FALL 2025

Abstract. This paper analyzes the value of information for targeting price discounts in shopping applications. It applies methods that combine standard consumer choice models from marketing and economics with matrix factorization techniques from machine learning, whereby users have latent preferences and products have latent attributes, and these are learned from data about consumer choice in a setting where prices vary over time. The paper applies the model to individual panel data from supermarket shopping for a large retailer. The paper analyzes the value of data for increasing profits through personalized price targeting, assessing the relative importance of enriching the model (adding more latent factors) versus more precisely estimating parameters of a fixed model, finding that enriching the model as data grows is an important contributor to improved performance. The paper shows that increasing the length of the history of data used for given set of individuals is substantially more valuable for targeting to those users than adding data about more products or additional users. The results have implications for privacy policy and competition policy.


Abstract. Online privacy protection has gained momentum in recent years and spurred both government regulations and private-sector initiatives. A centerpiece of this movement is the removal of third-party cookies, which are widely employed to track online user behavior and implement targeted ads, from web browsers. Using banner ad auction data from Yahoo, we study the effect of a third-party cookie ban on the online advertising market. We first document stylized facts about the value of third-party cookies to advertisers. Adopting a structural approach to recover advertisers' valuations from their bids in these auctions, we simulate a few counterfactual scenarios to quantify the impact of Google's plan to phase out third-party cookies from Chrome, its market-leading browser. Our counterfactual analysis suggests that an outright ban would reduce publisher revenue by 54% and advertiser surplus by 40%. The introduction of alternative tracking technologies under Google's Privacy Sandbox initiative would partially offset these losses. In either case, we find that big tech firms can leverage their informational advantage over their competitors and gain a larger surplus from the ban.