scholarly journals Customer Churn Prediction Based on HMM in Telecommunication Industry

2020 ◽  
Author(s):  
Huisheng Zhu ◽  
Bin Yu
Keyword(s):  
Prediction Model ◽  
Information Gain ◽  
Rapid Development ◽  
Data Set ◽  
Customer Churn ◽  
Numerical Attributes ◽  
China Telecom ◽  
Market Communication ◽  
Customer Churn Prediction ◽  
Monthly Change

The rapid development of technology and increasing numbers of customers have saturated the communication market. Communication operators must give focused attention to the problem of customer churn. Analyzing the customer’s communication behavior and building a prediction model of customer churn can provide the advance evidence for communication operators to minimize churn. This paper describes how to design a HMM to predict customer churn based on communication data. First, we oversample churners to increase the number of positive samples and establish the relative balance of positive and negative samples. Second, the continuous numerical attributes that affect communication customer churn are relatively discretized and their monthly values are converted into monthly change tendencies. Next, we select the communication features by calculating the information gains and information gain rates of all communication attributes. We then construct and optimize a prediction model of customer churn based on HMM. Finally, we test and evaluate the model by using a Spark cluster and the communication data set of Taizhou Branch of China Telecom. Experimental evaluation provides proof that our prediction model is exceptionally reliable.

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

2020 ◽  
Vol 10 (2) ◽  
pp. 1406
Author(s):  
Asia Mahdi Naser alzubaidi ◽  
Eman Salih Al-Shamery
Keyword(s):  
Discriminant Analysis ◽  
Random Forest ◽  
Prediction Model ◽  
Lift Coefficient ◽  
Telecommunications Industry ◽  
Feature Analysis ◽  
Support Vector ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

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.

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