Is first- or third-party audience data more effective for reaching the ‘right’ customers? The case of IT decision-makers

Nico Neumann (), Catherine E. Tucker (), Kumar Subramanyam () and John Marshall ()
Additional contact information
Nico Neumann: University of Melbourne
Catherine E. Tucker: MIT Sloan School of Management, Cambridge, MA and Research Associate at the NBER
Kumar Subramanyam: HP Inc.
John Marshall: HP Inc.

Quantitative Marketing and Economics (QME), 2023, vol. 21, issue 4, No 3, 519-571

Abstract: Abstract Often marketers face the challenge of how to communicate best with the customers who have the right responsibilities, influence or purchasing power, especially in business-to-business (B2B) settings. For example, B2B marketers selling software and IT need to identify IT decision-makers (ITDMs) within organizations. The modern digital environment in theory allows marketers to target individuals in organizations through specifically designed third-party audience segments based on deterministic prospect lists or probabilistic inference. However, in this paper we show that in our context, such ‘off-the-shelf’ segments perform no better at reaching the right person than random prospecting. We present evidence that even deterministic attribute information is flawed for ITDM identification, and that the poor campaign results can be partly linked to incorrect assignment of established prospect profiles to online identifiers. We then use access to our publisher network data to investigate what would happen if the advertiser had used first-party data that are less susceptible to the identified issues. We demonstrate that first-party demographics or contextual information allows advertisers and publishers to outperform both third-party ITDM audience segments and random prospecting. Our findings have implications for understanding the shift in digital advertising away from third-party cookie tracking, and how to execute digital marketing in the context of broad privacy regulation.

Keywords: Digital targeting; User profiling; Data brokers; Third-party data (search for similar items in EconPapers)
JEL-codes: L8 M3 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11129-023-09268-7

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