Cluster-Based Approaches toward Developing a Customer Loyalty Program in a Private Security Company

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.