The value of relevant and reliable information in this globalized. competitive andrndynamic business environment is 100 high as it allows optimal decision making in allrnrespects. Especially, in customer-oriented businesses like in the telecom industry,rnadequate in((ml1ation regarding Customers is villa so that appropriate customerrnrelationships that maximize mutual benefit can be established)rnBehavioral based customer segmentation, ,which is one of the core applications ofrnCustomer Relationship Management (CRM). provides useful insights for designingrnand implementing an appropriate CHM strategies and programs that remit inrnsuccess. To this end. data mining has strong potential in exploring naturalrnsegmentation schemes that lies within customers' data This study is an attempt to explore the customers' data to find underlying customerrnsegments that may be use/ill Jar marketing decision making. The research followedrnthe CRISP data mining process model.rnFirst the business problem was analyzed and a corresponding data mining tool,rntechniques and algorithms were selected. Besides, relevant data was collected,rnanalyzed and prepared. Then, an automatic cluster detection models were bull to choose the best attributesrnfor the final segmentation. Next, the final automatic cluster detection model wasrngenerated, analyzed and evaluated together with domain experts with differentrnparameters. Finally, a decision tree model, selling cluster index as dependentrnvariable, was built Corresponding rules to the decision tree were also generated.rnThe results obtained were encouraging and can be further refined .for possiblerndeployment