<![CDATA[In the digital era, transaction data analysis has become crucial for enhancing business competitiveness. PT XYZ, a steel distributor in Palembang City, faces challenges in understanding customer purchasing patterns, which impacts the optimization of marketing strategies and sales volume. This study aims to analyze customer purchasing patterns using the Apriori algorithm to develop more effective marketing strategies. The research follows the CRISP-DM methodology, which includes data collection, cleaning, analysis, and visualization through an interactive dashboard. Sales transaction data from August 2023 to August 2024 is analyzed to identify frequently purchased product combinations. The results indicate that the Apriori algorithm generates association rules that can be leveraged to design bundling packages that align with customer preferences. With the interactive dashboard, the analysis results can be visually presented, supporting faster and more accurate business decision-making. This study offers a data-driven strategy to improve operational efficiency, optimize sales, and strengthen the company's competitiveness.]]>
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