Customer Churn Prediction Research Articles

Q-ensemble learning for customer churn prediction with blockchain-enabled data transparency

Q-ensemble learning for customer churn prediction with blockchain-enabled data transparency

Application of machine learning techniques for churn prediction in the telecom business

The telecom business generates a significant amount of data on a daily basis due to its massive client base. Acquiring a fresh client base is more expensive than retaining existing customers, whereas churn refers to customers transitioning from one company to another within a specified timeframe. Telecom managers and analysts investigate why customers cancel their subscriptions and analyze the behavior patterns of customers who have stopped using the services. In this work employs categorization methodologies to determine the instances of leave subscriptions and gathers the rationales behind client leave subscriptions in the telecommunications sector. The primary objective of this work is to examine various machine learning algorithms necessary for creating customer churn prediction (CP) models and identifying the reasons for churn. This work aims to provide retention strategies and plans to address churn. This work utilizes machine learning (ML) technique such as random forests (RF) to collect and classify client data for leave subscriptions. These results compare with other ML algorithm such as support vector machines (SVM), gradient boosting (GB), Extreme Gradient Boosting (XGBoost), and light gradient boosting machines (LGBM), The business model provides a practical analysis of customer churn data, enabling accurate forecasts of customers likely to churn. This allows business management to take timely action to prevent churn and minimize profit loss. In this work obtains an accuracy of 98.1 % by utilizing the random forest classifier for churn prediction. The classifier matrix has obtained a precision of 92.8 % and a recall factor of 92.7 %, resulting in an overall accuracy of 95.6 %. Similarly, our research endeavors enhance churn prediction, encompass additional business domains, and furnish prediction models to retain their current consumers, improve customer service, and efficiently prevent churn.

Multi-Layer Perceptron and Radial Basis Function Networks in Predictive Modeling of Churn for Mobile Telecommunications Based on Usage Patterns

Customer retention is a key priority for mobile telecommunications companies, as acquiring new customers is significantly more costly than retaining existing ones. A major challenge in this field is predicting customer churn—users discontinuing services. Traditional predictive models such as rule-based systems often struggle with the complex, non-linear nature of customer behavior. To address this, we propose the use of deep learning techniques, specifically multi-layer perceptron (MLP) and radial basis function (RBF) networks, to improve the accuracy of churn predictions. However, while neural networks excel in predictive performance, they are often criticized for being “black-box” models, lacking interpretability. A real-world data set is considered, which originally contained information about 15,000 randomly selected clients. Various network structures and configurations are analyzed. The obtained results are compared with results generated using fuzzy rule-based and rough-set rule-based systems. The MLP model achieved an almost perfect accuracy of 0.999 with an F-measure of 0.989, outperforming traditional methods such as fuzzy rule-based and rough-set systems. Although the RBF model slightly lagged in accuracy, it demonstrated a superior recall of 0.993, indicating better identification of potential churners. These results demonstrate that neural network models significantly enhance predictive performance in churn modeling. The interpretability of the model is also discussed since it bears significance in real applications. Our contribution lies in showing that deep learning methods significantly enhance churn prediction accuracy, though the challenge of model interpretability remains a critical area for future work.

A Comparative Study of Machine Learning Models for Predicting Customer Churn in Retail Banking: Insights from Logistic Regression, Random Forest, GBM, and SVM

Customer churn poses a significant challenge in the retail banking sector, leading to substantial financial losses and undermining long-term growth. This study explores the effectiveness of various machine learning models, including Logistic Regression, Random Forest, Gradient Boosting Machine (GBM), and Support Vector Machine (SVM), in predicting customer churn. Utilizing a comprehensive dataset derived from a leading bank, we conducted extensive data preprocessing and feature engineering before evaluating model performance through metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Our findings reveal that the Gradient Boosting Machine outperforms its counterparts, achieving an accuracy of 87.2%, with an AUC-ROC score of 0.91, indicating its exceptional ability to distinguish between churned and non-churned customers. Random Forest follows closely, exhibiting robust performance, while SVM and Logistic Regression demonstrate moderate accuracy levels. This research underscores the transformative potential of machine learning in enhancing customer retention strategies within the banking industry. By identifying at-risk customers and understanding the underlying factors contributing to churn, banks can implement targeted interventions to improve customer satisfaction and loyalty. The study further suggests avenues for future research, including the exploration of real-time data analysis and the integration of qualitative customer insights, to refine predictive models and retention strategies.

