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Open Access Journal Article

Predictive Analytics for Customer Churn Prevention in Telecommunications

by Michael Harris 1,*
1
Michael Harris
*
Author to whom correspondence should be addressed.
ISTI  2023, 38; 5(1), 38; https://doi.org/10.69610/j.isti.20230312
Received: 11 January 2023 / Accepted: 23 February 2023 / Published Online: 12 March 2023
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Abstract

This paper investigates the application of predictive analytics in the telecommunications industry to prevent customer churn. Customer churn, the loss of customers to competitors, is a significant concern for telecommunication companies as it negatively impacts revenue and market share. The research focuses on the development and implementation of predictive models that can forecast customer churn with high accuracy. By leveraging advanced statistical techniques and machine learning algorithms, the study identifies key factors influencing customer churn, such as service quality, pricing, and customer satisfaction. The findings indicate that predictive analytics can significantly reduce churn rates by enabling telecom companies to proactively target at-risk customers and tailor personalized retention strategies. Furthermore, the paper discusses the challenges and limitations associated with predictive analytics in the context of customer churn prevention, emphasizing the need for ongoing model refinement and data-driven decision-making processes.

Keywords: Predictive Analytics, Customer Churn, Telecommunications, Machine Learning, Retention Strategies.;
Copyright: © 2023 by Harris. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Harris, M. Predictive Analytics for Customer Churn Prevention in Telecommunications. Information Sciences and Technological Innovations, 2023, 5, 38. https://doi.org/10.69610/j.isti.20230312
AMA Style
Harris M. Predictive Analytics for Customer Churn Prevention in Telecommunications. Information Sciences and Technological Innovations; 2023, 5(1):38. https://doi.org/10.69610/j.isti.20230312
Chicago/Turabian Style
Harris, Michael 2023. "Predictive Analytics for Customer Churn Prevention in Telecommunications" Information Sciences and Technological Innovations 5, no.1:38. https://doi.org/10.69610/j.isti.20230312
APA style
Harris, M. (2023). Predictive Analytics for Customer Churn Prevention in Telecommunications. Information Sciences and Technological Innovations, 5(1), 38. https://doi.org/10.69610/j.isti.20230312

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