Analyse Customer Behaviour and Sentiment Using Natural Language Processing (NLP) Techniques to Improve Customer Service and Personalize Banking Experiences

Abstract

In the ever-evolving landscape of banking services, the quest for superior customer experiences and personalized interactions remains paramount. Leveraging Natural Language Processing (NLP) techniques, this research endeavors to dissect customer behavior and sentiment to enhance service delivery and tailor banking experiences. Through a comprehensive literature review, we trace the evolution of customer service in banking and highlight the pivotal role of NLP in analyzing customer sentiment and behavior. Methodologically, we outline data collection strategies and NLP methodologies for sentiment analysis and customer behavior analysis. Employing real-world datasets, we conduct sentiment analysis to gauge customer sentiments across various banking touchpoints and delve into customer behavior analysis to unveil patterns and preferences. Findings reveal actionable insights for banking institutions to improve service delivery and offer personalized experiences. By integrating NLP-powered analytics into banking operations, institutions can foster deeper customer relationships, driving competitive advantage and long-term sustainability. This research not only contributes to the burgeoning field of NLP applications in banking but also serves as a catalyst for future research endeavors aimed at redefining customer-centric banking paradigms.