Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023)

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Customer Churn Prediction Based on Big Data and Machine Learning Approaches

Authors
Ziyu Zhu1, *
1Eli Broad College of Business, Michigan State University, Okemos, East Lansing, USA
*Corresponding author. Email: zhuziyu3@msu.edu
Corresponding Author
Ziyu Zhu
Available Online 15 May 2023.
DOI
10.2991/978-94-6463-142-5_2How to use a DOI?
Keywords
Customer Churn Prediction Model; Logistic Regression model; Prediction Algorithms; telecom
Abstract

Telecom companies are facing fierce competition in the market. For telecom operators, customers are living. Due to the high upfront investment in acquiring new customers, they prefer to retain existing customers rather than acquire new ones. The loss of old customers means that telecom operators are losing their share in the telecom market. To prevent customer churn, business analysts and customer relationship management (CRM) analysts need to understand and analyze the behavioral patterns of existing customer churn data. The study used three models (i.e., LGBM, Logistic Regression, and Random Forest) to construct a valid and accurate churn prediction model for the telecom industry. Furthermore, the empirical evaluation results suggest that Logistic Regression selected by the AUC metric is the most suitable model. These results provide an understanding of customer features and preferences to predict customers that might to be churn and the reasons for churn and to take preventive measures in advance.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023)
Series
Advances in Economics, Business and Management Research
Publication Date
15 May 2023
ISBN
978-94-6463-142-5
ISSN
2352-5428
DOI
10.2991/978-94-6463-142-5_2How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ziyu Zhu
PY  - 2023
DA  - 2023/05/15
TI  - Customer Churn Prediction Based on Big Data and Machine Learning Approaches
BT  - Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023)
PB  - Atlantis Press
SP  - 4
EP  - 15
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-142-5_2
DO  - 10.2991/978-94-6463-142-5_2
ID  - Zhu2023
ER  -