Customer Segmentation Analysis with RFM Model (Recency, Frequency, Monetary) and K-Means Clustering: Case Study of Bottled Water Sales at PT XYZ
Keywords:
Customer Segementation, Data Mining, K-Means Clustering, Marketing Strategy, RFMAbstract
Customer segmentation is a crucial process in understanding consumer behavior patterns to support strategic decision making in marketing. The main challenge faced by companies is to accurately group customers based on transaction data. The purpose of this study is to find out and segment customers using the algorithm K-Means clustering based on RFM model (Recency, Frequency, Monetary) on Bottled Water sales transaction data at PT XYZ. The research method involves analysis of 111 customer data processed using software Orange Data Mining, with validation of results using Silhouette Score which is useful in determining the amount cluster ideal. This research produced four cluster customers, with Cluster 4 reflects customers with the highest level of loyalty, marked by a value Frequency And Monetary the dominant one, while Cluster 3 describes customers with low loyalty potential. The results of this study provide a scientific basis for the development of more focused and efficient data-based marketing strategies.
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