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Open Access Journal Article

Predicting Customer Churn in Telecommunication Industry Using Big Data Analytics

by Olivia Johnson 1,*
1
Olivia Johnson
*
Author to whom correspondence should be addressed.
TASC  2023, 41; 5(2), 41; https://doi.org/10.69610/j.tasc.20230822
Received: 15 June 2023 / Accepted: 20 July 2023 / Published Online: 22 August 2023
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Abstract

The telecommunication industry, with its rapid technological advancements and increasingly competitive market, faces the critical challenge of customer churn. This study aims to explore the potential of big data analytics in predicting customer churn within the telecommunication sector. By employing advanced data mining techniques and machine learning algorithms, this research endeavors to identify patterns and correlations in customer behavior that can be indicative of churn risk. The analysis involves the collection and integration of large-scale customer data, encompassing various dimensions such as service usage, billing history, and customer feedback. The findings suggest that a combination of predictive analytics and customer segmentation can significantly enhance the accuracy of churn predictions. This paper contributes to the field by proposing a comprehensive framework for churn prediction, which not only helps telecommunication companies in retaining customers but also aids in optimizing their service offerings and marketing strategies. Ultimately, the adoption of such predictive models can lead to substantial cost savings and improved customer satisfaction.

Keywords: Customer Churn, Telecommunication Industry, Big Data Analytics, Predictive Modeling, Data Mining Techniques.;
Copyright: © 2023 by Johnson. 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
Johnson, O. Predicting Customer Churn in Telecommunication Industry Using Big Data Analytics. Transactions on Applied Soft Computing, 2023, 5, 41. https://doi.org/10.69610/j.tasc.20230822
AMA Style
Johnson O. Predicting Customer Churn in Telecommunication Industry Using Big Data Analytics. Transactions on Applied Soft Computing; 2023, 5(2):41. https://doi.org/10.69610/j.tasc.20230822
Chicago/Turabian Style
Johnson, Olivia 2023. "Predicting Customer Churn in Telecommunication Industry Using Big Data Analytics" Transactions on Applied Soft Computing 5, no.2:41. https://doi.org/10.69610/j.tasc.20230822
APA style
Johnson, O. (2023). Predicting Customer Churn in Telecommunication Industry Using Big Data Analytics. Transactions on Applied Soft Computing, 5(2), 41. https://doi.org/10.69610/j.tasc.20230822

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