Survival analysis as a tool for predicting and reducing customer churn

With the rising growth of the telecommunication industry, the customer churn problem has grown in significance as well. One of the most critical challenges in the data and voice telecommunication service industry is retaining customers, thus reducing customer churn by increasing customer satisfaction. Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. The related research reveals that many studies have focused on developing churner prediction models based on historical data. These models face delay issues and lack timelines for targeting customers in real-time. In addition, these models lack the ability to tap into Arabic language social media for real-time analysis. As a result, the design of a customer churn model based on real-time analytics is needed. Therefore, this study offers a new approach to using social media mining to predict customer churn in the telecommunication field. This represents the first work using Arabic Twitter mining to predict churn in Saudi Telecom companies. The newly proposed method proved its efficiency based on various standard metrics and based on a comparison with the ground-truth actual outcomes provided by a telecom company.

This study investigates the application of advanced machine learning techniques for customer churn prediction in the rapidly evolving aquaculture technology sector. We employ and compare three distinct models—Logistic Regression, Random Forest, and XGBoost—to analyze a synthesized dataset representative of the industry. The research encompasses comprehensive data preprocessing, feature engineering, and model evaluation using standard performance metrics. Our findings demonstrate the superior performance of XGBoost, achieving 88% accuracy in predicting customer churn. Through feature importance analysis, we identify key churn predictors, with the difference between a customer’s last order amount and their mean order amount emerging as the most significant factor. Additionally, we utilize SHAP (SHapley Additive exPlanations) analysis to interpret model outcomes, revealing nuanced relationships between features and churn probability. The study highlights the critical role of consistent engagement, proactive customer support, and personalized retention strategies in reducing churn. Our research contributes to the growing body of knowledge on churn prediction in specialized technology sectors and provides actionable insights for improving customer retention strategies in the aquaculture industry. The paper concludes with recommendations for future research, including the integration of external data sources and exploration of deep learning approaches for temporal dependency analysis in customer behaviour.

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.

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.

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.

Abstract: The problem of customer churn is a continuous challenge for profitability in the telecommunications sector, particularly given significant competition within the industry. Therefore, the ability to use advanced analytics to identify compressed subscribers in advance, and potentially retain them, is critical. This study describes an end-to-end explainable AI framework for predicting and intervening with telco churn. We used the IBM Telco Customer Churn dataset and simulated relevant behavioural features, so we could train our system via informative preprocessing (handling missing values and inappropriate encodings), feature engineering (where we devised aspects such as charge ratio, tenure, and service stability), and XGBoost modelling (where our modelling is optimized via stratified cross-validation and business-based target metrics) to monitor our predictions. Importantly, we provide SHAP-based explainability for every prediction, where our predictions contain both global and local explainability features to provide an actionable bridge between the power of algorithmic manipulation and business consideration for the customer. Our prediction offers the functionality of a business logic layer allowing churn probabilities to be mapped to risk formulations, loyalty tier definitions, and ways to intervene with the customer prior to their leaving as well as knowing later via a secure Flask-based web app with live analysis and audit of actions performed. Our experiment produced strong prediction values (AUC-ROC value of 0.95; retention of churn class recall up to 0.93), consistent with feature ranking providing interpretably relevant information, and identifiable space for practical business consideration. In conclusion, we have provided what we believe to be a scalable and transparent model for interpretable churn management with an aim of providing timely, informed decisions and accountability to operate the retention strategy in the telecommunications environment. Index terms: Customer churn, Churn prediction, Telecom retention strategy, Explainable AI, XGBoost, SHAP (SHapley Additive exPlanations).

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 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.

Telecom companies are facing a serious problem of customer churn due to exponential growth in the use of telecommunication based services and the fierce competition in the market. Customer churns are the customers who decide to quit or switch use of the service or even company and join another competitor. This problem can affect the revenues and reputation of the telecom company in the business market. Therefore, many Customer Churn Prediction (CCP) models have been developed; however these models, mostly study in the context of within company CCP. Therefore, these models are not suitable for a situation where the company is newly established or have recently adopted the use of advanced technology or have lost the historical data relating to the customers. In such scenarios, Just-In-Time (JIT) approach can be a more practical alternative for CCP approach to address this issue in cross-company instead of within company churn prediction. This paper has proposed a JIT approach for CCP. However, JIT approach also needs some historical data to train the classifier. To cover this gap in this study, we built JIT-CCP model using Cross-company concept (i.e., when one company (source) data is used as training set and another company (target) data is considered for testing purpose). To support JIT-CCP, the cross-company data must be carefully transformed before being applied for classification. The objective of this paper is to provide an empirical comparison and effect of with and without state-of-the-art data transformation methods on the proposed JIT-CCP model. We perform experiments on publicly available benchmark datasets and utilize Naive Bayes as an underlying classifier. The results demonstrated that the data transformation methods improve the performance of the JIT-CCP significantly. Moreover, when using well-known data transformation methods, the proposed model outperforms the model learned by using without data transformation methods.

Churn is the phenomenon of a customer terminating their relationship or subscription with a company or service provider[1]. It represents the rate at which customers stop using a company's product or service during a specific period of time. Attrition rate is an important metric for businesses as it directly impacts revenue, growth and customer retention. In the context of the churn data set, the churn label indicates whether a customer has been churn. Lost customers are those who decide to stop buying the company's products. On the other hand, non-churn customers are those who continue to buy the company's products. Understanding customer churn is critical for businesses to identify the patterns, factors, and metrics that lead to customer churn. By analyzing churn behavior and its associated characteristics, companies can develop strategies to retain existing customers, improve customer satisfaction, and reduce customer churn. Predictive modeling techniques can also be applied to anticipate and proactively address potential customer churn, enabling companies to take proactive steps to retain at-risk customers[2].

Customer churn is a key issue in the telecom industry and has a significant impact on the revenue of related companies. For telecom companies, establishing an accurate customer churn prediction model is crucial for retaining existing customers. Therefore, it is necessary to develop reliable prediction models to effectively manage and reduce churn rates. This study explores how to use machine learning technology to predict customer churn based on various customer characteristics and usage patterns, and its application in accurately predicting customer churn based on demographic data, service usage behavior, and billing information. Through systematic analysis of model performance, the advantages and disadvantages of each method are highlighted, with particular emphasis on the superior predictive power of the integrated method when dealing with imbalanced data sets. This proves the potential of these methods in accurately predicting customer churn behavior, thereby providing relevant companies with methods to retain customers. In addition, the study explored the economic implications of the accuracy of attrition predictions and advocated the use of lower-cost methods to ensure the rational allocation of resources.

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.

Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons(MLP) whose training is obtained using negative correlation learning(NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achievebetter generalization performance (high churn rate) compared with ensembleof MLP without NCL (flat ensemble) and other common datamining techniques used for churn analysis.

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.

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.