A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Sudharsan, R.; Ganesh, E. N.
• Is Part Of: Connection science. Volume 34:Issue 1(2022)
• Page Start: 1855
• Page End: 1876
• Publication Date: 2022-12-31
• Subjects: Customer churn prediction -- butterfly optimisation algorithm -- recurrent neural network (RNN) -- brownian motion clustering large application -- swish activation function; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2022.2083584
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : 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.