AI-Powered Customer Segmentation in Grocery Retail: Leveraging Big Data for Hyper-Personalization
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© 2025 by IJCTT Journal
Volume-73 Issue-4
Year of Publication : 2025
Authors : Abhirup Mazumder
DOI :  10.14445/22312803/IJCTT-V73I4P111

How to Cite?

Abhirup Mazumder, "AI-Powered Customer Segmentation in Grocery Retail: Leveraging Big Data for Hyper-Personalization," International Journal of Computer Trends and Technology, vol. 73, no. 4, pp. 79-87, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I4P111

Abstract
Customer segmentation is a major concept in contemporary grocery retailing since it creates distinction according to customers’ needs and wants. The past segmentation approaches are based on demographic data and, more often, on the transactional data known to the organization, thus providing a limited view of customer behavior. The advantages of AI and Big Data for the segmentation strategy are that they are more sophisticated segmentation that reflects individual consumer behavior, psychographic factors, and immediate purchase data. This paper aims to highlight customer segmentation using AI where techniques covered are specifically clustering models like K-Means, DBSCAN, deep learning, and NLP. The paper also outlines how AI influences hyper-personalization in grocery retail in the context of processing a large number of customers and providing special discounts. The research develops an AI-based segmentation approach that uses both supervised and unsupervised learning to improve the segmentation accuracy further. The accuracy of the segmentation has been enhanced, as supported by the experimental findings, thus translating to better sales and customer loyalty. The paper also brings some drawbacks of this approach, such as data privacy, computational cost, and model interpretation. The idea of the new work on retailer segmentation through AI is the best example of how Big Data can revolutionize retail personalization and define new ways of effective marketing.

Keywords
AI-Driven segmentation, Big Data analytics, Grocery retail, Hyper-personalization, Machine Learning, Customer behavior, Clustering algorithms.

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