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EMAC 2021 Annual Conference

Marketing Analytics and Privacy

Published: May 25, 2021


Jaap Wieringa, University of Groningen ; Thomas Reutterer, WU Vienna University of Economics and Business; René Laub, Goethe University Frankfurt; PK Kannan, Robert H. Smith School of Business, University of Maryland; Michael Platzer, MOSTLY AI; Gilian Ponte, Rijksuniversiteit Groningen


"In almost everything we do, we leave a trail of data that exposes our interests, traits, beliefs and intentions. In doing so, we provide information to firms and governmental institutions, which allows them to track individuals and collect personal information to predict customer behavior and deliver an unprecedented level of personalization. This offers great opportunities for marketing analytics. At the same time, increasing privacy concerns and stricter privacy regulations within the European Union and the United States limit firms in their ability to process individual-level data and to develop effective marketing analytics programs. The goal of this special session is to address the marketing challenges in today’s privacy environment. Our joint contribution is to strike a balance between data-utility and privacy protection. We cover the trade-off from data collection to deriving insights in a privacy-preserving way. Laub, Miller and Skiera derive the monetary value that publishers are losing due to restrictions on online tracking. In their paper entitled “The Value of User Tracking and Behavioral Targeting for Publishers”, they decompose the value of a third-party cookie into a privacy-intrusive part, that stems from behavioral targeting, and a privacy non-intrusive part, that stems from other features such as success measurement of online advertising. The authors further illustrate how the value of a cookie differs across publishers in the market. Kannan and Lu discuss how with increased privacy concerns, tracking of consumers becomes more and more difficult. Consequently, marketing technology solutions may no longer be helpful for marketers. In their presentation “A World Without Cookies: Challenges for Attribution and Media Mix Modeling” they outline possible work-arounds using modeling approaches that preserve privacy but also enable attribution estimates and optimal allocation of media for effective outcomes. Platzer, Reutterer and Vamosi investigate the utility-privacy trade-off in the context of anonymizing behavioral marketing data. In their presentation “AI-based re-identification exposes privacy risk of behavioral data. A case for synthetic data”, they demonstrate that standard “anonymization” techniques fail to protect individual-level sequences of behavioral data. They show that data synthetization can effectively reduce the risk of privacy intrusion and help to conserve the value of the data for data-driven marketing. Ponte and Wieringa develop generative adversarial networks (GANs) that generate individual-level artificial data that, when analyzed, deliver the same marketing insights as real consumer data. In their presentation “Privacy-preserving Generative Adversarial Networks to Share Data and Derive Marketing Insights”, they show that the artificial data estimations occasionally even outperform real data estimations in terms of predictive validity. Their approach allows for data sharing even under strict privacy regulations."