A Demand Forecasting Model Leveraging Machine Learning to Decode Customer Preferences for New Fashion Products

Abstract

Demand forecasting for new products in the fashion industry has always been challenging due to changing trends, longer lead times, seasonal shifts, and the proliferation of products. Accurate demand forecasting requires a thorough understanding of consumer preferences. This research suggests a model based on machine learning to analyse customer preferences and forecast the demand for new products. To understand customer preferences, the fitting room data are analysed, and customer profiles are created. K-means clustering, an unsupervised machine learning algorithm, is applied to form clusters by grouping similar profiles. The clusters were assigned weights related to the percentage of product in each cluster. Following the clustering process, a decision tree classification model is used to classify the new product into one of the predefined clusters to predict demand for the new product. This demand forecasting approach will enable retailers to stock products that align with customer preferences, thereby minimising excess inventory.