A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection

Sana, Joydeb Kumar; Abedin, Mohammad; Rahman, M. Sohel; Rahman, M. Saifur

doi:10.1371/journal.pone.0278095

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A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection

Joydeb Kumar Sana, Mohammad Abedin

, M. Sohel Rahman, M. Saifur Rahman

PLOS ONE, Volume: 17, Issue: 12, Start page: e0278095

Swansea University Author: Mohammad Abedin

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DOI (Published version): 10.1371/journal.pone.0278095

Abstract

Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM) data through the use of various machine learning models and data transformation methods to identify the customers who are likely t...

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Published in:	PLOS ONE
ISSN:	1932-6203
Published:	Public Library of Science (PLoS) 2022
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa64261

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spelling	2023-09-19T16:23:02.0646866 v2 64261 2023-08-31 A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2023-08-31 CBAE Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM) data through the use of various machine learning models and data transformation methods to identify the customers who are likely to churn. While several studies have been conducted in the customer churn prediction (CCP) context in TCI, a review of performance of the various models stemming from these studies show a clear room for improvement. Therefore, to improve the accuracy of customer churn prediction in the telecommunication industry, we have investigated several machine learning models, as well as, data transformation methods. To optimize the prediction models, feature selection has been performed using univariate technique and the best hyperparameters have been selected using the grid search method. Subsequently, experiments have been conducted on several publicly available TCI datasets to assess the performance of our models in terms of the widely used evaluation metrics, such as AUC, precision, recall, and F-measure. Through a rigorous experimental study, we have demonstrated the benefit of applying data transformation methods as well as feature selection while training an optimized CCP model. Our proposed technique improved the prediction performance by up to 26.2% and 17% in terms of AUC and F-measure, respectively. Journal Article PLOS ONE 17 12 e0278095 Public Library of Science (PLoS) 1932-6203 Customer churn, telecommunication industry, customer relationship management 1 12 2022 2022-12-01 10.1371/journal.pone.0278095 http://dx.doi.org/10.1371/journal.pone.0278095 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2023-09-19T16:23:02.0646866 2023-08-31T17:59:42.0643479 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Joydeb Kumar Sana 1 Mohammad Abedin 0000-0002-4688-0619 2 M. Sohel Rahman 3 M. Saifur Rahman 0000-0002-9887-4456 4 64261__28565__278e9f973af74b22ad4c152fe035fed6.pdf 64261.VOR.pdf 2023-09-18T17:13:30.6980335 Output 2829709 application/pdf Version of Record true © 2022 Sana et al. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title	A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
spellingShingle	A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection Mohammad Abedin
title_short	A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
title_full	A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
title_fullStr	A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
title_full_unstemmed	A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
title_sort	A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
author_id_str_mv	4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv	4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author	Mohammad Abedin
author2	Joydeb Kumar Sana Mohammad Abedin M. Sohel Rahman M. Saifur Rahman
format	Journal article
container_title	PLOS ONE
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container_issue	12
container_start_page	e0278095
publishDate	2022
institution	Swansea University
issn	1932-6203
doi_str_mv	10.1371/journal.pone.0278095
publisher	Public Library of Science (PLoS)
college_str	Faculty of Humanities and Social Sciences
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hierarchy_top_title	Faculty of Humanities and Social Sciences
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department_str	School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url	http://dx.doi.org/10.1371/journal.pone.0278095
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description	Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM) data through the use of various machine learning models and data transformation methods to identify the customers who are likely to churn. While several studies have been conducted in the customer churn prediction (CCP) context in TCI, a review of performance of the various models stemming from these studies show a clear room for improvement. Therefore, to improve the accuracy of customer churn prediction in the telecommunication industry, we have investigated several machine learning models, as well as, data transformation methods. To optimize the prediction models, feature selection has been performed using univariate technique and the best hyperparameters have been selected using the grid search method. Subsequently, experiments have been conducted on several publicly available TCI datasets to assess the performance of our models in terms of the widely used evaluation metrics, such as AUC, precision, recall, and F-measure. Through a rigorous experimental study, we have demonstrated the benefit of applying data transformation methods as well as feature selection while training an optimized CCP model. Our proposed technique improved the prediction performance by up to 26.2% and 17% in terms of AUC and F-measure, respectively.
published_date	2022-12-01T05:24:12Z
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A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection

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