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Systematic Literature Review (SLR) Model LRFM dan Pengelompokan K-Means untuk Strategi Retensi Pelanggan B2B

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 13 No. 2 (2026) · pp. 459–464

DOI
10.25126/jtiik.132
ISSN
2528-6579, 2355-7699
Published
2026-04-30
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
id
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Indonesian

Dalam era persaingan pasar yang semakin ketat, strategi retensi pelanggan berbasis data menjadi krusial, khususnya dalam konteks Business-to-Business (B2B) yang masih relatif terbatas dibahas dalam literatur. Penelitian ini merupakan Systematic Literature Review (SLR) yang bertujuan untuk memetakan dan mensintesis penelitian terkait penerapan model LRFM (Length, Recency, Frequency, Monetary) dan algoritma K-Means dalam strategi retensi pelanggan. Model metodologi SLR mengikuti protokol Kitchenham et al. (2009) melalui tahapan perumusan pertanyaan penelitian, pencarian literatur, seleksi studi, dan sintesis hasil. Hasil kajian menunjukkan bahwa sebagian besar penelitian LRFM dan K-Means masih berfokus pada konteks B2C, sementara penerapannya dalam lingkungan B2B relatif terbatas dan belum terkonseptualisasi secara memadai. Berdasarkan kesenjangan tersebut, artikel ini mengusulkan kerangka konseptual LRFM-B2B sebagai agenda penelitian masa depan dengan mempertimbangkan karakteristik spesifik B2B, tanpa dilakukan pengujian atau analisis empiris. Penelitian ini berkontribusi pada pemetaan literatur, identifikasi kesenjangan riset, serta perumusan arah pengembangan analitik pelanggan dalam konteks B2B. Abstract In an era of increasingly intense market competition, data-driven customer retention strategies have become crucial, particularly in the Business-to-Business (B2B) context, which remains underexplored in the existing literature. This study presents a Systematic Literature Review (SLR) that aims to map and synthesize prior research on the application of the LRFM (Length, Recency, Frequency, Monetary) model and K-Means clustering for customer retention strategies. The review follows the Kitchenham et al. (2009) protocol, including research question formulation, literature search, study selection, and result synthesis. The findings indicate that most existing studies focus on B2C contexts, while B2B applications remain limited and conceptually underdeveloped. Based on the identified research gaps, this article proposes a conceptual LRFM-B2B framework as a future research agenda by incorporating B2B-specific characteristics such as contractual value and relationship depth. PT. XYZ is included solely as an illustrative case to contextualize general B2B challenges, without conducting any empirical testing or data analysis. This study contributes by providing a structured literature mapping, identifying critical research gaps, and outlining directions for future B2B customer analytics research.

English

Dalam era persaingan pasar yang semakin ketat, strategi retensi pelanggan berbasis data menjadi krusial, khususnya dalam konteks Business-to-Business (B2B) yang masih relatif terbatas dibahas dalam literatur. Penelitian ini merupakan Systematic Literature Review (SLR) yang bertujuan untuk memetakan dan mensintesis penelitian terkait penerapan model LRFM (Length, Recency, Frequency, Monetary) dan algoritma K-Means dalam strategi retensi pelanggan. Model metodologi SLR mengikuti protokol Kitchenham et al. (2009) melalui tahapan perumusan pertanyaan penelitian, pencarian literatur, seleksi studi, dan sintesis hasil. Hasil kajian menunjukkan bahwa sebagian besar penelitian LRFM dan K-Means masih berfokus pada konteks B2C, sementara penerapannya dalam lingkungan B2B relatif terbatas dan belum terkonseptualisasi secara memadai. Berdasarkan kesenjangan tersebut, artikel ini mengusulkan kerangka konseptual LRFM-B2B sebagai agenda penelitian masa depan dengan mempertimbangkan karakteristik spesifik B2B, tanpa dilakukan pengujian atau analisis empiris. Penelitian ini berkontribusi pada pemetaan literatur, identifikasi kesenjangan riset, serta perumusan arah pengembangan analitik pelanggan dalam konteks B2B. Abstract In an era of increasingly intense market competition, data-driven customer retention strategies have become crucial, particularly in the Business-to-Business (B2B) context, which remains underexplored in the existing literature. This study presents a Systematic Literature Review (SLR) that aims to map and synthesize prior research on the application of the LRFM (Length, Recency, Frequency, Monetary) model and K-Means clustering for customer retention strategies. The review follows the Kitchenham et al. (2009) protocol, including research question formulation, literature search, study selection, and result synthesis. The findings indicate that most existing studies focus on B2C contexts, while B2B applications remain limited and conceptually underdeveloped. Based on the identified research gaps, this article proposes a conceptual LRFM-B2B framework as a future research agenda by incorporating B2B-specific characteristics such as contractual value and relationship depth. PT. XYZ is included solely as an illustrative case to contextualize general B2B challenges, without conducting any empirical testing or data analysis. This study contributes by providing a structured literature mapping, identifying critical research gaps, and outlining directions for future B2B customer analytics research.

