Exploring Hybrid and Ensemble Models for Customer Churn Prediction in Telecom Sector

Pamina, J.; Rajkumar, T. Dhiliphan; Kiruthika, S.; Suganya, T.; Femila, F

doi:10.35940/ijrte.a9170.078219

Cited by 6 publications

(4 citation statements)

References 31 publications

(39 reference statements)

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“…This dataset enables a comprehensive analysis of customer behavior, offering valuable insights into the factors that contribute to churn in the telecommunications industry [7]. This dataset is available at this URL link: https://www.kaggle.com/datasets/jpacse/datasets-for-churn-telecom [8]. The telecommunications industry experiences a high churn rate.…”

Section:  Issn: 2088-8708mentioning

confidence: 99%

Predicting churn with filter-based techniques and deep learning

Quek Jia Yi,

Ying Han,

Zheng You

et al. 2024

IJECE

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Customer churn prediction is of utmost importance in the telecommunications industry. Retaining customers through effective churn prevention strategies proves to be more cost-efficient. In this study, attribute selection analysis and deep learning are integrated to develop a customer churn prediction model to improve performance while reducing feature dimensions. The study includes the analysis of customer data attributes, exploratory data analysis, and data preprocessing for data quality enhancement. Next, significant features are selected using two attribute selection techniques, which are chi-square and analysis of variance (ANOVA). The selected features are fed into an artificial neural network (ANN) model for analysis and prediction. To enhance prediction performance and stability, a learning rate scheduler is deployed. Implementing the learning rate scheduler in the model can help prevent overfitting and enhance convergence speed. By dynamically adjusting the learning rate during the training process, the scheduler ensures that the model optimally adapts to the data while avoiding overfitting. The proposed model is evaluated using the Cell2Cell telecom database, and the results demonstrate that the proposed model exhibits a promising performance, showcasing its potential as an effective churn prediction solution in the telecommunications industry.

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Section:  Issn: 2088-8708mentioning

confidence: 99%

Predicting churn with filter-based techniques and deep learning

Quek Jia Yi,

Ying Han,

Zheng You

et al. 2024

IJECE

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“…In addition to these studies, some authors make literature surveys about customer churn in telecommunication sector: Pamina et al [27] conduct a literature survey to assist the researchers or data analysts to find out the most appropriate techniques to predict customer churn in telecommunication sector. Jain et al [28] present a literature review about customer churn and its effects, the root causes of customer churn, the requirements of businesses and the method and techniques to predict customer churn.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

BİLİŞİK,

SARP

2023

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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In today's conditions, customer loyalty has gained importance with the increase in the competitive environment between companies, the development of marketing strategies and the improvement of companies. Therefore, it is essential to acquire customers for a company to survive. Retaining an existing customer in the telecommunication sector is less costly than gaining a new customer. Customer churn analysis is the process of predicting customers with high abandonment requests by examining the offers and utilizable behaviors. Customer churn analysis provides services to develop various campaigns aiming to increase the company’s loyalty by predicting the customers who are planning to move to another company. In this way, it gives the company a competitive advantage. This study aims to make predictions by developing models for customer churns through data mining and machine learning methods in the telecommunication sector. In addition, we believe that the application in this article will contribute to data analysts and academicians who will want to analyze customer churn with different data sets in telecommunication and other sectors in the future. The analysis in this study is carried out on a data set obtained from an open-access database, including 20 transaction records for the customer from 7043 customers and whether the customer left the company. Among the data mining methods, Random Forest (RF), Support Vector Machines (SVM) and Multilayer Artificial Neural Networks (ANN) are modeled in open-source Phyton environment. The results have shown that ANN has fared better at classifying customers than other machine learning methods.

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“…Online First ensemble, as well as other classic data mining methods. A detailed investigation of churn-based machine learning prediction in the telecoms business for eight years prompted the suggestion of investigating synthetic and ensemble techniques in the telecommunications industry [19]. It was discovered that telecommunication churn concerns and problems could not be predicted and that proposals and solutions to these problems were presented.…”

Section: Eai Endorsed Transactions On Mobile Communications and Appli...mentioning

confidence: 99%

A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms

Abdulsalam

Ajao

Balogun

et al. 2022

EAI Endorsed Trans Mob Com Appl

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INTRODUCTION: Customer churn is a severe problem of migrating from one service provider to another. Due to the direct influence on the company's sales, companies are attempting to promote strategies to identify the churn of prospective consumers. Hence it is necessary to examine issues that influence customer churn to yield effective solutions to minimize churn. OBJECTIVES: The major purpose of this work is to create a model of churn prediction that assists telecom operatives to envisage clients that are more probably to be prone to churn. METHODS: The experimental strategy for this study leverages the machine learning techniques on the telecom churn dataset, employing an improved Relief-F feature selection algorithm to extract related features from the enormous dataset. RESULTS: The result demonstrates that CNN has a high prediction capability of 94 percent compared to the 91 percent Random Forest classifier. CONCLUSION: The results are of enormous relevance to the telecommunication business in improving churners and loyal clients.

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Exploring Hybrid and Ensemble Models for Customer Churn Prediction in Telecom Sector

Cited by 6 publications

References 31 publications

Predicting churn with filter-based techniques and deep learning

Predicting churn with filter-based techniques and deep learning

Analysis of Customer Churn in Telecommunication Industry with Machine Learning Methods

A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms

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