Enhancing E-Business Communication with a Hybrid Rule-Based and Extractive-Based Chatbot

Abstract

E-businesses often face challenges related to customer service and communication, leading to increased dissatisfaction among customers and potential damage to the brand. To address these challenges, data-driven and AI-based approaches have emerged, including predictive analytics for optimizing customer interactions and chatbots powered by AI and NLP technologies. This study focuses on developing a hybrid rule-based and extractive-based chatbot for e-business, which can handle both routine and complex inquiries, ensuring quick and accurate responses to improve communication problems. The rule-based QA method used in the chatbot demonstrated high precision and accuracy in providing answers to user queries. The rule-based approach achieved impressive 98% accuracy and 97% precision rates among 1684 queries. The extractive-based approach received positive feedback, with 91% of users rating it as “good” or “excellent” and an average user satisfaction score of 4.38. General user satisfaction was notably high, with an average Likert score of 4.29, and 54% of participants gave the highest score of 5. Communication time was significantly improved, as the chatbot reduced average response times to 41 s, compared to the previous 20-min average for inquiries.