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Competitive Model Selection in Algorithmic Targeting

Author

Listed:
  • Ganesh Iyer

    (University of California at Berkeley, Berkeley, California 94720)

  • T. Tony Ke

    (Chinese University of Hong Kong, Hong Kong)

Abstract

We study how market competition influences the algorithmic design choices of firms in the context of targeting. Firms face a general bias-variance trade-off when choosing the design of a supervised learning algorithm in terms of model complexity or the number of predictors to accommodate. Each firm has a data analyst who uses the chosen algorithm to estimate demand for multiple consumer segments, based on which it devises a targeting policy to maximize estimated profits. We show that competition induces firms to strategically choose simpler algorithms that involve more bias but lower variance. Therefore, more complex/flexible algorithms may have higher value for firms with greater monopoly power.

Suggested Citation

  • Ganesh Iyer & T. Tony Ke, 2024. "Competitive Model Selection in Algorithmic Targeting," Marketing Science, INFORMS, vol. 43(6), pages 1226-1241, November.
    • Handle: RePEc:inm:ormksc:v:43:y:2024:i:6:p:1226-1241
    DOI: 10.1287/mksc.2023.0175
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