# Cluster-Based Approaches toward Developing a Customer
## 4 Results
### Evaluation  
In addition to creating a loyalty program for the company's most valuable customers, the main objective of this study was also to provide decision-making alternatives to the main business managers in the process of managing this group of customers. In this way, the company can develop strategies to motivate these customers to stay longer with the company. A questionnaire was developed for the previously interviewed respondents, as presented in Table 2, t thus incorporating the main results from the k-means implementation for K = 4, K = 5, and K = 6. The purpose of this questionnaire was to engage key stakeholders in the creation of a loyalty program and facilitate decision making in the program's design. Before presenting the results of the identified groups to all respondents, a brief explanation of the silhouette coefficient was provided as a metric for evaluating the most valuable groups found. Table 15 displays the explanation silhouette coefficient concept to interviewees. Next, the results of the clustering technique were presented to the organization's professionals/decision-makers, hiding the methodology, which was the value of K used. Only a corresponding alias was presented, as shown in Table 16.  
Along with the results presented in Table 16, the interviewees were encouraged to answer two questions as part of the evaluation process for the created loyalty program. A questionnaire for identifying the best loyalty program is shown in Table 17.  
Table 18 presents the result of question 1 of the questionnaire proposed to the group of respondents, as shown in Table 17. Program 3 received the highest score for the best loyalty program, with three votes out of a total of four. The justifications for each respondent's choices are presented in Table 19. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**K Value**} & \multirow{2}{*}{**Alias**} & **Customer** & **Percentage of** & **Gross** & **Silhouette** \\  & & **Quantity** & **Total Customer** & **Revenue** & **Coefficient** \\  & & **Base** & **(Euros)** & **Coefficient** \\ \hline
4 & Program 1 & 6589 & 9.886\% & 4540.79 & 0.757 \\
5 & Program 2 & 5613 & 8.421\% & 7153.40 & 0.684 \\
6 & Program 3 & 2170 & 3.256\% & 8683.11 & 0.655 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Summary of the results found in the implementation of k-means clustering for presentation to the interviewees (source: the authors). \begin{table}
\begin{tabular}{c c} \hline \hline
**Question Number** & **Question** \\ \hline \hline \multirow{4}{*}{1} & Based on the results presented, mark with an ’x’ \\  & & which of these programs you would \\ \cline{1-1}  & & implement as a loyalty program for the \\ \cline{1-1}  & & company’s most valuable customers. \\ \hline
2 & Justify your choice to the previous question. \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Questionnaire for identifying the best loyalty program (source: the authors). \begin{table}
\begin{tabular}{c c} \hline \hline
**Silhouette Coefficient Concept** \\ \hline The silhouette coefficient is a metric used to evaluate the clusters of customers identified. The \\ coefficient ranges between \(-1\) (indicative of poor clustering) and \(+1\) (indicative of good \\ \cline{1-1} clustering). The aim is to maximize its value. Upon analyzing the justifications, some common points were highlighted by most of the individuals surveyed regarding the choice of the best loyalty program. The first was that program 3 has a smaller customer group, which could lead to greater efficiency in managing the customers included in the program. This can be explained by the fact that with fewer customers in the program, a higher level of service can be provided. The second common point was related to the cost of the program, which was mentioned by two of the directors. A loyalty program with a smaller customer group is less financially burdensome for the company.