CUSTOMER CHURN PREDICTION USING THE RANDOM FOREST ALGORITHM

This research proposes machine learning—a subfield of artificial intelligence-based approaches to forecast bank client attrition. The study promotes investigating the possibility of churn through the examination of consumer behaviour. In this paper, a method to predict customer churn in a bank using machine learning techniques, which is a branch of artificial intelligence, is proposed. The research encourages the exploration of the likelihood of churn by analysing customer behaviour. The number of service providers is increasing very rapidly in every business these days. As a result, customer churn and engagement have become one of the top issues for most of the banks. In this work, the Random Forest, SVC, XGB, LGBM, and Logistic Regression classifiers are employed. A few feature selection strategies have also been applied to validate system performance and determine which features are more relevant. The test was conducted using the churn modelling dataset from IEEE dataset. The results are compared to find an appropriate model with increased precision and predictability. As a result, when utilised after oversampling, the Random Forest model outperforms other models in terms of accuracy. The Random Forest model showed a significant improvement in accuracy compared to the other classifiers, making it the preferred choice for predicting customer churn in the banking sector. The feature selection strategies helped identify the most relevant features that contribute to accurate predictions. Overall, this study highlights the importance of implementing advanced machine learning techniques and feature selection methods to enhance customer retention strategies in the banking industry.

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.

In recent years, with the continuous improvement of the financial system and the rapid development of the banking industry, the competition of the banking industry itself has intensified. At the same time, with the rapid development of information technology and Internet technology, customers’ choice of financial products is becoming more and more diversified, and customers’ dependence and loyalty to banking institutions is becoming less and less, and the problem of customer churn in commercial banks is becoming more and more prominent. How to predict customer behavior and retain existing customers has become a major challenge for banks to solve. Therefore, this study takes a bank’s business data on Kaggle platform as the research object, uses multiple sampling methods to compare the data for balancing, constructs a bank customer churn prediction model for churn identification by GA-XGBoost, and conducts interpretability analysis on the GA-XGBoost model to provide decision support and suggestions for the banking industry to prevent customer churn. The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. The GA-XGBoost classifier was identified as the best solution for the customer churn problem. (3) Using Shapley values, we explain how each feature affects the model results, and analyze the features that have a high impact on the model prediction, such as the total number of transactions in the past year, the amount of transactions in the past year, the number of products owned by customers, and the total sales balance. The contribution of this paper is mainly in two aspects: (1) this study can provide useful information from the black box model based on the accurate identification of churned customers, which can provide reference for commercial banks to improve their service quality and retain customers; (2) it can provide reference for customer churn early warning models of other related industries, which can help the banking industry to maintain customer stability, maintain market position and reduce corporate losses.

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.

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.

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

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.

Customer churn prediction is a critical challenge for modern businesses seeking to maintain long-term customer relationships and reduce revenue loss. This study explores the application of deep learning and machine learning models for predicting customer churn using real-world business datasets. The dataset includes demographic, account, and behavioural attributes of customers that influence churn likelihood. Models such as Logistic Regression, Random Forest, and Deep Neural Networks (DNN) were trained and evaluated using metrics including accuracy, precision, recall, F1-score, and ROC-AUC. The experimental results reveal that the Deep Neural Network model achieves the highest prediction accuracy of 94.1%, outperforming traditional machine learning algorithms. Random Forest offers an effective trade-off between accuracy and interpretability, while Logistic Regression serves as a reliable baseline. This research provides a comparative framework for organizations aiming to leverage AI-based methods to predict and prevent customer churn, leading to improved retention strategies and business profitability.

Due to the fierce competition in the market, customers are often faced with multiple choices when choosing products and services. So many industries, including banking, are now facing the problem of how to address customer churn. At the same time, in order to improve the quality of service for users, banks and other institutions need to conduct in-depth research on the characteristics of customers. This paper provides solutions to the above two problems by using data analysis and mining technology and machine learning technology in artificial intelligence. The study provides an in-depth exploration of cus-tomer churn likelihood by analyzing customer behavior and characteristics. This study used data analysis methods such as Chi-square test, Mann Whitney U test, linear regres-sion, and machine learning methods such as logistic regression, random forest, and XGBoost. This research uses public datasets on Kaggle. This study uses data analysis techniques to provide recommendations on how the banking industry should improve service quality, and establishes 6 models with better performance to predict customer churn. In addition, this paper also uses a variety of evaluation indicators to compare the model performance, and selects the random forest model with high predictive ability as the most suitable model. In addition, the order of importance of the factors responsible for customer churn was successfully derived.

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.

This research is dedicated to forecasting bank customer churn by using machine learning methods. In the highly competitive landscape of the banking industry, retaining customers has emerged as a critical factor for a bank's success. Eleven distinct machine learning models were utilized in the analysis of a bank customer dataset. Prior to model implementation, comprehensive data preprocessing was conducted, encompassing the management of missing values, encoding of categorical variables, and detection of outliers. The models were then evaluated under various data split ratios and sampling methods, with recall serving as the primary evaluation metric.The study's findings indicate that LightGBM, CatBoost, and XGBoost demonstrate superior performance in predicting customer churn. Additionally, Gradient Boost, Ada Boost, and Bagging prove to be effective in handling imbalanced data. Several key predictors of customer churn, such as Total_Trans_Amt and Total Trans ct, were identified. These results offer valuable practical implications for banks in formulating customer retention strategies. However, it is necessary to recognize the research's limitations. There may be deficiencies in data representation. What's more, external factors that could influence customer churn have been excluded.

With the ongoing economic growth and the rise of new companies, customer churn has become a renewed focus for many businesses, particularly in the telecommunications and SaaS sectors. Accurately identifying lost customers using artificial intelligence technology is crucial for helping companies develop effective strategies to retain these valuable clients. This paper provides a comprehensive overview of recent research on the application of various machine learning models, including logistic regression, random forests, decision trees, XGBoost, and advanced deep learning models like Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), to different types of datasets in the telecommunications and SaaS sectors. The study examines the workflow of each model, offering an in-depth analysis of their effectiveness and performance in predicting customer churn. In addition to evaluating the strengths and weaknesses of these models, the paper also addresses key challenges in machine learning, such as interpretability, applicability, and privacy concerns. It highlights current solutions employed by researchers, including SHapley Additive exPlanations (SHAP) for model interpretability, transfer learning, domain adaptation, and federated learning, to overcome these challenges. Furthermore, this paper emphasizes the ongoing need for further research to enhance the robustness and practical deployment of churn prediction models in these industries. In conclusion, this paper offers a comprehensive overview of current research on customer churn prediction models, highlighting the application of various machine learning techniques and addressing key challenges and solutions within the telecommunications and SaaS sectors.

Customer churn, a critical metric in the telecommunications industry, reflects the propensity of customers to discontinue service usage, thereby affecting the revenue and growth of a company. Customer churn is greatly affected by individuals and products, and there is no obvious rule, which greatly increases the difficulty of customer loss prediction. With the development of machine learning, the prediction in telecom churn using machine learning has attracted wide attention from researchers. However, there are great differences between different algorithms, which increases the difficulty of application. Therefore, this paper studies the issue of customer churn prediction from the aspect of algorithm. This paper introduces the common data sets and evaluation indexes of customer churn prediction, and then introduces logistic regression, decision tree, K-nearest neighbor, random forest, Gaussian naive Bayes, XGBoost, gradient enhancement and neural network algorithms. Then, through the experimental analysis, the differences between these models are compared. Finally, the paper summarizes the full text and discusses the future research hotspots.

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.

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%.