Research on Customer Management in EMS Based on RFM

Jin-yao, Luo; Shao, Peiji; Luo, Bin

doi:10.1109/itcs.2009.142

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…However, It can be seen from the PCI value of the cluster that reaches 0.86, which means the formed clusters have a good quality based on the validation criterion. The RFM approach that was implemented before the FCM method also influenced the segmentation results as indicated in [1]- [5], which shows that the application of RFM in customer segmentation also gives relatively good results.…”

Section: Cluster Analysis Toward Cross-tabulation Among Partnership School Type and School Regionmentioning

confidence: 99%

“…The evaluation approach that can be applied is the evaluation of loyalty. One method that is widely applied to evaluate loyalty is the analysis of recency, frequency, and monetary (RFM) [1]- [5]. RFM analysis generally applied to customer transaction data, namely the transaction date, transaction amount, and financial amount per transaction [6], [7].…”

Section: Introductionmentioning

confidence: 99%

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

Hidayat

Rismayati

Tajuddin

et al. 2020

Jurnal Teknologi dan Sistem Komputer

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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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Section: Cluster Analysis Toward Cross-tabulation Among Partnership School Type and School Regionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Hidayat

Rismayati

Tajuddin

et al. 2020

Jurnal Teknologi dan Sistem Komputer

View full text Add to dashboard Cite

show abstract

“…RFM model is used to represent customer behavior characteristics. This approach records three dimensions of customer transactional data, namely recency, frequency and monetary value, to classify customer behavior (Jinyao Luo, 2009). Combining RFM analysis with statistical method to judge customer value is more convenient than the two methods mentioned above.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method to Identify Customer Value in Tourist Hotel Management

Li¹

2011

Proceedings of the 13th International Conference on Enterprise Information Systems

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Fierce market competition forces tourist enterprise to put more and more attention on the demands of the customer. Customer relationship management (CRM) becomes increasingly important in tourist industry. Identify valuable customers and cultivate these valuable customers is the two basic tasks of CRM. So it has significant meaning for manager to indentify the value of customers. In this paper, we introduce a more convenient statistical method based on RFM (Recency, Frequency, Monetary value) analysis (Xiaoyu Zhao, 2005) to the customer value identification in tourist hotel. We study how to use this method in the management of a tourist hotel. The convenience and importance of this method are demonstrated through comprehensive analysis.

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Research on Customer Management in EMS Based on RFM

Cited by 2 publications

References 4 publications

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

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

An Efficient Method to Identify Customer Value in Tourist Hotel Management

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