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Frontiers: The Identity Fragmentation Bias

Author

Listed:
  • Tesary Lin

    (Questrom School of Business, Boston University, Boston, Massachusetts 02215)

  • Sanjog Misra

    (Booth School of Business, The University of Chicago, Chicago, Illinois 60637)

Abstract

Consumers interact with firms across multiple devices, browsers, and machines; these interactions are often recorded with different identifiers for the same consumer. The failure to correctly match different identities leads to a fragmented view of exposures and behaviors. This paper studies the identity fragmentation bias , referring to the estimation bias resulted from using fragmented data. Using a formal framework, we decompose the contributing factors of the estimation bias caused by data fragmentation and discuss the direction of bias. Contrary to conventional wisdom, this bias cannot be signed or bounded under standard assumptions. Instead, upward biases and sign reversals can occur even in experimental settings. We compare several corrective measures and discuss their advantages and caveats.

Suggested Citation

  • Tesary Lin & Sanjog Misra, 2022. "Frontiers: The Identity Fragmentation Bias," Marketing Science, INFORMS, vol. 41(3), pages 433-440, May.
    • Handle: RePEc:inm:ormksc:v:41:y:2022:i:3:p:433-440
    DOI: 10.1287/mksc.2022.1360
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    References listed on IDEAS

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    Cited by:

    1. Nico Neumann & Catherine E. Tucker & Kumar Subramanyam & John Marshall, 2023. "Is first- or third-party audience data more effective for reaching the ‘right’ customers? The case of IT decision-makers," Quantitative Marketing and Economics (QME), Springer, vol. 21(4), pages 519-571, December.
    2. Jessica Clark & Jean-François Paiement & Foster Provost, 2023. "Who’s Watching TV?," Information Systems Research, INFORMS, vol. 34(4), pages 1622-1640, December.

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