Echo State Network with SVM-readout for customer churn prediction

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.

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.

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.

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.

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

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.

The Telecommunications (telecom) Industry is saturated and marketing strategies are focusing on customer retention and churn prevention. Churning is when a customer stops using a company’s service thereby opting for the next available service provider. This churn threat has led to various Customer Churn Prediction (CCP) studies and many models have been developed to predict possible churn cases for timely retention strategies. This review looks at the existing models in literature, using 30 selected CCP studies particularly from 2014 to 2017. Data preparation methods and churn prediction challenges have also been explored. This study reveals that Support Vector Machines, Naïve Bayes, Decision Trees and Neural Networks are the mostly used CCP techniques. Feature selection is the mostly used data preparation method followed by Normalization and Noise removal. Undersampling is the mostly preferred technique for handling telecom data class imbalances. Imbalanced, large and high dimensional datasets are the key challenges in telecom churn prediction. The issue of large telecom datasets is handled by use of new big data technologies such as Hadoop, Hbase and NoSQL (Not only Structured Query Language) though their adoption is still at a low rate.

Churn prediction is an important task for Customer Relationship Management (CRM) in telecommunication companies. Accurate churn prediction helps CRM in planning effective strategies to retain their valuable customers. However, churn prediction is a complex and challenging task. In this paper, a hybrid churn prediction model is proposed based on combining two approaches; Neighborhood Cleaning Rules (NCL) and Particle Swarm Optimization (PSO). NCL is applied in the preprocessing stage for handling the imbalanced churn data; and eliminating outliers and unrepresentative data. In the next stage, a Constricted PSO is applied for developing the final prediction model. The developed model is evaluated and compared with a baseline PSO model. The proposed hybrid model is compared also with Artificial Neural Networks (ANN) and Decision trees (DT) models which are traditional and common approaches used in the literature for churn prediction. The experimental results show that the proposed hybrid model outperforms the baseline PSO model, ANN and DT in terms of accuracy and actual churn rate.

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.

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.

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 still a big problem for the retail industry because it affects profits and long-term survival. Machine learning (ML) has become a powerful tool in the last few years for predicting and preventing churn by finding customers who are likely to leave and allowing data-driven retention strategies. This paper examines the utilisation of machine learning techniques for predicting customer churn in the retail sector, integrating results from previous research and suggesting a comprehensive methodological framework for effective implementation. The study analyses 30 peer-reviewed journal articles published until 2023, focusing on significant machine learning models like logistic regression, decision trees, random forests, gradient boosting machines, and deep learning techniques, emphasising their relative advantages and disadvantages in churn prediction scenarios. The methodology section outlines a systematic pipeline that encompasses data preprocessing, feature engineering, model training, evaluation metrics, and deployment considerations. Results from empirical implementations across diverse retail datasets demonstrate that ensemble methods (e.g., XGBoost, LightGBM) and neural networks consistently outperform traditional statistical models in predictive accuracy, though model interpretability remains a critical concern for practitioners. The paper also talks about how explainable AI can help people trust and use ML-driven churn prediction systems. The results highlight the necessity of incorporating ML models with customer relationship management (CRM) systems to facilitate prompt interventions, tailored offers, and the enhancement of loyalty programs. This study enhances academic scholarship and industry practice by addressing methodological, practical, and ethical considerations. The paper ends with suggestions for retailers who want to use ML tools and ideas for future research.

This study uses machine learning algorithms to analyse customer churn prediction and analysis. Churn prediction predicts customer attrition or the likelihood of a customer discontinuing their company relationship. The study explores the use of machine learning algorithms to predict customer churn and analyse the factors that contribute to it. Customer churn prediction and analysis is one of the essential factors to consider while building the customer relationship for any company. The competitive telecom market always demands better customer service and better product quality. Based on those service quality factors, whether the customers will continue the service or change to another service provider is decided. The dataset used in this study is from a fictional telecom company, 'Tel', obtained by the IBM developer platform. The dataset includes a churn mark label indicating whether or not the client left in the preceding month, along with other characteristics like account information, demographic data, and subscribed services.

