Analysis And Classification of Customer Churn Using Machine Learning Models - Journal Article

Aug 2, 2026

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Customer churn analysis supports profitability growth and stronger customer relationships by identifying customers likely to leave. The study applies exploratory data analysis for visualization and evaluates multiple machine learning classification models, including Logistic Regression, Random Forest, SVM, Gradient Boosting, AdaBoost, and XGBoost. Class imbalance is handled using SMOTE, and results indicate XGBoost achieves the highest accuracy (0.829424). Permutation Feature Importance from XGBoost highlights tenure, monthly contracts, and TV streaming as key drivers of churn.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Abstract

Introduction

Customer retention strategy and churn concept

Prior research and predictive approaches

Accredited Ranking SINTA 2 Decree of the Director General of Higher Education, Research and Technology, No. 158/E/KPT/2021 Validity period from Volume 5 Number 2 of 2021 to Volume 10 Number 1 of 2026 Published online on: [http://jurnal.iaii.or.id](http://jurnal.iaii.or.id) JURNAL RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol. 7 No. 6 (2023) 1253-1259 ISSN Media Electronic: 2580-0760 Analysis And Classification of Customer Churn Using Machine Learning Models Muhammad Maulana Sidiq 1, Dyah Anggraini2 1,2Master of Information Systems Management, Department of Information System Software, Gunadarma University, Jakarta, Indonesia [1](1 maulanasidiqwork@gmail.com)[ maulanasidiqwork@gmail.com](1 maulanasidiqwork@gmail.com), [2](2 dyahangg@staff.gunadarma.ac.id)[ dyahangg@staff.gunadarma.ac.id](2 dyahangg@staff.gunadarma.ac.id), Abstract Analysis studies of customer loss (customer churn) have been used for years to increase profitability and build customer relationships with companies. Customer analysis using exploratory data analysis (EDA) for visualizing data and the use of machine learning for the classification of customer churn are often used by past analysts. This study uses several machine learning models that can be used for customer churn classification, namely Logistic Regression, Random Forest, Support Vector Machine (SVM), Gradient Boosting, AdaBoost, and Extreme Gradient Boosting (XGBoost). However, there is a class imbalance factor in the dataset, which is the biggest challenge that is usually faced by analysts to get good results in the classification of machine learning models. The Synthetic Minority Over-sampling Technique (SMOTE) method is a popular method applied to deal with class imbalances in datasets. The results of the analysis show that the classification of churn customers using the XGBoost algorithm has the best level of accuracy compared to other algorithms, with an accuracy value of 0.829424, and the oversampling method with SMOTE tends to reduce the accuracy value of each classification algorithm. The Permutation Feature Importance (PFI) technique from the XGBoost model gets the result that tenure, monthly contracts, and TV streaming are the features that affect customer churn the most. Keywords: data mining; machine learning models; imbalance data; SMOTE; confusion matrix 1. Introduction Economic growth and development began to grow and recover since the COVID-19 virus spread rate decreased in mid-2022. With this opportunity, many new companies have emerged with innovations to lure consumers to use their products or services. Of course, this makes the atmosphere of business competition so intense that it makes businesspeople have to use various strategies to survive. As a result of this massive business competition, business actors sometimes legalize various kinds of ways to gain profits. To survive in this competitive market depends on several strategies. There are three common strategies used to increase the company's revenue, which are: (1) acquiring new customers, (2) upselling existing customers, and (3) increasing customer retention period. Comparing these strategies by considering the return on investment (RoI) value of each strategy shows that the third strategy is the better strategy to pursue. Retaining existing customers costs much less than acquiring new customers. in addition, it is also considered much easier than the upselling strategy. To implement the third strategy, companies must reduce the potential for losing customers, known as"switching customers from one provider to another”. It is important that every business needs customers to remain viable and profitable [1]. Getting customers who are willing and able to buy the business's products is oneof the main objectives of setting up a business. Every step taken in the management of the company is focused on customer needs. This includes the implementation of various marketing strategies. Co

FAQs

Which machine learning models are evaluated for customer churn classification?

The study evaluates Logistic Regression, Random Forest, Support Vector Machine (SVM), Gradient Boosting, AdaBoost, and Extreme Gradient Boosting (XGBoost).

How does the study address class imbalance in the dataset?

It uses SMOTE (Synthetic Minority Over-sampling Technique) to deal with class imbalance before classification.

What features most influence customer churn according to the results?

Permutation Feature Importance (PFI) based on XGBoost shows that tenure, monthly contracts, and TV streaming affect churn the most.

Aug 2, 2026

Upload by Eliana