Analyzing Consumer Purchasing Behavior in Electrical Supply Stores Using Association Rules
DOI:
https://doi.org/10.35335/8ed7jg14Keywords:
Apriori, Association Rules, Confidence, EDA, SupportAbstract
This study aims to improve inventory management and to analyze consumer purchasing behavior through data exploration and the application of association rule mining. The dataset used in this research consists of sales transaction records of electrical products collected over a one-year period. Due to the wide variety of items sold, product categorization is conducted to support more effective analysis and interpretation of purchasing patterns. The method applied in this study is association rule mining using the Apriori algorithm. This method is employed to discover relationships and co-occurrence patterns among items in transaction data. The minimum thresholds used in this study are support ≥ 10% and confidence ≥ 30%, ensuring that only significant and reliable association rules are generated. The results of the analysis reveal several important patterns, with the strongest rule identified as: “Lakban, Switch, and Socket → Cable,” which has a confidence value of 46%. This indicates that customers who purchase Lakban, switches, and sockets have a 46% likelihood of also purchasing cables. The findings provide insights into customer purchasing behavior that can be utilized to optimize inventory control, improve product arrangement, and develop effective cross-selling strategies. Furthermore, this study demonstrates that the application of association rule mining can support data-driven decision-making, enhance operational efficiency, and contribute to increased sales performance and customer satisfactionReferences
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