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Research Article

Predictive Analytics for Telecom Customer Churn: Enhancing Retention Strategies in the US Market

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Abstract

The telecommunications industry in America has been characterized by exponential technological advancements and escalated competition, leading to heightened client expectations. Consequently, client retention has emerged as a crucial metric for telecom companies, directly influencing profitability and market share. The chief objective goal of this study was to build strong predictive models that could correctly identify at-risk customers in the US telecom market. This research paper aimed to use machine learning algorithms and advanced data analytics to uncover patterns and trends in customer dissatisfaction or intent to churn. This study centered particularly on the American telecom market, examining relevant client data drawn from various sources, entailing billing records, client service interactions, and usage patterns. The dataset for the current study was retrieved from proven and verified sources. This dataset provided intensive insight into customer behavior in terms of churning in the telecom industry. It contained highly elaborate information on customer demographics, service usage, and several indicators that are substantial for the analysis of customer retention and churn. The dataset was designed for the exploration of factors that influence customer churn and retention. The given dataset provided a very good basis for building predictive models aimed at finding customers who are at risk and understanding the dynamics of customer turnover. Among the different models that can be used are Logistic Regression, Support Vector Machines, and Random Forests, among others, each with its advantages and disadvantages. The Random Forest algorithm attained the highest accuracy, indicating exceptional performance in effectively identifying both churn and non-churn instances.

Article information

Journal

Journal of Computer Science and Technology Studies

Volume (Issue)

7 (1)

Pages

30-45

Published

2025-01-17

How to Cite

Mohaimin, M. R., Das, B. C., Akter, R., Anonna, F. R., Hasanuzzaman, M., Chowdhury, B. R., & Alam, S. (2025). Predictive Analytics for Telecom Customer Churn: Enhancing Retention Strategies in the US Market. Journal of Computer Science and Technology Studies, 7(1), 30-45. https://doi.org/10.32996/jcsts.2025.7.1.3

References

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Keywords:

Customer Churn, Telecom Industry, Predictive Analytics, Retention Strategies, Machine Learning, U.S Telecom Market