Predicting customer abandonment in recurrent neural networks using short-term memory

• Autores: Beltozar-Clemente, Saul, Iparraguirre-Villanueva, Orlando, Pucuhuayla-Revatta, Félix, Zapata-Paulini, Joselyn, Cabanillas-Carbonell, Michael
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Autónoma del Perú
• Repositorio: AUTONOMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.autonoma.edu.pe:20.500.13067/3175
• Enlace del recurso: https://hdl.handle.net/20.500.13067/3175; https://doi.org/10.1016/j.joitmc.2024.100237
• Nivel de acceso: acceso abierto
• Materia: Deep learning; LSTM; Churn prediction; Customer churn; Telecommunication; https://purl.org/pe-repo/ocde/ford#2.02.04

Customer retention, a critical business priority, has become a growing concern, especially in the telecommunications industry. This study addresses the need to anticipate and understand customer churn through the application of Deep Learning models. The central focus of the research was the development and evaluation of a short-term memory model (LSTM) specifically designed to predict customer leakage. The choice of LSTM as the mainstay of the research is based on its proven ability to model long-term dependencies in sequences, its resilience to recurrent challenges in neural networks, and its success in various sequence prediction tasks. The model implementation, configured sequentially with Keras, comprised of an initial LSTM layer of 64 units, followed by a 20% removal layer to mitigate overfitting. The second LSTM layer, with 32 units, was supplemented with another elimination layer. Model training was conducted using a dataset consisting of 20 attributes and 4250 records. The model evaluation was based on crucial measures such as precision, accuracy, sensitivity and F1 count, revealing exceptional results with 95% performance on all metrics. This study, therefore, highlights the effectiveness of the LSTM model in predicting customer churn, providing companies with a valuable tool to improve retention and mitigate associated losses.