A Bayesian Approach to Estimating Household Parameters

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Abstract
The authors present a Bayesian approach to the estimation of household parameters. Applied to the standard logit model, the procedure produces household-level estimates of all model parameters, enabling researchers to identify differences in household reaction to all variables in the marketing mix. Simulated data are used to study the small-sample performance of the estimator. The estimator can be easily implemented with standard algorithms used to maximize likelihood functions. In application to tuna scanner panel data, strong evidence of heterogeneity in price, display, and feature response (slope) parameters is detected. Approaches that fail to take into account slope heterogeneity are shown to underestimate the value of feature advertising and in-store displays in this dataset. In addition, the household price sensitivity estimates are strongly related to coupon usage and demonstrate how the estimates can be used to implement a targeted household drop.