To survive in today's telecommunication business it is imperative to distinguish customers who are not reluctant to move toward a competitor. Therefore, customer churn prediction has become an essential issue in telecommunication business. In such competitive business a reliable customer predictor will be regarded priceless. This paper has employed data mining classification techniques including Decision Tree, Artificial Neural Networks, K-Nearest Neighbors, and Support Vector Machine so as to compare their performances. Using the data of an Iranian mobile company, not only were these techniques experienced and compared to one another, but also we have drawn a parallel between some different prominent data mining software. Analyzing the techniques’ behavior and coming to know their specialties, we proposed a hybrid methodology which made considerable improvements to the value of some of the evaluations metrics. The proposed methodology results showed that above 95% accuracy for Recall and Precision is easily achievable. Apart from that a new methodology for extracting influential features in dataset was introduced and experienced. Nowadays business managers have started to appreciate the important role of churn prediction in their way of prosperity. In the literature, it has been repeatedly indicated that customer retention in comparison to absorbing new customers is significantly more achievable and less expensive. In today's competitive business environment, losing a customer should be considered as a real disaster. Loss of a customer can be contemplated in three different aspects. First, losing existing customers is figuratively equivalent to have a critical machine irreparably broken down due to the fact that they are any company's most precious assets. Furthermore, by the same imaginary assumption, losing a customer would mean passing our asset intentionally to our competitor. Finally it is too laborious a task to gain a new customer. To make the matters worse, even if a new customer were absorbed, they even would not be as loyal as the old customers. It may take some time for just a proportion of them to become slightly loyal. Therefore, the prevention strategy is absolutely worthwhile. Customer retention plays a major role in many enterprises, especially matured ones, including telecommunications and finances [1]. Acquiring it requires and rears churn prediction, which is another term and keyword in customer retention. It can be explained as predicting customers’ probable tendency to switch to our competitor. In today's telecommunication business environment, competition is tremendously fierce. The services and customers’ options also have become more comparable and more competitive. This is the reason why customer loyalty tends to erode. It costs customers figuratively nothing to switch from a service provider to another. They are customers after all and they have freewill to switch to a better and probably more inexpensive service in a competitive market. We, as company managers, ought to take every necessary step so as to get in their way of leaving. It is imperative to distinguish customers who are not reluctant to move toward another competitor before they actually consider so. Therefore, dealing with the probability of customers’ churn has become an inevitable issue in telecommunication industry [2]. The telecommunication service companies are annually facing with loss of valuable customers to competitors. Due to the changes and improvements in telecommunications’ services technologies in the last few years, customers’ churn has resulted in magnificent losses and it has proven itself as a real issue [3]. 
Improved churn prediction in telecommunication industry using data mining techniques