Journal articles: 'Customer churn prediction'

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Xu, Tianpei, Ying Ma, and Kangchul Kim. "Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping." Applied Sciences 11, no. 11 (May 21, 2021): 4742. http://dx.doi.org/10.3390/app11114742.

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In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems.

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B, Senthilnayaki, Swetha M, and Nivedha D. "CUSTOMER CHURN PREDICTION." IARJSET 8, no. 6 (June 30, 2021): 527–31. http://dx.doi.org/10.17148/iarjset.2021.8692.

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Zhao, Ming, Qingjun Zeng, Ming Chang, Qian Tong, and Jiafu Su. "A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China." Discrete Dynamics in Nature and Society 2021 (August 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/7160527.

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Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain “high-value” customers, and continue to provide customers with “value” and reduce the cost of maintaining customers.

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Coussement, Kristof. "Improving customer retention management through cost-sensitive learning." European Journal of Marketing 48, no. 3/4 (April 8, 2014): 477–95. http://dx.doi.org/10.1108/ejm-03-2012-0180.

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Purpose – Retailers realize that customer churn detection is a critical success factor. However, no research study has taken into consideration that misclassifying a customer as a non-churner (i.e. predicting that (s)he will not leave the company, while in reality (s)he does) results in higher costs than predicting that a staying customer will churn. The aim of this paper is to examine the prediction performance of various cost-sensitive methodologies (direct minimum expected cost (DMECC), metacost, thresholding and weighting) that incorporate these different costs of misclassifying customers in predicting churn. Design/methodology/approach – Cost-sensitive methodologies are benchmarked on six real-life churn datasets from the retail industry. Findings – This article argues that total misclassification cost, as a churn prediction evaluation measure, is crucial as input for optimizing consumer decision making. The practical classification threshold of 0.5 for churn probabilities (i.e. when the churn probability is greater than 0.5, the customer is predicted as a churner, and otherwise as a non-churner) offers the worst performance. The provided managerial guidelines suggest when to use each cost-sensitive method, depending on churn levels and the cost level discrepancy between misclassifying churners versus non-churners. Practical implications – This research emphasizes the importance of cost-sensitive learning to improve customer retention management in the retail context. Originality/value – This article is the first to use the concept of misclassification costs in a churn prediction setting, and to offer recommendations about the circumstances in which marketing managers should use specific cost-sensitive methodologies.

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Mishachandar, B., and Kakelli Anil Kumar. "Predicting customer churn using targeted proactive retention." International Journal of Engineering & Technology 7, no. 2.27 (August 2, 2018): 69. http://dx.doi.org/10.14419/ijet.v7i2.27.10180.

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With the advent of innovative technologies and fierce competition, the choices for customers to choose from have increased tremendously in number. Especially in the case of a telecommunication industry, where deregulation is at its peak. Every year a new company springs up offering fitter options for its customers. This has turned the concentration of the business doers on churn prediction and business management models to sustain their places. Businesses approach churn in two ways, one is through targeted customer retention and through cause identification strategy. The literature of this paper provides a comprehensible understanding of the so far employed techniques in predicting customer churn. From that, it is quite evident that less attention has been given to the accuracy and the intuitiveness of churn models developed. Therefore, a novel approach of combining the models of Machine Learning and Big Data Analytics tools was proposed to deal churn prediction effectively. The purpose of this proposed work is to apply a novel retention technique called the targeted proactive retention to predict customer churning behavior in advance and help in their retention. This proposed work will help telecom companies to comprehend the risk associated with customer churn by predicting the possibility and the time of occurrence.

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Rodan, Ali, Ayham Fayyoumi, Hossam Faris, Jamal Alsakran, and Omar Al-Kadi. "Negative Correlation Learning for Customer Churn Prediction: A Comparison Study." Scientific World Journal 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/473283.

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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 achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis.

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Manasa, Morla. "Telecom Customer Churn Prediction." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 2857–62. http://dx.doi.org/10.22214/ijraset.2020.5479.

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D., Jainam, Fenil D., and Mrugendra Rahevar. "Customer Churn Prediction Analysis." International Journal of Computer Applications 182, no. 29 (November 15, 2018): 15–17. http://dx.doi.org/10.5120/ijca2018918145.

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Adesua, O., P. A. Danquah, and O. B. Longe. "A Comparative Study of Predicting Customer Churn and Lifetime." advances in multidisciplinary & scientific research journal publication 26, no. 1 (December 10, 2020): 1–6. http://dx.doi.org/10.22624/isteams/v26p1-ieee-ng-ts.

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The problem to be investigated in this research is that of predicting customers who are at risk of leaving the company, a term called churn prediction in telecommunication. The aim of this research is to predict customer churn and further focus on creating customer lifetime profiles. These profiles will allow the company to fit their customer base into n categories and make a long estimation on when a customer is potentially going to terminate their service with the company. The research then proceeds to provide a comparative analysis of neural networks and survival analysis in their capabilities of predicting customer churn and lifetime. . Key words: GSM networks, Base station, Mobile station, Signal strength, GSM service provider

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Almuqren, Latifah, Fatma S. Alrayes, and Alexandra I. Cristea. "An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach." Future Internet 13, no. 7 (July 5, 2021): 175. http://dx.doi.org/10.3390/fi13070175.

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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.

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Faris, Hossam. "A Hybrid Swarm Intelligent Neural Network Model for Customer Churn Prediction and Identifying the Influencing Factors." Information 9, no. 11 (November 17, 2018): 288. http://dx.doi.org/10.3390/info9110288.

