Advanced NLP and ML Techniques in E-Commerce: Enhancing Customer Experience with AI Chatbots

Due to the exponential growth of e-cornmerce, it has become important to come up with new concepts to improve the customer experience (CX) as well as manage the competition factors. This research aims to explore the advances made in applying AI in the area of chatbots with a specific focus on the e-commerce industry. Leaning from the principles of Natural Language Processing (NLP) and Machine Learning (ML), we created a high-performance chatbot capable of performing numerous customer service functions such as product inquiries, tracking order status, and addressing concerns. To do this, the data gathered by the current study is a broad dataset, including records of 50,000 customer interactions collected from different e-commerce platforms along with customer satisfaction rating, response time, and resolution success. The chatbot was trained using a supervised learning approach and evaluated on multiple performance parameters: response time, resolution rate, customer satisfaction, engagement rate, and cost savings. The results demonstrate that the chatbot achieved an average response time of 1.5 seconds, a resolution rate of 92.3%, an average customer satisfaction rating of 4.6 out of 5, an engagement rate of 78.5%, and monthly cost savings of $45,000. These findings underscore the chatbot's effectiveness in providing rapid, accurate, and personalized responses, leading to enhanced customer satisfaction and operational efficiency. A comparison of the study with existing modern approaches for constructing and training chatbots shows the effectiveness of the developed chatbot, predominantly in terms of the resolution rate.