An Integrated Model of Grocery Store Shopping Path and Purchase Behavior

Abstract

As behavioral hypotheses about in-store decision making become more common in the marketing literature, there is a growing need for richer, more complete datasets in order to test them more carefully. We introduce a novel PathTracker® dataset that captures consumers' in-store shopping processes, thus allowing researchers to start thinking about how to run such tests using actual field data. We propose an individual-level probability model that jointly captures three key aspects of a consumer's within-store behavior: which zones she visits, how long she stays in each zone, and what purchases she makes within that zone. After showing that our model offers an adequate description of the PathTracker® data, we discuss the issues involved in testing several behavioral hypotheses using the proposed framework.