Abstract

The retail industry has undergone a significant transformation over the last few decades, primarily fueled by rapid technology adoption and advancing consumer interests. In an expanding digital landscape, novel technologies like social media, mobile applications, and analytics have dramatically altered how consumers shop. Today, consumers have developed a penchant for personalized experiences, on-demand services, and instant information gratification. Consequently, retailers are compelled to craft more individualized shopping experiences by developing collaborations across various touchpoints and channels. The emergence of shopping channels allows consumers to be both active customers and online audience members. The growing online presence and varied shopping behavior create a massive volume of information about consumers and their preferences, referred to as 'big data.' This information, when harnessed intelligently, can significantly benefit retailers by generating insights into customer preferences and past behavior. A well-harnessed big data infrastructure enables retailers to tailor their communications and target specific customers adeptly. The advancements in artificial intelligence and machine learning fields over the last decade have provided novel opportunities for retailers to exploit big data for personalization. Data mining techniques, including those derived from the multidisciplinary field of statistical natural language processing, can be harnessed for this purpose. In this work, the impact of big data, AI, and ML on the retail sector and the rise of personalized retail experiences is discussed. This also presents a background of the stimulating factors that prompted personalized retail experiences and an overview of the current vision of big data and AI and ML use in retail scenarios.