Providing a New Approach for Segmenting Customers Based on Their Purchasing Behavior Change over Time in Electronic Business

Document Type: Research Paper


1 MSc. of Information Technology Engineering, Graduate University of Advanced Technology, Kerman, Iran

2 MSc. of Assessment and Measurement, Allameh Tabataba'i University, Tehran, Iran


Usual methods of segmentation have been designed, relying solely on the components of Recency (R), Frequency (F) and Monetary (M) in which customers’ behavior changes over time are not considered. Accordingly, in order to achieve a desired segmentation method, this study aims to apply a set of statistical calculations, such as line slope and the derivative with respect to time and data mining methods such as K-means and Self-Organizing Maps (SOM) to define new parameters for studying the changes trending of customer purchasing behavior. The results show that considering the slope of the line of customer behavior changes (R, F, and M) and the higher value for recent behaviors of customers compared to that of their past behavior in customer segmentation would thereby increase the accuracy of predicting the future behavior and cause the customers of each section to become more homogeneous. Based on the suggested method, customers are categorized into four segments: best, spender, repeater and missed ones each of them are divided into two subcategories of ascending and descending segments, which leads to better and more accurate understanding of customers in different segments according to how of their purchasing behavior change. Finally, the characteristics of each segments and sub-segments are described and appropriate strategies are provided for managing its customers.


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