Customer Retention: Detecting Churners in Telecoms Industry using Data Mining Techniques

Abstract

Customers are more concerned with the quality of services that companies can provide. Customer churn is the percentage of service for subscribers, who stop their subscriptions or the proportion of customers, who discontinue using the product of the firm or service within a given time frame. Services by various service providers or sellers are not very distinct that raise rivalry between firms to maintain the quality of their services and upgrade them. This paper aims at manifesting the service quality effect on customer satisfaction and churn prediction to reveal customers who have meant to leave a service. Predictive models can give the extent of the service quality effect on customer satisfaction for the correct determination of possible churners shortly for the provision of a retention solution. This paper analyses the impact of service quality and prediction models that depend on data mining (DM) techniques. The present model contains five steps: data-pre-processing, feature selection, sampling of data, training our classifier, testing for prediction, and output of prediction. A data set with 17 attributes and 5000 records used - which consist of 75% training the model and 25% testing- are randomly selected. The DM techniques applied in this paper are Boruta algorithm, C5.0, Neural Network, Support Vector Machine, and random forest via open-source software R and WEKA.