Customer Segmentation-Based Promotion Recommendation Using RFM and K-Means Clustering on a Web Platform - Kemal Fiqri Handoko; Nugraha Nugraha; Alun Sujjada (Komunitas Dosen Indonesia, 2026)
Traditional micro-scale retail businesses, such as neighborhood stores, frequently apply promotions uniformly due to limited analytical capacity, leading to inefficient resource use and weak customer retention. Existing promotion recommendation studies predominantly focus on large-scale or online retail settings, leaving a methodological and translational gap in data-driven promotional decision support for micro-scale traditional retail contexts. This study aims to address this gap by developing and evaluating a web-based promotion recommendation system tailored to operational constraints of small neighborhood stores. Customer purchasing behavior is modeled using the Recency, Frequency, and Monetary (RFM) framework, and customer segmentation is performed with the K-Means clustering algorithm. The study utilizes transaction records from 1,043 registered customers comprising 1,500 transactions collected between February and April 2025. Five customer segments are identified, namely VIP customers, frequent buyers, occasional shoppers, at-risk customers, and new customers. Clustering quality is assessed using the Silhouette Score, achieving a value of 0.4464, which indicates moderate cluster cohesion and separation. Promotional performance is evaluated through a pre–post implementation comparison, where the observed 157.09% sales increase reflects an associative improvement rather than a causal estimate of system impact. Analytically, the study contributes a validated customer segmentation pipeline suitable for sparse micro-retail data, while at the system level it delivers an operational web-based decision support tool that translates segmentation results into actionable promotional recommendations. Although practically useful, the evaluation covers one store and a short period, limiting generalizability and causal inference.