عنوان مقاله [English]
The competitive characteristics of telecom industry and customers’ demands along with advances in telecommunication technologies make the necessity of determining customer relationship management new strategies more evident. One approach to reach this reformation is to analyze large volume of customers’ data warehouses in order to identify different classes of customers and presenting appropriate services according to their transactional and behavioral features, subsequently. In this research, the researchers had employed data mining algorithms, especially clustering one and also RFM analysis on the customers of Guilan province Telecom Bureau, for a six month period of observation of their payment and debt information in order to identify different clusters of customers. After the process of clustering results evaluation, the best clustering method had been chosen and each cluster was allocated to each known group of customers according to the Loyalty matrix. In this research, after selecting appropriate attributes of customer’s data using RFM analysis method, three specific clusters of customers had been identified using K-means clustering algorithm and then according to the behavior features and product consumption patterns of customers, the appropriate services to each cluster is suggested and presented in order to improve the customer relationship management practices and increasing the customer life value.