Churn Prediction Research Articles

Machine Learning Based Customer Churn Prediction in Banking Sector

Machine Learning Based Customer Churn Prediction in Banking Sector

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

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.

Role of Machine Learning Algorithms in Enhancing Customer Relationship Management Systems in Rwanda

Purpose: The aim of the study was to assess the role of machine learning algorithms in enhancing customer relationship management systems in Rwanda. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Algorithms analyze vast amounts of data, allowing businesses to personalize interactions and anticipate customer needs more effectively. For instance, predictive analytics can forecast customer churn, prompting proactive retention strategies. Moreover, sentiment analysis tools gauge customer emotions from feedback, enabling tailored responses and improved service delivery. Machine learning also automates routine tasks like email marketing segmentation, optimizing resource allocation and enhancing overall efficiency. Consequently, CRM systems empowered by machine learning algorithms foster stronger customer relationships, driving business growth through enhanced customer satisfaction and loyalty. Implications to Theory, Practice and Policy: Technology acceptance model (TAM), resource-based view and diffusion of innovations theory may be used to anchor future studies on assessing the role of machine learning algorithms in enhancing customer relationship management systems in Rwanda. Businesses should adopt advanced ML techniques, such as Long Short-Term Memory (LSTM) models and ensemble learning, to enhance the accuracy of customer segmentation and churn prediction, leading to more personalized marketing strategies and improved customer retention. Policymakers should develop and enforce stringent data privacy and security regulations to protect customer data used in ML-enhanced CRM systems.

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

Bank Churn Prediction: Leveraging Advanced Analytics for Customer Retention in the Banking Sector

Bank Churn Prediction: Leveraging Advanced Analytics for Customer Retention in the Banking Sector

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

Churn prediction using machine learning: A coupon optimization technique

Churn prediction using machine learning: A coupon optimization technique

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

An analysis on classification models for customer churn prediction

An analysis on classification models for customer churn prediction

Predictive Customer Analytics: Machine Learning for Churn Prediction and Retention

Customer churn presents a big challenge in the industry. Businesses have to deal with the problem of customers stopping using their products and services due to dissatisfaction, competitive offers, more affordable alternatives, or changing needs. Churn can be damaging to businesses since it causes revenue loss and higher costs. To address this issue, our research aimed to develop a prediction model that helps predict customer churn. We started with getting the data set about telecommunication. Our analysis and model development were based on this dataset. Then we did data visualization to gain a better understanding of the data through multiple charts. After that, we performed data preparation. First, we did data transformation, data cleansing to address missing values and outliers; feature selection was done, and finally, in this step, the data set was split into testing and training sets. Multiple machine learning algorithms were used for modeling, such as decision trees, random forests, logistic regression, support vector machines, Naïve Bayes, and neural networks. Following model development, we evaluated the model performance with each algorithm using tune model hyperparameters. The decision tree algorithm performed the best with %96.7 accuracy, %96.9 precision, %99.3 recall, and %98.1 F1-score. These findings showed how effective decision tree algorithms are in predicting customer churn. This predictive model will enable telecommunication businesses to predict potential churn, make retention strategies, reduce customer churn and increase customer retention rates.

Research on User Churn Prediction of Traditional Supermarket Digital Platform

Research on User Churn Prediction of Traditional Supermarket Digital Platform

Neural Network Based a Comparative Analysis for Customer Churn Prediction

Customer churn refers to a customer's disconnection from a business. The expense associated with customer churn encompasses both the forfeited revenue and the marketing expenditures required to acquire new customers. Mitigating customer churn stands as the foremost objective for every business. Customer churn prediction will contribute to developing strategies enabling businesses to retain these customers by identifying customers with a high risk of loss. In the digital world, the importance of developing customer churn prediction models is increasing daily. In this study, MLP based artificial neural network model was developed for customer churn prediction using customer data from an anonymous telecommunications company. The developed model was compared with kNN, LR, NB, RF, and SVM. The prediction results of the applied models were discussed, and the experimental results showed that all the models compared had over 70% accuracy. Experimental results showed that the developed MLP-based artificial neural network model has the most successful classification performance compared to other models with approximately 95% accuracy.

