Penerapan Algoritma Fuzzy C-Means untuk Klasterisasi Customer Lifetime Value menggunakan Model LRFMD

 (*)Indah Ramadhani Mail (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 M Afdal (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Mustakim Mustakim (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Zarnelly Zarnelly (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: April 2, 2024; Published: July 26, 2024

Abstract

PT X is a retail company engaged in printing. The company has not differentiated between information about profitable and unprofitable customers for the company. Transaction data is only used as profit and loss information so they do not know the characteristics of the customers they have. In addition, the lack of extensive services in the merchandise category is one of the reasons the company's revenue has not reached the predetermined target. Currently, the company has opened additional services in the merchandise field. This research aims to identify customer segmentation as well as analyze the characteristics and provide a strategy proposal that will be submitted to PT. X. Customer loyalty and characteristics have a significant impact on a company. To identify customers who show loyalty to the company, the Fuzzy C-Means algorithm is used to perform clustering, using the Davies Bouldin Index (DBI) to evaluate the clustering results. The model used is in accordance with the principles of Length, Recency, Frequency, Monetary and Diversity (LRFMD) to categorize purchasing patterns. By analyzing LRFMD variables, it is possible to identify customers who are loyal to the company and those who are not. This research produces 6 clusters with the best cluster or supestar customer in cluster 6, the second best value customer or golden customer is cluster 2, the average value customer or typical customer is cluster 4 and 5 and the lowest cluster or dormant customer is in cluster 3.

Keywords


Clustering; Fuzzy C-Means; Customer Characteristics; LRFMD; Segmentation

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References

M. Y. Smaili and H. Hachimi, “New RFM-D classification model for improving customer analysis and response prediction,” Ain Shams Eng. J., no. xxxx, p. 102254, 2023, doi: 10.1016/j.asej.2023.102254.

M. Abdul, M. Mushafiq, S. Khan, and Z. Ali, “Journal of Retailing and Consumer Services RFM-based repurchase behavior for customer classification and segmentation,” J. Retail. Consum. Serv., vol. 61, no. March, p. 102566, 2021, doi: 10.1016/j.jretconser.2021.102566.

L. C. T. B. Wellerson V. Oliveira, Daniel S. A. Araujo, “Supermarket customer segmentation : a case study in a large Brazilian retail chain,” Conf. Bus. Informatics, no. November 2022, 2022.

K. G. Grunert, “International segmentation in the food domain: Issues and approaches,” Food Res. Int., vol. 115, no. November 2018, pp. 311–318, 2019, doi: 10.1016/j.foodres.2018.11.050.

W. D. Dahana, Y. Miwa, and M. Morisada, “Linking lifestyle to customer lifetime value: An exploratory study in an online fashion retail market,” J. Bus. Res., vol. 99, no. February, pp. 319–331, 2019, doi: 10.1016/j.jbusres.2019.02.049.

B. C. Laksono and I. Y. Wulansari, “Pemodelan Dan Penerapan Metode Rfm Pada Estimasi Nilai Konsumen (Customer Lifetime Value) Menggunakan K-Means Clustering Machine Learning,” Semin. Nas. Off. Stat., vol. 2020, no. 1, pp. 1277–1285, 2021, doi: 10.34123/semnasoffstat.v2020i1.689.

M. Pratiwi, U. I. Arsyah, R. L. G. Rahma, A. A. Rahma, and F. Aldi, “Efektifitas Penerapan Customer Relationship Management (CRM) padaToko Nil Maizar Sport Apparel,” J. BIT, vol. 16, no. 2, pp. 7–12, 2019, [Online]. Available: https://journal.budiluhur.ac.id/index.php/bit JURNAL

Y. Juniarti, “ANALISIS SEGMENTASI PELANGGAN BERDASARKAN RECENCY, FREQUENCY, MONETARY (RFM) DAN DEMOGRAFI MENGGUNAKAN ALGORITMA DBSCAN,” Skripsi tidak ditampilkan, Program Studi Sistem Informasi, Universitas Islam Negeri Sultan Syarif Kasim, RIAU, 2021.

