ABSTRACT

This study focused on the development of a Customer Relationship Management (CRM) system integrated with AI-powered sentiment analysis to enhance customer engagement and satisfaction. By utilizing machine learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, the system analyzes customer feedback to provide real-time sentiment interpretation. Built using the Django framework, the CRM system delivers insights into customer emotions, enabling businesses to make data-driven decisions that improve service quality and customer retention. The research included a detailed comparative analysis of different models, demonstrating that the CNN-LSTM hybrid model outperforms traditional approaches in accuracy, precision, and recall. Additionally, the system’s compliance with ISO/IEC 25010 software quality standards ensures functional suitability, performance efficiency, and security. This study also examined the challenges of deploying AI-driven CRM systems in markets such as China and the Philippines, were local cultural and technological differences impact implementation. The findings highlight the potential of AI in transforming CRM systems into intelligent platforms that anticipate customer needs, optimize business strategies, and drive customer loyalty. This study provides actionable recommendations for improving AI-enhanced CRM systems, including the use of advanced natural language processing (NLP) techniques and expanding data sources to include social media and real-time interactions.

Keywords: Customer relationship management (CRM) AI sentiment analysis, convolutional neural network (CNN), long short-term memory (LSTM), customer feedback analysis
https://doi.org/10.57180/duix4514