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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. This paper was accepted by David Simchi-Levi, marketing. Funding: Financial support from the National Science Foundation [CAREER Award 6923256] and an anonymous panel company is gratefully acknowledged. Supplemental Material: The web appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4979. [ABSTRACT FROM AUTHOR]

The explosion of available data has created much excitement among marketing practitioners about their ability to better understand the impact of marketing investments. Big data allows for detecting patterns and often it seems plausible to interpret them as causal. While it is quite obvious that storks do not bring babies, marketing relationships are usually less clear. Apparent “causalities” often fail to hold up under examination. If marketers want to be sure not to walk into a causality trap, they need to conduct field experiments to detect true causal relationships. In the present digital environment, experiments are easier than ever to execute. However, they need to be prepared and interpreted with great care in order to deliver meaningful and genuinely causal results that help improve marketing decisions.

Fast-paced IT advances have made it increasingly possible and useful for firms to collect data on their customers on an unprecedented scale. One downside of this is that firms can experience negative publicity and financial damage if their data are breached. This is particularly the case in the medical sector, where we find empirical evidence that increased digitization of patient data is associated with more data breaches. The encryption of customer data is often presented as a potential solution, because encryption acts as a disincentive for potential malicious hackers, and can minimize the risk of breached data being put to malicious use. However, encryption both requires careful data management policies to be successful and does not ward off the insider threat. Indeed, we find no empirical evidence of a decrease in publicized instances of data loss associated with the use of encryption. Instead, there are actually increases in the cases of publicized data loss due to internal fraud or loss of computer equipment. (Contains 11 tables, 1 figure and 11 footnotes.)