Customer Behavior Using RFM Model and K-Means Algorithm in Aesthetic Clinic

In an era that is increasingly competitive and rapidly developing in the beauty industry, understanding customer behavior has a very important role. Aesthetic clinics or beauty clinics, have become the main destination for individuals seeking various types of treatments to improve their physical appearance. Technological advances and rapidly evolving beauty trends have made beauty clinics a popular destination for many people. A strong analytical approach is needed to extract insights from customer behavior data in beauty clinics. One effective approach is to use the Recency, Frequency, and Monetary (RFM) model, which has proven successful in analyzing customer behavior in various industries. By applying the K-Means algorithm to beauty clinic customer data, we can identify groups of customers who have similar preferences, thereby enabling clinic managers to adjust services and offers according to the needs and preferences of each group. The data used in this study is sales data at aesthetic clinics for the period January - July 2023 as many as 2248 data. It is possible to conclude from the research that 2248 customers engaged in transactions from January 2023 to July 2023. Three clusters are found to be the ideal number of clusters when the elbow technique is used to determine the best number of clusters. The K-Means algorithm is used for clustering, and there are 3 clusters altogether. This yields a cluster proportion of 531 loyal customer: 484 promising customer: 1233 active customer.