Improving customer churn models as one of customer relationship management business solutions for the telecommunication industry

Nowadays, when companies are dealing with severe global competition, they are making serious investments in Customer Relationship Management (CRM) strategies. One of the cornerstones in CRM is customer churn prediction, the practice of determining a mathematical relation between customer characteristics and the likelihood to end the business contract with the company. This paper focuses on how to better support marketing decision makers in identifying risky customers in telecom industry by using Predictive Models. Based on historical data regarding the customer base for a telecom company, we proposed a Predictive Model using Logistic Regression technique and evaluate its efficiency as compared to the random selection. In the future, we will focus on extending our study by integrating more business considerations and mining models in order to adjust the churn models or redesign marketing activities for the telecom industry.