Analyzing Customer Loyalty Levels through Segmentation in Aesthetic Clinics Using K-Means and RFAM

Effective customer segmentation is crucial in optimizing marketing strategies, particularly in customer-oriented aesthetic clinics. This research aims to enhance customer segmentation in aesthetic clinics using a K-Means approach based on the RFAM (Recency, Frequency, Average-Monetary) model. This approach is utilized to leverage historical customer data to identify customer segments based on their purchasing behavior, including visit frequency, average purchase amount, and the last time they visited the clinic. The K-Means clustering method maps customers into homogeneous groups, enabling aesthetic clinics to adapt more focused and personalized marketing strategies. The research results indicate insights obtained from the analysis and interpretation of RFAM conducted on 493 data points, resulting in the formation of two distinct clusters. In Cluster 1, denoting low loyalty, there are 156 customers, while Cluster 2 comprises 337 customers, reflecting high loyalty. Practical implications of this research include improvements in service customization and promotions tailored to customer needs and preferences. In conclusion, the K-Means approach based on the RFAM model can be utilized as an effective tool to enhance customer segmentation in the aesthetic clinic industry.