This study focuses on analyzing retail store sales data using data analytics and visualization techniques to understand overall business performance. In today’s competitive environment, organizations rely on data-driven decision-making to improve profitability, efficiency, and customer satisfaction. The main objective of this study is to evaluate sales performance by examining key factors such as region, product category, customer segment, discount strategy, and profit.

The dataset used in this research consists of transactional sales data, including details such as orders, customers, product categories, sales amount, quantity, discounts, and profit. Various data visualization techniques, including bar charts, line graphs, and comparative analysis, were used to identify patterns and trends within the data.

The analysis reveals that sales performance differs across regions and product categories. It was observed that higher sales do not always result in higher profit, especially in cases where heavy discounts are applied. The study also highlights the importance of customer segmentation in improving marketing strategies and business outcomes.

Overall, the findings demonstrate that data analytics can help businesses convert raw data into meaningful insights. These insights support better decision-making, improve profitability, and enhance overall business performance.

Keywords— Data Analytics; Sales Performance Analysis; Retail Business; Data Visualization; Business Intelligence; Profitability Analysis