Personal data strategies in digital advertising: Can first-party data outshine third-party data?

Abstract

As Google explores new personal data strategies, it aims to strike a balance between enhancing privacy and maintaining personalization, ensuring that consumers' data is handled responsibly while still delivering relevant and personalized advertising. This study, based on the privacy calculus framework, employs a mixed-methods approach to address two key objectives: 1) understanding how different levels of personalization impact advertising performance based on the type of data utilized, and 2) exploring the underlying mechanism of consumer reactions to personalized advertising using different types of personal data. To achieve the first research goal, Study 1 integrates Analytic Hierarchy Process (AHP) analysis of survey data from 25 advertisers with econometric analysis of advertising data from a European beauty company. To achieve the second research objective, Study 2 explores consumer perceptions through in-depth interviews and an online survey. The key findings of this study are as follows. The AHP analysis revealed that advertisers prioritize first-party data, especially purchase history, for enhancing personalized targeting. The econometric analysis suggested that while third-party data is currently most effective for enhancing advertising performance, first-party data emerges as a promising alternative in light of evolving advertising policies. Qualitative and quantitative analyses revealed complex interactions between personalization levels, data types, and consumer responses, highlighting the tension between personalization benefit and risk. These insights provide valuable guidance for advertisers, platforms, and policymakers in navigating the changing landscape of digital advertising and personal data privacy.