This paper studies consumers' privacy choices when firms can use their data to make personalized offers. We first introduce a general framework of personalization and privacy choice, and then apply it to personalized recommendations, personalized prices, and personalized product design. We argue that due to firms' reaction in the product market, consumers who share their data often impose a negative externality on other consumers. Due to this privacy-choice externality, too many consumers share their data relative to the consumer optimum; moreover, more competition, or improvements in data security, can lower consumer surplus by encouraging more data sharing.
We study personalized pricing in a general oligopoly model. When the market structure is fixed, the impact of personalized pricing relative to uniform pricing hinges on the degree of market coverage. If market conditions are such that coverage is high, personalized pricing harms firms and benefits consumers, whereas the opposite is true if coverage is low. However, when the market structure is endogenous, personalized pricing benefits consumers because it induces socially optimal firm entry. Finally, when only some firms have data to personalize prices, consumers can be worse off compared to when either all or no firms personalize prices.
We study personalized pricing (or first-degree price discrimination) in a general oligopoly model. In the short-run, when the market structure is fixed, the impact of personalized pricing hinges on the degree of market coverage (i.e., how many consumers buy). If coverage is high (e.g., because the production cost is low, or the number of firms is large), personalized pricing intensifies competition and so harms firms but benefits consumers, whereas the opposite is true if coverage is low. However in the long-run, when the market structure is endogenous, personalized pricing always benefits consumers because it induces the socially optimal level of firm entry. We also study the asymmetric case where some firms can use consumer data to price discriminate while others cannot, and show it can be worse for consumers than when either all or no firms can personalize prices.
This paper develops a new framework for studying multiproduct intermediaries when consumers demand multiple products and face search frictions. We show that a multiproduct intermediary is profitable even when it does not improve consumer search efficiency. In its optimal product selection, it stocks high-value products exclusively to attract consumers to visit, then profits by selling non-exclusive products which are relatively cheap to buy from upstream suppliers. However, relative to the social optimum, the intermediary tends to be too big and stock too many products exclusively. As applications we use the framework to study the optimal design of a shopping mall, and the impact of direct-to-consumer sales by upstream suppliers on the retail market.