A Customer Churn Prediction Model in Telecom Industry Using Boosting

Lu Ning · Hua Lin · Jie Lu B · Guangquan Zhang

185 citations0/0 files storedIEEE Transactions on Industrial Informatics

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

With the rapid growth of digital systems and associated information technologies, there is an emerging trend in the global economy to build digital customer relationship management (CRM) systems. This trend is more obvious in the telecommunications industry, where companies become increasingly digitalized. Customer churn prediction is a main feature of in modern telecomcommunication CRM systems. This research conducts a real-world study on customer churn prediction and proposes the use of boosting to enhance a customer churn prediction model. Unlike most research that uses boosting as a method to boost the accuracy of a given basis learner, this paper tries to separate customers into two clusters based on the weight assigned by the boosting algorithm. As a result, a higher risk customer cluster has been identified. Logistic regression is used in this research as a basis learner, and a churn prediction model is built on each cluster, respectively. The result is compared with a single logistic regression model. Experimental evaluation reveals that boosting also provides a good separation of churn data; thus, boosting is suggested for churn prediction analysis.

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References

2005A Strategic Framework for Customer Relationship Management 2003The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration 2011Enterprise Systems: State-of-the-Art and Future Trends 1999Building Data Mining Applications for CRM 2008Handling class imbalance in customer churn prediction 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach 2002Turning telecommunications call details to churn prediction: a data mining approach 2006Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques

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2014Improved churn prediction in telecommunication industry using data mining techniques 2015Telco Churn Prediction with Big Data 2016A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn 2017Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior 2020Customer switching behavior analysis in the telecommunication industry via push-pull-mooring framework: A machine learning approach

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