Predictive Model on Churn Customers using SMOTE and XG-Boost Additive Model and Machine Learning Techniques in Telecommunication Industries

In this research paper the researcher builds a predictive model on churn customers using SMOTE and XG-Boost additive model and machine learning techniques in Telecommunication Industries. Customer’s churning is one of the global research issues in telecommunication industries. In somehow customers are not satisfying from telecommunication customer services, call rate, international plan, data pack, and others which are having a significant impact on customer’s services. The researcher used the SMOTE and XGboost technique to handle the imbalanced dataset and gives the higher-level accuracy for predictive model to identify the category of customer whether they are in churn or not churn. The researcher used the comparative study between logistics regression and random forest algorithms to classify the category of churn customers and non-churn customers in Telecommunication Industries. The predictive model is verifying at 96% accuracy level and can be capable to handle imbalance dataset. As per the data analysis the score of the confusion matrix is such as accuracy 94%, Precision for “ did not leave “ is 0.97 whereas recall is 0.96, and F1score is 0.97 with the support features of 903. For the churn customers precision is 0.80, recall is 0.81, F1-score is 0.80 and support features is 160, the data analysis report shows that the predictive model is having 94% accuracy whereas at 6% does not predict accurately about the customers status. Finally, the researcher concluded that the predictive model is more accurate and can be capable to handle imbalance dataset. The researchers assure that the predictive model would be benefited for the telecommunication industries to categories the churn/ non-churn customers and accordingly the organization can make changes their business plan and policies which would be benefited for the customers.

[1]  Muddesar Iqbal,et al.  Customer Churn Prediction in Telecommunication A Decade Review and Classification , 2013 .

[2]  Cheng-Seen Ho,et al.  Toward a hybrid data mining model for customer retention , 2007, Knowl. Based Syst..

[3]  Kristof Coussement,et al.  Improved marketing decision making in a customer churn prediction context using generalized additive models , 2010, Expert Syst. Appl..

[4]  Wagner A. Kamakura,et al.  Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models , 2006 .

[5]  Jae-Hyeon Ahn,et al.  Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry , 2006 .

[6]  Gavril Toderean,et al.  CHURN PREDICTION IN THE TELECOMMUNICATIONS SECTOR USING SUPPORT VECTOR MACHINES , 2013 .

[7]  Su-Yeon Kim,et al.  Customer segmentation and strategy development based on customer lifetime value: A case study , 2006, Expert Syst. Appl..

[8]  Chen-Fu Chien,et al.  Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry , 2008, Expert Syst. Appl..

[9]  Rahul J. Jadhav,et al.  Churn Prediction in Telecommunication Using Data Mining Technology , 2011 .

[10]  Eric W. T. Ngai,et al.  Customer churn prediction using improved balanced random forests , 2009, Expert Syst. Appl..

[11]  A. Hudaib,et al.  Hybrid Data Mining Models for Predicting Customer Churn , 2015 .

[12]  Chih-Ping Wei,et al.  Turning telecommunications call details to churn prediction: a data mining approach , 2002, Expert Syst. Appl..

[13]  Ashutosh Tiwari,et al.  Computer assisted customer churn management: State-of-the-art and future trends , 2007, Comput. Oper. Res..

[14]  Chih-Fong Tsai,et al.  Customer churn prediction by hybrid neural networks , 2009, Expert Syst. Appl..

[15]  Eser Kandogan,et al.  Visualizing multi-dimensional clusters, trends, and outliers using star coordinates , 2001, KDD '01.

[16]  David C. Yen,et al.  Applying data mining to telecom churn management , 2006, Expert Syst. Appl..

[17]  David C. Yen,et al.  Data mining techniques for customer relationship management , 2002 .

[18]  Vadlamani Ravi,et al.  Predicting credit card customer churn in banks using data mining , 2008, Int. J. Data Anal. Tech. Strateg..

[19]  Shu-Hsien Liao,et al.  Data mining techniques and applications - A decade review from 2000 to 2011 , 2012, Expert Syst. Appl..

[20]  Dirk Van den Poel,et al.  Customer attrition analysis for financial services using proportional hazard models , 2004, Eur. J. Oper. Res..

[21]  Euiho Suh,et al.  An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry , 2004, Expert Syst. Appl..

[22]  Miguel A. P. M. Lejeune,et al.  Measuring the impact of data mining on churn management , 2001, Internet Res..

[23]  Y. Ilker Topcu,et al.  Applying Bayesian Belief Network approach to customer churn analysis: A case study on the telecom industry of Turkey , 2011, Expert Syst. Appl..