Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments

Neeraj Arora and Joel Huber

Journal of Consumer Research, 2001, vol. 28, issue 2, 273-83

Abstract: We propose aggregate customization as an approach to improve individual estimates using a hierarchical Bayes choice model. Our approach involves the use of prior estimates to build a common design customized for the average respondent. We conduct two simulation studies to investigate conditions that are most conducive to aggregate customization. The simulations are validated by a field study showing that aggregate customization results in better estimates of individual parameters and more accurate predictions of individuals' choices. The proposed approach is easy to use, and a simulation study can assess the expected benefit from aggregate customization prior to its implementation. Copyright 2001 by the University of Chicago.

Date: 2001
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Persistent link: https://EconPapers.repec.org/RePEc:oup:jconrs:v:28:y:2001:i:2:p:273-83

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