We study personalized price competition with costly advertising among n quality-cost differentiated firms. Strategies involve mixing over both prices and whether to advertise. In equilibrium, only the top two firms advertise, earning "Bertrand-like" profits. Welfare losses initially rise then fall with the ad cost, with losses due to excessive advertising and sales by the "wrong" firm. When firms are symmetric, the symmetric equilibrium yields perverse comparative statics and is unstable. Our key results apply when demand is elastic, when ad costs are heterogeneous, and with noise in consumer tastes.
We study list price competition when firms can individually target consumer discounts (at a cost) afterwards, and we address recent privacy regulation (like the GDPR) allowing consumers to choose whether to opt-in to targeting. Targeted consumers receive poaching and retention discount offers. Equilibrium discount offers are in mixed strategies, but only two firms vie for each contested consumer and final profits on them are Bertrand-like. When targeting is unrestricted, firm list pricing resembles monopoly. For plausible demand conditions and if targeting costs are not too low, firms and consumers are worse off with unrestricted targeting than banning it. However, targeting induces higher (lower) list prices if demand is convex (concave), and either side of the market can benefit if list prices shift enough in its favor. Given the choice, consumers opt in only when expected discounts exceed privacy costs. Under empirically plausible conditions, opt-in choice makes all consumers better off.
Personalized advertising is becoming one of the most defining features of the twenty-first century marketplace. The two essays in this dissertation analyze two important theoretical and empirical dimensions of personalized advertising. The first essay develops a model of costly advertising and price competition among n quality-cost di↵erentiated firms in which the individual consumer is the basic unit of analysis. Strategies involve mixing over both prices and whether to advertise. In equilibrium, only the top two firms advertise, earning "Bertrand-like" profits. Welfare losses initially rise then fall with the ad cost, with losses due to excessive advertising and sales by the "wrong" firm. Additionally, taking the limit of advertising costs to zero selects the equilibrium where the most e cient firm prices (with probability one) at the cost of its closest rival. The second essay develops a model of personalized advertisements and their impact on customers' purchase paths in a context of limited consideration sets. Personalized advertisements are more likely to be considered relative to generic advertisements. The consideration set expands from the status quo shopping list to the product in the advertisement when it is considered. Consideration of products outside a customer's status quo purchase path has the greatest expected increase on her consideration set and purchase basket while products along her purchase path have limited impact. I empirically test these predictions and find that personalized campaigns increase sales in the promoted department and in the store overall. Campaigns for regularly purchased products have little impact on sales in the department or store, even though redemption rates are higher. Generic campaigns have the least impact on sales.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.