User churn prediction hierarchical model based on graph attention convolutional neural networks

User churn prediction hierarchical model based on graph attention convolutional neural networks

Customer Churn Analysis in Telecom Industry using Kaplan–Meier and Cox Proportional Hazards Model

Customer churn poses a significant challenge for companies in the telecom industry, impacting revenue and profitability. In this paper, we present a comprehensive survey of customer churn analysis methodologies, focusing on the application of logistic regression, Kaplan-Meier survival analysis, and the Cox Proportional Hazards Model. Logistic regression serves as a fundamental technique for binary classification, enabling the prediction of churn based on customer attributes and behaviors. Kaplan-Meier survival analysis offers insights into customer survival probabilities over time, facilitating the identification of high-risk cohorts. Additionally, the Cox Proportional Hazards Model provides a flexible framework for analyzing the impact of predictor variables on churn timing, accounting for censoring and time-varying covariates. Through a review of existing literature, case studies, and practical applications, we explore the strengths and limitations of these methodologies in customer churn prediction. We also discuss challenges in data collection, feature engineering, and model evaluation, along with future research directions to enhance churn analysis effectiveness. By leveraging logistic regression with Kaplan-Meier and the Cox model, telecom companies can develop robust churn prediction models to improve customer retention strategies and mitigate revenue loss. Keywords: Customer churn analysis, Telecom industry, Logistic regression, Kaplan-Meier survival analysis, Cox Proportional Hazards Model, Retention strategies.

Customer Churn Prediction using Machine Learning

Companies have to fight hard to lure in new customers from their suppliers. Client retention is a trendy issue for investigation since it directly impacts a business's revenue; early discovery of client churn allows organizations to take proactive steps to retain consumers. Thus, through customer retention programs, all businesses could employ a range of strategies to recognize their clientele early on. Consequently, this study tries to advise on the ideal machine- learning technique for early client churn prediction. All customer information dating back around nine months prior to the churn is included in the data used in this research. Anticipating the reactions of current clients is the aim in order to retain them. Several algorithms, including k- nearest neighbors, random forest, logistics regression etc have been tested in this work. As theThe aforementioned algorithms had accuracy rates of 78.1%, 82.6%, 83.9%, and 82.9%, respectively. By analyzing these algorithms and debating the best of the four from various angles, we have obtained the most efficient outcomes.

Explaining customer churn prediction in telecom industry using tabular machine learning models

The study addresses customer churn, a major issue in service-oriented sectors like telecommunications, where it refers to the discontinuation of subscriptions. The research emphasizes the importance of recognizing customer satisfaction for retaining clients, focusing specifically on early churn prediction as a key strategy. Previous approaches mainly used generalized classification techniques for churn prediction but often neglected the aspect of interpretability, vital for decision-making. This study introduces explainer models to address this gap, providing both local and global explanations of churn predictions. Various classification models, including the standout Gradient Boosting Machine (GBM), were used alongside visualization techniques like Shapley Additive Explanations plots and scatter plots for enhanced interpretability. The GBM model demonstrated superior performance with an 81% accuracy rate. A Wilcoxon signed rank test confirmed GBM’s effectiveness over other models, with the p-value indicating significant performance differences. The study concludes that GBM is notably better for churn prediction, and the employed visualization techniques effectively elucidate key churn factors in the telecommunications sector.

Incorporating usage data for B2B churn prediction modeling

The potential for predicting churn in B2B contexts remains untapped despite the growing availability of digital customer traces, particularly usage data. Nevertheless, this source provides valuable insights into customer behavior and product interaction, serving as a powerful tool for understanding customer needs and improving retention efforts. This paper aims to explore the value of usage data for B2B customer churn prediction using a real-world dataset with 3959 subscriptions from a European software provider. The contribution to literature is two-fold. First, we define a framework to structure usage data into cross-sectional features that could be included in predictive models. We study the impact of usage information on churn prediction performance along three primary components– timing, granularity, and expertise. The findings confirm the value of this data source and two of the three examined components, particularly in terms of AUC and TDL. Second, we provide insights into the carbon footprint of the entire modeling process required to integrate usage data into different machine-learning algorithms. Furthermore, to strengthen the robustness of our findings, we compare five common machine-learning algorithms in CCP. Consequently, this study uncovered unexplored aspects of usage data and practical implications for enhancing churn prediction in a B2B setting.

Identifying Key Learning Algorithm Parameter of Forward Feature Selection to Integrate with Ensemble Learning for Customer Churn Prediction

Identifying Key Learning Algorithm Parameter of Forward Feature Selection to Integrate with Ensemble Learning for Customer Churn Prediction