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

Syahroni Hidayat · Ria Rismayati · Muhammad Tajuddin · Ni Luh Putu Merawati

11 citations0/0 files storedJurnal Teknologi dan Sistem Komputer

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

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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References

2011Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study 2016Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis 2011Estimating customer future value of different customer segments based on adapted RFM model in retail banking context 2012Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees 2011Group RFM analysis as a novel framework to discover better customer consumption behavior 2017Clustering and profiling of customers using RFM for customer relationship management recommendations 2009Comparison of customer response models 2015Use of Data Mining for Prediction of Customer Loyalty

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2021Churn Prediction on Higher Education Data with Fuzzy Logic Algorithm

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