skip to main content

Segmentation of university customers loyalty based on RFM analysis using fuzzy c-means clustering

Department of Computer Science, Universitas Bumigora, Indonesia

Received: 6 May 2019; Revised: 8 Mar 2020; Accepted: 11 Mar 2020; Available online: 18 Mar 2020; Published: 30 Apr 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
One of the strategic plans of the developing universities in obtaining new students is forming a partnership with surrounding high schools. However, partnerships made does not always behave as expected. This paper presented the segmentation technique to the previous new student admission dataset using the integration of recency, frequency, and monetary (RFM) analysis and fuzzy c-means (FCM) algorithm to evaluate the loyalty of the entire school that has bound the partnership with the institution. The dataset is converted using the RFM approach before processed with the FCM algorithm. The result reveals that the schools can be segmented, respectively, as high potential (SP), potential (P), low potential (CP), and very low potential (KP) categories with PCI value 0.86. From the analysis of SP, P, and CP, only 71 % of 52 school partners categorized as loyal partners.

Note: This article has supplementary file(s).

Fulltext View|Download |  Data Set
Research Dataset
Type Data Set
  Download (79KB)    Indexing metadata
Email colleagues
Keywords: new student recruitment strategy; fuzzy c-means; RFM analysis; customers loyalty
Funding: Bumigora University

Article Metrics:

