AI-Powered Customer Engagement : How Personalization, Chatbots, and Recommendation Systems Influence E-commerce Customer Satisfaction?

Abstract

This thesis investigates how AI-powered personalization, chatbots, and recommendation systems influence customer satisfaction in e-commerce. The research question asks to what extent these AI tools enhance or negatively impact the online shopping experience for customers. To answer this, a mixed-method approach was used: quantitative survey of online shoppers and qualitative interviews with small e-commerce business owners. The survey data and interviews provide both a consumer perspective and a business viewpoint. Key findings indicate that consumers have positive perceptions of AI-driven personalization and recommendation systems, appreciating tailored content and product suggestions that make shopping more convenient. Conversely, AI chatbots were viewed negatively by a majority of respondents where most reported frustration or lack of improvement in their shopping experience with chatbots, preferring human customer service for complex queries. Additionally, privacy concerns emerged strongly. Consumers surveyed expressed strong worry about how their personal data is collected and used. These insights suggest that while AI tools can enhance customer satisfaction by providin convenience and individualized experiences, their implementation must be handled carefully. The conclusion drawn is that ecommerce firms can boost customer satisfaction with these AI-tools if they address chatbot limitations and privacy/transparency issues, ensuring these technologies truly serve customer needs without undermining trust or overall experience.