Who’s Watching TV?

Jessica Clark (), Jean-François Paiement () and Foster Provost ()
Additional contact information
Jessica Clark: Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742
Jean-François Paiement: AT&T Research, San Francisco, California 94108
Foster Provost: Leonard N. Stern School of Business, New York University, New York, New York 10012

Information Systems Research, 2023, vol. 34, issue 4, 1622-1640

Abstract: This work addresses the problem of “user disambiguation”—estimating the likelihood of each member of a small group using a shared account or device. The main focus is on television set-top box (STB) viewership data in multiperson households, in which it is impossible to tell with certainty which household members watch what. The first main contribution is formulating user disambiguation as a predictive problem. The second contribution is a solution for estimating the likelihood that each individual in a multiperson household watches each TV segment. Kernel theories from the marketing, economics, and sociology literature inform the design of our method. This method learns priors for viewership in single-person households and then adapts them to the specifics of each multiperson household’s viewership history. Finally, we formalize two ad hoc heuristics that are currently used in industry (and research) for estimating audience composition of STB data and conduct a comparative analysis using simulated data, real large-scale viewership data, and a fully labeled panel-based data source. We find that our method has superior performance and practical value. The proposed solution has implications for advertisers, researchers who seek better understanding of TV viewership, and anyone using data generated by shared devices or accounts. A major TV provider has deployed this new method for use in its TV ad-targeting system. No personally identifiable information was gathered or used in conducting this study. To the extent any data were analyzed, it was anonymous and/or aggregated data, consistent with the carrier’s privacy policy.

Keywords: machine learning; TV advertising; design science; audience estimation; user disambiguation (search for similar items in EconPapers)
Date: 2023
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