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

*Syahroni Hidayat orcid scopus  -  Department of Computer Science, Universitas Bumigora, Indonesia
Ria Rismayati  -  Department of Computer Science, Universitas Bumigora, Indonesia
Muhammad Tajuddin scopus  -  Department of Computer Science, Universitas Bumigora, Indonesia
Ni Luh Putu Merawati  -  Department of Computer Science, Universitas Bumigora, Indonesia
Received: 6 May 2019; Revised: 8 Mar 2020; Accepted: 11 Mar 2020; Published: 30 Apr 2020; Available online: 18 Mar 2020.
DOI: https://doi.org/10.14710/jtsiskom.8.2.2020.133-139 View
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Section: Original Research Articles
Language: EN
In Volume 8, Issue 2, Year 2020 (April 2020)
Statistics: 399 135
Abstract
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.

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Keywords: new student recruitment strategy; fuzzy c-means; RFM analysis; customers loyalty

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