From Patterns to Personas: Navigating the InStore Apparel Retail Landscape via ML Based Customer Segmentation

This study examines implementing a machine-learning-based system for customer segmentation in the apparel retail industry through the use of image- processing technologies powered by sensors located in retail stores. In this study, analyses have been conducted based on two primary raw data that includes real- life footfall and purchase data from a retail store over a three-month period in 2024. In this study, a deeper understanding of customer behavior has been gained upon conducting data preprocessing and feature engineering techniques on raw datasets including footfall data capturing the temporal locations of each unique visitor and transaction data. Model design and training were performed, applying five widely used machine learning algorithms for customer segmentation both in train and test sets. The process also included testing, validation, and cluster analysis to derive meaningful customer insights. Thus, this study introduces a novel perspective on analyzing in-store customer behavior and its shopping patterns by focusing on the temporal tracking of customers’ locations within the store environment. Accordingly, the distinctive data preprocessing and feature engineering techniques enabled better understanding of customer behavior and let the decision-makers offer customers a more tailored journey while maximizing customer satisfaction, engagement, and business profitability.