Multi-Stage Modeling in Marketing: Consumers’ Brand Choice, Category Purchase, Store Choice, and RetailFormat Choices

Abstract

Multi-stage modeling is an essential method for analyzing consumer behavior. Purchasing decisions often proceed through multiple stages, such as category purchase, brand choice, store choice, and retail format choice. Understanding these stage-specific processes and systematically analyzing them using statistical models is crucial for uncovering consumer behavioral characteristics and decision-making factors. This paper organizes the theoretical background of multi-stage modeling and primarily reviews its applications in the marketing field. Specifically, it provides an overview of prominent methods used in multi-stage modeling, including discrete choice models such as logit and probit models, hierarchical Bayesian models, and mixed logit models. It also addresses practical data formats, such as scanner panel data, which are commonly utilized for data collection and analysis in real-world applications. Furthermore, this paper examines approaches to integrating multiple stages into unified models, with a focus on methods that consider interdependencies between categories and heterogeneity among consumers. These insights have potential applications not only in marketing strategies but also in other fields, such as transportation behavior analysis and travel planning. The objective of this paper is to reevaluate the role of multi-stage modeling and clarify its practical value across various domains.