Customer Churn Prediction using Machine Learning Models

The classification model in machine learning has been employed to address different problems. Machine Learning classification is an effective method to realize customer churn prediction. This article provides a comparative study of machine learning from the perspective of predicting customer churn. Customer churn is one of the obstacles hindering the development of companies. Through classification approaches based on machine learning, customer churn can be predicted precisely, thus providing decision-making capabilities to these companies. Customer churn prediction is a typical classification problem and can be addressed by the use of a decision tree or random forest model. In this paper, decision tree and random forest models are employed to predict customer churn, using sales data provided by a chemical company from 2012 to 2020. We analyze the underlying risk of customer churn and the customer churn factors, including the low-priced count (LC), total amount of money (TM), and creation time (CT). The prediction results of the two models are evaluated by calculating various metrics. The experimental results indicate that the low-priced count (LC) is the most essential factor for customer churn. The results of the comparison, considering the training error and generalization error including: confusion matrix, ROC curve, AUC, precision, recall, and F1 score, reveal that the random forest model has better prediction accuracy than the decision tree model.

Customer churn is a serious problem in the telecommunications industry and occurs more often. The cost of maintaining existing customers is much lower than attracting new customers, and the literature stated that five times the cost of maintaining existing customers have to be spent on attracting new customers. In this article, we have identified customers who intend to stop using the organization's services. One of the most important problems in predicting customer churn is the imbalanced data, which has been tried to be solved and compared with different methods. The machine learning algorithms used in this paper are Decision Tree, Support Vector Machine, Multi-Layer Perceptron, Random Forest, and Gradient Boosting. Data was balanced by random over-sampling, random under-sampling and SMOTE methods. The methods of over-sampling and under-sampling had appropriate and almost similar results in terms of the area under the receiver character curve (AUC) index, the method of under-sampling has shown the better specificity, and the method over-sampling has shown the better sensitivity. Also, the performance of random forest and gradient boosting algorithms were better than other algorithms.

In all customer based industries, customer churn is considered as one of the most important and challenging concerns since it can lead to a serious profit loss. Therefore, developing accurate churn prediction models can significantly help Customer Relationship Management in planning effective retention campaigns and consequently helps in maximizing the profit of the service provider. In this paper, we propose the use of an Echo State Network (ESN) with a Support Vector Machine (SVM) training algorithm for predicting customer churn in telecommunication companies. The proposed approach is trained and tested based on two datasets: the first is a popular online available dataset while the second is obtained from a local service provider. Experiment results show that ESN with SVM readout outperform other popular machine learning models used in the literature for the same customer churn prediction problems.

Introduction: The Churn means Attrition. In the world of competition and customer needs, the superior quality service, to predict customer churn, has become a pressing issue for the HRMS Vendors or service providers. Machine Learning is capable to solve this challenging problem. The customer churn can be predicted, and proactively, HRMS vendors can apply a strategy to retain customers and manage the customer relationship. Objectives: The objective of this paper is to meticulously analyse and summarize the previous studies conducted for customer churn prediction. Furthermore, this paper will serve as a valuable resource for future research in the context of HRMS vendors or service providers. Methods: This paper focuses on machine learning algorithms like Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, Neural Network, etc. The paper highlights the accuracy of the outperformed models. This paper includes the previous studies between 2021 and 2025. In total, 30 papers were reviewed meticulously. To provide more clarity, all the previous studies are summarized in a table format. The paper was searched on Google with the keywords ‘Customer Churn’ AND ‘Machine Learning’. The Duplicates were removed, and the previous studies were analyzed. Results: The findings showcase that various machine learning models like RF, DT, Logistic Regression, Gradient Boosting are applied to predict churn. Furthermore, by this analysis it is clearly stated that hybrid or ensemble models work best for customer churn model prediction. This study guides on selecting the best model as per the relevant industry. Conclusions: This comprehensive survey has meticulously analysed the implementation of machine learning algorithms to predict customer churn and highlighted its importance to fully transform the business to build client retention strategies. In this paper, different machine models are examined, and for each previous study, the machine learning models used & their results are discussed.

Customer Churn Prediction is an increasingly pressing issue in today's ever-competitive commercial arena. Although there are several researches in churn prediction, but the accuracy rate, which is very important to business, is not high enough. Recently, Support Vector Machines (SVMs), based on statistical learning theory, are gaining applications in the areas of data mining, machine learning, computer vision and pattern recognition because of high accuracy and good generalization capability. But there has no report about using SVM to Customer Churn Prediction. According to churn data set characteristic, the number of negative examples is very small, we introduce an improved one-class SVM. And we have tested our method on the wireless industry customer churn data set. Our method has been shown to perform very well compared with other traditional methods, ANN, Decision Tree, and Naive Bays.

