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The Identity Fragmentation Bias

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  • Tesary Lin
  • Sanjog Misra

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 then compare several corrective measures, and discuss their respective advantages and caveats.

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  • Tesary Lin & Sanjog Misra, 2020. "The Identity Fragmentation Bias," Papers 2008.12849, arXiv.org, revised Feb 2021.
    • Handle: RePEc:arx:papers:2008.12849
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    1. Joel Barajas & Ram Akella & Marius Holtan & Aaron Flores, 2016. "Experimental Designs and Estimation for Online Display Advertising Attribution in Marketplaces," Marketing Science, INFORMS, vol. 35(3), pages 465-483, May.
    2. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    3. Ron Berman, 2018. "Beyond the Last Touch: Attribution in Online Advertising," Marketing Science, INFORMS, vol. 37(5), pages 771-792, September.
    4. Alexander Bleier & Maik Eisenbeiss, 2015. "Personalized Online Advertising Effectiveness: The Interplay of What, When, and Where," Marketing Science, INFORMS, vol. 34(5), pages 669-688, September.
    5. Martha J. Bailey & Connor Cole & Morgan Henderson & Catherine Massey, 2020. "How Well Do Automated Linking Methods Perform? Lessons from US Historical Data," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 997-1044, December.
    6. Klaus M. Miller & Bernd Skiera, 2017. "Economic Damage of Cookie Lifetime Restrictions," Working Papers 17-02, NET Institute.
    7. Oliver J. Rutz & Michael Trusov & Randolph E. Bucklin, 2011. "Modeling Indirect Effects of Paid Search Advertising: Which Keywords Lead to More Future Visits?," Marketing Science, INFORMS, vol. 30(4), pages 646-665, July.
    8. Kirthi Kalyanam & John McAteer & Jonathan Marek & James Hodges & Lifeng Lin, 2018. "Cross channel effects of search engine advertising on brick & mortar retail sales: Meta analysis of large scale field experiments on Google.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(1), pages 1-42, March.
    9. Thomas Blake & Chris Nosko & Steven Tadelis, 2016. "Returns to Consumer Search: Evidence from eBay," NBER Working Papers 22302, National Bureau of Economic Research, Inc.
    10. Thomas Blake & Sarah Moshary & Kane Sweeney & Steven Tadelis, 2018. "Price Salience and Product Choice," NBER Working Papers 25186, National Bureau of Economic Research, Inc.
    11. Li, Quan & Reuveny, Rafael, 2003. "Economic Globalization and Democracy: An Empirical Analysis," British Journal of Political Science, Cambridge University Press, vol. 33(1), pages 29-54, January.
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