Spatio-temporal Analysis of Retail Customer Behavior based on Clustering and Sequential Pattern Mining

In the era of big data, the availability of large amounts of spatio-temporal data provides a new path for customer analytics. However, the related research in the retail industry is underdeveloped. In this paper, we introduce spatio-temporal data mining into retail customer analytics and conduct experiments on large real-world data sets from a retail company containing millions of customer purchase records. Spatio-temporal clustering and a new hybrid sequential pattern mining method are used to discover the characteristics of customer behavior at the aggregation as well as the individual level. The typical spatial and temporal distribution of customers and main customer clusters are obtained. Some interesting sequential purchase patterns are also found. Our research will provide not only a new analytic framework for academia but also some guidelines for better development of marketing strategies in the retail industry.