A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection.

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 te

SUBMITTER: Sana JK 

PROVIDER: S-EPMC9714823 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature