Journal article 221 views 55 downloads
A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
PLOS ONE, Volume: 17, Issue: 12, Start page: e0278095
Swansea University Author: Abedin Abedin
-
PDF | Version of Record
© 2022 Sana et al. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0).
Download (2.7MB)
DOI (Published version): 10.1371/journal.pone.0278095
Abstract
Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM) data through the use of various machine learning models and data transformation methods to identify the customers who are likely t...
| Published in: | PLOS ONE |
|---|---|
| ISSN: | 1932-6203 |
| Published: |
Public Library of Science (PLoS)
2022
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa64261 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract: |
Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM) data through the use of various machine learning models and data transformation methods to identify the customers who are likely to churn. While several studies have been conducted in the customer churn prediction (CCP) context in TCI, a review of performance of the various models stemming from these studies show a clear room for improvement. Therefore, to improve the accuracy of customer churn prediction in the telecommunication industry, we have investigated several machine learning models, as well as, data transformation methods. To optimize the prediction models, feature selection has been performed using univariate technique and the best hyperparameters have been selected using the grid search method. Subsequently, experiments have been conducted on several publicly available TCI datasets to assess the performance of our models in terms of the widely used evaluation metrics, such as AUC, precision, recall, and F-measure. Through a rigorous experimental study, we have demonstrated the benefit of applying data transformation methods as well as feature selection while training an optimized CCP model. Our proposed technique improved the prediction performance by up to 26.2% and 17% in terms of AUC and F-measure, respectively. |
|---|---|
| Keywords: |
Customer churn, telecommunication industry, customer relationship management |
| College: |
Faculty of Humanities and Social Sciences |
| Issue: |
12 |
| Start Page: |
e0278095 |