Membangun Model Prediksi Churn Pelanggan yang Akurat

Customer churn prediction models have become an important tool in the telecommunications industry to reduce churn rates and improve customer retention. This research focuses on building an accurate customer churn prediction model using machine learning algorithms for TELCO Company. By applying diverse feature engineering techniques and prediction models such as RandomForestClassifier, DecisionTreeClassifier, and XGBoost, this study showcases a significant improvement in prediction accuracy compared to previously implemented rule-based methods. The findings of this research allow TELCO Company to identify high-risk customers more effectively and implement targeted retention strategies. Results show that the resulting model can identify customers at risk of churn more effectively, enabling more targeted retention actions..

CTGAN-ENN: a tabular GAN-based hybrid sampling method for imbalanced and overlapped data in customer churn prediction

Class imbalance is one of many problems of customer churn datasets. One of the common problems is class overlap, where the data have a similar instance between classes. The prediction task of customer churn becomes more challenging when there is class overlap in the data training. In this research, we suggested a hybrid method based on tabular GANs, called CTGAN-ENN, to address class overlap and imbalanced data in datasets of customers that churn. We used five different customer churn datasets from an open platform. CTGAN is a tabular GAN-based oversampling to address class imbalance but has a class overlap problem. We combined CTGAN with the ENN under-sampling technique to overcome the class overlap. CTGAN-ENN reduced the number of class overlaps by each feature in all datasets. We investigated how effective CTGAN-ENN is in each machine learning technique. Based on our experiments, CTGAN-ENN achieved satisfactory results in KNN, GBM, XGB and LGB machine learning performance for customer churn predictions. We compared CTGAN-ENN with common over-sampling and hybrid sampling methods, and CTGAN-ENN achieved outperform results compared with other sampling methods and algorithm-level methods with cost-sensitive learning in several machine learning algorithms. We provide a time consumption algorithm between CTGAN and CTGAN-ENN. CTGAN-ENN achieved less time consumption than CTGAN. Our research work provides a new framework to handle customer churn prediction problems with several types of imbalanced datasets and can be useful in real-world data from customer churn prediction.

Customer Churn Prediction for Life Insurance Using Binary Logistic Regression

One of a major problem for many industries including life insurance is customer churn. Because insurance contracts are often renewed every year, insurance businesses have a much harder time retaining customers than other businesses. The main objective of this research is to mitigate expected customer loss and retain potentially lost customers by increasing incentive product and service offerings on behalf of PT. XYZ Insurance, one of the life insurance companies in Indonesia which was founded in 2008. With a total of 123,982 policyholder data were included in the data set for this research, which covers a one-year data period as of December 2022. These data include details about the insurance holder, age of the insured, payment frequency, tenor, premium, and the product chosen by the policyholder of customers at PT. XYZ Insurance. In this research, the data is processed based on binary logistic regression in SPSS, where the data is processed in such a way as to produce output that meets the researchers' expectations. From the results of this research, there are around 16,951 insurance customers who have the potential to churn customers. So, company must implement customized value propositions based on research findings to help retain customers and reduce churn rates effectively in the competitive insurance market. Then the results of this research can also be used to target identified customers in marketing campaigns aimed at reducing churn rates while increasing profitability

Predicting customer churn in telecommunication sector using Naïve Bayes algorithm

The telecom sector creates huge amounts of information every day as a result of its large customer base. Business professionals and decision-makers emphasized that maintaining existing clients is less expensive than recruiting new ones. Business analysts and customer relationship management (CRM)need to know the reasons why customers leave and the behavior patterns from earlier churn consumer’s data. Today, there is a problem with customer churn examination and prediction in the telecom industry since it is crucial for the sector to examine customer behavior to identify those who are going to stop their subscriptions. Customer retention could be increased by utilizing detection system to detect consumer behavior. Recent advancements in machine learning(ML)have made churn prediction more precise and practical. It is essential for identifying customers ready to leave using company’s products and services in the early stage. Hence in this work, predicting customers churn in telecommunication sector usingNaïveBayes(NB) model is presented. The performance of presentedNBalgorithmis evaluated using the parameters accuracy, precision, and sensitivity. The NB algorithm will have better performance than pervious approaches.