R. Heldt, C. S. Silveira, and F. B. Luce, “Predicting customer value per product: From RFM to RFM/P,” J. Bus. Res., vol. 127, no. March 2018, pp. 444–453, 2021, doi: 10.1016/j.jbusres.2019.05.001.

A. Ebrahimi, K. Askarifar, and A. Nikbakht, “Designing and evaluating insurance customer loyalty programs for different customer groups based on their lifetime value,” J. Financ. Serv. Mark., no. October, 2023, doi: 10.1057/s41264-023-00242-8.

S. Y. Anu Gupta Aggarwal, “Customer Segmentation Using Fuzzy-AHP and RFM Model,” Int. Conf. Reliab. Infocom Technol. Optim. (Trends Futur. Dir., no. September 2020, pp. 77–80, 2020.

S. S. Prasetyo, A. R. Hakim, D. Statistika, F. Sains, and U. Diponegoro, “PENERAPAN FUZZY C-MEANS KLUSTER UNTUK SEGMENTASI PELANGGAN E-COMMERCE DENGAN METODE RECENCY FREQUENCY MONETARY (RFM),” J. GAUSSIAN, vol. 9, pp. 421–433, 2020.

K. Zhou and S. Yang, “Effect of cluster size distribution on clustering: a comparative study of k-means and fuzzy c-means clustering,” Pattern Anal. Appl., vol. 23, pp. 455–466, 2020.

D. L. Aditya and D. Fitrianah, “Comparative Study of Fuzzy C-Means and K-Means Algorithm for Grouping Customer Potential in Brand Limback,” J. Ris. Inform., vol. 3, no. 4, pp. 327–334, 2021, doi: 10.34288/jri.v3i4.241.

M. N. Dayat, N. Suarna, and Y. A. Wijaya, “Analisa Clustering untuk Mengelompokan Data Penayangan Film Bioskop Menggunakan Algoritma K-Means,” Intern. (Information Syst. Journal), vol. 6, no. 1, pp. 68–78, 2023, [Online]. Available: https://jurnal.masoemuniversity.ac.id/index.php/internal/article/view/686

W. Gie and D. Jollyta, “Perbandingan Euclidean dan Manhattan Untuk Optimasi Cluster Menggunakan Davies Bouldin Index: Status Covid-19 Wilayah Riau,” Pros. Semin. Nas. Ris. Dan Inf. Sci., vol. 2, no. April, pp. 187–191, 2020.

Y. A. Wijaya, D. A. Kurniady, E. Setyanto, W. S. Tarihoran, D. Rusmana, and R. Rahim, “Davies Bouldin Index Algorithm for Optimizing Clustering Case Studies Mapping School Facilities,” TEM J., vol. 10, no. 3, pp. 1099–1103, 2021, doi: 10.18421/TEM103.

B. E. Adiana, I. Soesanti, and A. E. Permanasari, “Analisis segmentasi pelanggan menggunakan kombinasi RFM model dan teknik clustering,” J. Terap. Teknol. Inf., vol. 2, no. 1, pp. 23–32, 2018.

A. Z. Putri, M. Afdal, S. Monalisa, and I. Permana, “Penerapan Algoritma Fuzzy C-Means Pada Segmentasi Pelanggan B2B dengan Model LRFM,” J. Media Inform. Budidarma, vol. 7, pp. 1423–1432, 2023, doi: 10.30865/mib.v7i3.6150.

S. Patil, H. Khan, S. Mehta, and P. U. Mandawkar, “ISSN NO : 0377-9254 STUDY OF CUSTOMER SEGMENTATION USING k-MEANS CLUSTERING AND RFM MODELLING Page No : 556,” J. Eng. Sci., vol. 12, no. 06, pp. 556–559, 2021.

