Source of Publication
Customer churn, a phenomenon that causes large financial losses when customers leave a business, makes it difficult for modern organizations to retain customers. When dissatisfied customers find their present company's services inadequate, they frequently migrate to another service provider. Machine learning and deep learning (ML/DL) approaches have already been used to successfully identify customer churn. In some circumstances, however, ML/DL-based algorithms lacks in delivering promising results for detecting client churn. Previous research on estimating customer churn revealed unexpected forecasts when utilizing machine learning classifiers and traditional feature encoding methodologies. Deep neural networks were also used in these efforts to extract features without taking into account the sequence information. In view of these issues, the current study provides an effective method for predicting customer churn based on a hybrid deep learning model termed BiLSTM-CNN. The goal is to effectively estimate customer churn using benchmark data and increase the churn prediction process's accuracy. The experimental results show that when trained, tested, and validated on the benchmark dataset, the proposed BiLSTM-CNN model attained a remarkable accuracy of 81%.
Springer Science and Business Media LLC
Customer churn, Deep learning, BiLSTM-CNN, Churn prediction
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Khattak, Asad; Mehak, Zartashia; Ahmad, Hussain; Asghar, Muhammad Usama; Asghar, Muhammad Zubair; and Khan, Aurangzeb, "Customer churn prediction using composite deep learning technique" (2023). All Works. 6131.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series