Application of GWO-attention-ConvLSTM model in customer churn prediction and satisfaction analysis in customer relationship management

Customer Relationship Management (CRM) is vital in modern business, aiding in the management and analysis of customer interactions. However, existing methods struggle to capture the dynamic and complex nature of customer relationships, as traditional approaches fail to leverage time series data effectively. To address this, we propose a novel GWO-attention-ConvLSTM model, which offers more effective prediction of customer churn and analysis of customer satisfaction. This model utilizes an attention mechanism to focus on key information and integrates a ConvLSTM layer to capture spatiotemporal features, effectively modeling complex temporal patterns in customer data. We validate our proposed model on multiple real-world datasets, including the BigML Telco Churn dataset, IBM Telco dataset, Cell2Cell dataset, and Orange Telecom dataset. Experimental results demonstrate significant performance improvements of our model compared to existing baseline models across these datasets. For instance, on the BigML Telco Churn dataset, our model achieves an accuracy of 95.17%, a recall of 93.66%, an F1 score of 92.89%, and an AUC of 95.00%. Similar results are validated on other datasets. In conclusion, our proposed GWO-attention-ConvLSTM model makes significant advancements in the CRM domain, providing powerful tools for predicting customer churn and analyzing customer satisfaction. By addressing the limitations of existing methods and leveraging the capabilities of deep learning, attention mechanisms, and optimization algorithms, our model paves the way for improving customer relationship management practices and driving business success.

Multi-Level Machine Learning Model to Improve the Effectiveness of Predicting Customers Churn Banks

Abstract This study presents a novel multi-level Stacking model designed to enhance the accuracy of customer churn prediction in the banking sector, a critical aspect for improving customer retention. Our approach integrates four distinct machine-learning algorithms – K-Nearest Neighbor (KNN), XGBoost, Random Forest (RF), and Support Vector Machine (SVM) – at the first level (Level 0). These algorithms generate initial predictions, which are then combined and fed into higher-level models (Level 1) comprising Logistic Regression, Recurrent Neural Network (RNN), and Deep Neural Network (DNN). We evaluated the model through three scenarios: Scenario 1 uses Logistic Regression at Level 1, Scenario 2 employs a Deep Convolutional Neural Network (DNN), and Scenario 3 utilizes a Deep Recurrent Neural Network (RNN). Our experiments on multiple datasets demonstrate significant improvements over traditional methods. In particular, Scenario 1 achieved an accuracy of 91.08%, a ROC-AUC of 98%, and an AUC-PR of 98.15%. Comparisons with existing research further underscore the enhanced performance of our proposed model.

Machine Learning Based Customer Churn Prediction in Banking Sector

Machine Learning Based Customer Churn Prediction in Banking Sector

Machine Learning Algorithms Based Prediction for Customer Churn in Banks

Currently, competition within the service sector is intensifying rapidly. Acquiring new customers is becoming increasingly costly for banks compared to retaining existing ones. Consequently, it becomes imperative to forecast customer attrition in banks. This paper presents a method to predict customer churn in banks using machine learning. The study explored the possibility of customer churn in banks through a dataset of customer information on Kaggle. This study uses four kinds of machine learning such as ANN, Random Forest, Decision tree and Logistic regression. And the correlation analysis of the data set is carried out. Finally, the ROC curves of four kinds of machine learning were compared to get the model with the highest accuracy. Therefore, the accuracy of ANN is higher than that of the other three models. Then the importance is analyzed, and the most important feature of this experiment is the number of banking products owned by customers.

Implementing machine learning techniques for customer retention and churn prediction in telecommunications

This review paper explores the application of machine learning techniques in predicting customer churn and enhancing customer retention within the telecommunications industry. The paper begins by discussing the significance of customer churn, its causes, and the limitations of traditional churn prediction methods. It then delves into machine learning algorithms, including decision trees, support vector machines, and ensemble methods. It highlights their effectiveness in handling large and complex datasets typical of the telecom sector. The discussion extends to the challenges faced in data quality, model selection, implementation, and ethical considerations in using customer data for predictive analytics. The paper also compares machine learning models with traditional methods, emphasizing the advantages of scalability, accuracy, and real-time processing. Furthermore, it identifies potential innovations, such as improved data integration, interpretable models, and personalized retention strategies. Finally, the paper reflects on future trends, predicting the growing role of AI and machine learning in telecommunications, particularly in customer service automation and network optimization. The review underscores the importance of adopting machine learning to reduce churn and improve customer retention while considering the field's ethical implications and future opportunities. Keywords: Customer Churn Prediction, Machine Learning, Telecommunications, Customer Retention, Predictive Analytics, AI in Telecom

From Data to Decisions: Predictive Machine Learning Models for Customer Retention in Banking

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.

