Analisis Loyalitas Pelanggan Berbasis Model Recency, Frequency, dan Monetary (RFM) dan Decision Tree pada PT. Solo

Penulis

Basri Basri, Windu Gata, Risnandar Risnandar

Abstrak

Perkembangan bisnis alat tulis kantor dan sekolah saat ini banyak yang menjanjikan, maka banyak bermunculan pemasok baru dalam bisnis Alat Tulis Kantor dan Sekolah (ATKS). PT Solo yang bergerak di bidang bisnis ATKS harus memiliki strategi dalam setiap persaingan usaha, khususnya dalam meraih loyalitas pelanggan. Loyalitas pelanggan sering dipengaruhi oleh faktor jumlah aktivitas transaksi, nilai nominal transaksi, waktu transaksi di perusahaan, dan atribut outlet. Penelitian ini mengusulkan model Recency, Frequency, dan Monetary (RFM) yang dikombinasikan dengan Decision Tree. Model RFM digunakan untuk proses klasterisasi data pelanggan berdasarkan jumlah transaksi, nilai nominal transaksi, waktu transaksi, dan atribut outlet. Sedangkan Decision Tree dapat menggambarkan tingkat loyalitas pelanggan. Data transaksi dalam penelitian ini dilakukan sepanjang 1 Januari hingga 31 Desember 2018 terhadap 1.203 pelanggan dan 18.087 transaki melalui faktur pembelian. Hasil penelitian ini menunjukan bahwa state-of-the-art pada model RFM dan Decision Tree yang diusulkan lebih unggul dibandingkan hanya dengan menggunakan model RFM saja. Cluster ke-1 memiliki 860 pelanggan menghasilkan loyalitas pelanggan sedang (biru), cluster ke-2 memiliki 69 pelanggan menghasilkan loyalitas pelanggan yang tinggi (hijau), dan cluster ke-3 memiliki 274 pelanggan menghasilkan loyalitas pelanggan yang rendah (merah). Model klasterisasi RFM dan klasifikasi Decision Tree telah menghasilkan atribut outlet yang berpengaruh terhadap nilai akurasi sebesar 67,54%.

 

Abstract

 

The development of office and school stationery business at this time, many promising, so many new suppliers have sprung up in the office and school stationery business. PT Solo, which has the office and school stationery business, must have a strategy in every business competition, especially in achieving customer loyalty. Customer loyalty is often influenced by factors in the number of transaction activities, transaction nominal value, transaction time at the company, and outlet attributes. This research proposes a Recency, Frequency, and Monetary (RFM) model combined with a Decision Tree. RFM model is used to process customer data clustering based on number of transactions, transaction nominal value, transaction time, and outlet attributes. Whereas Decision Tree can describe the level of customer loyalty. Transaction data in this study were conducted from 1 January to 31 December 2018 to the 1,203 customers and 18,087 transactions through purchase invoices. The results of this study indicate that the state-of-the-art in the proposed RFM and Decision Tree models is outperform compared to only using the RFM model. Cluster 1 has 860 customers resulting in moderate customer loyalty (blue), Cluster 2 has 69 customers resulting in high customer loyalty (green), and Cluster 3 has 274 customers resulting in lower customer loyalty (red). RFM clustering model and Decision Tree classification have produced outlet attributes that affect the accuracy value of 67.54%.


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Referensi


AIT DAOUD, RACHID, ABDELLAH AMINE, BELAID BOUIKHALENE, AND RACHID LBIBB. 2016. Combining RFM Model and Clustering Techniques for Customer Value Analysis of a Company Selling Online. In Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA,.

CHO, YOUNG SUNG, SONG CHUL MOON, SI CHOON NOH, AND KEUN HO RYU. 2012. Implementation of Personalized Recommendation System Using K-Means Clustering of Item Category Based on RFM. In 2012 IEEE 6th International Conference on Management of Innovation and Technology, ICMIT 2012,.