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Customer churn is one of the most challenging problems for telecommunication companies. In fact, this is because customers are considered as the real asset for the companies. Therefore, more companies are increasing their investments in developing practical solutions that aim at predicting customer churn before it happens. Identifying which customer is about to churn will significantly help the companies in providing solutions to keep their customers and optimize their marketing campaigns. In this work, an intelligent hybrid model based on Particle Swarm Optimization and Feedforward neural network is proposed for churn prediction. PSO is used to tune the weights of the input features and optimize the structure of the neural network simultaneously to increase the prediction power. In addition, the proposed model handles the imbalanced class distribution of the data using an advanced oversampling technique. Evaluation results show that the proposed model can significantly improve the coverage rate of churn customers in comparison with other state-of-the-art classifiers. Moreover, the model has high interpretability, where the assigned feature weights can give an indicator about the importance of their corresponding features in the classification process.

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Kumar, S. Likhit. "Bank Customer Churn Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 15, 2021): 727–32. http://dx.doi.org/10.22214/ijraset.2021.37467.

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Banking is one of the highly competitive sectors where customer relations is of utmost importance for any bank. Each customer is considered as a customer for life by the banks. The term “Customer Churn” refers to the state in which the customer or the subscriber stops involving in business transactions with a company or a service provider. To deal with this, the paper presents the work done towards predicting the customer churn rate, using machine learning models which will indicate whether a customer will leave the bank or not based on many factors, this in turn will help the bank in knowing which category of customers generally tend to leave the bank. Further the banks can bring in exciting offers so that it can retain its customers. In this predictive process popular models such as logistic regression, decision trees, random forest and other boosting techniques have been used to achieve a decent level of accuracy, for the banks to rely upon.

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Jamalian, E., and R. Foukerdi. "A Hybrid Data Mining Method for Customer Churn Prediction." Engineering, Technology & Applied Science Research 8, no. 3 (June 19, 2018): 2991–97. http://dx.doi.org/10.48084/etasr.2108.

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The expenses for attracting new customers are much higher compared to the ones needed to maintain old customers due to the increasing competition and business saturation. So customer retention is one of the leading factors in companies’ marketing. Customer retention requires a churn management, and an effective management requires an exact and effective model for churn prediction. A variety of techniques and methodologies have been used for churn prediction, such as logistic regression, neural networks, genetic algorithm, decision tree etc.. In this article, a hybrid method is presented that predicts customers churn more accurately, using data fusion and feature extraction techniques. After data preparation and feature selection, two algorithms, LOLIMOT and C5.0, were trained with different size of features and performed on test data. Then the outputs of the individual classifiers were combined with weighted voting. The results of applying this method on real data of a telecommunication company proved the effectiveness of the method.

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Li, Koh Guan, and Booma Poolan Marikannan. "Hybrid Particle Swarm Optimization-Extreme Learning Machine Algorithm for Customer Churn Prediction." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3432–36. http://dx.doi.org/10.1166/jctn.2019.8304.

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Customer churn which also commonly referred to as customer attrition, occurs when a customer ceases or stops doing business with a particular company or service. Predicting customer churn had become one of the major aim of businesses is various sectors, namely the telecommunication sector as the markets are very saturated. Apart from that, the cost to retain existing customers is much lesser as compared to the cost to acquire or attract new customers through marketing. This paper proposes a new hybrid algorithm which incorporate the algorithms of Particle Swarm Optimization (PSO) as well as Extreme Learning Machine (ELM) to build a telecommunication churn prediction model which can accurately predict churners and non-churners. This model will be named as Particle Swarm Extreme Learning Machine (PSELM) model. PSO algorithm is able to effectively scale the selected features so that ELM algorithm can classify data based on these features more easily and provide accurate classification.

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Wardani, Ni Wayan, Gede Rasben Dantes, and Gede Indrawan. "Prediksi Customer Churn dengan Algoritma Decision Tree C4.5 Berdasarkan Segmentasi Pelanggan untuk Mempertahankan Pelanggan pada Perusahaan Retail." Jurnal RESISTOR (Rekayasa Sistem Komputer) 1, no. 1 (April 21, 2018): 16–24. http://dx.doi.org/10.31598/jurnalresistor.v1i1.219.

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Customer is a very important asset for retail companies. This is the reason why retail companies should plan and use a fairly clear strategy in treating customers. With the large number of customers, the problem that must be faced is how to identify the characteristics of all customers and able to retain existing customers in order not to stop buying and moving to a competitor retail company. By applying the concept of CRM, a company can identify customers by segmenting customers while also being able to implement customer retention programs by predicting potential churn on each customer class. The data used comes from UD.Mawar Sari. Customer segmentation process uses RFM model to get customer class. UD. Mawar Sari customer class is dormant, everyday, golden and superstar. The construction of prediction models using the Decision Tree C4.5. The application of the prediction model obtains performance results, that is: Dormant: Recall 97.51%, Precision 75.18%, Accuracy 76.18%. Everyday: Recall 100%, Precision 99.04%, Accuracy 99.04%. Golden: Recall 100%, Precision 98.84%, Accuracy 98.84%. Superstar: Recall 96.15%, Precision 99.43%, Accuracy 95.63%. Results of the evaluation with confusion matrix it can be concluded that the dormant customer class is a potentially churn customer class.

