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

  • Rahma Wati Sembiring Brahmana Department of Information Technology, Faculty of Engineering, Udayana University
  • Fahd Agodzo Mohammed Department of Computer Engineering, Chandigarh University
  • Kankamol Chairuang Department of Business Administration, Chandigarh University

Abstract

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.

Downloads

Download data is not yet available.

References

[1] I. D. A. A. Y. Primandari, I. K. G. D. Putra, and I. M. Sukarsa, "Customer Segmentation Using Particle Swarm Optimization and K-Means Algorithm," International Journal of Digital Content Technology and its Application, vol. 10, no. 4, pp. 22-28, 2016.
[2] I. K. Gede, D. Putra, and D. S. H, "Combination of Adaptive Resonance Theory 2 and RFM Model for Customer Segmentation In Retail Company," International Journal of Computer Applications, vol. 48, no. 2, pp. 18–23, 2012.
[3] K. M. Manero, R. Rimiru, and C. Otieno, "Customer Behaviour Segmentation among Mobile Service Providers in Kenya using K-Means Algorithm," International Journal of Computer Science, vol. 15, no. 5, pp. 67–76, 2018.
[4] R. A. Carrasco, M. F. Blasco, J. García-Madariaga, and E. Herrera-Viedma, "A Fuzzy Linguistic RFM Model Applied to Campaign Management," International Journal of Interactive Multimedia and Artificial Intelligence, vol. 5, no. 4, p. 21-27, 2019.
[5] S. A. Mustaniroh, U. Effendi, and R. L. R. Silalahi, "Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster," IOP Conference Series: Materials Science and Engineering, vol. 336, pp. 1-6, 2017.
[6] Z. Rustam and A. S. Talita, "Fuzzy Kernel K-Medoids Algorithm for Multiclass Multidimensional Data Classification," Journal of Theoretical and Applied Information Technology, vol. 80, no. 1, pp. 147–151, 2015.
[7] N. Made, A. Santika, I. K. Gede, D. Putra, and I. M. Sukarsa, “Implementasi Metode Clustering DBSCAN pada Proses Pengambilan Keputusan,” Lontar Komputer, vol. 6, no. 3, pp. 185–191, 2015.
[8] D. Virmani, S. Taneja, and G. Malhotra, "Normalization Based K-Means Clustering Algorithm," Journal of Advanced Engineering Research and Science, vol. 5, no. 6, pp. 1–5, 2015.
[9] M. Madhiarasan and S. N. Deepa, "A Novel Criterion to Select Hidden Neuron Numbers in Improved Back Propagation Networks for Wind Speed Forecasting," Application Intelligence, vol. 44, no. 4, pp. 878–893, 2016.
[10] P. Pengelompokan, R. Kost, D. I. Kelurahan, and T. Semarang, “Perbandingan Metode K-Means dan Metode DBSCAN pada Pengelompokan Rumah Kost Mahasiswa di Kelurahan Tembalang Semarang,” Jurnal Gaussian, vol. 5, pp. 757–762, 2016.
[11] I. Kamila et al., “Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau,” Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi, vol. 5, no. 1, pp. 119–125, 2019.
[12] I. V Anikin, "Privacy Preserving DBSCAN Clustering Algorithm for Vertically Partitioned Data in Distributed Systems," International Siberian Conference on Control and Communications, vol. 10, pp.1-4, 2017.
[13] K. Hamdi and A. Zamiri, "Identifying and Segmenting Customers of Pasargad Insurance Company Through RFM Model (RFM)," International Business Management, vol. 10, no. 18. pp. 4209–4214, 2016.
[14] Y. Nugraheni, "Data Mining Using Fuzzy Method for Customer Relationship Management In Retail Industry," Lontar Komputer, vol. 4, no. 1, pp. 188–200, 2013.
[15] B. Jumadi Dehotman Sitompul, O. Salim Sitompul, and P. Sihombing, "Enhancement Clustering Evaluation Result of Davies-Bouldin Index with Determining Initial Centroid of K-Means Algorithm," International Conference on Computing and Applied Informatics, vol. 1235, no. 1, pp. 1-6, 2019.
[16] A.-M. Shoolihah, M. T. Furqon, and A. W. Widodo, “Implementasi Metode Improved K-Means untuk Mengelompokkan Titik Panas Bumi,” Jurnal Pengembangan Teknolologi Informasi dan Ilmu Komputer Universitas Brawijaya, vol. 1, no. 11, pp. 1270–1276, 2017.
Published
2020-04-30
How to Cite
SEMBIRING BRAHMANA, Rahma Wati; MOHAMMED, Fahd Agodzo; CHAIRUANG, Kankamol. Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 11, n. 1, p. 32-43, apr. 2020. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/58025>. Date accessed: 13 nov. 2024. doi: https://doi.org/10.24843/LKJITI.2020.v11.i01.p04.