Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/2284 2019 2019 Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes (UNIANDES), Equador Luis Llerena Ocaà Ocaña Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes (UNIANDES), Equador Dionisio Ponce Ruiz Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes (UNIANDES), Equador Betty Valle Fiallos 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. 2024 2024 75 83 10.54216/JISIoT.110108 https://www.americaspg.com/articleinfo/18/show/2284