Path Data in Marketing: An Integrative Framework and Prospectus for Model-Building

Abstract

Many datasets, from different and seemingly unrelated marketing domains, all involve "paths" - records of consumers' movements in a spatial configuration. Path data contain valuable information for marketing researchers because they describe how consumers interact with their environment and make dynamic choices. As data collection technologies improve and researchers continue to ask deeper questions about consumers' motivations and behaviors, path datasets will become more common, and play a more central role in marketing research. To guide future research in this area, we review the previous literature, propose a formal definition of paths, and derive a unifying framework that allows us to classify different kinds of paths. We identify and discuss two primary dimensions (characteristics of the spatial configuration and the agent) as well as six underlying sub-dimensions. Based on this framework, we cover a range of important operational issues that should be taken into account as researchers begin to build formal models of path-related phenomena. We close with a brief look into the future of path-based models, and a call for researchers to address some of these emerging issues.