Personalized algorithm is a set of complex mathematical models that use various techniques such as machine learning and data mining. Individuals’ personal information is collected and processed in various ways to create personalized experiences. This article aims to have a thorough outlook of personalization in a varied retail touchpoint. The study initiated the work by conducting comprehensive systematic literature review of personalization by utilizing the techniques of science mapping, performance analysis and formulating the PRISMA model framework. A systematic review was done using two significant databases, Scopus and Web of Science, based on which significant themes were identified. At the end, study judiciously concludes by offering the future research direction to incorporate the technological advancements in the form of big data analysis, Internet of Things, artificial intelligence and humanoid shopping assistant to lay out the adaptation and feasibility of personalization in the omnichannel retailing.
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