Frontiers: How Effective Is Third-Party Consumer Profiling? Evidence from Field Studies

Nico Neumann (), Catherine E. Tucker () and Timothy Whitfield ()
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Nico Neumann: Melbourne Business School, Carlton, Victoria 3053, Australia
Catherine E. Tucker: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142; National Bureau of Economic Research, Cambridge, Massachusetts 02138
Timothy Whitfield: Burst SMS, Sydney, New South Wales 2000, Australia

Marketing Science, 2019, vol. 38, issue 6, 918-926

Abstract: Data brokers often use online browsing records to create digital consumer profiles that they sell to marketers as predefined audiences for ad targeting. However, this process is a “black box”—little is known about the reliability of the digital profiles that are created or of the audience identification provided by buying platforms. In this paper, we investigate using three field tests the accuracy of a variety of demographic and audience-interest segments. We examine the accuracy of more than 90 third-party audiences across 19 data brokers. Audience segments vary greatly in quality and are often inaccurate across leading data brokers. In comparison with random audience selection, the use of black box data profiles, on average, increased identification of a user with a desired single attribute by 0%–77%. Audience identification can be improved, on average, by 123% when combined with optimization software. However, given the high extra costs of targeting solutions and the relative inaccuracy, we find that third-party audiences are often economically unattractive except for higher-priced media placements.

Keywords: digital advertising; data brokers; profiling; algorithms; machine learning; big data (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (25)

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