Systematic Literature Review (SLR) Model LRFM dan Pengelompokan K-Means untuk Strategi Retensi Pelanggan B2B - Apriyana, Yeni; Trimanadi, Raden; Sensuse, Dana Indra; Lusa, Sofian (Jurnal Teknologi Informasi dan Ilmu Komputer, 2026)
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.