## 4 Results
### Clustering Parameter Selection and Result of Cluster
The findings indicate no significant difference between K-means clustering and [PERSON]'s method in terms of the clustering method. However, K-means clustering was selected for its flexibility and ease of use. Figure 6 displays the results of the analysis of mean for cluster number and metric in the customer-oriented group. The analysis shows no significant effect of the \"cluster number\" factor, necessitating further analysis using the Sil metric to determine the optimal number of clusters for customer segmentation. Figure 4: ANOM Result for Model and Metric (Customer-Oriented Group). Figure 5: ANOM Result for Method and Metric (Customer-Oriented Group). Figure 8 shows the results of the analysis of mean for method and metric in the product-oriented group. The analysis revealed no significant difference between K-means clustering and [PERSON]'s method in terms of the clustering method. Therefore, K-means clustering was chosen based on its flexibility and ease of use. Figure 9 displays the results of the analysis of mean for cluster number and metric in the product-oriented group. The analysis indicates no significant effect of the \"cluster number\" factor, requiring further analysis using the Sil metric to determine the optimal number of clusters for product segmentation. Figure 10 illustrates the results of the Sil analysis for the customer-oriented and product-oriented groups. As mentioned, Sil follows the \"higher is better\" rule, implying that a higher value signifies better clustering performance. Better performance in the clustering process indicates the ability of clusters to effectively group data based on their character  
Figure 8: ANOM Result for Method and Metric (Product-Oriented Group). Figure 7: ANOM Result for Model and Metric (Product-Oriented Group). istics. Figure 10a demonstrates that the highest Sil value is obtained with two clusters, indicating that it is preferable. Figure 11 presents the results of the elbow method using the sum of squared error with Euclidean distance for the product-oriented group. The figure shows a significant drop from one cluster to three clusters, and from three clusters onwards, the decrease becomes more stable. The finding indicates that the optimal number of clusters representing the data characteristics in the product-oriented group is three. Figure 12 depicts the clustering results for both the customer-oriented and product-oriented groups using K-means clustering. Table 9 illustrates the results of clustering segmentation, which categorizes customer behavior into two groups: potential customers and loyal customers. Cluster 1, referred to as potential customers, exhibited a lower customer engagement index (CEI) mean value (0.986) compared to cluster 2 (0.988), indicating less activity. Additionally, the average duration between transactions (periodicity) for cluster 1 (132.77 days) was longer than that of cluster 2 (16.022 days). These results validate the CEI calculatio  
Figure 10: (**a**) Result of Silhouette Index for Customer-Oriented Group. (**b**) Result of Silhouette Index for Product-Oriented Group. Figure 9: ANOM Result for Cluster Number and Metric (Product-Oriented Group). to make repeat transactions every 16-17 days, while cluster 1 made repeat purchases after 132 days. In terms of monetary value, cluster 1 (USD 538.38) had a higher mean value than cluster 2 (USD 399.096), suggesting that customers in cluster 1 are more profitable for the company, whereas cluster 2 is more profitable in terms of transaction frequency. In the actual world, potential customers spend less, are less active, and wait longer between purchases than loyal customers. However, with the correct marketing plan, potential customers can turn into devoted patrons. A loyal customer is usually found as a frequent customer, while a potential customer will appear if there is any additional motivation, for example, if there is any promotion or needs at any certain time.