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We propose a framework for the joint study of the consumer's decision of where to buy and what to buy. The framework is rooted in utility theory where the utility is for a particular channel/brand combination. The framework contains firm actions, the consumer search process, the choice process, and consumer learning. We develop research questions within each of these areas. We then discuss methodological issues pertaining to the use of experimentation and econometrics. Our framework suggests that brand and channel choices are closely intertwined, and therefore studying them jointly will reveal a deeper understanding of consumer decision making in the modern marketing environment. [ABSTRACT FROM AUTHOR]

When managers and researchers encounter a data set, they typically ask two key questions: (1) Which model (from a candidate set) should I use? And (2) if I use a particular model, when is it going to likely work well for my business goal? This research addresses those two questions and provides a rule, i.e., a decision tree, for data analysts to portend the "winning model" before having to fit any of them for longitudinal incidence data. We characterize data sets based on managerially relevant (and easy-to-compute) summary statistics, and we use classification techniques from machine learning to provide a decision tree that recommends when to use which model. By doing the "legwork" of obtaining this decision tree for model selection, we provide a time-saving tool to analysts. We illustrate this method for a common marketing problem (i.e., forecasting repeat purchasing incidence for a cohort of new customers) and demonstrate the method's ability to discriminate among an integrated family of a hidden Markov model (HMM) and its constrained variants. We observe a strong ability for data set characteristics to guide the choice of the most appropriate model, and we observe that some model features (e.g., the "back-and-forth" migration between latent states) are more important to accommodate than are others (e.g., the inclusion of an "off" state with no activity). We also demonstrate the method's broad potential by providing a general "recipe" for researchers to replicate this kind of model classification task in other managerial contexts (outside of repeat purchasing incidence data and the HMM framework). [ABSTRACT FROM AUTHOR]

An item response theory model for ordinal customer satisfaction data is presented which considers 'no answer' responses. The proposed model, which is integrated into a Bayesian hierarchical approach developed by Albert and Chib, takes the probability of each response as a function of latent person and question variables and of cutoffs for the ordinal response categories. This framework supports model inferences about satisfaction of individual customers as well as relationships between customer characteristics and satisfaction levels or the tendency to respond., 1. INTRODUCTION Corporations survey customer satisfaction to support crucial decisions about market segmentation, product repositioning, allocation of sales personnel, new research and development, and marketing campaigns. Given the importance and [...]