Consumer Scores and Price Discrimination

Abstract

We study the implications of tracking consumers and aggregating purchase histories into scores, numerical proxies for their unobserved willingness to pay. In our framework, a consumer interacts with a sequence of firms in a stationary Gaussian setting. Each firm relies on the consumer’s current score–modeled as a linear aggregate of past quantity signals discounted exponentially–to learn about her preferences and to set prices. In equilibrium, the consumer reduces her demand to drive average prices below the no-information benchmark. Firms’ learning–and thus, their ability to price discriminate–is maximized by scores that overweigh signals relative to Bayes’ rule when observing disaggregated data. In markets with high average willingness to pay, the benefits from low prices dominate the losses from better tailored prices, and thus consumers want to be tracked. Finally, hidden scores–those only observed by firms– reduce demand sensitivity, increase expected prices, and reduce expected quantities

Gonzalo Cisterna
MIT Sloan