Introduction. Customer churn is one of the key challenges in the telecommunications sector, significantly impacting companies' financial performance. Losing customers leads to substantial economic losses, as acquiring new users is typically more expensive than retaining existing ones. This creates a need for the development of effective analytical approaches to predict customer churn and identify the factors influencing it. Objective. This study aims to assess the effectiveness of survival analysis methods in predicting customer churn in the telecommunications sector. The primary objective is to identify key factors affecting churn rates and evaluate the impact of targeted retention strategies on customer behaviour. Methodology. The study employs survival analysis methods to evaluate customer churn dynamics. The Kaplan-Meier estimator is used to analyse the temporal patterns of customer retention, identifying high-risk periods. The Cox proportional hazards model is applied to determine the influence of various factors on churn probability. The dataset consists of real-world telecommunications customer data, including demographic characteristics, contract details, and financial indicators. Preprocessing steps include data cleaning, feature selection, and transformation of categorical variables into numerical format. Model performance is evaluated using metrics such as the Concordance Index (C-index) and Brier Score. Finally, A/B testing is conducted to assess the effectiveness of retention strategies in reducing customer churn. Results. The findings indicate that the highest risk of churn occurs within the first 20 months after subscription activation. It was determined that long-term contracts and high-quality service reduce the likelihood of churn, whereas high monthly costs and certain payment methods increase it. The developed predictive models achieved an accuracy level of 85–87%. Implemented retention strategies within A/B testing resulted in an increase in the retention rate from 74% to 91%. The proposed methods can be applied by telecommunications companies, financial institutions, and other businesses operating in the subscription-based service industry to enhance loyalty programs and reduce customer churn.

In the telecommunications sector, customer churn prediction is of critical importance as it directly affects customer loyalty and revenue. Developments in the field of artificial intelligence play an important role in the development of customer churn prediction models. This study presents a comparative evaluation using machine learning (ML) methods for customer churn prediction in the telecommunications sector. The study was conducted on open-source IBM Telco, Orange Telecom and Maven Analytic Telco datasets. 16 different machine learning classification models were evaluated separately on the datasets and the 3 models that gave the best results were selected and their customer churn prediction performances were evaluated with classification metrics. The Linear Discriminant model showed the most successful performance with 80% test accuracy on the IBM Telco dataset, the Gradient Boosting model with 94% test accuracy on Orange Telecom and the Linear Regression model with 99% test accuracy on Maven Analytic Telco.

The telecommunication sector becomes popular in recent years and service companies suffer from a loss of valued customers to competitors. In the operational area, customer churn is a challenging issue, which makes companies, loses customers, and therefore is of bigger issue for the industry. Recent technologies and competitors were developing quickly and churn prediction becomes essential for telecom companies. Customer Churn Prediction (CCP) models offer precise detection of significant churners and therefore a retaining solution can be offered to them. Data analytics and machine learning (ML) models can be designed and used for churn investigation to reduce customer churns and improve profit. In this aspect, this article investigates a novel Tomtit Flock Optimization with Deep Variational Autoencoder for Customer Churn Prediction (TFODVAE-CCP) in the telecommunication sector. The presented TFODVAE-CCP technique mainly concentrates on the identification and classification of customer churns. To accomplish this, the presented TFODVAE-CCP technique performs data pre-processing at the primary stage. Next, the TFODVAE-CCP technique employs TFO algorithm to elect an optimal subset of features and thereby improve classification accuracy. Finally, the DVAE method can be leveraged for classifying churner/non-churner. A detailed set of experimental analyses is made on telecommunication churn dataset and the results illustrate the betterment of the TFODVAE-CCP method over other existing models.

Customer churn prediction plays a vital role in modern business, accurately influencing strategic and operational decisions that influence customer loyalty to a service. Customer churn focuses on customer retention being more profitable than attracting new customers because long-term customers provide lower profits and costs while losing customers increases the costs and need to attract new customers. However, customer churn still occurs frequently and cannot be predicted. If customer churn is left unchecked, it will endanger the company or banking industry because it can cause loss of income, damage reputation, and decrease market share. Random Forest, a data mining technique, was used in this research because of its ability to predict and handle many variables. This research aims to predict customer churn using the Random Forest method with datasets from Europe, especially France, Spain, and Germany, hoping to benefit the banking industry by identifying customers at high risk of abandoning services. This research is expected to benefit business people from customer churn predictions. Especially in the banking industry, it can help identify customers at high risk of abandoning service. Thus, companies can take appropriate steps to retain these customers, increase customer retention, strengthen customer loyalty and optimize their business performance. The results of this research are an accurate system for predicting customer churn in the future. The research obtained accuracy results of 87% in predicting customer churn using accuracy testing in the form of a confusion matrix.

