A Method for Dynamic Segmentation of Customer Base in an Adaptive Business Intelligence System

The core of every intelligent system is its ability to adapt to environmental changes, but there is not enough attention to the compatibility issue in these systems. Hence, the aim of this study is to provide a method in adaptive business intelligence system that dynamically segment customers and update this segmentation by monitoring purchasing behavior and analysis of "Recency", "Frequency" and "Monetary" of each one of them. In this research, data mining techniques have been applied on the adaptive modulus of the adaptive business intelligence system so that the current clients of the organization are classified and over time and with learning from the environment, this classification is improved to provide customized services to customers. This method can modify initial clustering with respect to environmental changes in less than 0.5 seconds and reduce the number of execution of conventional clustering algorithms (Approximately 22% of cases). The proposed method, considering different customer segments, their current RFM values and changes made by repeating purchases at these values, provides a better and more comprehensive clustering of the organization's customers that can be useful in improving the performance of an adaptive business intelligent system.