Abstract

Objectives: The research seeks to introduce a fresh method to improve the online shopping experience by incorporating diverse Artificial Intelligence (AI) strategies. The goals include facilitating personalized shopping experiences, lowering customer churn in e-commerce platforms, and delivering efficient product suggestions. The study also aims to investigate the constraints and difficulties linked to integrating AI technologies in e-commerce. Methods: The research utilizes an extensive examination of AI methods such as ML, neural networks, fuzzy logic, and evolutionary computation, along with their use in personalized marketing, product suggestions, chatbots, and fraud detection. The study combines current literature, case studies, and practical applications from top e-commerce platforms like Amazon and Alibaba, examining the utilization of AI methods to enhance user satisfaction and operational productivity. Results: Incorporating AI methods in online business has displayed considerable promise in improving customer happiness, boosting revenue, and lowering operating expenses. Tailored recommendation systems and conversational agents have proven highly successful in enhancing user interaction and minimizing the time shoppers dedicate to product exploration. Still, obstacles like maintaining data privacy, connecting to current systems, and the high cost of implementing AI technology are major hindrances to wider acceptance, especially for smaller online retail sites. Conclusion: AI-powered solutions could completely transform the e-commerce industry by providing personalized, efficient, and secure shopping experiences. Yet, achieving success involves addressing obstacles concerning data accuracy, integrating systems, and privacy issues. Future studies should concentrate on creating AI solutions that are more scalable and can be easily implemented across e-commerce platforms of all sizes.