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Kulkarni, Aditya, Amruta Patil, Madhushree Patil, and Sachin Bhoite. "Customer Churn Analysis and Prediction." International Journal of Computer Applications Technology and Research 8, no. 9 (September 17, 2019): 363–66. http://dx.doi.org/10.7753/ijcatr0809.1005.

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Huang, Bingquan, Mohand Tahar Kechadi, and Brian Buckley. "Customer churn prediction in telecommunications." Expert Systems with Applications 39, no. 1 (January 2012): 1414–25. http://dx.doi.org/10.1016/j.eswa.2011.08.024.

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Meghyasi, Homa, and Abas Rad. "Customer Churn Prediction in Telecommunication Industry Using Data Mining Methods." Innovaciencia Facultad de Ciencias Exactas Físicas y Naturales 8, no. 1 (December 1, 2020): 1–8. http://dx.doi.org/10.15649/2346075x.999.

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At present, in competitive space between companies and organizations, customers churn is their most important challenge. When a customer becomes churn, organizations lose one of their most important assets, which can lead to financial losses and even bankruptcy. Customer churn prediction using data mining techniques can alleviate these problems to some extent. The aim of the present study is to provide a hybrid method based on Genetic Algorithm and Modular Neural Network to customer churn prediction in telecommunication industries and use Irancell data as a sample. The accuracy result of this study which is 95.5% get the highest accuracy rank in comparisons with the result of other methods, which shows using modular neural network with two modules of feedforward neural network and also using genetic algorithm to obtain optimal structure for modules of the neural network are the most important indicators of this method to each the highest accuracy result among the rest of methods.

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Karuppaiah, Sivasankar, and N. P. Gopalan. "Enhanced Churn Prediction Using Stacked Heuristic Incorporated Ensemble Model." Journal of Information Technology Research 14, no. 2 (April 2021): 174–86. http://dx.doi.org/10.4018/jitr.2021040109.

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In a rapidly growing industry like telecommunications, customer churn prediction is a crucial challenge affecting the sustainability of the business as a whole. The fact that retaining a customer is more profitable than acquiring new customers is important to predict potential churners and present them with offers to prevent them from churning. This work presents a stacked CLV-based heuristic incorporated ensemble (SCHIE) to enable identification of potential churners so as to provide them with offers that can eventually aid in retaining them. The proposed model is composed of two levels of prediction followed by a recommendation to reduce customer churn. The first level involves identifying effective models to predict potential churners. This is followed by result segregation, CLV-based prediction, and user shortlisting for offers. Experimental results indicate high efficiencies in predicting potential churners and non-churners. The proposed model is found to reduce the overall loss by up to 50% in comparison to state-of-the-art models.

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Chouiekh, Alae, and El Hassane Ibn El Haj. "Deep Convolutional Neural Networks for Customer Churn Prediction Analysis." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 1 (January 2020): 1–16. http://dx.doi.org/10.4018/ijcini.2020010101.

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Several machine learning models have been proposed to address customer churn problems. In this work, the authors used a novel method by applying deep convolutional neural networks on a labeled dataset of 18,000 prepaid subscribers to classify/identify customer churn. The learning technique was based on call detail records (CDR) describing customers activity during two-month traffic from a real telecommunication provider. The authors use this method to identify new business use case by considering each subscriber as a single input image describing the churning state. Different experiments were performed to evaluate the performance of the method. The authors found that deep convolutional neural networks (DCNN) outperformed other traditional machine learning algorithms (support vector machines, random forest, and gradient boosting classifier) with F1 score of 91%. Thus, the use of this approach can reduce the cost related to customer loss and fits better the churn prediction business use case.

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Turkmen, Ahmet, Cenk Anil Bahcevan, Youssef Alkhanafseh, and Esra Karabiyik. "User behaviour analysis and churn prediction in ISP." New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, no. 12 (April 30, 2020): 57–67. http://dx.doi.org/10.18844/gjpaas.v0i12.4987.

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There is no doubt that customer retention is vital for the service sector as companies’ revenue is significantly based on their customers’ financial returns. The prediction of customers who are at the risk of leaving a company’s services is not possible without using their connection details, support tickets and network traffic usage data. This paper demonstrates the importance of data mining and its outcome in the telecommunication area. The data in this paper are collected from different sources like Net Flow logs, call records and DNS query logs. These different types of data are aggregated together to decrease the missing information. Finally, machine learning algorithms are evaluated based on the customer dataset. The results of this study indicate that the gradient boosting algorithm performs better than other machine learning algorithms for this dataset. Keywords: Data analysis, customer satisfaction, subscriber churn, machine learning, telecommunication.

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Eria, Kamya, and Booma Poolan Marikannan. "Significance-Based Feature Extraction for Customer Churn Prediction Data in the Telecom Sector." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3428–31. http://dx.doi.org/10.1166/jctn.2019.8303.

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The telecom industry is saturated with many service providers competing for highly rational customers. The current big data and highly technological era calls for real-time churn analysis and decision making which has also been highlighted in previous studies. However, telecom data is highly dimensional in nature thus when this is coupled with this big data era increases the computational and processing costs. Therefore, this complexity and dimensionality of telecom data coupled with the current need for near or real-time churn analysis demands feature selection-based models that efficiently consider the most relevant variables in explaining customer churn behaviors. This study proposes a feature extraction-based churn prediction model that concentrates on the most relevant features with significant discriminatory power for churn. The data has been reduced on the basis of missing values and irrelevant variables. Irrelevant variables were first identified by use of Random Forest and Logistic Regression models. The findings of the study provide churn analysts with insights about the prediction errors to consider and minimize in their future churn analyses. It also contributes to reducing computational costs incurred by churn analysts working with big data in their churn prediction and analysis.

