Projection pursuit random forest using discriminant feature analysis model for churners prediction in telecom industry

Abstract

A major and demanding issue in the telecommunications industry is the prediction of churn customers. Churn describes the customer who is attrite from one Telecom service provider to competitors searching for better services offers. Companies from the Telco sector frequently have customer relationship management offices it is the main objective in how to win back defecting clients because preserve long-term customers can be much more beneficial to a company than gain newly recruited customers. Researchers and practitioners are paying great attention and investing more in developing a robust customer churn prediction model, especially in the telecommunication business by proposed numerous machine learning approaches. Many approaches of Classification are established, but the most effective in recent times is a tree-based method. The main contribution of this research is to predict churners/non-churners in the Telecom sector based on project pursuit Random Forest (PPForest) that uses discriminant feature analysis as a novelty extension of the conventional Random Forest approach for learning oblique Project Pursuit tree (PPtree). The proposed methodology leverages the advantage of two discriminant analysis methods to calculate the project index used in the construction of PPtree. The first method used Support Vector Machines (SVM) as a classifier in the construction of PPForest to differentiate between churners and non-churners customers. The second method is a Linear Discriminant Analysis (LDA) to achieve linear splitting of variables node during oblique PPtree construction to produce individual classifiers that are robust and more diverse than classical Random Forest. It found that the proposed methods enjoy the best performance measurements e.g. Accuracy, hit rate, ROC curve, Gini coefficient, Kolmogorov-Smirnov statistic and lift coefficient, H-measure, AUC. Moreover, PPForest based on direct applied of LDA on the raw data delivers an effective evaluator for the customer churn prediction model.

A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation

“…We aimed to cover this research gap using a Fisher discriminant analysis and a logistic regression analysis of telecom customer churn related to diverse factors. Moreover, the discriminant function and logistic regression analysis are proven to predict telecom customer churn [42]. In this study, through a Wilks' lambda discriminant test, we be concluded that the discriminant equation is valid and can explain the reasons for churn.…”

Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

“…Firstly, they segment the customers, secondly, they take the loyalty levels of each segment, thirdly they use classification algorithms based on these descriptives and finally they evaluate these models based on several criteria and develop some rules. Alzubaidi and Al-Shamery [20] conduct research by using a methodology called project pursuit Random Forest (PPForest), which uses discriminant feature analysis to predict churners/non-churners in the telecommunication sector. Rao et al [21] develop an algorithm via machine learning to test the key performance indicators (KPIs) and weed out the troublesome sites from a network's traffic data dump spanning over a month which would help to identify the problematic websites, thus addressing the customer's problems.…”