Study on customer rating using RFM and K-means

Abstract

The RFM (Recency, Frequency, Monetary) market analysis technique is a widely used in the marketing field to analyze customer behavior. The interest in machine learning has recently increased to utilize the increase in accumulated data. Therefore, an attempt was made to analyze data by combining the RFM technique and various algorithms. In this study, we attempted to classify customers through the RFM technique and k-means algorithm, which is a typical clustering algorithm. In a conventional experiment, there are many cases where the k value is designated as 8 or 9. However, in this experiment, the optimal k value for the data set was obtained using an internal evaluation method.