CHRISNANTO, YULISON HERRY, AND ADE KANIANINGSIH. 2019. Pengelompokan Ekuitas Pelanggan Berbasis Recency Frequency Monetary ( RFM ) Menggunakan K-Means Clustering. 2019(Sentika): 13–14.

HIDAYATULLAH1, DANIEL PRADIPTA, RETNO INDAH ROKHMAWATI2, AND ANDI REZA PERDANAKUSUMA3. 2018. Analisis Pemetaan Pelanggan Potensial Menggunakan Algoritma K-Means dan LRFM Model Untuk Mendukung Strategi Pengelolaan Pelanggan (Studi Pada Maninjau Center Kota Malang). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya 2(8): 2548–2964. http://j-ptiik.ub.ac.id.

HU, YA HAN, AND TZU WEI YEH. 2014. Discovering Valuable Frequent Patterns Based on RFM Analysis without Customer Identification Information. Knowledge-Based Systems 61: 76–88. http://dx.doi.org/10.1016/j.knosys.2014.02.009.

KANDEIL, DALIA ABDELRAZEK, AMANI ANWAR SAAD, AND SHERIN MOUSTAFA YOUSSEF. 2014. A Two-Phase Clustering Analysis for B2B Customer Segmentation. In Proceedings - 2014 International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS 2014,.

LI, ZHIBIN ET AL. 2018. Analisis Segmentasi Pelanggan Menggunakan Kombinasi RFM Model dan Teknik Clustering. Jutei 2(2): 23–32.

MAHBOUBEH, KHAJVAND ET AL. 2011. Estimating Customer Lifetime Value Based on RFM Analysis of Customer Purchase Behavior: Case Study. In Procedia Computer Science 3 (2011): 57-63.

MARYANI, INA, AND DWIZA RIANA. 2017. Clustering and Profiling of Customers Using RFM for Customer Relationship Management Recommendations. In 2017 5th International Conference on Cyber and IT Service Management, CITSM 2017,.

NGAI, E. W.T., LI XIU, AND D. C.K. CHAU. 2009. Application of Data Mining Techniques in Customer Relationship Management: A Literature Review and Classification. Expert Systems with Applications 36(2 PART 2): 2592–2602. http://dx.doi.org/10.1016/j.eswa.2008.02.021.

QI, LILEI, AND SONGJUAN ZHANG. 2012. The Development of Customer Relationship Management System Based on Rough Set. Communications in Computer and Information Science 315: 328–33.

REZA ALLAHYARI SOEINI, AND EBRAHIM FATHALIZADE. 2012. Customer Segmentation Based on Modified RFM Model in Insurance Industry. Ipcsit 25: 101–4. www.ipcsit.com/vol25/020-ICMLC2012-L0071.pdf.

SANDI, TOMMI ALFIAN ARMAWAN, RIDWAN, MUGI RAHARJO, AND JORDY LASMANA PUTRA. 2018. Clustering Kesetiaan Pelanggan E-Ritel Dengan Model Rfm. PILAR Nusa Mandiri 14(2): 239–46.

SAVITRI, AULIA DEWI, FITRA ABDURRACHMAN BACHTIAR, AND NANANG YUDI SETIAWAN. 2018. Segmentasi Pelanggan Menggunakan Metode K-Means Clustering Berdasarkan Model RFM Pada Klinik Kecantikan (Studi Kasus : Belle Crown Malang). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya 2(9): 2957–66.

SHESHASAAYEE, ANANTHI, AND L. LOGESHWARI. 2019. Implementation of RFM Analysis Using Support Vector Machine Model. In Proceedings of the International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2018.

SUDRIYANTO. 2017. Prosiding SNATIF Ke-4 Clustering Loyalitas Pelanggan Dengan Metode RFM (Recenty,Frequency,Monetary) Dan Fuzzy C-Means.




DOI: http://dx.doi.org/10.25126/jtiik.2020752284