Implementation of Clustering Algorithm Method for Customer Segmentation

Implementation of Clustering Algorithm Method for Customer Segmentation

• By Nurmalasari; Mukhayaroh, Anna; Marlina,Siti; Hartini,Sari; Muryani,Sri; Sinnun,Ahmad; Nurajizah,Siti; Adiwihardja,Cep;
• Jurnal Internasional Bereputasi
• 397
• 1-8

The intense competition in the sale of goods and services in the digital era of e-commerce requires to manage customers optimally. Some online shops try to improve their marketing strategies by classifying their customers. This study aims to determine potential customers, namely loyal cus- tomers. Potential customers can be determined by customer segmentation. Sampling from several online shops in Indonesia. The model used for segmentation is RFM (Recency, Frequency, and Monetary) and data mining techniques, namely clustering method with the K-Means algorithm. The results of this segmentation research divide the customer into 2 clusters. The best number of clus- ters is determined based on the Davies Bouldin index. The first cluster is cluster 0 consisting of 261 customers with RFM Score between 111–543. The first cluster includes the Everyday Shop- per group. The second cluster, cluster 1 consists of 102 customers with RFM Score 443–555. The second cluster includes the Golden Customer group. With the existence of research on customer segmentation, it is expected to help in grouping customers so that companies can determine the right strategy for each group of customers.

• Katakunci Clustering, K-Means, Customer Segmentation
• URL http://www.aspbs.com/ctn/
• Penerbit ,

REFERENSI

1. Suyanto, 2017. Data Mining for Data Classification and Clustering. Bandung, Informatika Bandung.
2. Sutrisno, S., Afriyudi, A. and Widiyanto, W., 2013. Application of data mining at sales using clustering method case study Pt. indo- marco palembang. Penerapan Data Mining Pada Penjualan Meng- gunakan Metode Clustering, pp.1–11.
3. Wulandari, G.F., 2014. Customer segmentation using K-Means algo- rithm for customer relationship management (CRM) at Miulan Hijab. pp.5–6.
4. Widiarina and Wahono, R.S., 2015. Dynamic cluster algorithm for cluster optimization in K-means algorithm in mapping potential cus- tomers. Journal of Intelligent Systems, 1(1), pp.33–36.
5. Turban, E., Aronson, J. and Liang, T., 2005. Decission Support Sys- tem and Intelligent System. Indiana, Pearsan Hall Universitas.
6. Larose, D.T. and Larose, C.D., 2014. Discovering Knowledge in Data: An Introduction to Data Mining. Second edn., New Jersey, John Willey & Sons, Inc.
7. Thavalingam, A., Bicanic, N., Robinson, J. and Ponniah, D., 2001. Computational Framework for Discontinuous Modelling of Masonry Arch Bridges. Elsevier.
8. Berry, M.J. and Linoff, G.S., 2004. Data Mining Techniques for Marketing, Sales, and Customer Relationship Management. United Stated, Wiley Publishing.
9. Vulandari, T.R., 2017. Data Mining Teori dan Aplikasi Rapidminer. Surakarta, Java Media.
10. Han, J., Kamber, M. and Pei, J., 2011. Concepts and Techniques.
11. Gudono, 2011. First Edition Multivariate Data Analysis. Yogyakarta, BPFE.
12. Laeli, S., 2014. Cluster Analysis with Average Linkage Method and Ward’s Method for Unit Link Life Insurance Customer Respondents Data. Universitas Negeri Yogyakarta.
13. Azis, W.S. and Atmajaya, D., 2016. Grouping of student inter- ests using the K-means method. ILKOM Scientific Journal, 8(2), pp.89–94.
14. Munigsih, E. and Kiswati, S., 2015. Application of the K-means method for clustering online shop products in stock determination. Journal Bianglala Informatika, 3(1), pp.10–17.
15. Hardiani, T., Sulistyo, S. and Hartanto, R., 2015. Savings Cus- tomer Segmentation Using RFM (Recency, Frequency, Monetary) and K-Means Models in Microfinance Institutions. Seminar Nasional Teknologi Informasi dan Komunikasi Terapan (SEMANTIK), pp.463– 468, DOI: 10.1016/S1875-5364(14)60001-7.
16. Yuliari, N.P.P., Putra, I.K.G.D. and Rusjayanti, N.K.D., 2015. Customer segmentation through fuzzy C-means and fuzzy RFM method. Journal of Theoretical and Applied Information Technology, 78(3), pp.380–385.