for the centralized monopoly to use pricing metrics than decentralized duopoly.