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Nagadevara, Vishnuprasad. "CUSTOMER CHURN PREDICTION IN BANKING INDUSTRY." California Business Review 3, no. 1 (March 1, 2015): 41–46. http://dx.doi.org/10.18374/cbr-3-1.6.

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Qi, Jiayin, Li Zhang, Yanping Liu, Ling Li, Yongpin Zhou, Yao Shen, Liang Liang, and Huaizu Li. "ADTreesLogit model for customer churn prediction." Annals of Operations Research 168, no. 1 (August 9, 2008): 247–65. http://dx.doi.org/10.1007/s10479-008-0400-8.

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N., Sandhya, Philip Samuel, and Mariamma Chacko. "Feature intersection for agent-based customer churn prediction." Data Technologies and Applications 53, no. 3 (July 1, 2019): 318–32. http://dx.doi.org/10.1108/dta-03-2019-0043.

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Purpose Telecommunication has a decisive role in the development of technology in the current era. The number of mobile users with multiple SIM cards is increasing every second. Hence, telecommunication is a significant area in which big data technologies are needed. Competition among the telecommunication companies is high due to customer churn. Customer retention in telecom companies is one of the major problems. The paper aims to discuss this issue. Design/methodology/approach The authors recommend an Intersection-Randomized Algorithm (IRA) using MapReduce functions to avoid data duplication in the mobile user call data of telecommunication service providers. The authors use the agent-based model (ABM) to predict the complex mobile user behaviour to prevent customer churn with a particular telecommunication service provider. Findings The agent-based model increases the prediction accuracy due to the dynamic nature of agents. ABM suggests rules based on mobile user variable features using multiple agents. Research limitations/implications The authors have not considered the microscopic behaviour of the customer churn based on complex user behaviour. Practical implications This paper shows the effectiveness of the IRA along with the agent-based model to predict the mobile user churn behaviour. The advantage of this proposed model is as follows: the user churn prediction system is straightforward, cost-effective, flexible and distributed with good business profit. Originality/value This paper shows the customer churn prediction of complex human behaviour in an effective and flexible manner in a distributed environment using Intersection-Randomized MapReduce Algorithm using agent-based model.

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Kamalakannan, T., and P. Mayilvaghnan. "Optimal customer relationship management in telecalling industry by using data mining and business intelligence." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 12. http://dx.doi.org/10.14419/ijet.v7i1.1.8907.

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Decision making system in telecommunication industries plays a more important role where it is required to find customer churn. Customer churn prediction requires finding out and analyzing the information about the business data intelligence techniques which can be done efficiently by adapting the business intelligence techniques. Business intelligence provides tools to predict and analyze the historical, current and predictive views of business operations. However, this would be more complex task with high volume of data which are gathered from million of telephone users for the time being. It can be handled effectively by introducing the data mining techniques which select the most useful information from the gathered data set from which decision making can be done efficiently. In this research method, telecommunication industry is considered in which customer churn prediction application is focused. The main goal of this research method is to introduce the data mining technique which can select the most useful information from the telecommunication industry dataset. This is done by introducing the Hybrid Genetic Algorithm with Particle Swarm Optimization (HGAPSO) method which can select the most useful information. In this research, the hybrid HGAPSO combines the advantages of PSO and GA optimally. From the selected information, decision making about the customer churn prediction can be done accurately. Finally decision making is done by predicting the customer behaviour using Support Vector Machine classification approach. The performance metrics are considered such as precision, recall, f-measure, accuracy, True Positive Rate (TPR), False Positive Rate (FPR), time complexity and ROC. Experimental results demonstrated that HGAPSO provides highly scalable which is used for prediction examination in the business intelligence.

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Ahmed, Mehreen, Hammad Afzal, Awais Majeed, and Behram Khan. "A Survey of Evolution in Predictive Models and Impacting Factors in Customer Churn." Advances in Data Science and Adaptive Analysis 09, no. 03 (July 2017): 1750007. http://dx.doi.org/10.1142/s2424922x17500073.

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The information-based prediction models using machine learning techniques have gained massive popularity during the last few decades. Such models have been applied in a number of domains such as medical diagnosis, crime prediction, movies rating, etc. Similar is the trend in telecom industry where prediction models have been applied to predict the dissatisfied customers who are likely to change the service provider. Due to immense financial cost of customer churn in telecom, the companies from all over the world have analyzed various factors (such as call cost, call quality, customer service response time, etc.) using several learners such as decision trees, support vector machines, neural networks, probabilistic models such as Bayes, etc. This paper presents a detailed survey of models from 2000 to 2015 describing the datasets used in churn prediction, impacting features in those datasets and classifiers that are used to implement prediction model. A total of 48 studies related to churn prediction in telecom industry are discussed using 23 datasets (3 public and 20 private). Our survey aims to highlight the evolution of techniques from simple features/learners to more complex learners and feature engineering or sampling techniques. We also give an overview of the current challenges in churn prediction and suggest solutions to resolve them. This paper will allow researchers such as data analysts in general and telecom operators in particular to choose best suited techniques and features to prepare their churn prediction models.

