<![CDATA[PT XYZ is a manufacturing company that produces various types of industrial valves. Despite having entered the export market, the company continues to experience monthly fluctuations in order volumes due to the suboptimal use of data in formulating effective sales strategies. This study aims to identify association rules using the Apriori algorithm as the basis for sales recommendations. The analysis was conducted on transaction data from January 2024 to February 2025, using a minimum support threshold of 20% and a minimum confidence threshold of 65%, determined through exploratory analysis. The results yielded 14 first-level (L1) rules, 21 second-level (L2) rules, and 8 third-level (L3) rules, indicating associations between products that can inform cross-selling schemes and product sampling strategies. These patterns were used to design sales strategies, such as cross-selling and product bundling, to increase the average value per transaction, as well as product sampling to introduce less popular items. GV and CV products showed the strongest association, with a support value of 43%, a confidence level of 77%, and a lift value of 1.5—indicating a strong potential for increased sales when these products are offered together. These personalized strategy recommendations are expected to improve customer loyalty, expand market reach, and drive sustainable growth in the company’s sales volume.]]>
Copyrights © 2025
