Disentangling Preferences and Learning in Brand Choice Models
Posted: 24 Oct 2012
Date Written: 2012
In recent years there has been a growing stream of literature in marketing and economics that models consumers as Bayesian learners. Such learning behavior is often embedded within a discrete choice framework that is then calibrated on scanner panel data. At the same time, it is now accepted wisdom that disentangling preference heterogeneity and state dependence is critical in any attempt to understand either construct. We posit that this confounding between state dependence and heterogeneity often carries through to Bayesian learning models. That is, the failure to adequately account for preference heterogeneity may result in over- or underestimation of the learning process because this heterogeneity is also reflected in the initial conditions. Using a unique data set that contains stated preferences (survey) and actual purchase data (scanner panel) for the same group of consumers, we attempt to untangle the effects of preference heterogeneity and state dependence, where the latter arises from Bayesian learning. Our results are striking and suggest that measured brand beliefs can predict choices quite well and, moreover, that in the absence of such measured preference information, the Bayesian learning behavior for consumer packaged goods is vastly overstated. The inclusion of preference information significantly reduces evidence for aggregate-level learning and substantially changes the nature of individual-level learning. Using individual-level outcomes, we illustrate why the lack of preference information leads to faulty inferences.
Keywords: Bayesian learning, brand choice, preferences, state dependence, Markov chain Monte Carlo
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