  1. I. Maryani and D. Riana, “Clustering and profiling of customers using rfm for customer relationship management recommendations,” in 5th International Conference on Cyber and IT Service Management, Denpasar, Indonesia, Aug. 2017, pp. 2–7. doi: 10.1109/CITSM.2017.8089258
  2. S. C. Hsu, “The RFM-based institutional customers clustering: case study of a digital content provider,” Information Technology Journal, vol. 11, no. 9, pp. 1193–1201, 2012. doi: 10.3923/itj.2012.1193.1201
  3. L. I. U. Jiale and D. Huiying, “Study on airline customer value evaluation based on RFM model,” in 2010 International Conference on Computer Design and Appliations, Qinhuangdao, China, Jun. 2010, pp. 278–281. doi: 10.1109/ICCDA.2010.5541151
  4. R. A. Daoud, B. Bouikhalene, A. Amine, and R. Lbibb, “Combining RFM model and clustering techniques for customer value analysis of a company selling online,” in 12th International Conference of Computer Systems and Applications, Marrakech, Morocco, Nov. 2015, pp. 1–6. doi: 10.1109/AICCSA.2015.7507238
  5. J. Luo, P. Shao, and B. Luo, “Research on customer management in EMS Based on RFM,” in 2009 International Conference on Information Technology and Computer Science, Kiev, Ukraine, Jul. 2009, pp. 19–23. doi: 10.1109/ITCS.2009.142
  6. R. C. Blattberg, B.-D. Kim, and S. A. Neslin, RFM analysis. New York, NY: Springer, 2008, pp. 323–337
  7. Y. L. Chen, M. H. Kuo, S. Y. Wu, and K. Tang, “Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data,” Electronic Commerce Research and Applications, vol. 8, no. 5, pp. 241–251, 2009. doi: 10.1016/j.elerap.2009.03.002
  8. M. Khajvand and M. J. Tarokh, “Estimating customer future value of different customer segments based on adapted RFM model in retail banking context,” Procedia Computer Science, vol. 3, pp. 1327–1332, 2011. doi: 10.1016/j.procs.2011.01.011
  9. M. Khajvand, K. Zolfaghar, S. Ashoori, and S. Alizadeh, “Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study,” Procedia Computer Science, vol. 3, pp. 57–63, 2011. doi: 10.1016/j.procs.2010.12.011
  10. H. C. Chang and H. P. Tsai, “Group RFM analysis as a novel framework to discover better customer consumption behavior,” Expert Systems with Applications, vol. 38, no. 12, pp. 14499–14513, 2011. doi: 10.1016/j.eswa.2011.05.034
  11. M. Mohammadzadeh, Z. Z. Hoseini, and H. Derafshi, "A data mining approach for modeling churn behavior via RFM model in specialized clinics Case study: A public sector hospital in Tehran," Procedia Computer Science, vol. 120, pp. 23-30, 2017. doi: 10.1016/j.procs.2017.11.206
  12. Y. H. Hu and T. W. Yeh, "Discovering valuable frequent patterns based on RFM analysis without customer identification information," Knowledge-Based Systems, vol. 61, pp. 76-88, 2014. doi: 10.1016/j.knosys.2014.02.009
  13. A. Dursun and M. Caber, "Using data mining techniques for profiling profitable hotel customers: an application of RFM analysis," Tourist Management Perspectives, vol. 18, pp. 153-160, 2016. doi: 10.1016/j.tmp.2016.03.001
  14. V. Golmah, "A case study of applying som in market segmentation of automobile insurance customers," International Journal of Database Theory and Applications, vol. 7, no. 1, pp. 25-36, 2014. doi: 10.14257/ijdta.2014.7.1.03
  15. Y. Kameoka, K. Yagi, S. Munakata, and Y. Yamamoto, "Customer segmentation and visualization by combination of self-organizing map and cluster analysis," in 2015 International Conference on ICT and Knowledge Engineering, Bangkok, Thailand, Nov. 2015, pp. 19-23. doi: 10.1109/ICTKE.2015.7368465
  16. D. L. Olson, Q. Cao, C. Gu, and D. Lee, "Comparison of customer response models," Service Business, vol. 3, pp. 117-130, 2009. doi: 10.1007/s11628-009-0064-8
  17. S. S. Güllüoğlu, "Segmenting customers with data mining techniques," in International Conference on Digital Information, Networking, and Wireless Communications, Moscow, Russia, Feb. 2015, pp. 154-159. doi: 10.1109/DINWC.2015.7054234
  18. A. Wijaya and A. S. Girsang, "The use of data mining for prediction of customer loyalty," CommIT (Communication and Information Technology), vol. 10, no. 1, pp. 41-47, 2016. doi: 10.21512/commit.v10i1.1660
  19. F. Afrin and M. Tabassum, "Comparative performance of using PCA with k-means and fuzzy c-means clustering for customer segmentation," International Journal of Scientific & Technology Research, vol. 4, no. 10, pp. 70-74, 2015
  20. S. Al-Augby, S. Majewski, A. Majewska, and K. Nermend, "A comparison of k-means and fuzzy c-means clustering methods for a sample of cooperation council stock markets," Folia Oeconomica Stetin., vol. 14, no. 2, pp. 19-36, 2015. doi: 10.1515/foli-2015-0001
  21. A. Taufik and S. S. S. Ahmad, "A comparative study of fuzzy c-means and k-means clustering techniques," in Malaysia University Conference on Engineering and Technology, Melaka, Malaysia, Nov. 2014, pp. 1-7
  22. A. Sheshasayee and P. Sharmila, "Comparative study of fuzzy c means and k means algorithm for requirements clustering," Indian Journal of Science and Technology, vol. 7, no. 6, pp. 853-857, 2014
  23. S. Ghosh and S. K. Dubey, "Comparative analysis of k-means and fuzzy c- means algorithms," International Journal of Advanced Computer Science and Applications, vol. 4, no. 4, pp. 35-39, 2013. doi: 10.14569/IJACSA.2013.040406
  24. W. Widiarina and R. S. Wahono, "Algoritma cluster dinamik untuk optimasi cluster pada algoritma k-means dalam pemetaan nasabah potensial," Journal of Intelligent Systems, vol. 1, no. 1, pp. 33-36, 2015
  25. A. Aviliani, U. Sumarwan, I. Sugena, and A. Saefudin, "Segmentasi nasabah tabungan mikro berdasarkan recency, frequency, dan monetary: kasus bank BRI," Jurnal Keuangan dan Perbankan, vol. 13, no. 1, pp. 95-109, 2011
  26. K. Coussement, F. A. M. V. den Bossche, and K. W. De Bock, "Data accuracy’s impact on segmentation performance : benchmarking RFM analysis, logistic regression, and decision trees," Journal of Business Research, vol. 67, no. 1, pp. 2751-2758, 2012. doi: 10.1016/j.jbusres.2012.09.024
  27. E. Prasetyo, Data mining konsep dan aplikasi menggunakan MATLAB. Yogyakarta: Penerbit Andi, 2013
  28. N. I. Selviana and M. Mustakim, "Analisis Perbandingan k-means dan fuzzy c-means untuk pemetaan motivasi balajar mahasiswa," in Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI), Riau, Indonesia, Oct. 2016, pp. 95-105
  29. J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms. Plenum Press, 1981. doi: 10.1007/978-1-4757-0450-1
  30. B. Balasko, J. Abonyi, and B. Feil, “Fuzzy clustering and data analysis toolbox,” [online]. Available:
  31. Y. Hu, C. Zuo, Y. Yang, and F. Qu, "A cluster validity index for fuzzy c-means clustering," in International Conference on System Science, Engineering Design and Manufacturing Informatization, Guiyang, China, Oct. 2011, pp. 263-266. doi: 10.1109/ICSSEM.2011.6081293

Last update:

  1. Research on Consumer Purchasing Prediction Based on XGBoost Algorithm

    Shengyin Luo, Sibo Zhang, Hang Cong. 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), 2021. doi: 10.1109/ICAICA52286.2021.9497944

Last update: 2022-10-06 22:07:47

No citation recorded.