Data Deserts and Black Boxes: The Impact of Socio-Economic Status on Consumer Profiling

Nico Neumann (), Catherine E. Tucker (), Levi Kaplan (), Alan Mislove () and Piotr Sapiezynski ()
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Nico Neumann: Melbourne Business School, University of Melbourne, Carlton, Victoria 3053, Australia
Catherine E. Tucker: MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; National Bureau of Economic Research, Cambridge, Massachusetts 02138
Levi Kaplan: Northeastern University, Boston, Massachusetts 02115
Alan Mislove: Northeastern University, Boston, Massachusetts 02115
Piotr Sapiezynski: Northeastern University, Boston, Massachusetts 02115

Management Science, 2024, vol. 70, issue 11, 8003-8029

Abstract: Data brokers use black-box methods to profile and segment individuals for ad targeting, often with mixed success. We present evidence from 5 complementary field tests and 15 data brokers that differences in profiling accuracy and coverage for these attributes mainly depend on who is being profiled. Consumers who are better off—for example, those with higher incomes or living in affluent areas—are both more likely to be profiled and more likely to be profiled accurately. Occupational status (white-collar versus blue-collar jobs), race and ethnicity, gender, and household arrangements often affect the accuracy and likelihood of having profile information available, although this varies by country and whether we consider online or offline coverage of profile attributes. Our analyses suggest that successful consumer-background profiling can be linked to the scope of an individual’s digital footprint from how much time they spend online and the number of digital devices they own. Those who come from lower-income backgrounds have a narrower digital footprint, leading to a “data desert” for such individuals. Vendor characteristics, including differences in profiling methods, explain virtually none of the variation in profiling accuracy for our data, but explain variation in the likelihood of who is profiled. Vendor differences due to unique networks and partnerships also affect profiling outcomes indirectly due to differential access to individuals with different backgrounds. We discuss the implications of our findings for policy and marketing practice.

Keywords: digital advertising; marketing: segmentation; consumer profiling; algorithmic fairness; digital privacy (search for similar items in EconPapers)
Date: 2024
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