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Sebastian, Helen, and Rupali Wagh. "Churn Analysis in Telecommunication Using Logistic Regression." Oriental journal of computer science and technology 10, no. 1 (March 24, 2017): 207–12. http://dx.doi.org/10.13005/ojcst/10.01.28.

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Since the beginning of data mining the discovery of knowledge from the Databases has been carried out to solve various problems and has helped the business come up with practical solutions. Large companies are behind improving revenue due to the increase loss in customers. The process where one customer leaves one company and joins another is called as churn. This paper will be discussing how to predict the customers that might churn, R package is being used to do the prediction. R package helps represent large dataset churn in the form of graphs which will help to depict the outcome in the form of various data visualizations. Churn is a very important area in which the telecom domain can make or lose their customers and hence the business/industry spends a lot of time doing predictions, which in turn helps to make the necessary business conclusions. Churn can be avoided by studying the past history of the customers. Logistic Regression is been used to make necessary analysis. To proceed with logistic regression we must first eliminate the outliers that are present, this has be achieved by cleaning the data (for redundancy, false data etc) and the resultant has been populated into a prediction excel using which the analysis has been performed.

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Mahajan, Vishal, and Renuka Mahajan. "Variable Selection of Customers for Churn Analysis in Telecommunication Industry." International Journal of Virtual Communities and Social Networking 10, no. 1 (January 2018): 17–32. http://dx.doi.org/10.4018/ijvcsn.2018010102.

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The telecommunication industry considers customer relationship management as a significant issue for organizational adaptation. Mobile service providers have enforced CRM with the objective to reduce the number of customers that churn. The objective of this article is to detect high impact factors leading to customer churn in the mobile industry over the present-day market situation in Delhi-NCR by using a questionnaire survey and examine their importance. The study is done to understand usage patterns of customers using mobile data services. The data collected was analyzed using descriptive statistics to identify the most common issues to identify attributes of selecting a service provider, cellular usage, and service quality. Thus, the authors have selected possible variables for modeling the decision tree to build a churn prediction model. A renewed customer service, after analyzing this experience, could predict those customers who are at risk of switching to a different provider.

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Khodabandehlou, Samira, and Mahmoud Zivari Rahman. "Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior." Journal of Systems and Information Technology 19, no. 1/2 (March 13, 2017): 65–93. http://dx.doi.org/10.1108/jsit-10-2016-0061.

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Purpose This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business. Design/methodology/approach The six stages are as follows: first, collection of customer behavioral data and preparation of the data; second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis; third, selection of training and testing data and reviewing their proportion; fourth, the development of prediction models using simple, bagging and boosting versions of supervised machine learning; fifth, comparison of churn prediction models based on different versions of machine-learning methods and selected variables; and sixth, providing appropriate strategies based on the proposed model. Findings According to the results, five variables, the number of items, reception of returned items, the discount, the distribution time and the prize beside the recency, frequency and monetary (RFM) variables (RFMITSDP), were chosen as the best predictor variables. The proposed model with accuracy of 97.92 per cent, in comparison to RFM, had much better performance in churn prediction and among the supervised machine learning methods, artificial neural network (ANN) had the highest accuracy, and decision trees (DT) was the least accurate one. The results show the substantially superiority of boosting versions in prediction compared with simple and bagging models. Research limitations/implications The period of the available data was limited to two years. The research data were limited to only one grocery store whereby it may not be applicable to other industries; therefore, generalizing the results to other business centers should be used with caution. Practical implications Business owners must try to enforce a clear rule to provide a prize for a certain number of purchased items. Of course, the prize can be something other than the purchased item. Business owners must accept the items returned by the customers for any reasons, and the conditions for accepting returned items and the deadline for accepting the returned items must be clearly communicated to the customers. Store owners must consider a discount for a certain amount of purchase from the store. They have to use an exponential rule to increase the discount when the amount of purchase is increased to encourage customers for more purchase. The managers of large stores must try to quickly deliver the ordered items, and they should use equipped and new transporting vehicles and skilled and friendly workforce for delivering the items. It is recommended that the types of services, the rules for prizes, the discount, the rules for accepting the returned items and the method of distributing the items must be prepared and shown in the store for all the customers to see. The special services and reward rules of the store must be communicated to the customers using new media such as social networks. To predict the customer behaviors based on the data, the future researchers should use the boosting method because it increases efficiency and accuracy of prediction. It is recommended that for predicting the customer behaviors, particularly their churning status, the ANN method be used. To extract and select the important and effective variables influencing customer behaviors, the discriminant analysis method can be used which is a very accurate and powerful method for predicting the classes of the customers. Originality/value The current study tries to fill this gap by considering five basic and important variables besides RFM in stores, i.e. prize, discount, accepting returns, delay in distribution and the number of items, so that the business owners can understand the role services such as prizes, discount, distribution and accepting returns play in retraining the customers and preventing them from churning. Another innovation of the current study is the comparison of machine-learning methods with their boosting and bagging versions, especially considering the fact that previous studies do not consider the bagging method. The other reason for the study is the conflicting results regarding the superiority of machine-learning methods in a more accurate prediction of customer behaviors, including churning. For example, some studies introduce ANN (Huang et al., 2010; Hung and Wang, 2004; Keramati et al., 2014; Runge et al., 2014), some introduce support vector machine ( Guo-en and Wei-dong, 2008; Vafeiadis et al., 2015; Yu et al., 2011) and some introduce DT (Freund and Schapire, 1996; Qureshi et al., 2013; Umayaparvathi and Iyakutti, 2012) as the best predictor, confusing the users of the results of these studies regarding the best prediction method. The current study identifies the best prediction method specifically in the field of store businesses for researchers and the owners. Moreover, another innovation of the current study is using discriminant analysis for selecting and filtering variables which are important and effective in predicting churners and non-churners, which is not used in previous studies. Therefore, the current study is unique considering the used variables, the method of comparing their accuracy and the method of selecting effective variables.

