Abstract

The retail landscape thrives on the synthesis of advanced analytics and business intelligence techniques, pivotal in navigating the complexities of consumer behavior and market dynamics. This study addresses the imperative to optimize retail strategies by leveraging historical sales data from 45 diverse stores with multifaceted departments. The challenge of predicting retail sales prices guided our methodology, employing convolutional neural network architectures and Root Mean Square Error (RMSE) as the principal error metric. Through iterative computations and feature extractions, our model aimed to discern intricate patterns and correlations within the retail domain, underpinning strategic decision-making processes. Analysis of the integrated methodologies illuminated critical insights into the intricate interplay of factors impacting retail operations. The findings underscored the significance of these techniques in informing strategic decisions, highlighting their potential to elevate sales performance and operational efficiencies. Our study culminates in advocating for the application and refinement of predictive models across diverse retail contexts, proposing further research into real-time application and interpretability methods.