Natural Language Processing-Enabled Customer Engagement Interface for Retail Chatbot Systems

This paper features a Natural Language Processing (NLP) powered customer interaction interface that would be useful in increasing the use of retail chatbot capabilities in terms of carrying out natural dialogues. Using the most technologically-advanced NLP technologies, including BERT, GPT, and sentiment analysis models, the system is able to understand and react to customer requests in the contextual meaning and with an emotional understanding. The interface links to retail databases and provides them with individual product recommendations, solves complaints, and automates customer service. The integration of intent recognition, entity extraction, and conversational memory enables the chatbot to provide high quality and interactive experiences to ensure customer satisfaction and the efficient nature of operations. The present paper discusses the architecture of the system, methodology and performance indicators that prove the potential of the NLP technologies to transform the digital merchandising of the retailer and redesign the models of consumer interaction.