# Cluster-Based Approaches toward Developing a Customer
## 5 Discussion  
As the current literature does not present studies that contemplate the creation of a loyalty program or even customer segmentation in this industry segment, H1 was created. Based on the results presented in this study, Table 16 fully confirms H1. Thus, this study expands recent findings on loyalty programs. The results found are aligned with previous studies which emphasized the creation of a loyalty program in various industry segments [4; 10; 17; 18; 19]. Regarding H2, which is related to the creation of a loyalty program based solely on RFM variables, Figures 5 and 6 present the confirmation of the corresponding hypothesis raised. A study conducted by [PERSON] et al. [4] in which the variables of the RFM model were applied to the k-means technique to create customer segmentation in a retail industry also validates this hypothesis. However, Figures 5 and 6 also show how unsatisfactory the segmentation of the clusters found is and their respective silhouette coefficient results with the use of only RFM variables. \begin{table}
\begin{tabular}{c c c c} \hline
**Interviewee ID** & **Question 1** & \multicolumn{2}{c}{**Question 2**} \\ \hline  & **Yes** & **No** & **Yes** & **No** \\ \hline
1 & x & & & x \\
2 & x & & x & \\
3 & x & & x & \\
4 & x & & x & x \\ Total & 4 & 0 & 2 & 2 \\ Average & 1 & 0 & 0.5 & 0.5 \\ \hline \end{tabular}
\end{table} 
Table 21: Results of the interviewee’ answers to the applied questionnaire to identify the utility and existence of the program (source: the authors). \begin{table}
\begin{tabular}{c c c} \hline
**Question Number** & **Response ID** & **Summary of Responses** \\ \hline  & 3.1 & “The program is extremely useful and objective. It is the first time that we will be able to identify and retain customers who bring more value to the business”. \\ \cline{2-3}  & 3.2 & “Positive aspects of the program: identification of the best customers; loyalty and creation of targeted Marketing”. \\ \cline{2-3}  & 3.3 & “Identification of the most valuable customers and creation of a specific and excellent Marketing for these customers”. \\ \cline{2-3}  & 3.4 & “Knowing who are the most valuable and creating benefits for them”. \\ \hline
4 & 4.1 & “The second group of most valuable customers should be presented, because in this way we could do Marketing for this group to become the most valuable too”. \\ \hline \end{tabular}
\end{table} 
Table 22: Evaluation of the creation of the loyalty program (source: the authors). The result presented in the previous hypothesis refers to H3, which raises the assumption that variables associated with RFM attributes present better results in the creation of a loyalty program. Table 23 confirms and validates this hypothesis and presents its respective silhouette coefficient values, which are higher than the results found using only RFM variables. Therefore, this study joins the findings of [PERSON] et al. [9], in which behavioral, demographic and financial variables were used with the aim of carrying out  
Figure 5: Results of the silhouette coefficient obtained through the implementation of k-means clustering for RFM variables (source: the authors). Figure 6: Average silhouette coefficient for each k found and their distributions in each group for RFM variables (source: the authors). (each color represent a different cluster). customer segmentation in the telecommunications industry. Furthermore, the use of more variables in the current clustering model presented an average improvement of 27% in the silhouette coefficient results when compared to the model with only RFM variables. However, to the best of our knowledge, this is the first study that proposes to create a loyalty program for the most valuable customers in a private security company. In view of this hypothesis, H4 was suggested, which relates to the creation of marketing strategies for the segmentation of customers found. Table 22 confirms the last-mentioned hypothesis in which the creation of the loyalty program is evaluated.