A Study of Customer Segmentation Based on RFM Analysis and K-Means

The rapid growth of the competitive market in today’s world has encouraged marketers to have more data-driven marketing strategies. As customers come from diverse backgrounds and have different requirements and expectations, understanding their demands and preferences is key to efficient customer relationships. This study shows the effect of different machine learning technologies in integration with the recency, frequency, and monetary (RFM) model and K-means to get meaningful customer segments. K-means is the most widely used method for customer segmentation because of its easy implementation, fast results, and accuracy. This study discovers the use of various extended RFMs in the literature for getting better segmentation results.