Telecom churn prediction and used techniques, datasets and performance measures: a review

Jain, Hemlata; Khunteta, Ajay; Srivastava, Sumit

doi:10.1007/s11235-020-00727-0

Cited by 29 publications

(17 citation statements)

References 37 publications

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“… Jain, Khunteta & Srivastava (2020) discussed overview of different classification algorithms. These algorithms are Multi-Layer-Perception, KNN measure, Fuzzy Cluster and Deep Learning CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry

Bilal

Almazroi

Bashir

et al. 2022

PeerJ Computer Science

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Mobile communication has become a dominant medium of communication over the past two decades. New technologies and competitors are emerging rapidly and churn prediction has become a great concern for telecom companies. A customer churn prediction model can provide the accurate identification of potential churners so that a retention solution may be provided to them. The proposed churn prediction model is a hybrid model that is based on a combination of clustering and classification algorithms using an ensemble. First, different clustering algorithms (i.e. K-means, K-medoids, X-means and random clustering) were evaluated individually on two churn prediction datasets. Then hybrid models were introduced by combining the clusters with seven different classification algorithms individually and then evaluations were performed using ensembles. The proposed research was evaluated on two different benchmark telecom data sets obtained from GitHub and Bigml platforms. The analysis of results indicated that the proposed model attained the highest prediction accuracy of 94.7% on the GitHub dataset and 92.43% on the Bigml dataset. State of the art comparison was also performed using the proposed model. The proposed model performed significantly better than state of the art churn prediction models.

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Section: Literature Reviewmentioning

confidence: 99%

An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry

Bilal

Almazroi

Bashir

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Antecedents of customer churn under Brand Image category include Fair Company, Friendly Company, and Innovative Company. Jain et al [1] found out that Lack of engagement, Lack of new offers/promotions, Lack of customer service support, High call rates/SMS charges, Nonpayment bills, Fraud or Misuse of services by customers, and Change of location or position of customers are the main factors why customers churn. According to various studies conducted on factors that determine customer churn, service quality has been quoted as a vital determinant [1,34,35].…”

Section: Customer Churn Determinantsmentioning

confidence: 99%

“…In the telecom sector, situations, where customers withdraw from company services, are common. e customer either voluntarily leaves the services because of reasons like being unsatis ed with company's existing plans and services, changing their jobs and relocating to a di erent area where they cannot access the services, going out of the country, nding a job mandating them to use a particular service provider, or the service provider involuntarily terminates their services due to some reasons like failure to pay bills or using the services for illegal purposes [1]. Churn is movement of clients from one service provider to another in a speci c period with di erent reasons, reducing revenues for the former service provider [2].…”

Section: Introductionmentioning

confidence: 99%

“…Due to increasing competition in the telecom industry, clients have taken an advantage of available choices for transferring to better and cheaper services. Customers are the main source of revenue for any organization [1]. A precise customer churn prediction can help companies to retain their customers at a large base and thus maintain their revenues at a higher level.…”

Section: Introductionmentioning

confidence: 99%

“…Huge volumes of data stored in customer relationship management systems have been currently adapted in predicting customer churn and revealed the factors that drive customers to churn [3]. Acquiring a new client is expensive considering the cost of advertising, promotion, human resource, and time involved [1]. Retaining customers is becoming a major consideration for telecom service providers because it is easier and more economic [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting the Risk of Customer Churn in Telecom Sector: A Comparative Study

Edwine

Wang

Song

et al. 2022

Mathematical Problems in Engineering

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Churn rate describes the rate at which customers abandon a product or service. Identifying churn-risk customers is essential for telecom sectors to retain old customers and maintain a higher competitive advantage. The purpose of this paper is to explore an effective method for detecting the risk of customer churn in telecom sectors through comparing the advanced machine learning methods and their optimization algorithms. Based on two different telecom datasets, Mutual Information classifier was firstly utilized to select the most critical features relevant to customer churn. Next, the controlled-ratio undersampling strategy was employed to balance both minority and majority classes. Key hyperparameter optimization algorithms of Grid Search, Random Search, and Genetic Algorithms were then combined to fit the three promising machine learning models-Random Forest, Support Vector Machines, and K-nearest neighbors into the customer churn prediction problem. Six evaluation metrics-Accuracy, Recall, Precision, AUC, F1-score and Mean Absolute Error, were last used to evaluate the performance of the proposed models. The experimental results have revealed that the RF algorithm optimized by Grid Search based on a low-ratio undersampling strategy (RF-GS-LR) outperformed other models in extracting hidden information and understanding future churning behaviors of customers on both datasets, with the maximum accuracy of 99% and 95% on the applied dataset 1-2 respectively.

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A Cost-Sensitive Ensemble Model for e-Commerce Customer Behavior Prediction with Weighted SVM

Jing

Avant³

2023

Lecture Notes on Data Engineering and Communications Technologies

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Telecom churn prediction and used techniques, datasets and performance measures: a review

Cited by 29 publications

References 37 publications

An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry

An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry

Detecting the Risk of Customer Churn in Telecom Sector: A Comparative Study

A Cost-Sensitive Ensemble Model for e-Commerce Customer Behavior Prediction with Weighted SVM

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