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Open Access Journal Article

Predictive Models for Churn Analysis in Telecom Industry

by Emma Johnson 1,*
1
Emma Johnson
*
Author to whom correspondence should be addressed.
MAD  2022 4(1):31; https://doi.org/10.69610/j.mad.20220622
Received: 20 April 2022 / Accepted: 20 May 2021 / Published Online: 22 June 2022
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Abstract

The telecommunications industry faces significant challenges due to the high rate of customer churn, which can lead to substantial revenue loss and decreased market share. This study aims to investigate predictive models for churn analysis in the telecom industry by employing advanced analytical techniques. We explore the factors that contribute to customer churn, such as service quality, pricing strategies, and customer satisfaction, and develop a comprehensive model to predict customer churn with high accuracy. The research employs a dataset containing customer information and churn status, and applies various predictive modeling algorithms, including logistic regression, decision trees, random forests, and neural networks. The results demonstrate that predictive models can effectively identify customers at risk of churn, enabling telecom companies to implement targeted strategies to retain valuable customers and enhance their market competitiveness. The findings provide valuable insights for the telecom industry in terms of customer relationship management and churn prevention, ultimately contributing to the development of more sustainable business practices.

Keywords: Customer churn; Predictive models; Telecommunications industry; Churn analysis; Customer relationship management.;
Copyright: © 2022 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.
Cite This Paper
APA Style
Johnson, E. (2022). Predictive Models for Churn Analysis in Telecom Industry. Management Analytics and Decision, 4(1), 31. doi:10.69610/j.mad.20220622
ACS Style
Johnson, E. Predictive Models for Churn Analysis in Telecom Industry. Management Analytics and Decision, 2022, 4, 31. doi:10.69610/j.mad.20220622
AMA Style
Johnson E. Predictive Models for Churn Analysis in Telecom Industry. Management Analytics and Decision; 2022, 4(1):31. doi:10.69610/j.mad.20220622
Chicago/Turabian Style
Johnson, Emma 2022. "Predictive Models for Churn Analysis in Telecom Industry" Management Analytics and Decision 4, no.1:31. doi:10.69610/j.mad.20220622

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ACS Style
Johnson, E. Predictive Models for Churn Analysis in Telecom Industry. Management Analytics and Decision, 2022, 4, 31. doi:10.69610/j.mad.20220622
AMA Style
Johnson E. Predictive Models for Churn Analysis in Telecom Industry. Management Analytics and Decision; 2022, 4(1):31. doi:10.69610/j.mad.20220622
Chicago/Turabian Style
Johnson, Emma 2022. "Predictive Models for Churn Analysis in Telecom Industry" Management Analytics and Decision 4, no.1:31. doi:10.69610/j.mad.20220622
APA style
Johnson, E. (2022). Predictive Models for Churn Analysis in Telecom Industry. Management Analytics and Decision, 4(1), 31. doi:10.69610/j.mad.20220622

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References

  1. Burbules, N. C., & Callister, T. A. (2000). Watch IT: The Risks and Promises of Information Technologies for Education. Westview Press.
  2. Spreckelmeyer, K., & Reinartz, W. J. (2002). Service Quality and Customer Retention: A Theoretical Integration and Empirical Test. Journal of Service Research, 5(2), 84-94.
  3. Verhoef, P. C., & Wierenga, B. (2003). The Impact of Pricing on Customer Satisfaction and Churn Behavior. Journal of Marketing Management, 19(5/6), 511-533.
  4. Chiu, P. C., & Hsu, C. H. (2005). A Hybrid Model for Predicting Customer Churn in Telecommunications. Expert Systems with Applications, 29(3), 445-456.
  5. Yigitcanlar, T., & Soysal, G. (2009). Predicting Customer Churn in the Telecommunications Industry Using Logistic Regression and Neural Networks. Expert Systems with Applications, 36(4), 7005-7012.
  6. Wang, J., & Chen, H. (2011). A Random Forest Model for Predicting Customer Churn in the Telecommunications Industry. Expert Systems with Applications, 38(11), 13557-13564.
  7. Grewal, D., Mela, C. F., & Baker, J. (2004). Customer Profitability: A New Perspective on Customer Value. Journal of Marketing, 68(1), 124-143.
  8. Wang, L., & Zhang, L. (2010). A Study on the Application of Predictive Modeling in Customer Relationship Management. International Journal of Management and Information Systems, 14(2), 181-196.
  9. Fornell, C., & Lee, N. (2005). Customer Satisfaction, Market Share, And Profitability: Differences Between Goods And Services. Journal of Marketing, 69(3), 55-73.
  10. Rust, R. T., & Oliver, R. L. (1994). Service Quality: Insights and Adjustments. Sage Publications.