Customer churn is a prominent issue facing companies. Preventing customer churn, trying to retain and retain customers has become an important issue for business operations and development. Most of the current customer churn predictions use a single prediction model, which makes it difficult to accurately predict customer churn. Based on the prediction results and confidence of decision tree and neural network model, this paper designs a combined prediction model of customer churn and conducts empirical research on the effectiveness of the model. The prediction results show that compared with the single customer churn prediction model, the combined prediction model has higher accuracy and better prediction effect, and can more intuitively display the basic characteristics of the churn customers.

There are many purchases in the digital banking platform occurred daily. The electronic banking contains various transactions with different purchase behavior. Customer attrition from a digital store to another has become a challenge to the business owners. So, Businesses should measure their customer churn rate at regular intervals, as it is an important metric. Digital Banks started in building intelligent models to increase the customers satisfaction. Customer churn prediction (CCP) is an important strategy involved in the customer relationship management (CRM) strategies to forecast the probability of their attrition. This paper presents a classifier-based model to predict customer churn behavior based on the customer profile data. In this research, several supervised classification algorithms have been applied and compared like, KNN (k-Nearest Neighbors), Logistic Regression, AdaBoost, Gradient_Boosting and Random Forest. A unified voting is next applied for the set of classifiers in order to produce a high prediction accuracy. The model performance is enhanced by applying hyperparameters optimization and tuning. The most important features are defined and ranked using the Random Forest model. In the presented experimental case study with a dataset of size 10K, the model achieved an accuracy value 87%.

Improving the diagnosis and prediction of customer churn: A heterogeneous hazard modeling approach

This thesis proposes customer churn prediction model for a Software-as-a-Service inventory management company in Thailand. Software-as-a-Service is the fast growing and high market values industry as a new emerging online business. Customer churn is a critical measure for this business. Thus, this thesis focuses on seeking a customer churn prediction model specified for a Software-as-a-Service inventory management company in Thailand which is facing a high churn rate issue. This thesis executes the prediction models with four machine learning algorithms: logistic regression, support vector machine, decision tree and random forest. The results show that the optimized decision tree model is capable to outperform other classification models toward recall scorer with validated testing scores of 94.4% of recall and 88.2% of F1-score. Moreover, feature importance scores can highlight useful insights of case-study that business metrics are significantly related to churn behavior. As a result, this paper is beneficial to the case-study company to help indicate real churn customer correctly and enhance the effectiveness in making executive decision and marketing campaign.

Purpose: Customer churn is a crucial issue for companies, especially those in the telecommunications sector, as it has a direct impact on revenue and new customer acquisition costs. The purpose of this research is to create a customer churn prediction model through performance comparison between the Logistic Regression algorithm and Ridge Classifier, considering the effect of data balancing. Methods: This study developed a churn classification model by comparing the Logistic Regression and Ridge Classifier algorithms in three scenarios: without data balancing, balancing using SMOTE, and balancing using GAN. The dataset used was Telco Customer Churn from Kaggle. Model evaluation was performed using a confusion matrix with accuracy, precision, recall, and F1-score metrics, with a primary focus on the accuracy metric. Result: The results show that data balancing using SMOTE and GAN does not improve model accuracy. The highest accuracy was achieved by the Ridge Classifier without data balancing, at 82.47%, followed by Logistic Regression at 82.25%. However, the recall and F1-score metrics improved when using SMOTE. The highest recall was achieved by Ridge Classifier at 75.34% and Logistic Regression at 75.07% in the SMOTE 50:50 scenario. The highest F1-score was also achieved by Ridge Classifier at 64.76% and Logistic Regression at 64.68% followed by the SMOTE 50:30 scenario. Meanwhile, the precision metric tends to decrease after data balancing. Novelty: The uniqueness of this study lies in the comparison of the performance of the Ridge Classifier and Logistic Regression in data balancing scenarios using SMOTE and GAN, which has not been widely discussed in previous studies. The main findings show that the highest accuracy is achieved when the Ridge Classifier model uses original data or without applying SMOTE or GAN data balancing. However, data balancing using SMOTE has been proven to significantly improve the recall and F1-score metrics.

Customer churn is one of the most critical issues faced by thetelecommunications industry. In the telecommunications industry, it is more expensive to acquire a new customer as compared to retaining the current one. Hence, customer churn prediction is currently the main mechanism employed by the industry in order to prevent customers from churning. The objective of churn prediction is to identify customers that are going to leave the telecommunications service provider in advance. Customer churn prediction would allow the telecommunications service provider to plan their customer retention strategy. The high volume of data generated by the industry, with the help of data mining techniques implementation, becomes the main asset for predicting customer churn. Due to this reason, recent literature of different data mining techniques and most popular data mining algorithms for customer churn prediction are reviewed in this paper. Additionally, recent literature on newly developed algorithms based on the popular algorithms are also reviewed.