Customer Churn Prediction using Machine Learning Models

Glory Sam · Philip Michael Asuquo · Bliss Utibe-Abasi Stephen

26 citations0/0 files storedJournal of Engineering Research and Reports

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

Abstract

Customer churn is a critical concern for the telecommunication industry. Understanding and predicting customer churn can lead to more effective retention strategies and an increase in profitability. Predicting customer churn allows telecommunication companies to identify potentially dissatisfied customers early on and take proactive measures to retain them. Due to a large client base, the telecom industry generates a large volume of data on a daily basis. Decision makers and business analysts stressed that acquiring new customers is more expensive than retaining existing ones. Business analysts and customer relationship management (CRM) analysts must understand the reasons for customer churn as well as behaviour patterns from existing churn data. This paper proposes a churn prediction model that uses classication and clustering techniques to identify churn customers and provides the factors that contribute to customer churning in the telecom sector. The results presented shows that XBoost and Random Forest achieved higher prediction accuracy when compared to K- Nearest Neighbours, Support Vector Machines and Decision Trees in terms of accuracy, precision, F1-Score and recall.

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References

2015A comparison of machine learning techniques for customer churn prediction 2006Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques 2017Big data technologies: An empirical investigation on their adoption, benefits and risks for companies 2021Customer churn prediction system: a machine learning approach 2020Churn Prediction in Telecommunication using Logistic Regression and Logit Boost 2023Customer churning analysis using machine learning algorithms 2023Customer churn prediction using composite deep learning technique 2022Machine Learning to Develop Credit Card Customer Churn Prediction

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2025MNeuralTab: Integrating meta-modeling and neural networks for customer churn prediction in e-commerce 2025RetenNet: A Deployable Machine Learning Pipeline with Explainable AI and Prescriptive Optimization for Customer Churn Management

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