Study of the effectiveness of chatbots in customer service on e-commerce websites

Abstract

Efficient customer service solutions are needed to improve virtual interactions as electronic commerce grows exponentially. AI and NLP-powered chatbots are a potential alternative. This research compares chatbots against human customer care agents on e-commerce platforms and investigates how machine learning algorithms might improve their effectiveness. This study examines customer service in the e-commerce market and optimizes operations for a major website. It discusses chatbot pros and cons and how machine learning algorithms may improve them. Developing a chatbot prototype involves qualitative and quantitative research methods, including surveys, interviews, and case studies. This study examined a specific e-commerce website using a case study. Machine learning, natural language processing, and sentiment analysis were used to analyze the data. The study shows that chatbots can provide customer assistance on e-commerce platforms, resulting in faster responses and 24/7 availability. Chatbots have limitations, such as difficulty understanding complex client inquiries and empathy. To overcome these limitations, customer service chatbots should be supplemented by human operators. Machine learning algorithms should be used to develop chatbot technology to improve chatbot customer assistance.

Keywords: chatbots, customer service, e-commerce, machine learning, artificial intelligence