Abstract: “Churn prediction is considered a big issue in the Telecom market because customer acquisition costs five to eight times than retaining an existing one. Customer retention is the action that a service provider undertakes in order to reduce customer dissatisfaction and decrease the probability of leaving a service provider. This paper proposes a churn prediction retention framework based on data mining techniques. Deploying the proposed framework into a business intelligence system can help in enhancing the efficiency of customer relationship management. Moreover it can help customer churn management department to easily predict and retain the expected future churners in many business areas. The proposed framework mainly composed of seven phases and sub-phases. A case study is demonstrated to evaluate the framework throughout 5000 customers’ records in an anonymous telecom company.”

Keywords: Churn prediction, customer retention, data mining, clustering, classification