Version 1
: Received: 8 March 2024 / Approved: 11 March 2024 / Online: 14 March 2024 (05:27:06 CET)
How to cite:
Imani, M. Customer Churn Prediction in Telecommunication Industry: A Literature Review. Preprints2024, 2024030585. https://doi.org/10.20944/preprints202403.0585.v1
Imani, M. Customer Churn Prediction in Telecommunication Industry: A Literature Review. Preprints 2024, 2024030585. https://doi.org/10.20944/preprints202403.0585.v1
Imani, M. Customer Churn Prediction in Telecommunication Industry: A Literature Review. Preprints2024, 2024030585. https://doi.org/10.20944/preprints202403.0585.v1
APA Style
Imani, M. (2024). Customer Churn Prediction in Telecommunication Industry: A Literature Review. Preprints. https://doi.org/10.20944/preprints202403.0585.v1
Chicago/Turabian Style
Imani, M. 2024 "Customer Churn Prediction in Telecommunication Industry: A Literature Review" Preprints. https://doi.org/10.20944/preprints202403.0585.v1
Abstract
In the dynamic landscape of the telecommunications industry, understanding and mitigating customer churn is paramount for maintaining competitive advantage and sustaining revenue streams. This comprehensive literature review delves into the intricacies of customer churn prediction, a crucial aspect in fortifying the bond between telecom businesses and their customers. The review methodically analyzes 11 articles published between 2000 and March 2024, spotlighting the strategic role of Customer Relationship Management and the prevalence of high churn rates that necessitate a shift from customer acquisition to retention strategies.Furthermore, the review navigates through the challenges inherent in churn prediction, including the complexities of managing large, imbalanced, and noisy datasets and the intricacies involved in model selection and optimization. Despite these challenges, the paper highlights the superior capability of machine learning techniques in handling complex datasets to provide accurate predictions, albeit acknowledging their practical complexities. The main aim of the study is to provide an overview, serving as a knowledge base for researchers interested in machine learning techniques within the realm of telecommunications.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
