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Customer Lifetime Value Clustering Using K-Means Algorithm with Length Recency Frequency Monetary Model to Enhance Customer Relationship Management

Journal of Advances in Information Systems and Technology · Vol. 6 No. 1 (2024) · hlm. 149–165

DOI
10.15294/jaist.v6i1.5011
ISSN
2715-999X, 2714-9714
Terbit
2024-08-23
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

The current era of business growth is fraught with challenges and competition due to rapid technological advancements, rapid market growth, and globalization. This research discusses customer management strategies to enhance Customer Relationship Management (CRM) at PT Digibook Sarana Promosi Indonesia, a company in the digital printing industry. With the emergence of numerous competitors in this challenging business growth era, the k-means algorithm and Length, Recency, Frequency, Monetary (LRFM) model are employed for customer clustering. The results identify two main customer groups. The first group falls into the category of almost lost or uncertain lost customers with the symbol L↓R↑F↓M↓, exhibiting low Customer Lifetime Value (CLV), suggesting a "let go" strategy to focus on more valuable customers. The second group comprises high-value loyal customers with the symbol L↑R↓F↑M↑, demonstrating high CLV, recommending an "enforced" strategy to maintain customer loyalty through loyalty programs. This research indicates that the optimal number of clusters is 2, validated using the ClValid method, with the best values on connectivity, Dunn index, and silhouette.

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  • State University of Semarang
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Riza ArifudinState University of Semarang5
Alamsyah AlamsyahState University of Semarang3
Kholiq BudimanAfiliasi tidak diketahui2
Untsa Zaaidatunni'mahState University of Semarang2
Nuril AsyrofiyyahState University of Semarang2
Dany PradanaState University of Semarang2
Dwijanto DwijantoState University of Semarang2
Amin SuyitnoState University of Semarang2
Amin SuyitnoState University of Semarang2
Budi PrasetiyoState University of Semarang1
Much Aziz MuslimState University of Semarang1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Anggyi Trisnawan PutraAfiliasi tidak diketahui1
zaenal abidinAfiliasi tidak diketahui1
Yahya Nur IfrizaState University of Semarang1
Tiffany Ovilia Dwi LestariState University of Semarang1
Kevin Tito HutahaeanState University of Semarang1
Defa RiandinaState University of Semarang1
Isa AkhlisState University of Semarang1
Taufik Fajar MubaroqAfiliasi tidak diketahui1
Rahmalia NuridaAfiliasi tidak diketahui1
Apriani SolikhatunAfiliasi tidak diketahui1
Safit FirmansyahAfiliasi tidak diketahui1
Anita Ayu SeptiantinaAfiliasi tidak diketahui1
Arief Broto SusiloState University of Semarang1
Tomi Bagus NugrohoState University of Semarang1
Wahyu SyafrizalState University of Semarang1
Oktaria Gina KhoirunnisaState University of Semarang1
Yusuf AffandiState University of Semarang1
Erika Noor DiantiState University of Semarang1
Akbar Lintang AjiState University of Semarang1
Fabian Dominggus Eka BoroState University of Semarang1
Muhammad AfifudinState University of Semarang1
Ahmad Ramadhan AuniState University of Semarang1
Muhammad KharisAfiliasi tidak diketahui1
Fajar Eska PradhanaAfiliasi tidak diketahui1
Ratnaningtyas Widyani PurnamasariAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/jaist/oai, pertama di-harvest 6 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">Customer Lifetime Value Clustering Using K-Means Algorithm with Length Recency Frequency Monetary Model to Enhance Customer Relationship Management</dc:title>
	<dc:creator xml:lang="en">Chairun Nisak</dc:creator>
	<dc:creator xml:lang="en">Endang Sugiharti</dc:creator>
	<dc:subject xml:lang="en">CLV</dc:subject>
	<dc:subject xml:lang="en">K-Means</dc:subject>
	<dc:subject xml:lang="en">LRFM</dc:subject>
	<dc:subject xml:lang="en">CRM</dc:subject>
	<dc:description xml:lang="en">



The current era of business growth is fraught with challenges and competition due to rapid technological advancements, rapid market growth, and globalization. This research discusses customer management strategies to enhance Customer Relationship Management (CRM) at PT Digibook Sarana Promosi Indonesia, a company in the digital printing industry. With the emergence of numerous competitors in this challenging business growth era, the k-means algorithm and Length, Recency, Frequency, Monetary (LRFM) model are employed for customer clustering. The results identify two main customer groups. The first group falls into the category of almost lost or uncertain lost customers with the symbol L↓R↑F↓M↓, exhibiting low Customer Lifetime Value (CLV), suggesting a "let go" strategy to focus on more valuable customers. The second group comprises high-value loyal customers with the symbol L↑R↓F↑M↑, demonstrating high CLV, recommending an "enforced" strategy to maintain customer loyalty through loyalty programs. This research indicates that the optimal number of clusters is 2, validated using the ClValid method, with the best values on connectivity, Dunn index, and silhouette.



</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2024-08-23</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/jaist/article/view/5011</dc:identifier>
	<dc:identifier>10.15294/jaist.v6i1.5011</dc:identifier>
	<dc:source xml:lang="en">Journal of Advances in Information Systems and Technology; Vol. 6 No. 1 (2024): April; 149 - 165</dc:source>
	<dc:source>2715-999X</dc:source>
	<dc:source>2714-9714</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/jaist/article/view/5011/1292</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Journal of Advances in Information Systems and Technology</dc:rights>
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