Enhancing Fashion Retail Sales Optimization

Abstract

This study delves into the critical realm of sales optimization in the fashion industry, employing machine learning techniques to forecast monthly sales and optimize production quantities, with a specific focus on standard clothing sizes. Leveraging the Bagging Classifier algorithm, the research achieves a commendable accuracy range of 75% to 85%, providing fashion businesses with invaluable insights to enhance inventory management, pricing strategies, and targeted marketing efforts. Additionally, the study introduces pivotal components including shopping cart management, discount management, and product rating, all aimed at improving the overall customer experience while gleaning essential data on customer preferences and behaviours. However, limitations, such as data reliance on an external e-commerce platform and potential inaccuracies in size mapping, are acknowledged. Despite these challenges, this research presents a comprehensive framework to empower fashion enterprises in aligning production with consumer demand, optimizing resource allocation, and establishing a competitive edge in the ever-evolving fashion landscape, with opportunities for future research to delve into finer customer segmentation and the consideration of additional factors impacting sales trends.