Data transformation based optimized customer churn prediction model for the telecommunication industry
This is an incremental improvement for the telecommunications industry to better predict customer attrition.
The paper tackled customer churn prediction in telecommunications by combining data transformation methods with machine learning models, resulting in significant performance improvements as confirmed by comparisons and statistical tests on public datasets.
Data transformation (DT) is a process that transfers the original data into a form which supports a particular classification algorithm and helps to analyze the data for a special purpose. To improve the prediction performance we investigated various data transform methods. This study is conducted in a customer churn prediction (CCP) context in the telecommunication industry (TCI), where customer attrition is a common phenomenon. We have proposed a novel approach of combining data transformation methods with the machine learning models for the CCP problem. We conducted our experiments on publicly available TCI datasets and assessed the performance in terms of the widely used evaluation measures (e.g. AUC, precision, recall, and F-measure). In this study, we presented comprehensive comparisons to affirm the effect of the transformation methods. The comparison results and statistical test proved that most of the proposed data transformation based optimized models improve the performance of CCP significantly. Overall, an efficient and optimized CCP model for the telecommunication industry has been presented through this manuscript.