Investigating emergent nested geographic structure in consumer purchases: a Bayesian dynamic multi-scale spatiotemporal modeling approach

• Published in: Journal of applied statistics Vol. 48; no. 3; pp. 410 - 433
• Main Authors: Wang, Xia, Pancras, Joseph, Dey, Dipak K.
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 17.02.2021; Taylor & Francis Ltd
• Subjects: Clustering; dynamic linear models; Empirical analysis; empirical Bayes methods; Markov chain Monte Carlo methods; Markov chains; Modelling; Monte Carlo simulation; multi-scale modeling; Scale models; spatial models; Statistical methods; multi-scale modeling; spatial models; dynamic linear models; empirical Bayes methods; Clustering; Markov chain Monte Carlo methods
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2020.1725810

Spatial modeling of consumer response data has gained increased interest recently in the marketing literature. In this paper, we extend the (spatial) multi-scale model by incorporating both spatial and temporal dimensions in the dynamic multi-scale spatiotemporal modeling approach. Our empirical application with a US company's catalog purchase data for the period 1997-2001 reveals a nested geographic market structure that spans geopolitical boundaries such as state borders. This structure identifies spatial clusters of consumers who exhibit similar spatiotemporal behavior, thus pointing to the importance of emergent geographic structure, emergent nested structure and dynamic patterns in multi-resolution methods. The multi-scale model also has better performance in estimation and prediction compared with several spatial and spatiotemporal models and uses a scalable and computationally efficient Markov chain Monte Carlo method that makes it suitable for analyzing large spatiotemporal consumer purchase datasets.