Customer Segmentation by Various Clustering Approaches and Building an Effective Hybrid Learning System on Churn Prediction Dataset

Abstract

Success of every organization or firm depends on Customer Preservation (CP) and Customer Correlation Management (CCM). These are the two parameters determining the rate at which the customers decide to subscribe with the same organization. Thus higher service quality reduces the chance of customer churn. It involves various attributes to be analyzed and predicted in industries like telecommunication, banking, and financial institutions. Customer churn forecast helps the organization to retain the valuable customers and it avoids failure of the particular organization in a competitive market. Single classifier does not result in higher churn forecast accuracy. Nowadays, both unsupervised and supervised techniques are being combined to get better classification accuracy. Also unsupervised classification plays a major role in hybrid learning techniques. Hence, this work focuses on various unsupervised learning techniques which are comparatively studied using algorithms like Fuzzy C-Means (FCM), Possibilistic Fuzzy C-Means (PFCM), K-Means clustering (K-Means), where similar type of customers is grouped within a cluster and better customer segmentation is predicted. The clusters are divided for training and testing by Holdout method, in which training is carried out by decision tree and testing is done by the model generated. The results of the churn prediction data set experiment show that; K-Means clustering algorithm along with the decision tree helps improving the result of churn prediction problem present in the telecommunication industry.