A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy

• Published in: Connection science Vol. 34; no. 1; pp. 1855 - 1876
• Main Authors: Sudharsan, R., Ganesh, E. N.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 31.12.2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: brownian motion clustering large application; butterfly optimisation algorithm; Classification; Competition; Criteria; Customer churn prediction; Customers; Deep learning; Markets; Monitoring; Neural networks; recurrent neural network (RNN); Recurrent neural networks; swish activation function
• ISSN: 0954-0091; 1360-0494
• DOI: 10.1080/09540091.2022.2083584

Owing to saturated markets, fierce competition, dynamic criteria, along with introduction of new attractive offers, the considerable issue of customer churn was faced by the telecommunication industry. Thus, an efficient Churn Prediction (CP) model is required for monitoring customer churn. Therefore, this work proposes a novel framework to predict customer churn through a deep learning model namely Swish Recurrent Neural Network (S-RNN). Finally, SRNN is adapted to classify the Churn Customer (CC) and a normal customer. If the result is a churn customer, network utilisation history is analysed for retention process. Whereas, the number of churn customers based on the area network usage is not recognised in this frameworkOwing to saturated markets, fierce competition, dynamic criteria, along with introduction of new attractive offers, the considerable issue of customer churn was faced by the telecommunication industry. Thus, an efficient Churn Prediction (CP) model is required for monitoring customer churn. Therefore, this work proposes a novel framework to predict customer churn through a deep learning model namely Swish Recurrent Neural Network (S-RNN). Finally, S-RNN is adapted to classify the Churn Customer (CC) and a normal customer. If the result is a churn customer, network utilisation history is analysed for retention process.