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An empirical comparison of clustering approaches for recency, frequency, and monetary customer segmentation
IAES International Journal of Artificial Intelligence
ISSN 2252-8938, 2089-4872
Terbit 2026-08-01
Penerbit Institute of Advanced Engineering and Science
Bahasa eng
Abstrak
This study evaluates effectiveness of three clustering techniques—k-means, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN)—applied to the recency-frequency-monetary (RFM) model for customer segmentation in the retail sector. Using sales transaction data from a distributor of computer accessories and printing products. The results show that k-means achieved the best clustering validation scores and effectively identified high-value customers, hierarchical clustering generated less meaningful groupings than k-means, and DBSCAN misclassified key customers as noise. These findings highlight k-means as the most suitable technique for RFM-based segmentation in this retail business context. The study offers practical insights for retail and distribution businesses aiming to adopt data-driven customer strategies and suggests future research to enhance segmentation robustness and refine the RFM framework.
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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:title xml:lang="en-US">An empirical comparison of clustering approaches for recency, frequency, and monetary customer segmentation</dc:title>
<dc:creator>Lau, Upekkha</dc:creator>
<dc:creator>Wasesa, Meditya</dc:creator>
<dc:subject xml:lang="en-US"></dc:subject>
<dc:subject xml:lang="en-US">Business analytics; Customer segmentation; DBSCAN clustering; Hierarchical clustering; K-means clustering</dc:subject>
<dc:description xml:lang="en-US">This study evaluates effectiveness of three clustering techniques—k-means, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN)—applied to the recency-frequency-monetary (RFM) model for customer segmentation in the retail sector. Using sales transaction data from a distributor of computer accessories and printing products. The results show that k-means achieved the best clustering validation scores and effectively identified high-value customers, hierarchical clustering generated less meaningful groupings than k-means, and DBSCAN misclassified key customers as noise. These findings highlight k-means as the most suitable technique for RFM-based segmentation in this retail business context. The study offers practical insights for retail and distribution businesses aiming to adopt data-driven customer strategies and suggests future research to enhance segmentation robustness and refine the RFM framework.</dc:description>
<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
<dc:contributor xml:lang="en-US">Institut Teknologi Bandung</dc:contributor>
<dc:date>2026-08-01</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en-US"></dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/29126</dc:identifier>
<dc:identifier>10.11591/ijai.v15.i4.pp3402-3410</dc:identifier>
<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 4: August 2026; 3402-3410</dc:source>
<dc:source>2252-8938</dc:source>
<dc:source>2089-4872</dc:source>
<dc:source>10.11591/ijai.v15.i4</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/29126/15301</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2026 Upekkha Lau, Meditya Wasesa</dc:rights>
<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
Sitasi dan pengindeksan
Jumlah menurut tiap indeks pada 13 Nov 2026; setiap indeks menghitung sitasi dengan cara berbeda.
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Sitasi artikel ini
APA 7
Lau, U., & Wasesa, M. (2026). An empirical comparison of clustering approaches for recency, frequency, and monetary customer segmentation. IAES International Journal of Artificial Intelligence, 15(4), 3402–3410. https://doi.org/10.11591/ijai.v15.i4.pp3402-3410
Salin
IEEE
U. Lau and M. Wasesa, “An empirical comparison of clustering approaches for recency, frequency, and monetary customer segmentation,” IAES International Journal of Artificial Intelligence, vol. 15, no. 4, pp. 3402–3410, 2026, doi: 10.11591/ijai.v15.i4.pp3402-3410.
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