Developing AI Models for Personalized Shopping Experiences in Retail

Abstract

Personalization is often seen as a critical element in shaping customer satisfaction and loyalty, especially in the context of shopping experiences. Many personalization methods for just-in-time goods have been extended into e-commerce platforms where they can notify users when an event happens that would demand a purchase to be made. Therefore, with significant advancements in technology, retailers are now quickly evolving and trying to offer better experiences to shoppers by personalizing recommendations for their needs and requirements. In earlier days, the retail approach did not add the delight factor after understanding the choices of shoppers, since a manual method of recommendation delivery was not correct and efficient. Nowadays, these preferences have moved largely from demographic to personalized retail partnerships, making it necessary to use state-of-the-art algorithms. AI has revolutionized the retail sector in terms of decision-making. This is all possible thanks to the use of decision-making methodologies. Major companies use big data to analyze customer purchasing habits, as studying consumer behavior patterns is difficult for out-of-range employees. This preference data will help retailers grasp trends and decide which products to endorse and set area standards. Helping a customer identify their choices and needs has always been interesting. This cannot work out if the seller appears to know nothing about the needs and preferences of the consumers.