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Naser alzubaidi, Asia Mahdi, and Eman Salih Al-Shamery. "Projection pursuit random forest using discriminant feature analysis model for churners prediction in telecom industry." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (April 1, 2020): 1406. http://dx.doi.org/10.11591/ijece.v10i2.pp1406-1421.

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A major and demanding issue in the telecommunications industry is the prediction of churn customers. Churn describes the customer who is attrite from one Telecom service provider to competitors searching for better services offers. Companies from the Telco sector frequently have customer relationship management offices it is the main objective in how to win back defecting clients because preserve long-term customers can be much more beneficial to a company than gain newly recruited customers. Researchers and practitioners are paying great attention and investing more in developing a robust customer churn prediction model, especially in the telecommunication business by proposed numerous machine learning approaches. Many approaches of Classification are established, but the most effective in recent times is a tree-based method. The main contribution of this research is to predict churners/non-churners in the Telecom sector based on project pursuit Random Forest (PPForest) that uses discriminant feature analysis as a novelty extension of the conventional Random Forest approach for learning oblique Project Pursuit tree (PPtree). The proposed methodology leverages the advantage of two discriminant analysis methods to calculate the project index used in the construction of PPtree. The first method used Support Vector Machines (SVM) as a classifier in the construction of PPForest to differentiate between churners and non-churners customers. The second method is a Linear Discriminant Analysis (LDA) to achieve linear splitting of variables node during oblique PPtree construction to produce individual classifiers that are robust and more diverse than classical Random Forest. It found that the proposed methods enjoy the best performance measurements e.g. Accuracy, hit rate, ROC curve, Gini coefficient, Kolmogorov-Smirnov statistic and lift coefficient, H-measure, AUC. Moreover, PPForest based on direct applied of LDA on the raw data delivers an effective evaluator for the customer churn prediction model.

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Xia, Tai Wu. "Research on Customer Churn Model with Least Square Support Vector Machine." Applied Mechanics and Materials 236-237 (November 2012): 869–74. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.869.

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China telecommunications market is becoming more competitive, the operators are facing the severe costumer churn problem, and how to predict and effectively reduce the costumer churn directly concerns the survival and development of every operator. Therefore, the least squares support vector machine(LS-SVM) algorithm is adopted to build customer churn model, mainly including data cleaning, normalization, building forecasting model, model prediction, etc. The case study shows that the customer churn prediction using the LS-LSV has high precision, small error and remarkable effect.

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Zhao, Xi, Yong Shi, Jongwon Lee, Heung Kee Kim, and Heeseok Lee. "Customer Churn Prediction Based on Feature Clustering and Nonparallel Support Vector Machine." International Journal of Information Technology & Decision Making 13, no. 05 (September 2014): 1013–27. http://dx.doi.org/10.1142/s0219622014500680.

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Bank customer churn prediction is one of the key businesses for modern commercial banks. Recently, various methods have been investigated to identify the customers who would leave away. This paper proposed a new framework based on feature clustering and classification technique to help commercial banks make an effective decision on customer churn problem. The proposed method benefits from the result of data explorations, clusters the customer features, and makes a decision with a state-of-the-art classifier. When facing the data with large amount of missing items, it does not directly remove the features by some subjective threshold, but clusters the features through the consideration of the relationship and the missing ratio. Real-world data from a major commercial bank of China verifies the feasibility of our framework in industrial applications.

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Sreeejesh, S. "Cellular Customer Churns Due to Mobile Number Portability." International Journal of Interdisciplinary Telecommunications and Networking 5, no. 1 (January 2013): 43–57. http://dx.doi.org/10.4018/jitn.2013010104.

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Retaining existing customer has been considered to be one of the most critical challenges for telecommunication service providers than for attracting new ones. In telecommunication, the service offered is different from that of a general commodity sale as in the former case the service is considered to be a continuous process, wherein the service provider can offer the differentiated services throughout the customer’s tenure. This differentiation in service offered creates a demarcation from the competitors and hence establishes competitive advantage for that service provider for attracting new customers and retaining the existing ones, which ultimately determines the profitability. In this paper, the author captures this differentiation factor by investigating customer switching behavior under Mobile Number Portability (MNP) in Indian telecommunication market. It is shown that only limited attention has been paid to the customer churn under MNP and none of the existing studies incorporated psychological constructs as the determinants of customer churn. In this context, the study used discriminant analysis to understand the factors that best discriminate between switchers and non-switchers and predict (develop a churn prediction model) the customer churn behavior through incorporating psychological constructs. The findings indicate that service quality, customer satisfaction, attachment, commitment and switching costs are the major factors differentiating the switching and non-switching decisions. Service quality of the service provider found to be as the differentiating factor in churning decision. The results of the study have implications for both academicians and relationship mangers; they are using psychological constructs to predict customer switching behavior.

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Jadhav, Prasadkumar. "Web Log Analyser for Customer Churn Prediction." International Journal for Research in Applied Science and Engineering Technology 7, no. 5 (May 31, 2019): 3456–60. http://dx.doi.org/10.22214/ijraset.2019.5566.

