Customer Behavior Mining Framework (CBMF) using clustering and classification techniques
الموضوعات :Farshid Abdi 1 , Shaghayegh Abolmakarem 2
1 - Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
الکلمات المفتاحية: Customer attractiveness . Customer behavior . Customer portfolio analysis . Segmentation . Churn prediction . Data mining,
ملخص المقالة :
The present study proposes a Customer Behavior Mining Framework on the basis of data mining techniques in a telecom company. This framework takes into account the customers’ behavior patterns and predicts the way they may act in the future. Firstly, clustering technique is used to implement portfolio analysis and previous customers are divided based on socio-demographic features usingk-means algorithm. Then, the cluster analysis is conducted based on two criteria, i.e., the number of hours the telecom services used and the number of the services selected by customers of each group. Six groups of customers are identified in three levels of attractiveness according to the results ofthecustomer portfolio analysis. The second phase has been devoted to mining the future behavior of the customers. Predicting the level of attractiveness of newcomer customers and also the churn behavior of these customers are accomplished in the second phase. This framework effectively helps the telecom managers mine the behavior of their customers. This may lead to develop appropriate tactics according to customers’ attractiveness and their churn behavior. Improving managers’ abilities in customer relationship management is one of the obtained results of the study.