Keywords

Collaboration network of the authors

38 authors · 103 co-authorship links · 30 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Indonesia
  • Institut Pariwisata Trisakti
  • National Research and Innovation Agency
  • Universitas Teknokrat Indonesia
  • Badan Pusat Statistik
  • Bakrie University
  • STMIK Bumigora Mataram
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Prasetyo Adi Wibowo PutroUniversity of Indonesia4
Erisva Hakiki PurwaningsihUniversity of Indonesia3
Deden Sumirat HidayatNational Research and Innovation Agency3
Ryan Randy SuryonoUniversitas Teknokrat Indonesia2
Nandhita Zefania MaharaniUniversity of Indonesia2
Shabrina Salsabila KurniawanUniversity of Indonesia2
Puja Putri AbdullahUniversity of Indonesia2
Rina RahmawatiUniversity of Indonesia2
Assaf AriefUniversity of Indonesia2
Prasetyo AdiUniversity of Indonesia2
Ferdian Maulana AkbarUniversity of Indonesia2
Robby HermansyahUniversity of Indonesia2
Nadya SafitriUniversity of Indonesia2
Damayanti ElisabethUniversity of Indonesia2
Elin CahyaningsihBakrie University2
Iqbal Caraka AltinoUniversity of Indonesia2
Wahyu Setyawan WibowoAffiliation unknown2
Husain HusainSTMIK Bumigora Mataram2
Handika MasbudiAffiliation unknown2
Sofianti IndriasariAffiliation unknown2
Rini MuliahatiAffiliation unknown1
Herlambang PermadiBadan Pusat Statistik1
Rias Kumalasari DeviUniversity of Indonesia1
KautsarinaAffiliation unknown1
Muhamad Adhytia Wana Putra RahmadhanUniversity of Indonesia1
Kautsarina KautsarinaAffiliation unknown1
Rahma DestrianiUniversity of Indonesia1
Raihansyah Yoga AdhitamaUniversity of Indonesia1
Imairi EitiveniUniversity of Indonesia1
Nur Isni NirwanAffiliation unknown1
Apriyanti SijabatAffiliation unknown1
Adi Suryaputra ParamitaUniversitas Ciputra1
Harjanto PrabowoAffiliation unknown1
Arief RamadhanAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jtiik.ub.ac.id/index.php/jtiik/en/oai, first harvested 27 Oct 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="id">Systematic Literature Review (SLR) Model LRFM dan Pengelompokan K-Means untuk Strategi Retensi Pelanggan B2B</dc:title>
	<dc:title xml:lang="en">Systematic Literature Review (SLR) Model LRFM dan Pengelompokan K-Means untuk Strategi Retensi Pelanggan B2B</dc:title>
	<dc:creator xml:lang="en">Apriyana, Yeni</dc:creator>
	<dc:creator xml:lang="en">Trimanadi, Raden </dc:creator>
	<dc:creator xml:lang="en">Sensuse, Dana Indra </dc:creator>
	<dc:creator xml:lang="en">Lusa, Sofian </dc:creator>
	<dc:creator xml:lang="id">Apriyana, Yeni</dc:creator>
	<dc:creator xml:lang="id">Trimanadi, Raden </dc:creator>
	<dc:creator xml:lang="id">Sensuse, Dana Indra </dc:creator>
	<dc:creator xml:lang="id">Lusa, Sofian </dc:creator>
	<dc:subject xml:lang="id">K-Means</dc:subject>
	<dc:subject xml:lang="id">B2B</dc:subject>
	<dc:subject xml:lang="id">Retensi Pelanggan</dc:subject>
	<dc:subject xml:lang="id">Model LRFM</dc:subject>
	<dc:subject xml:lang="en">Customer retention</dc:subject>
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Abstract
In an era of increasingly intense market competition, data-driven customer retention strategies have become crucial, particularly in the Business-to-Business (B2B) context, which remains underexplored in the existing literature. This study presents a Systematic Literature Review (SLR) that aims to map and synthesize prior research on the application of the LRFM (Length, Recency, Frequency, Monetary) model and K-Means clustering for customer retention strategies. The review follows the Kitchenham et al. (2009) protocol, including research question formulation, literature search, study selection, and result synthesis. The findings indicate that most existing studies focus on B2C contexts, while B2B applications remain limited and conceptually underdeveloped. Based on the identified research gaps, this article proposes a conceptual LRFM-B2B framework as a future research agenda by incorporating B2B-specific characteristics such as contractual value and relationship depth. PT. XYZ is included solely as an illustrative case to contextualize general B2B challenges, without conducting any empirical testing or data analysis. This study contributes by providing a structured literature mapping, identifying critical research gaps, and outlining directions for future B2B customer analytics research.</dc:description>
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Abstract
In an era of increasingly intense market competition, data-driven customer retention strategies have become crucial, particularly in the Business-to-Business (B2B) context, which remains underexplored in the existing literature. This study presents a Systematic Literature Review (SLR) that aims to map and synthesize prior research on the application of the LRFM (Length, Recency, Frequency, Monetary) model and K-Means clustering for customer retention strategies. The review follows the Kitchenham et al. (2009) protocol, including research question formulation, literature search, study selection, and result synthesis. The findings indicate that most existing studies focus on B2C contexts, while B2B applications remain limited and conceptually underdeveloped. Based on the identified research gaps, this article proposes a conceptual LRFM-B2B framework as a future research agenda by incorporating B2B-specific characteristics such as contractual value and relationship depth. PT. XYZ is included solely as an illustrative case to contextualize general B2B challenges, without conducting any empirical testing or data analysis. This study contributes by providing a structured literature mapping, identifying critical research gaps, and outlining directions for future B2B customer analytics research.</dc:description>
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	<dc:rights xml:lang="en">Copyright (c) 2026 Yeni Apriyana, Raden  Trimanadi, Dana Indra  Sensuse, Sofian  Lusa</dc:rights>
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