Analysis of path data in marketing with applications to grocery shopping
Path data, i.e., records of consumers' movements over time, contain valuable information for marketing researchers because they describe how consumers interact with their environment and make dynamic choices. This dissertation focuses on the analysis of path data, with particular emphasis on the study of a novel data set--integrated grocery shopping path and purchasing (scanner) data obtained from grocery carts affixed with Radio Frequency Identification (RFID) tags. The dissertation is comprised of three essays, each of which studies paths using a different approach. In the first essay, we identify the different dimensions and components of a path model and develop a unifying framework that allows us to apply tools developed in other disciplines (e.g., models of birds, pedestrians, and traffic) to path data in marketing. In the second essay, we develop a stochastic model for store shopping trips to capture the relationship between consumers' shopping paths through the store and their purchasing behavior. Using this model, we test several behavioral hypotheses about consumer grocery shopping behavior (e.g., shopping momentum, licensing, goal gradient, crowding/herding). In the third essay, we view grocery shopping trips through the lens of the "Traveling Salesman Problem" (TSP), a classic optimization framework in operations research. We calculate the TSP-optimal (shortest travel distance) path for each grocery shopper and compare each observed path with its optimal counterpart. We then decompose the systematic deviations from optimality into "order deviation" and "travel deviation," and study the relationship between these deviations and shopping behavior. The dissertation concludes with a short chapter, briefly describing areas for future research.
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