In today's competitive world, companies need to analyze, identify and predict the behavior of their customers and respond to their demands earlier than their competitors.Moreover, in many industries such as mobile telecommunications, the cost of maintaining existing customers (customer retention) is much lower than the cost of attracting a new customer.Therefore, the problem of identifying customers who are going to leave the company, so-called Customer Churn Prediction (CCP), and preventing them by offering Incentives is essential in these industries.In this direction, researchers have presented competent methods using data mining and artificial intelligence tools to identify potential churners.Machine learning (ML) methods are one of the most powerful and widely used techniques to deal with the CCP problem, since they can properly extract and learn complex relationships between the customers' attributes and their churn intention.Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), Logistic Regression (LR), and Nave Bayes (NB) are among the well-known ML models utilized in numerous studies to tackle the CCP problem.Also, ensemble learning techniques such as Adaboost, Gradient Boost, and Extreme Gradient Boost (XG_boost) have been widely used to solve the CCP problem since they can aggregate the capabilities of multiple ML models.Hence, in order to improve the process of predicting customer churn, in this paper we propose a novel ensemble learning based approach, which is designed based on the two-level stacking technique.We employ six prominent ML models in each level of our proposed ensemble model including MLP, SVM- 4 58 46 RBF, DT, NB, KNN, and LR.We also benefit from the Gray Wolf Optimization (GWO) algorithm as an efficient swarm intelligence based search algorithm to select the most effective features and also adjust the hyper-parameters in the proposed model.We have implemented our proposed model using Python and simulated it on two well-known customer churn datasets in the telecom market (IBM_Telco and Duke_Cell2Cell) to evaluate its performance.In this direction, we first demonstrated the optimal features' subset and the parameter values obtained from applying the GWO algorithm on each dataset.Next, we compared the performance of the proposed ensemble model with each of the base learners using common evaluation criteria including accuracy, precision, recall, F1 score and AUC.The results show that the proposed ensemble model can collect the capabilities of all the base learners and it works better than each of the basic ML models.Afterward, we compared the obtained results from the suggested model with the common ensemble models (Adaboost, Gradient_boost, XG_boost, and Cat_boost) The experimental results show the superiority of the proposed method over other evaluated ensemble models in all the evaluation criteria.Eventually, our method is compared with two recent CCP approaches introduced in the literature.This analysis reveals that, except for the recall criterion in the Duke_Cell2Cell dataset, our introduced method achieves superior results compared to the considered approaches in both datasets.

Background: China telecom is the largest integrated information service provider in China, its business volume all over the world. It is interesting to note that China Unicom and other telecom companies have carried out similar businesses one after another. How to prevent the loss of existing customers in the fierce competition is an important issue for telecom companies to think about.Methods: This work aims to build a variety of algorithm models for target optimization and use them to predict whether telecom companies will lose customers, respond to the early warning of customer churn, and then implement active retention measures. Data characteristics affect the final loss prediction effect. In this study, the weight contribution rate of each characteristic variable is obtained by calculating the evidence weight and then the characteristic variable information value so as to optimize the prediction accuracy of the algorithm model. Through calculation, we noted the weight contribution rate of five characteristic variables to be the highest. Including total day charge, total day minutes customer service calls, international plan, and number of voicemail messages, linear regression, decision tree, Bayesian, artificial neural network, and support vector machine are used to predict customer churn on the customer dataset published by telecom companies. The experimental results are used to test the performance of the algorithm model.Results: It is found that the characteristic variables calculated after optimization are put into multialgorithm models to predict the churn of telecom customers. Finally, it is found that it is better for the optimized characteristic variables to use the decision tree algorithm model to predict the loss of telecom customers.