Analysis of regional classification of consumer behavior characteristics using big data on purchasing

Abstract

Consumer behavior has a wide range of characteristics, such as replenishment of daily necessities, grocery shopping, and whimsical shopping. In addition, purchasing information is analyzed and utilized on a store-by-store basis, so it has been considered difficult to use it in research. However, as the use of big data has been attracting attention in recent years, comprehensive data that is excellent for analyzing purchasing information has become available. In this study, we propose a clustering method that utilizes big data on purchases to characterize consumption behavior in inter-regional travel. This method constructs a dispersion representation of inter-regional travel and intuitively shows the distribution of purchase amounts. The results of this analysis showed that clusters representing characteristics were created and that useful spatial information was obtained for understanding consumption trends in the city.