Leveraging Neural Networks and Natural Language Processing for Enhanced Customer Insights in AI-Powered CRM Systems

Abstract

This research paper explores the integration of neural networks and natural language processing (NLP) to enhance customer insights within AI-powered customer relationship management (CRM) systems. As businesses increasingly seek to tailor customer interactions, the demand for advanced analytical tools has grown. The proposed framework leverages deep learning models, specifically recurrent neural networks (RNNs) and transformer architectures, to analyze unstructured data such as customer reviews, emails, and social media interactions. By employing sentiment analysis and topic modeling, the system identifies key customer concerns and preferences, allowing for more personalized and proactive engagement strategies. Additionally, the paper evaluates the effectiveness of these models in real-time data processing, highlighting improvements in the accuracy and relevance of customer insights over traditional CRM methods. Experimental results from implementing the system in a multi-industry dataset demonstrate significant enhancement in customer satisfaction scores and retention rates. The findings underscore the potential of these technologies to revolutionize CRM by transforming raw data into actionable intelligence, thereby fostering deeper customer relationships and driving competitive advantage. This study contributes to the field by providing a comprehensive architecture for integrating advanced AI techniques into CRM platforms, setting the stage for future innovations in customer experience management.