Master Stockist Customer Segmentation Using RFM Model and Self-Organizing Maps Algorithm

Master Stockist PT SNS 21 Bali struggles to identify member performance based on purchasing behavior because the applicable system only records transactions and stock of goods without providing insight into customers. Customer segmentation can be carried out to identify and understand differences in customer purchasing behavior. Therefore, this study aims to determine customer segmentation using the RFM (Recency, Frequency, Monetary) model and the Self-Organizing Maps (SOM) algorithm. Segmentation development uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) approach. The RFM model numerically represents customer behavior through three variables, while the Self-Organizing Maps algorithm groups customers into segments with similar characteristics. In this research, the best SOM parameters are 750 iterations, learning rate 0.5, radius 0.5, and grid size 1x3, resulting in 3 clusters with a Silhouette Score of 0.647608 and a Davies-Bouldin Index of 0.536503. Cluster 1 consists of 226 new customers with low RFM values who need encouragement to be more active. Cluster 2, comprising seven members, has low recency, high frequency, and high monetary values, representing loyal customers who need to be retained. Cluster 3 consists of 239 inactive customers with high recency, low frequency, and low monetary values, requiring a reactivation strategy.