# Cluster-Based Approaches toward Developing a Customer
###### Abstract  
This study aimed to create a loyalty program for a private security company's most valuable customers using clustering techniques on a dataset from the company. K-means was employed as an unsupervised machine learning algorithm to segment customers. Performance evaluation metrics, including the silhouette coefficient, were utilized to compare various algorithmic approaches. As a distinctive feature of this study, in addition to the evaluation metric, strategic questionnaires were administered to business decision-makers to facilitate the integrated development of a loyalty program with key stakeholders invested in customer retention and profitability. The results show the existence of three customer clusters with an optimal silhouette coefficient for loyalty program development. Interestingly, the customer group to be targeted for the loyalty program did not exhibit the highest silhouette coefficient metric. Business leaders selected the group they perceived as most efficient for program implementation. Consequently, the study concludes that customer segmentation not only entails statistical analyses of individual user groups but also requires a comprehensive understanding of the business and collaboration with stakeholders. Furthermore, this study aligns with findings from other authors, demonstrating that private security companies can benefit from implementing a loyalty program, although avenues for further investigation remain.  
loyalty program; clustering; customer segmentation; k-means; private security companies +
Footnote †: journal:  
in an endeavor to enhance the profitability of the companies responsible for this segment. Additionally, according to [PERSON] et al. [6], in the United States, 75% of business in the retail network manage at least one loyalty program with their respective customers.  
Despite the current literature indicating that an increasing number of diverse industries are employing loyalty programs as catalysts in their respective businesses, no study has been found to demonstrate the creation of such programs within private security companies. Thus, the objective of this study is to develop a loyalty program for a private security company's most valuable customers based on customer segmentation techniques applied to a dataset from the company.  
One of the most frequently employed techniques for identifying similar customers and grouping them into segments with similar characteristics is referred to as cluster analysis. Cluster analysis techniques represent the most suitable methods that enable companies to discern segments or groups of customers with the aim of reaching a potential user base [7]. Originating from the field of marketing, the RFM model is a technique used to summarize a customer's past behavior based on recency, frequency, and monetary value attributes [8]. As per [PERSON] et al. [4], this technique has been employed for over 50 years for customer segmentation purposes. Therefore, this study proposes an integrated conceptual model that includes constructs based on previous research in customer segmentation and loyalty program development.