The price of discovering your needs online

Web users, even when not actively looking for a product, are continuously targeted by offers that potentially fit their interests. Does this kind of targeting always promote transactions with high social value? We consider a model in which a two-sided platform is visited by sellers seeking to promote their products and by users with state-contingent preferences about those products. Depending on its targeting ability, the platform could be able to target users by tailored advertising banners. We show that persuasion is stronger when targeting ability is maximal, fueling purchases that rational individuals may regret due to the persuasive nature of banners

Search advertising

Search engines enable advertisers to target consumers based on the query they have entered. In a framework with horizontal product differentiation, imperfect product information and in which consumers incur search costs, I study a game in which advertisers have to choose a price and a set of relevant keywords. The targeting mechanism brings about three kinds of efficiency gains, namely lower search costs, better matching, and more intense product market price-competition. A monopolistic search engine charges advertisers too high a price, and has incentives to provide a suboptimal matching quality. Competition among search engines eliminates the latter distortion, but exacerbates the former

Online advertising and privacy

An online platform makes a profit by auctioning an advertising slot that appears whenever a consumer visits its website. Several firms compete in the auction, and consumers differ in their preferences. Prior to the auction, the platform gathers data which is statistically correlated with consumers' tastes for products. We study the implications of the platform's decision to allow potential advertisers to access the data about consumers' characteristics before they bid. On top of the familiar trade-off between rent extraction and efficiency, we identify a new trade-off: the disclosure of information leads to a better matching between firms and consumers, but results in a higher equilibrum price on the product market. We find that the equilbrium price is an increasing function of the number of firms. As the number of firms becomes large, it is always profitable for the platform to disclose the information, but this need not be efficient, because of the distortion caused by the higher prices. When the quality of the match represents vertical shifts in the demand function, we provide conditions under which disclosure is optimal

Integration and search engine bias

Competition authorities all over the world worry that integration between search engines (mainly Google) and publishers could lead to abuses of dominant position. In particular, one concern is that of own-content bias, meaning that Google would bias its rankings in favor of the publishers it owns or has an interest in, to the detriment of competitors and users. In order to investigate this issue, we develop a theoretical framework in which the search engine (i) allocates users across publishers, and (ii) competes with publishers to attract advertisers. We show that the search engine is biased against publishers that display many ads - even without integration. Although integration may lead to own-content bias, it can also reduce bias by increasing the value of a marginal consumer to the search engine. Integration also has a positive effect on users by reducing the nuisance costs due to excessive advertising. Its net effect is therefore ambiguous in general, and we provide sufficient conditions for it to be desirable or not

Online advertising and privacy

An online platform makes a profit by auctioning an advertising slot that appears whenever a consumer visits its website. Several firms compete in the auction, and consumers differ in their preferences. Prior to the auction, the platform gathers data which is statistically correlated with consumers' tastes for products. We study the implications of the platform's decision to allow potential advertisers to access the data about consumers' characteristics before they bid. On top of the familiar trade-off between rent extraction and efficiency, we identify a new trade-off: the disclosure of information leads to a better matching between firms and consumers, but results in a higher equilibrum price on the product market. We find that the equilbrium price is an increasing function of the number of firms. As the number of firms becomes large, it is always profitable for the platform to disclose the information, but this need not be efficient, because of the distortion caused by the higher prices. When the quality of the match represents vertical shifts in the demand function, we provide conditions under which disclosure is optimal

Third-Degree Price Discrimination in Two-sided Markets

We investigate the welfare effects of third-degree price discrimination by a two-sided platform that enables interaction between buyers and sellers. Sellers are heterogenous with respect to their per-interaction benefit, and, under price discrimination, the platform can condition its fee on sellers' type. In a model with linear demand on each side, we show that price discrimination: (i) increases participation on both sides; (ii) enhances total welfare; (iii) may result in a strict Pareto improvement, with both seller types being better-off than under uniform pricing. These results, which are in stark contrast to the traditional analysis of price discrimination, are driven by the existence of cross-group network effects. By improving the firm's ability to monetize seller participation, price discrimination induces the platform to attract more buyers, which then increases seller participation. The Pareto improvement result means that even those sellers who pay a higher price under discrimination can be better-off, due to the increased buyer participation

Third-degree price discrimination in two-sided markets

We investigate the welfare effects of third-degree price discrimination by a two-sided platform that enables interaction between buyers and sellers. Sellers are heterogenous with respect to their per-interaction benefit, and, under price discrimination, the platform can condition its fee on sellers' type. In a model with linear demand on each side, we show that price discrimination: (i) increases participation on both sides; (ii) enhances total welfare; (iii) may result in a strict Pareto improvement, with both seller types being better-off than under uniform pricing. These results, which are in stark contrast to the traditional analysis of price discrimination, are driven by the existence of cross-group network effects. By improving the firm's ability to monetize seller participation, price discrimination induces the platform to attract more buyers, which then increases seller participation. The Pareto improvement result means that even those sellers who pay a higher price under discrimination can be better-off, due to the increased buyer participation