Customer-Specific Taste Parameters and Mixed Logit

Abstract

With flexible models of customers' choices among products and services, we estimate the tastes (part-worths) of each sampled customer as well as the distribution of tastes in the population. First, maximum likelihood procedures are used to estimate the distribution of tastes in the population using the pooled data for all sampled customers. Then, the distribution of tastes of each sampled customer is derived conditional on the observed data for that customer and the estimated population distribution of tastes (accounting for uncertainty in the population estimates.) The procedure provides the same type of information and is similar in spirit to hierarchical Bayes (HB.) The procedure is computationally attractive when it is easier to calculate the likelihood function for the population parameters than to draw from the posterior distribution of parameters as needed for HB. We apply the method to data on residential customers' choice among energy suppliers in conjoint-type experiments. The estimated distribution of tastes provides practical information that is useful for suppliers in designing their offers. The conditioning for individual customers is found to differentiate customers effectively for marketing purposes and to improve considerably the predictions in new situations.