Churn Prediction using Ensemble Learning

With a wealth of information on hand from the Internet, customers now can easily identify and switch to alternatives. In addition to this, a consensus has been reached that the cost of securing new customers is substantially higher than the cost of retaining the current customers. Therefore, customer retention has become an essential part of operating strategy for any organisation. Churn prediction is a practice of data analysis on the historical data, which is aiming to predict if a customer will be leaving the business or not in advance. A wide range of algorithms have been proposed for churn prediction in the past, however there is no agreement on choosing the best one. Therefore, this study presents a comparative study of the most widely used classification methods on the problem of customer churning in the telecommunication sector. The main goal of this study is to analyse and benchmark the performance of some widely used classification algorithms on a public dataset.