A novel classification algorithm for customer churn prediction based on hybrid Ensemble-Fusion model

Nowadays, customer churn issues are becoming more and more important, which is one of the most important metrics for evaluating the health of a business it is difficult to measure success without measuring customer churn metrics. However, it has become a challenge for the industry to predict when customers are churning or preparing to churn and to take the necessary action at the critical time before they do. At the same time, how to keep the place of deep research on the 17 machine learning algorithms in 9 major classes of machine learning classics production is the first problem we are facing. Through customer churn deep research, we mentioned the Ensemble-Fusion model based on machine learning and introduced a smart intelligent system to help reduce the actual customer churn about the production. Comparing with most popular predictive models, such as the Support vector machine algorithm, Random Forest algorithm, K-Nearest-Neighbor algorithm, Gradient boosting algorithm, Logistic regression algorithm, Bayesian algorithm, Decision tree algorithm, and Neural network algorithm are applied to check the effect on accuracy, AUC, and F1-score. By comparing with 17 algorithms in 9 categories of machine learning classics, the data prediction accuracy of the Ensemble-Fusion model reaches 95.35%, AUC score reaches 91% and F1-Score reaches 96.96%. The experimental results show that the data prediction accuracy of the Ensemble-Fusion model outperforms that of other benchmark algorithms.

Enhancing Customer Churn Prediction With Resampling: A Comparative Study

In this competitive business world, accurately predicting customer churn is crucial to maintaining and preventing revenue loss. However, due to the imbalanced nature of customer churn data, traditional machine learning algorithms often fail to identify churned customers accurately. This has led to exploring resampling techniques, demonstrating their efficacy in addressing this issue. However, current studies in the customer churn prediction field frequently overlook the untapped potential of comprehensive investigation and comparison of resampling techniques. Instead of exploring and comparing various resampling methods, many studies predominantly rely on a single resampling method, such as SMOTE. Hence, this paper aims to compare and evaluate the effectiveness of several resampling methods, including oversampling, undersampling, and hybrid techniques. We utilized the benchmark dataset, telecommunication customer churn, from IBM Watson, where approximately 26.5% of the customers have churned, indicating that the data is imbalanced. Our results demonstrate that the combination of random forest with a hybrid sampling method – SMOTE-ENN obtained the best result. The combination yields an F1 score of 95.3% and an accuracy of 96.0%, surpassing the studies that utilized the same dataset. This highlights the benefits of comparing resampling techniques in predicting customer churn, specifically in imbalanced datasets.

Machine Learning Approaches for Customer Churn Prediction in the Aquaculture Technology Sector

Machine Learning Approaches for Customer Churn Prediction in the Aquaculture Technology Sector

Customer data prediction and analysis in e-commerce using machine learning

Customer churn is a major challenge faced by e-commerce companies, as it leads to loss of revenue and decreased customer loyalty. In recent years, for predicting and reducing client churn machine learning techniques are powerful tools. This research aims to explore the use of machine learning algorithms for predicting customer churn, annual spending, and product on-time delivery in e-commerce. The study first conducted a comprehensive review of the literature on customer churn in machine learning. The literature showed that customer churn has been predicted successfully using a variety of machine learning algorithms, including support vector machine (SVM), random forest, and decision tree in various industries. To address this gap in the literature, the study conducted an empirical analysis of customer churn in e-commerce using machine learning algorithms. The data were then pre-processed and analyzed utilizing machine learning techniques for prediction. According to the study’s findings, machine learning algorithms are effective in predicting customer churn, and product on-time delivery in e-commerce. The best-performing algorithm SVM achieved an accuracy of 83.45% in predicting customer churn and 68.42% for product on-time delivery prediction.

Customer churn prediction using a novel meta-classifier: an investigation on transaction, Telecommunication and customer churn datasets

Customer churn prediction using a novel meta-classifier: an investigation on transaction, Telecommunication and customer churn datasets

An Efficient Customer Churn Prediction Technique Using Combined Machine Learning in Commercial Banks

An Efficient Customer Churn Prediction Technique Using Combined Machine Learning in Commercial Banks