CLV-Enhanced RFM Framework for Customer Segmentation in Indonesian SMEs Using K-Means Clustering
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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