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Tsai, Chih-Fong, and Yu-Hsin Lu. "Data Mining Techniques in Customer Churn Prediction." Recent Patents on Computer Science 3, no. 1 (February 1, 2010): 28–32. http://dx.doi.org/10.2174/1874479611003010028.

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Tsai, Chih-Fong, and Yu-Hsin Lu. "Data Mining Techniques in Customer Churn Prediction." Recent Patents on Computer Sciencee 3, no. 1 (January 1, 2010): 28–32. http://dx.doi.org/10.2174/2213275911003010028.

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Panjasuchat, M., and Y. Limpiyakorn. "Applying Reinforcement Learning for Customer Churn Prediction." Journal of Physics: Conference Series 1619 (August 2020): 012016. http://dx.doi.org/10.1088/1742-6596/1619/1/012016.

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Tsai, Chih-Fong, and Yu-Hsin Lu. "Customer churn prediction by hybrid neural networks." Expert Systems with Applications 36, no. 10 (December 2009): 12547–53. http://dx.doi.org/10.1016/j.eswa.2009.05.032.

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Verbeke, Wouter, David Martens, and Bart Baesens. "Social network analysis for customer churn prediction." Applied Soft Computing 14 (January 2014): 431–46. http://dx.doi.org/10.1016/j.asoc.2013.09.017.

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Burez, J., and D. Van den Poel. "Handling class imbalance in customer churn prediction." Expert Systems with Applications 36, no. 3 (April 2009): 4626–36. http://dx.doi.org/10.1016/j.eswa.2008.05.027.

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Kwon, Hongwook, Ho Heon Kim, Jaeil An, Jae-Ho Lee, and Yu Rang Park. "Lifelog Data-Based Prediction Model of Digital Health Care App Customer Churn: Retrospective Observational Study." Journal of Medical Internet Research 23, no. 1 (January 6, 2021): e22184. http://dx.doi.org/10.2196/22184.

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Background Customer churn is the rate at which customers stop doing business with an entity. In the field of digital health care, user churn prediction is important not only in terms of company revenue but also for improving the health of users. Churn prediction has been previously studied, but most studies applied time-invariant model structures and used structured data. However, additional unstructured data have become available; therefore, it has become essential to process daily time-series log data for churn predictions. Objective We aimed to apply a recurrent neural network structure to accept time-series patterns using lifelog data and text message data to predict the churn of digital health care users. Methods This study was based on the use data of a digital health care app that provides interactive messages with human coaches regarding food, exercise, and weight logs. Among the users in Korea who enrolled between January 1, 2017 and January 1, 2019, we defined churn users according to the following criteria: users who received a refund before the paid program ended and users who received a refund 7 days after the trial period. We used long short-term memory with a masking layer to receive sequence data with different lengths. We also performed topic modeling to vectorize text messages. To interpret the contributions of each variable to model predictions, we used integrated gradients, which is an attribution method. Results A total of 1868 eligible users were included in this study. The final performance of churn prediction was an F1 score of 0.89; that score decreased by 0.12 when the data of the final week were excluded (F1 score 0.77). Additionally, when text data were included, the mean predicted performance increased by approximately 0.085 at every time point. Steps per day had the largest contribution (0.1085). Among the topic variables, poor habits (eg, drinking alcohol, overeating, and late-night eating) showed the largest contribution (0.0875). Conclusions The model with a recurrent neural network architecture that used log data and message data demonstrated high performance for churn classification. Additionally, the analysis of the contribution of the variables is expected to help identify signs of user churn in advance and improve the adherence in digital health care.

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Dumitrache, Andreea, Denisa Melian, Delia Bălăcian, Alexandra Nastu, and Stelian Stancu. "Churn prepaid customers classified by HyperOpt techniques." Proceedings of the International Conference on Applied Statistics 2, no. 1 (December 1, 2020): 139–51. http://dx.doi.org/10.2478/icas-2021-0013.

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Abstract The telecommunications industry is representative when it comes to a country’s economy. In this industry, the customer plays a very important role in maintaining a stable income. The churn customer is one of the most important concerns for large companies. This increased attention is due to its direct effect on the revenues of large companies in the telecommunications industry, companies being in a constant search to develop ways to predict this type of customer. The aim of our paper is to identify potential customers at risk of churn using modern data mining techniques, often used in the business world. From the nine techniques tested, we choose as the churn prediction model, the technique with the highest performance. The effectiveness of the model is tested and evaluated by the f1-score. The model developed in the paper uses machine learning techniques on the Python platform, exploring a wide range of algorithms from logistic regression and the method of balancing the analyzed data set (Balanced Random Forest) to supervised learning methods (K-Nearest Neighbors, Naive Bayes) and optimization packages (Ligh GBM, CATBoost, ADABoost, RUSBoost, Stochastic Gradient Descent). The techniques analyzed in this paper cover a diverse range of methods that are compared in terms of performance. RUSBoost proves to be the best churn prediction model for telecom customers in this study. RUSBoost has the lowest loss function of all the tested techniques.

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Mohammadi, Golshan, Reza Tavakkoli-Moghaddam, and Mehrdad Mohammadi. "Hierarchical Neural Regression Models for Customer Churn Prediction." Journal of Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/543940.

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As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN), self-organizing maps (SOM), alpha-cut fuzzyc-means (α-FCM), and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, andα-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, theα-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.

