Enhancing Customer Relationship Management with Natural Language Processing: A Comparative Study of BERT and LSTM Algorithms

Abstract

This research paper explores the application of Natural Language Processing (NLP) in enhancing Customer Relationship Management (CRM) by examining the capabilities of two advanced algorithms: Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM). The study emphasizes the increasing need for robust NLP techniques to process and analyze vast amounts of customer interaction data, aiming to improve personalized customer experiences and operational efficiency. We conducted a comparative analysis of BERT and LSTM, focusing on their effectiveness in sentiment analysis, customer feedback categorization, and chat-based customer support automation. The research employed a comprehensive dataset gathered from various CRM systems, evaluating each algorithm's performance based on accuracy, processing time, and scalability. Our findings indicate that BERT outperforms LSTM in terms of accuracy and context understanding, attributed to its transformer-based architecture and bidirectional training approach. However, LSTM demonstrates superior efficiency in scenarios requiring lower computational resources and faster inference times, making it suitable for real-time applications. This paper concludes by discussing the trade-offs between these algorithms and proposes a hybrid model that leverages the strengths of both to optimize CRM processes, thereby offering valuable insights for organizations seeking to implement advanced NLP solutions in their customer engagement strategies.