CLUSTERING CUSTOMER FOR DETERMINE MARKET STRATEGY USING K-MEANS AND TOPSIS: CASE STUDY

Made Agung Raharja; I Kadek Ari Surya

Made Agung Raharja Universitas Udayana
I Kadek Ari Surya Universitas Udayana

Keywords: clustering, customer, K-Means, TOPSIS, silhouette coefficient, craft, RFM, LRFM

Abstract

determining marketing strategies by companies in the midst of intense market competition. The research stage starts from preprocessing the data and then calculating the Customer Lifetime Value (CLV) through weighting the RFM (Recency, Frequency, Monetary) and LRFM (Length, Recency, Frequency, Monetary) models. The weights are given by experts with the Analytic Hierarchy Process (AHP) calculation. Then K-Means is used for the clustering process and then evaluated with the Silhouette Coefficient (SC). The best clustering results are ranked using the Technique for Orders Preference by Similarity to Ideal Solution (TOPSIS) method to produce alternative decisions in cluster selection. This research takes a case study in one of the handicraft shops in Bali which experienced a decline in product demand due to the impact of intense market competition. The results show that the SC value tends to increase along with the addition of variations in the percentage of data. The weighting of the data model from the case study expert weight resulted in the best SC value at k = 2 with the value of the RFM model of 0.665 (medium structure) and the LRFM value of 0.641. The cluster ranking validation test and Rank Consistency on the best clustering results get valid calculation results and there is no rank reversal. The recommendation for customer clusters is that Cluster 2 in the first place has 158 customers with cluster type R↓F↑M↑ with Enforced Strategy and Cluster 1 in the second place has 817 customers with cluster type R↑F↓M↓ with Let-go Strategy.

References

Adiana, B.E., Soesanti, I. and Permanasari, A.E. (2018) ‘Analisis Segmentasi Pelanggan Menggunakan Kombinasi RFM Model Dan Teknik Clustering’, Jurnal Terapan Teknologi Informasi, 2(1), pp. 23–32. doi:10.21460/jutei.2018.21.76.
Angelie, A.V. (2017) Segmentasi Pelanggan Menggunakan Clustering K-Means Dan Model Rfm ( Studi Kasus : Pt . Bina Adidaya Surabaya ). ITS. Available at: https://core.ac.uk/download/pdf/291463031.pdf.
Azadnia, A.H. et al. (2011) ‘Integration model of Fuzzy C means clustering algorithm and TOPSIS method for customer lifetime value assessment’, in IEEE International Conference on Industrial Engineering and Engineering Management. doi:10.1109/IEEM.2011.6117870.
Dewi, D.A.I.C. and Pramita, D.A.K. (2019) ‘Analisis Perbandingan Metode Elbow dan Silhouette pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Bali’, Matrix : Jurnal Manajemen Teknologi dan Informatika, 9(3), pp. 102–109. doi:10.31940/matrix.v9i3.1662.
Dewi, D.P. et al. (2013) ‘Evaluasi dan Rekomendasi Peningkatan Nilai Perilaku Pembelian Pelanggan Berdasarkan Analisis RFM Perilaku Pembelian Pelanggan : Studi Kasus PT. XYZ’, 2(1).
Disperindag Bali (2019) ‘Laporan Kinerja Instansi Pemerintah (LKjIP) Dinas Perdagangan dan Perindustrian Provinsi Bali 2019’, Dinas Perdagangan dan Perindustrian Provinsi Bali [Preprint]. Available at: https://disperindag.baliprov.go.id/lkjip-2019-dinas-perindustrian-dan-perdagangan-provinsi-bali/.
Disperindag Bali (2020) ‘Laporan Kinerja Instansi Pemerintah (LKjIP) Dinas Perdagangan dan Perindustrian Provinsi Bali 2020’, Dinas Perdagangan dan Perindustrian Provinsi Bali [Preprint]. Available at: https://disperindag.baliprov.go.id/wp-content/uploads/2021/03/LKJiP-Disperindag-2020.pdf.
Ditendra, E. et al. (2020) ‘KLASTERISASI CLV DENGAN MODEL LRFM MENGGUNAKAN ALGORITMA FUZZY C-MEANS (Studi Kasus: Pangeran Gym Pekanbaru)’, Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi, 6(1), pp. 109–116.
Hastuti, A.B., Utami, E. and Luthfi, E.T. (2013) ‘Implementasi Metode Fuzzy C-Means Dan Topsis Dalam Membangun Sistem Pendukung Keputusan ( Studi Kasus : Penentuan Jurusan Di Sma Negeri 1 Wonosari )’, Jurnal Dasi, 14(2).
Monalisa, S. (2018) ‘KLASTERISASI CUSTOMER LIFETIME VALUE DENGAN MODEL LRFM MENGGUNAKAN ALGORITMA K-MEANS’, Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 5(2), pp. 247–252. doi:10.25126/jtiik.201852690.
Pramono, P.P., Surjandari, I. and Laoh, E. (2019) ‘Estimating customer segmentation based on customer lifetime value using two-stage clustering method’, in 2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019. IEEE, pp. 1–5. doi:10.1109/ICSSSM.2019.8887704.
Raharja, M.A. et al. (2021) ‘Analysis of membership function in implementation of adaptive neuro fuzzy inference system (ANFIS) method for inflation prediction’, Journal of Physics: Conference Series, 1722(1). doi:10.1088/1742-6596/1722/1/012005.
Raharja, M.A. and Supriana, I.W. (2019) ‘Analisis Klasifikasi Tinggkat Kesehatan Lembaga Perkreditan Desa (Lpd) Menggunakan Metode K-Means Clustering’, Jurnal Teknologi Informasi dan Komputer, 5(1), pp. 83–90. doi:10.36002/jutik.v5i1.641.
Sembiring Brahmana, R.W., Mohammed, F.A. and Chairuang, K. (2020) ‘Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods’, Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 11(1). doi:10.24843/lkjiti.2020.v11.i01.p04.
Usman Adi Nugroho, Ratih Kartika Dewi, M. (2019) ‘Implementasi TOPSIS pada Sistem Rekomendasi Tempat Pembelian Sayuran Organik di Malang Berbasis Lokasi’, Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(5), pp. 4680–4686.