Customer Profiling and Purchase Patterns Using K-Means and Apriori Algorithms
Keywords:
Apriori, Customer Segmentation, Data Mining, K-Means, Purchasing Pattern, RFMAbstract
This study aims to analyze customer segmentation and purchasing patterns at PT. Benteng Api Technic (BAT) using the K-Means and Apriori algorithms. Customer segmentation is based on the RFM (Recency, Frequency, Monetary) approach, which reflects customer purchasing behavior. The K-Means algorithm is applied to group customers into clusters with similar characteristics, while the Apriori algorithm is used to identify frequent product purchasing patterns within each cluster. The dataset used consists of sales transaction data from June 1, 2023, to June 30, 2024. The results show clear customer segmentation based on purchasing characteristics, and several associations between products frequently purchased together by customers in specific clusters were found. These findings are expected to help the company develop more targeted marketing strategies and improve inventory management efficiency.
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