Customer churn prediction for telecommunication industry: A Malaysian Case Study

Nurulhuda Binte Mustafa · Lew Sook Ling · Abdul Razak

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

Abstract

Background : Customer churn is a term that refers to the rate at which customers leave the business. Churn could be due to various factors, including switching to a competitor, cancelling their subscription because of poor customer service, or discontinuing all contact with a brand due to insufficient touchpoints. Long-term relationships with customers are more effective than trying to attract new customers. A rise of 5% in customer satisfaction is followed by a 95% increase in sales. By analysing past behaviour, companies can anticipate future revenue. This article will look at which variables in the Net Promoter Score (NPS) dataset influence customer churn in Malaysia's telecommunications industry. The aim of This study was to identify the factors behind customer churn and propose a churn prediction framework currently lacking in the telecommunications industry. Methods : This study applied data mining techniques to the NPS dataset from a Malaysian telecommunications company in September 2019 and September 2020, analysing 7776 records with 30 fields to determine which variables were significant for the churn prediction model. We developed a propensity for customer churn using the Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbours Classifier, Classification and Regression Trees (CART), Gaussian Naïve Bayes, and Support Vector Machine using 33 variables. Results : Customer churn is elevated for customers with a low NPS. However, an immediate helpdesk can act as a neutral party to ensure that the customer needs are met and to determine an employee's ability to obtain customer satisfaction. Conclusions : It can be concluded that CART has the most accurate churn prediction (98%). However, the research is prohibited from accessing personal customer information under Malaysia's data protection policy. Results are expected for other businesses to measure potential customer churn using NPS scores to gather customer feedback.

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References

2018Correlation Coefficients: Appropriate Use and Interpretation 2019Customer churn prediction in telecom using machine learning in big data platform 2011Measuring Customer Satisfaction with Service Quality Using American Customer Satisfaction Model (ACSI Model)2019Service quality and customer satisfaction: The moderating effects of hotel star rating 2006Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry 2020Employee turnover intentions and job performance from a planned change: the effects of an organizational learning culture and job satisfaction 2010Customer switching behaviour in the Chinese retail banking industry 2021The use of Net Promoter Score (NPS) to predict sales growth: insights from an empirical investigation

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