ENHANCING FASHION FORECASTING ACCURACY THROUGH CONSUMER DATA ANALYTICS: INSIGHTS FROM CURRENT LITERATURE

Abstract

The fashion industry is characterized by its fast-paced nature and constant evolution of consumer preferences, making accurate fashion forecasting essential for brands to remain competitive. Traditional forecasting methods, which rely heavily on historical sales data and expert intuition, are increasingly being complemented or replaced by advanced consumer data analytics. This article explores the integration of consumer data analytics into fashion forecasting, drawing insights from recent literature. By examining methodologies such as machine learning, big data analytics, and AI, as well as utilizing diverse data sources including social media, online shopping behaviors, and mobile data, this study highlights the significant improvements in trend prediction accuracy and operational efficiency. Key findings indicate that data-driven approaches provide more precise and real-time insights into consumer preferences, enabling brands to better anticipate market demands and optimize inventory management. The discussion underscores the transformative potential of consumer data analytics in enhancing the overall effectiveness of fashion forecasting.