Frontiers: The Identity Fragmentation Bias

Tesary Lin () and Sanjog Misra ()
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Tesary Lin: Questrom School of Business, Boston University, Boston, Massachusetts 02215
Sanjog Misra: Booth School of Business, The University of Chicago, Chicago, Illinois 60637

Marketing Science, 2022, vol. 41, issue 3, 433-440

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.

Keywords: fragmentation; cookies; bias; inference; privacy; measurement (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:41:y:2022:i:3:p:433-440

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