An improved convolutional neural network for churn analysis

Gopal, Priya; Mohdnawi, Nazri Bin

Abstract

The significance of customer churn analysis has escalated due to the increasing availability of relevant data and intensifying competition. Researchers and practitioners are focused on enhancing prediction accuracy in modeling approaches, with deep neural networks emerging as appealing due to their robust performance across domains. However, the computational demands surge due to the challenges posed by dimensionality and inherent characteristics of the data. To address these issues, this research proposes a novel hybrid model that strategically integrates Convolutional Neural Networks (CNN) and a modified Variational Autoencoder (VAE). By carefully adjusting the parameters of the VAE to capture the central tendency and range of variation, the study aims to enhance the effectiveness of classifying high-dimensional churn data. The proposed framework's efficacy is evaluated using six benchmark datasets from various domains, with performance metrics encompassing accuracy, f1-score, precision, recall, and response time. Experimental results underscore the prowess of the hybrid technique in effectively handling high-dimensional and imbalanced time series data, thus offering a robust pathway for enhanced churn analysis.

• Journal Article Type: Article
• Publication Date: 2023
• Deposit Date: Jan 8, 2025
• Publicly Available Date: Jan 30, 2025
• Journal: (IJACSA) International Journal of Advanced Computer Science and Applications
• Print ISSN: 2158-107X
• Electronic ISSN: 2156-5570
• Publisher: SAI Organization
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 9
• Pages: 204-210
• DOI: https://doi.org/10.14569/IJACSA.2023.0140921
• Keywords: Customer churn analysis; deep learning; variational autoencoder; convolutional neural networks; dimensionality reduction
• Public URL: https://uwe-repository.worktribe.com/output/13604437