## 4 Results
### Clustering Parameter Selection and Result of Cluster
Similarly, the \"method\" and \"cluster number\" factors show a significant impact. The result for the \"method\" factor (F = 6.88, \(p\) = 0.011) indicates that the specific clustering method employed, such as K-means clustering or Ward's method, plays a crucial role in determining the quality and accuracy of the resulting clusters. The result for the \"cluster number\" factor (F = 4.7, \(p\) < 0.001) indicates that the number of clusters chosen for the analysis has a substantial effect on the clustering outcomes and the ability to group customers based on their characteristics effectively. Additionally, the \"metrics\" factor significantly influences customer-oriented clustering (F = 423.32, \(p\) < 0.001). The choice of evaluation metrics, such as Sil, CH, or DB, greatly affected the assessment and comparison of different clustering models' performance. In addition, there are significant effects generated based on the interaction of the factors. For example, the combination of \"model\" and \"metrics\" showed a significant result (\(F\) = 260.08; \(p\) = 0.000), meaning that the selection of both factor combinations also gives a significant effect. This result suggests selecting both combinations, such as RFM, MPCEI, or FMP, with CH, DB, or Sil simultaneously. Another example is the \"method\" and \"metrics\" combination, which also showed significant results (\(F\) = 6.000, \(p\) = 0.004), meaning that the combination of both factors gives a significant additional interaction effect to the clustering quality. The result implies selecting both simultaneously, such as K-means clustering or Ward method, with CH, DB, or Sil. Finally, the \"cluster number\" and \"metrics\" combination caused a significant effect on clustering quality (\(F\) = 6.57, \(p\) = 0.000). This result shows that if the combination of both factors is chosen, a better clustering result will be obtained, for example, CH, DB, or Sil combined with the number of clusters from 2 to 10. Moreover, the combinations of model and metrics, method and metric, and cluster number and metric exhibit significant interaction effects on the clustering process. Based on these results, further analysis using the analysis of mean approach was conducted to determine the optimal parameters for the clustering process. Figure 4 presents the analysis of the mean for the model and metric in the customer-oriented group. Based on the literature review, the Davies-Bouldin index (DB) and silhouette index (Sil) are considered better when following the \"bigger is better\" rule. Conversely, the Calinski-Harabasz index (CH) is better when following the \"less is better\" rule. Applying these rules, the MCEIP model demonstrates the best result, exhibiting the highest Sil and the lowest CH. Both models yielded similar results for the DB. The RFM model is thus rejected due to its poorer performance with a high CH value and a low DB value. Furthermore, the choice of metric significantly impacted the evaluation of clustering results. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Source** & **DF** & **F** & \(p\)**-Value** \\ \hline Model & 2 & 149.74 & 0.000 \\ Method & 1 & 6.88 & 0.011 \\ Cluster Number & 8 & 4.7 & 0.000 \\ Metrics & 2 & 423.32 & 0.000 \\ Model and Method & 2 & 0.53 & 0.592 \\ Model and Cluster Number & 16 & 0.8 & 0.680 \\ Model and Metric & 4 & 260.08 & 0.000 \\ Method and Cluster Number & 8 & 0.4 & 0.914 \\ Method and Metric & 2 & 6 & 0.004 \\ Cluster Number and Metric & 16 & 6.57 & 0.000 \\ Model and Method and Cluster Number & 16 & 0.11 & 1.000 \\ Model and Method and Metric & 4 & 0.47 & 0.761 \\ Model and Cluster Number and Metric & 16 & 0.21 & 0.999 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: ANOVA Result for Customer-Oriented Group. In this study, SI was chosen instead of CH for its simplicity and compatibility with the \"bigger is better\" rule, which provides a clearer representation of clustering performance. Figure 5 illustrates the results of the analysis of mean for method and metric in the customer-oriented group.