Savings Customer Segmentation Using RFMB Model and K-Means Clustering
Article Sidebar
Main Article Content
Abstract
In increasing company growth, marketing strategies must be appropriate. Segmentation is carried out to create strategies by identifying potential customers. Recency Frequency Monetary (RFM) models analyze customer behavior. It is developed based on current transactions and balances and is called the RFMB model. Clustering can be interpreted as the process of grouping or classifying objects based on information obtained from data that can describe the relationships between objects. K-Means clustering algorithm that has the ability to group quite large amounts of data, and partition the dataset into several clusters. Base 65 thousand data from 1 branch, 147 thousand transactions in semester 1 2017, including cash withdrawals and deposits, book transfers, and ATM transactions. Results for 5 customer groups: SUPER, highest score for several attributes, 1,493 customers. PLATINUM, which has the highest balance, 44 customers. PREMIUM, 623 customers who have the largest transaction value. GOLD, 2,918 customers frequently transact, and CLASSIC's new transactions totaled 4062 customers.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Cheng, C.-H., & Chen, Y.-S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications, 4176–4184. https://doi.org/10.1016/j.eswa.2008.04.003.
Christy, A. J., Umamakeswari, A., Priyatharsini, L., & Neyaa, A. (2018). RFM Ranking – An Effective Approach to Customer Segmentation. Journal of King Saud University - Computer and Information, https://doi.org/10.1016/j.jksuci.2018.09.004.
Firdaus, U., & Utama, D. N. (2020, 05). Balance as one of the attributes in the customer segmentation analysis method: Systematic Literature Review. Advances in Science, Technology and Engineering Systems Journal, 5(3), 334-339.
Hardiani, T., Sulistyo, S., & Hartanto, R. (2015). Segmentasi Nasabah Tabungan Menggunakan Model RFM (Recency, Frequency,Monetary) dan K-Means Pada Lembaga Keuangan Mikro. Seminar Nasional Teknologi Informasi dan Komunikasi Terapan (SEMANTIK) (hal. 463-468). ISBN: 979-26-0280-1.
Harun, C. A., & Nattan, R. R. (2013). Non Core Deposit: Studi terhadap Dana Pihak Ketiga Perbankan Indonesia. Jakarta: Bank Indonesia.
Hu, Y.-H., & Yeh, T.-W. (2014). Discovering valuable frequent patterns based on RFM analysis without. Knowledge-Based Systems, 76-88. https://doi.org/10.1016/j.knosys.2014.02.009.
Mawoli, M. A. (2012). Effective Market Segmentation and Viability of Islamic Banking in Nigeria. Australian Journal of Business and Management Research Vol.1, 1-9.
Nainggolan, R., & Lumbantoruan, G. (2018). Optimasi performa cluster K-means menggunakan Sum of Squared Errors (SSE). METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 2 No.2, 103-108. ISSN: 2620-4339.
Newell, F. (1997). The new rules of marketing: How to use one-to-one relationship. New York: McGraw-Hills Companies.
Tavakoli, M., Molavi, M., Masoumi, V., Mobini, M., Etemad, S., & Rahmani, R. (2018). Customer Segmentation and Strategy Development based on User Behavior Analysis, RFM model and Data Mining Techniques: A Case Study. IEEE. International Conference on e-Business Engineering (ICEBE), (hal. 119-126. DOI 10.1109/ICEBE.2018.00027).