Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods

• Main Authors: Rahma Wati Sembiring Brahmana, Fahd Agodzo Mohammed, Kankamol Chairuang
• Format: Article
• Language: Indonesian
• Published: Universitas Udayana 2020-04-01
• Series: Lontar Komputer
• Online Access: https://ojs.unud.ac.id/index.php/lontar/article/view/58025
• ISSN: 2088-1541; 2541-5832

A problem that appears in marketing activities is how to identify potential customers. Marketing activities could identify their best customer through customer segmentation by applying the concept of Data Mining and Customer Relationship Management (CRM). This paper presents the Data Mining process by combining the RFM model with K-Means, K-Medoids, and DBSCAN algorithms. This paper analyzes 334,641 transaction data and converts them to 1661 Recency, Frequency, and Monetary (RFM) data lines to identify potential customers. The K-Means, K-Medoids, and DBSCAN algorithms are very sensitive for initializing the cluster center because it is done randomly. Clustering is done by using two to six clusters. The trial process in the K-Means and K-Medoids Method is done using random centroid values ??and at DBSCAN is done using random Epsilon and Min Points, so that a cluster group is obtained that produces potential customers. Cluster validation completes using the Davies-Bouldin Index and Silhouette Index methods. The result showed that K-Means had the best level of validity than K-Medoids and DBSCAN, where the Davies-Bouldin Index yield was 0,33009058, and the Silhouette Index yield was 0,912671056. The best number of clusters produced using the Davies Bouldin Index and Silhouette Index are 2 clusters, where each K-Means, K-Medoids, and DBSCAN algorithms provide the Dormant and Golden customer classes.