B. E. Adiana, I. Soesanti, A. E. Permanasari, J. G. No, J. G. No, and J. G. No, “Analisis Segmentasi Pelanggan Menggunakan Kombinasi RFM Model dan Teknik Clustering,” JUTEI, vol. 2, no. 2, pp. 23–32, 2018, doi: 10.21460/jutei.2017.21.76.

A. Nofiar, S. Defit, and Sumijan, “Penentuan Mutu Kelapa Sawit Menggunakan Metode K-Means Clustering,” J. KomtekInfo, vol. 5, no. 3, pp. 1–9, 2019, doi: 10.35134/komtekinfo.v5i3.26.

P. P. Pramono and E. Laoh, “Estimating Customer Segmentation based on Customer Lifetime Value Using Two-Stage Clustering Method,” 2019 16th Int. Conf. Serv. Syst. Serv. Manag., no. 1994, pp. 1–5, 2019.

R. Muliono and Z. Sembiring, “Data Mining Clustering Menggunakan Algoritma K-Means Untuk Klasterisasi Tingkat Tridarma Pengajaran Dosen,” CESS (Journal Comput. Eng. Syst. Sci., vol. 4, no. 2, pp. 2502–714, 2019.

H. Abdellahoum, N. Mokhtari, A. Brahimi, and A. Boukra, “CSFCM: An Improved Fuzzy C-Means Image Segmentation Algorithm Using a Cooperative Approach,” Expert Syst. Appl., p. 114063, 2020, doi: 10.1016/j.eswa.2020.114063.

M. A. Shah Putra, S. Monalisa, J. Julhandri, and I. Khoiru, “Penerapan Algoritma Fuzzy C-Means Menggunakan Model Rfm Dalam Klasterisasi Pelanggan Pada Toko Kue Feandra Cake,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 6, no. 1, p. 64, 2020, doi: 10.24014/rmsi.v6i1.8646.

A. Matz and A. T. Hermawan, “Customer Loyalty Clustering Model Using K-Means Algorithm with LRIFMQ Parameters,” J. Ilm. Bid. Teknol. Inf. dan Komun., vol. 5, no. 2, pp. 54–61, 2020.

F. Carneiro, R. M. A. Moreira, V. N. De Gaia, R. R. Frias, and V. Miguéis, “Applying Data Mining Techniques and Analytic Hierarchy Process to the Food Industry : Estimating Customer Lifetime Value,” Proc. Int. Conf. Ind. Eng. Oper. Manag., no. Kotler 2000, pp. 266–277, 2021.

F. Tempola and A. F. Assagaf, “Clustering of Potency of Shrimp In Indonesia With K-Means Algorithm And Validation of Davies-Bouldin Index,” vol. 1, no. Icst, pp. 730–733, 2018, doi: 10.2991/icst-18.2018.148.

R. K. Dinata, H. Novriando, N. Hasdyna, and S. Retno, “Reduksi Atribut Menggunakan Information Gain untuk Optimasi Cluster Algoritma K-Means,” J. Edukasi dan Penelit. Inform., vol. 6, no. 1, p. 48, 2020, doi: 10.26418/jp.v6i1.37606.

A. A. Zoeram and A. K. Mazidi, “A New Approach for Customer Clustering by Integrating the LRFM Model and Fuzzy Inference System,” 2018.

H. Lohonauman, “IPTEKS PENGHITUNGAN CUSTOMER LIFETIME VALUE Hans Lohonauman,” vol. 4, no. 1, pp. 19–23, 2020.

F. Marisa, S. S. S. Ahmad, Z. I. M. Yusof, Fachrudin, and T. M. A. Aziz, “Segmentation model of customer lifetime value in Small and Medium Enterprise (SMEs) using K-Means Clustering and LRFM model,” Int. J. Integr. Eng., vol. 11, no. 3, pp. 169–180, 2019, doi: 10.30880/ijie.2019.11.03.018.

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