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Research in Education, Technology, and Multiculture
Published by Institute of Multidisciplinary Research and Community Service
ISSN : -     EISSN : 30256763     DOI : http://doi.org/10.61436/rietm
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Research in Education, Technology, and Multiculture is an open-access, peer-reviewed journal that provides a comprehensive platform for the dissemination of scholarly works across three primary pillars: Technology and Applied Sciences, Technology-Enhanced Education, and Ethnics and Multiculturalism. The journal accommodates a broad spectrum of studies within these fields, encouraging both independent explorations and multidisciplinary approaches. The journal welcomes original research articles, conceptual papers, case studies, and community service reports that contribute to academic development and practical knowledge. Topics of interest comprehensively cover, but are not limited to: • Technology and Applied Sciences: information and communication technology, artificial Intelligence, block chain and digital transformation, STEM disciplines, alongside industrial and practical applications of applied sciences, as well as fundamental and applied research in mathematics and natural sciences. • Technology-Enhanced Education: Digital learning environments, educational technology (EdTech) innovations, and ICT based pedagogical strategies. • Ethnics and Multiculturalism: Multiculturalism in society and educational settings, ethnic relations, immigration and migrant workers’ studies, intercultural communication, cultural heritage, and diversity in social life.
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Search results for "clv-enhanced rfm framework for customer segmentation in indonesian smes using k-means clustering" : 1 Documents clear
CLV-Enhanced RFM Framework for Customer Segmentation in Indonesian SMEs Using K-Means Clustering Dio Rizkyandita; Heri Wijayanto; I Gde Putu Wirarama Wedashwara Wirawan; Mosiur Rahaman
Research in Education, Technology, and Multiculture Vol 5, No 1 (2026): Research in Education, Technology, and Multiculture
Publisher : Institute of Multidisciplinary Research and Community Service

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61436/rietm/v5i1.pp1-14

Abstract

Micro, Small, and Medium Enterprises (MSMEs) contribute more than 61% of Indonesia's Gross Domestic Product, yet most of them still face limitations in leveraging transactional data for customer retention strategies. Prior studies have extensively combined Recency, Frequency, and Monetary (RFM) analysis with the K-Means algorithm for customer segmentation, but the majority treat Customer Lifetime Value (CLV) only as a post-hoc label assigned to clusters after the clustering process is finalized, rather than as a feature that shapes the segment structure from the beginning. This study addresses three research questions: how CLV can be effectively integrated as a clustering input, what segmentation structure emerges from this approach, and what concrete retention strategies can be derived for MSMEs with limited analytical capabilities. The proposed framework incorporates CLV, calculated as the discounted historical net sales using a 10% annual discount rate, as the fourth feature in the K-Means feature space with K-Means++ initialization, alongside RFM variables standardized using Z-scores. The framework is applied to a real dataset from a coffee shop MSME in Indonesia, comprising 19,126 transactions, of which 4,310 are member transactions from 472 unique registered customers, recorded throughout January–December 2023. The optimal number of clusters is determined through the convergence of the Elbow Method and the Silhouette Coefficient, both indicating four clusters as the best solution with a silhouette score of 0.5105. The segmentation divided customers into four tiers: Platinum, Gold, Silver, and Bronze. A key finding was a highly concentrated value distribution, with just 1.48% of customers (n = 7) contributing 21.66% of total revenue and CLV. This pattern is significantly more skewed than the traditional 80/20 Pareto rule. This concentration is interpreted through the lenses of habit formation, small base amplification, and comparative empirical evidence. The four-tier framework translates into differentiated retention strategies: VIP retention, value uplift, repeat-purchase incentives, and win-back campaigns, with monetary thresholds calibrated to each segment's median value. Keywords: K-Means, Clustering, RFM, Customer Lifetime Value, Customer Segmentation, MSMEs.

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