scholarly journals Customer Churn Prediction and Upselling using MRF (Modified Random Forest) technique

2020 ◽  
Vol 9 (3) ◽  
pp. 475-482
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Random Forest ◽  
Prediction Model ◽  
Classification Accuracy ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction ◽  
Telecom Industry ◽  
Fierce Competition

Customer Churn Prediction has become one of the eminent topic in the telecom industry, it has gained a lot of attention in the research industry due to fierce competition from the various, and hence companies have focused on the larger size of the data for churning and upselling prediction. The model of customer churn prediction detects and identify the customer who are willing to terminate the subscription, customer churn prediction and upselling can be done through the data mining process. Hence, In this paper we have introduce a model Named MRF(Modified Random Forest), this model helps in enhancing the accuracy and also helps in ignoring the regression issue. Our methodology has been performed on the provided orange Datasets. For the evaluation of our algorithm comparative analysis between the existing and proposed methodology is done considering the two scenario i.e. churn and upselling. Later our model is compared with the various existing churn prediction model, the result of the analysis indicates that our model outperforms the existing method including the standard random forest in terms of AUC and classification accuracy.

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.

scholarly journals A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ming Zhao ◽  
Qingjun Zeng ◽  
Ming Chang ◽  
Qian Tong ◽  
Jiafu Su
Keyword(s):  
Prediction Model ◽  
Empirical Research ◽  
Customer Value ◽  
Customer Relationship ◽  
Churn Prediction ◽  
Customer Churn ◽  
Data Mining Algorithms ◽  
Customer Churn Prediction ◽  
Telecom Industry ◽  
Logistic Regression Algorithm

Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain “high-value” customers, and continue to provide customers with “value” and reduce the cost of maintaining customers.

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