# Cluster-Based Approaches toward Developing a Customer
## 6 Conclusions  
A loyalty program aims to attract and stimulate loyal customers, assuming that the program aggregates the most lucrative customer segment for the company. It is crucial for a company to discern which customers yield the highest profits, ascertain whether lucrative customers exhibit behaviors indicative of loyalty, and thereby determine the appropriateness of implementing a loyalty program as a relational marketing tool. This paper proposed the creation of a loyalty program for the most valuable customers to the company using machine learning techniques and historical customer information. In addition to quantitative resources, such as the selection of variables for the creation of a base RFM model, this study used qualitative resources; through providing a questionnaire to the company's main business managers, it was possible to add variables to the base model that were judged the most important. Furthermore, different segmentations of the most valuable customers were provided as an alternative to decision-making for the main business managers in the process of managing this group of customers. Additionally, the proposed model, in conjunction with the selection of variables determined to be the most important for the business, proved to be accurate in identifying the most valuable cluster. In this way, the company can develop strategies to motivate these customers to stay longer with the company. For instance, a company could implement more personalized promotions, prioritizing its most loyal customers as the primary focus for retention efforts. According to [PERSON] [28], owners of loyalty cards spend much money than people without them. This could be a marketing strategy for the company to adopt. This study contributed in several ways to the existing body of knowledge about the creation of a loyalty program. First, the relevance of the research design to building a loyalty program in conjunction with main stakeholders made a difference because it combined business experience with the proposed methodology. Secondly, it showed that private security companies can benefit from the creation of a loyalty program, as already clarified in the study of the state of the art of this same research area. Specifically, the results show \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Silhouette** & **Silhouette** & **Percentage Comparison** \\
**K Value** & **Coefficient with** & **Coefficient with** & **between the Silhouette** \\  & **Multiple Variables** & **RFM** & **Coefficient Multiple** \\ \hline
3 & 0.676 & 0.567 & 19.2\% \\
4 & 0.757 & 0.551 & 37.4\% \\
5 & 0.684 & 0.541 & 26.4\% \\
6 & 0.655 & 0.524 & 25.0\% \\ Total & 2.772 & 2.183 & \\ Average & 0.676 & 0.567 & 27.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Silhouette coefficient values for the 4 largest k values found in the implementation of k-means clustering with multiple variables and only with RFM variables (source: the authors). that typical data-based marketing strategies to deliver value from organizational data, such as clustering to segment customers using historic transactional data from customers to build valuable attributes such as RFM, are valid within the specific setting of the private security sector. This is a key contribution stemming from our study given the specificity of the products (e.g., alarms) and services being commercialized by this sector. Another important contribution was to show that despite presenting the best silhouette coefficient metric, the most valuable customer group was not chosen as the ideal cluster for the creation of the program. This finding affirms that customer segmentation not only involves statistical analyses of individual user groups but also requires business understanding and collaboration from stakeholders. This study has insightful findings and advantages. However, it also has some limitations.