<![CDATA[With the rise of digitalization and data growth, many business sectors are increasingly focused on data. As a result, developing a descriptive and predictive analytics dashboard is essential for extracting insights from complex datasets and supporting decision-making. This study addresses data analytics challenges in the retail industry by designing a dashboard system for analyzing and visualizing data. It also proposes future predictions for customer analytics, simplifying the interpretation of results from complex datasets obtained through business activities in the retail supply chain. These processes have implications for decision-making in areas such as customer segmentation, demographic analysis, and sales performance. The dashboard design includes a pre-processing stage for retail industry datasets and the creation of descriptive and predictive analytics models using clustering methods. It also simulates the development of these models into a comprehensive dashboard. The primary analysis employs the K-Means algorithm for RFM (Recency, Frequency, Monetary) analytics, customer segmentation, and demographic analysis. Results show that the use of this dashboard enhances the visualization of data, supporting decision-making processes related to marketing strategies, sales performance, and inventory management. By applying advanced analytic techniques to marketing strategies, supply chain management, and inventory planning, retail businesses can optimize their operations. This optimization is achieved through better data analytics tools.]]>
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