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Algorithmic Targeting and the Precision-Recall Tradeoff

Ganesh Iyer¹; Yunfei Yao²; Zemin Zhong³

¹ University of California, Berkeley, Berkeley, California · ² The Chinese University of Hong Kong, Shatin, Hong Kong · ³ Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada; and The City University of Hong Kong, Kowloon Tong, Hong Kong

We examine the implications of competitive algorithmic targeting when outcomes of targeting algorithms are the individual consumer-level predicted probabilities of conversion. In these situations, firms implicitly face the well-known precision-recall tradeoff while choosing their targeting strategies. They can choose to target a smaller set of consumers with a high probability of conversion (precision) but miss out on many consumers who might still be interested in their product. Conversely, firms can target a larger set of consumers (recall), but this results in a greater probability that their targeting is wasted on uninterested consumers. We analyze this precision-recall tradeoff under competition between firms that strategically choose their algorithmic targeting policies. We show that competing firms favor a targeting policy that has higher precision but lower recall compared with a monopoly. Firms target fewer consumers when their algorithms are more correlated. They also have the incentive to strategically decrease the precision of their targeting policies in order to reduce competition. If firms endogenously choose their algorithmic correlation, then there is an equilibrium incentive to decrease the correlation. History: Anthony Dukes served as the senior editor for this article. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mksc.2024.0930 .

DOI

10.1287/mksc.2024.0930

Volume

45 (4)

Pages

844-863

Language

en

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BibTeX

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