## 4 Results
### Clustering Parameter Selection and Result of Cluster
First, frequency and CEI index have a significant negative correlation (\(-0.99\)). Products that were once more popular now sell less frequently. Also, the correlation between product _frequency_ and _periodicity_ is moderately low (\(-0.23\)). Products with a higher purchase frequency have longer sales intervals. Despite being sold frequently, some products do not maintain regular sales intervals. Furthermore, the correlation between _monetary_ and _CEI_ is negative but relatively weak (\(-0.35\)). In other words, higher-value products may have a lower customer engagement index. In other words, high profits sometimes indicate better customer engagement with the business. The relationship between _monetary_ and _periodicity_ is minimal. However, the relationship between _CEI_ and _periodicity_ is positive, with a \(0.24\) value. Accordingly, products with higher customer engagement index values have more regular purchase intervals. A popular product is more likely to generate sales consistently. As a result, at least one variable from each combination, including the RFM model, MCEIP model, and FMP model, was selected for the model. These correlations provide valuable insights into product-oriented customer behavior, aiding in the development of effective marketing strategies and decision-making processes. This study investigated the significance of each factor's impact on the selected models. To achieve this, Table 6 presents the experiment factors utilized in the analysis of variance (ANOVA). Careful consideration was given to the chosen metrics, clustering methods, and cluster numbers to evaluate the models' performances comprehensively. The study involved three distinct models: RFM (recency, frequency, and monetary), MPCEI (monetary, periodicity, and CEI index), and FMP (frequency, monetary, and periodicity). Every model was assessed using several metrics, such as Sil, CH, and DB. By conducting the ANOVA analysis, the researchers aimed to identify any significant differences or correlations between these factors and the model's performance. The ANOVA results provided valuable insights into the relative importance of each factor in the clustering process, enabling a better understanding of the strengths and weaknesses of each model under various conditions and configurations. Furthermore, Table 7 presents the ANOVA results for the customer-oriented group. The analysis indicates that all four variables significantly affected the clustering process. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Metrics** & **Methods** & **Cluster** & **Number** \\ \hline \multirow{3}{*}{RFM} & Silhouette Index & 2 & 3 & 4 \\  & [PERSON]–Harabasz Index & K-Means Clustering & 5 & 6 & 7 \\  & [PERSON]–Bouldin Index & Ward’s Method & 8 & 9 & 10 \\ \hline \multirow{3}{*}{MPCEI} & Silhouette Index & 2 & 3 & 4 \\  & [PERSON]–Harabasz Index & 5 & 6 & 7 \\  & [PERSON]–Bouldin Index & Ward’s Method & 8 & 9 & 10 \\ \hline \multirow{3}{*}{FMP} & Silhouette Index & 2 & 3 & 4 \\  & [PERSON]–Harabasz Index & 5 & 6 & 7 \\  & [PERSON]–Bouldin Index & 8 & 9 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experiment Factors for ANOVA. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Product** & **Frequency** & **Monetary** & **CEI** & **Periodicity** \\ \hline Frequency & \(1.00\) & \(0.02\) & \(-0.99\) & \(-0.23\) \\ Monetary & \(0.02\) & \(1.00\) & \(-0.35\) & \(-0.08\) \\ CEI & \(-0.99\) & \(-0.35\) & \(1.00\) & \(0.24\) \\ Periodicity & \(-0.23\) & \(-0.08\) & \(0.24\) & \(1.00\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlation Matrix for RFMCEIP Product-Oriented Group. Several factors show statistically significant effects on the clustering process. For example, the \"model\" factor exhibits a highly significant effect on customer-oriented clustering (\(F\) = 149.74, \(p\) < 0.001). This result suggests that the choice of clustering models, such as RFM, MPCEI, or FMP, significantly influences the overall performance of the clustering process.