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Liu, D. S., and Chun Hua Ju. "Customer Churn Analysis Model in Manufacturing Industry." Advanced Materials Research 69-70 (May 2009): 675–79. http://dx.doi.org/10.4028/www.scientific.net/amr.69-70.675.

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To address the problem of customer churn in CRM in manufacturing industry, this paper proposes a prediction model based on Support Vector Machine (SVM). Considering the large-scale and imbalanced churn data, principal component analysis (PCA) is adopted to reduce dimensions and eliminate redundant information, which makes the sample space for SVM more compact and reasonable. An improved SVM is used to predict customer churn. Firstly, PCA is adopted to process 17 dimensional feature vectors of customer churn data, and then the application in manufacturing industry verifies that this model based on both PCA and SVM performs better than the model based on SVM only and other traditional models.

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Lee, Eui-Bang, Jinwha Kim, and Sang-Gun Lee. "Predicting customer churn in mobile industry using data mining technology." Industrial Management & Data Systems 117, no. 1 (February 6, 2017): 90–109. http://dx.doi.org/10.1108/imds-12-2015-0509.

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Purpose The purpose of this paper is to identify the influence of the frequency of word exposure on online news based on the availability heuristic concept. So that this is different from most churn prediction studies that focus on subscriber data. Design/methodology/approach This study examined the churn prediction through words presented the previous studies and additionally identified words what churn generate using data mining technology in combination with logistic regression, decision tree graphing, neural network models, and a partial least square (PLS) model. Findings This study found prediction rates similar to those delivered by subscriber data-based analyses. In addition, because previous studies do not clearly suggest the effects of the factors, this study uses decision tree graphing and PLS modeling to identify which words deliver positive or negative influences. Originality/value These findings imply an expansion of churn prediction, advertising effect, and various psychological studies. It also proposes concrete ideas to advance the competitive advantage of companies, which not only helps corporate development, but also improves industry-wide efficiency.

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Xiao, Jin, Bing Zhu, Geer Teng, Changzheng He, and Dunhu Liu. "One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values." Mathematical Problems in Engineering 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/869628.

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Scientific customer value segmentation (CVS) is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM) model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology in constructing the classification models. The empirical results in credit scoring dataset “German” from UCI and the real customer churn prediction dataset “China churn” show that the ODCEM outperforms four commonly used “two-step” models and the ensemble based model LMF and can provide better decision support for market managers.

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Jia, Yu Bo, Qian Zhang, Qian Qian Ding, and Dan Li Liu. "The Study and Realization of Customer-Churn Model Based on Date Mining in Telcom." Applied Mechanics and Materials 336-338 (July 2013): 2229–32. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2229.

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Customer frequent churn is a serious problem in telecom. In the three major telecom operators, the competition is quite fierce. Owing to lack of a high-efficient prediction model ,the existing means effect is far from enterprise target. This paper proposes a combination model CPM based on constraint model, prediction model and mark model responsible for different job. Customer subdivision is vital for pertinent service further to reduce the rate of latent customers run off.

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Lin, Wei-Chao, Chih-Fong Tsai, and Shih-Wen Ke. "Dimensionality and data reduction in telecom churn prediction." Kybernetes 43, no. 5 (April 29, 2014): 737–49. http://dx.doi.org/10.1108/k-03-2013-0045.

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Purpose – Churn prediction is a very important task for successful customer relationship management. In general, churn prediction can be achieved by many data mining techniques. However, during data mining, dimensionality reduction (or feature selection) and data reduction are the two important data preprocessing steps. In particular, the aims of feature selection and data reduction are to filter out irrelevant features and noisy data samples, respectively. The purpose of this paper, performing these data preprocessing tasks, is to make the mining algorithm produce good quality mining results. Design/methodology/approach – Based on a real telecom customer churn data set, seven different preprocessed data sets based on performing feature selection and data reduction by different priorities are used to train the artificial neural network as the churn prediction model. Findings – The results show that performing data reduction first by self-organizing maps and feature selection second by principal component analysis can allow the prediction model to provide the highest prediction accuracy. In addition, this priority allows the prediction model for more efficient learning since 66 and 62 percent of the original features and data samples are reduced, respectively. Originality/value – The contribution of this paper is to understand the better procedure of performing the two important data preprocessing steps for telecom churn prediction.

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Perisic, Ana, and Marko Pahor. "Extended RFM logit model for churn prediction in the mobile gaming market." Croatian Operational Research Review 11, no. 2 (2020): 249–61. http://dx.doi.org/10.17535/crorr.2020.0020.

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As markets are becoming increasingly saturated, many businesses are shifting their focus to customer retention. In their need to understand and predict future customer behavior, businesses across sectors are adopting data-driven business intelligence to deal with churn prediction. A good example of this approach to retention management is the mobile game industry. This business sector usually relies on a considerable amount of behavioral telemetry data that allows them to understand how users interact with games. This high-resolution information enables game companies to develop and adopt accurate models for detecting customers with a high attrition propensity. This paper focuses on building a churn prediction model for the mobile gaming market by utilizing logistic regression analysis in the extended recency, frequency and monetary (RFM) framework. The model relies on a large set of raw telemetry data that was transformed into interpretable game-independent features. Robust statistical measures and dominance analysis were applied in order to assess feature importance. Established features are used to develop a logistic model for churn prediction and to classify potential churners in a population of users, regardless of their lifetime.

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Journal articles on the topic 'Customer churn prediction'

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