Customer Churn Prediction Based on the Decision Tree in Personal Handyphone System Service

Juan Liu

72 citations0/0 files storedInternational Conference on Service Systems and Service Management

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

Abstract

Nowadays, churn prediction and management is critical for more and more companies in the fast changing and strongly competitive telecommunication market. In order to improve customer retention, telecommunication companies must be able to predict customers at risk who are prone to switch service provider. In this study, to overcome the limitations of lack of information of customers of Personal Handyphone System Service (PHSS) and to build an effective and accurate customer churn model, three research experimentations (changing sub-periods for training data sets, changing misclassification cost in churn model, changing sample methods for training data sets) are put forward to improve the prediction performance of churn model by using decision tree which is used widely, some optimal parameters (the time of sub-period being 10 days, misclassification cost being 1:5, and random sample method for train set) of models are found under the help of three research experimentations. The empirical evaluation results suggest that customer churn models built have a good performance through the course of model optical selecting, and show that the methods and techniques proposed are effective and feasible under the condition that information of customers is very little and class distribution is skewed. This study benefits not only churn prediction research and practice but also other data mining applications with similar characteristics.

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References

2005Applying data mining to telecom churn management 2002Turning telecommunications call details to churn prediction: a data mining approach 2004Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market 2000Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry 2004Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services 1999Subscriber churn in the Australian ISP market

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2018Application of interpretable machine learning models for the intelligent decision 2016Particle classification optimization-based BP network for telecommunication customer churn prediction 2009A new feature set with new window techniques for customer churn prediction in land-line telecommunications 2016Customer Churn in Mobile Markets: A Comparison of Techniques 2015Customer Churn in Mobile Markets: A Comparison of Techniques 2024Explaining customer churn prediction in telecom industry using tabular machine learning models

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