## 6 Conclusions  
This research endeavored to construct a precise and targeted recommendation system rooted in an in-depth understanding of customer-purchasing and product-selling behaviors. Our approach hinges on the analysis of customer-purchasing patterns, enabling us to categorize customers based on their distinctive characteristics and tailor our promotional strategies accordingly. Simultaneously, we delve into product-selling behavior, shedding light on the characteristics that drive customer interest and profitability. To achieve this, we augmented the RFM analysis with periodicity and CEI variables and use clustering algorithms to group customers and products based on their similarities. ANOVA and ANOM analyses were conducted to select the optimal clustering parameters, including models, metrics, methods, and cluster numbers. The MCEIP model, K-means clustering, and silhouette index were employed for the customer-oriented group. In contrast, the product-oriented group utilized the FMP model, K-means clustering, and silhouette index. The results indicate that customer behavior can be divided into two groups, namely loyal and potential customers, while product behavior is categorized into three groups: best-seller, profitable, and VIP products. Association rules were analyzed to identify purchasing combinations within product categories, which were subsequently utilized in a \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product** & **Customer** & **Amount of Transaction** & **Percentage** \\ \hline \multirow{2}{*}{Best-Seller} & Loyal & 582,581 & 74.74 \\  & Potential & 176,809 & 22.68 \\ \multirow{2}{*}{VIP} & Loyal & 2674 & 0.34 \\  & Potential & 1171 & 0.15 \\ \multirow{2}{*}{Profitable} & Loyal & 13,194 & 1.69 \\  & Potential & 3019 & 0.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Relationship Between Customer and Product. classifier-based recommendation system. Among the classifiers tested, K-nearest neighbor demonstrated the most suitable performance, with low overfitting probability and high performance index. In the real-world situation, by using K-nearest neighbor, the prediction will be more precise, stable, and reliable based on the training set. By predicting the recommendation as precisely as the purchasing behavior, the rate of transaction might be increased. This study also shows the suggestions for a marketing strategy that can be applied for the specific segment, either from a customer or product perspective, while both can be applied and affect each other to improve customer loyalty and retail sales amount. Although our data-driven approach enables us to offer tailored and specific recommendations, it also poses some limitations. The analysis is based on a secondary dataset, which raises the possibility of producing disparate results based on actual transaction data. As a result, we should continue to investigate our findings in practice to verify them. To increase the robustness of our recommendation system, we acknowledge the importance of expanding the horizons of our research by exploring alternative classifier options. Several strategic considerations drove our choice of data from 2009 to 2011 relative to our data selection. Firstly, the data were obtained from a company that analyzes long-term customer behavior. We gained valuable insights from this historical dataset about customer and product interactions. Our analysis, however, does not solely depend on historical data from this period. Instead, we leveraged this historical dataset as a foundational platform to develop and validate models, frameworks, and methodologies designed for broad applicability. Regardless of the dataset's vintage, it was designed for adaptability and application to current and future data. This older dataset was selected for empirical research and as a basis for establishing a suitable recommendation system model. Nevertheless, as stated in the conclusion, our findings should be applied to real-world datasets for thorough evaluation. Conceptualization, A.H.-L.C.