We propose a unifying framework for benefit and demographic segmentation based on the analysis of rank-order choice data collected in conjoint studies. The model identifies a number of unobserved segments, estimates the conjoint model within each segment, and at the same time estimates the...

The authors develop a class of mixtures of piece-wise exponential hazard models for the analysis of brand switching behavior. The models enable the effects of marketing variables to change nonproportionally over time and can, simultaneously, be used to identify segments among which switching and...

We attempt to provide insights into how heterogeneity has been and can be addressed in choice modeling. In doing so, we deal with three topics: Models of heterogeneity. Methods of estimation and Substantive issues. In describing models we focus on discrete versus continuous representations of...

Response latencies provide information about consumers' choice behavior in a conjoint choice experiment. The authors use filtered response latencies to scale the covariance matrix of a multinomial probit model and show that this leads to better model fit and holdout predictions, even if the...

The authors study the estimation of factor models and the imputation of missing data and propose an approach that provides direct estimates of factor weights without the replacement of missing data with imputed values. First, the approach is useful in applications of factor analysis in the...

This study concerns list augmentation in direct marketing. List augmentation is a special case of missing data imputation. We review previous work on the mixed outcome factor model and apply it for the purpose of list augmentation. The model deals with both discrete and continuous variables and...