Tracking-Based Advertising After Apple's App Tracking Transparency: Firm-Level Evidence and Policy Implications
We discuss the impact of Apple’s App Tracking Transparency's (“ATT”) on targeted, online advertising. We overview the empirical results of Aridor, Che, Hollenbeck, Kaiser & McCarthy (2025) that measured the impact of ATT on e-commerce firms. The results point to a large reduction in the efficacy of targeted advertising and subsequently large revenue losses, borne primarily by smaller firms.
AdGazer: Improving Contextual Advertising with Theory-Informed Machine Learning
Contextual advertising involves matching features of ads to features of the media context where they appear. We propose AdGazer, a new machine learning procedure to support contextual advertising. It comprises a theoretical framework organizing high and low-level features of ads and contexts, feature engineering models grounded in this framework, an XGBoost model predicting ad and brand attention, and an algorithm optimally assigning ads to contexts. AdGazer includes a Multimodal Large Language Model to extract high-level topics predicting the ad-context match.
AI for Customer Journeys: A Transformer Approach
AI for Customer Journeys: A Transformer Approach
Zipei Lu and P. K. Kannan, Smith School of Business, University of Maryland
(forthcoming Journal of Marketing Research)