Bayesian Theory with Application to Customer Segment and Consuming Behavior Control for Online Business

In this paper, we follow the model of interpurchase times to achieve heterogeneity across customers. We employ a mixture model to segment customers into three states: super-active, active and inactive. The interpurchase model and mixture model are solved by the hierarchical Bayes via Markov Chain Monte Carlo method. We employ CUSUM chart based on the density of active state to monitor consumer behavior. Factors on the CUSUM chart include interpurchase time and recency (the interval from the last visit day to the analyzing day) for individual customers. Control charts such as CUSUM chart have been used by industries to control product quality. They are useful time-series tools enable supervisors to correct the process in time when the chart shows a significant warning signal. In this research, we combined two information, interpurchase time and recency, from an individual customer and use CUSUM chart to control the consuming behavior. A real case study from an online company employed on CUSUM chart shows the type I and type II errors are less than 5% and 10%, respectively.