Shaping the Future of Retail

Abstract

To succeed in today's fast-paced retail industry, businesses must be able to predict their customers' actions. The goal of this study is to improve the accuracy of customer behaviour forecasts through the development of retail prediction analytics models. By applying state-of-the-art data analytics and machine learning methods, this study aims to understand better how to build and use predictive models that can foresee consumer behaviour, preference, and trend adoption. Researchers begin by looking at predictive analytics and how it may help the retail industry. It explains why retailers can't reliably forecast future customer behaviour using current data and analytics. The authors also examine some potential benefits of using more powerful prediction models. Some of the methods and algorithms studied in this study include those used for customer segmentation, sales forecasting, and churn prediction. It does this by performing a comprehensive study of the related literature.