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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.

Figure 3: Comparison across Different Data and Estimators

Note: The horizontal dashed lines indicate the estimated effect sizes, with black, red and blue representing the true (unfragmented), fragmented (naive estimator using fragmented data) and aggregated (stratefied aggregation on fragmented data) estimates. The vertical lines the 95% confidence region.
Citation

Lin, Tesary, and Sanjog Misra. “Frontiers: the identity fragmentation bias.” Marketing Science 41.3 (2022): 433-440.

@article{lin2022frontiers,
  title={Frontiers: the identity fragmentation bias},
  author={Lin, Tesary and Misra, Sanjog},
  journal={Marketing Science},
  volume={41},
  number={3},
  pages={433--440},
  year={2022},
  publisher={INFORMS}
}