SME Business Intelligence Support Using Retrieval-Augmented Generation and RFM Segmentation - Rosalina Rosalina; Noor Lees Ismail; Genta Sahuri; Joseph Tedja Nugraha Wibawa (Bright Publisher, 2026)
This study presents the design and evaluation of a cloud-based business intelligence support system for small and medium enterprises that integrates retrieval-grounded text generation with recency–frequency–monetary customer segmentation to enhance digital customer communication and promotional decision making. The primary objective is to assist individual small businesses in responding accurately to customer inquiries while simultaneously leveraging historical transaction data to identify actionable customer groups, all within their existing messaging workflows through a mobile keyboard interface. The proposed framework combines two complementary components. The first component automatically generates customer replies by retrieving semantically relevant information from a structured business knowledge base and using it to produce grounded, context-aware responses. The second component analyzes invoice records to segment customers into loyal, moderate, and at-risk groups, enabling sellers to tailor promotional strategies based on observed purchasing behavior. The system is implemented as a cloud service accessed by individual enterprises without requiring local infrastructure or model training. System evaluation was conducted using real small business data collected over several weeks. Experimental procedures included retrieval faithfulness analysis, response correctness evaluation with confidence intervals, customer cluster validation using silhouette analysis, end-to-end latency measurement, and structured user acceptance testing. Performance results demonstrate that the retrieval mechanism consistently provides accurate knowledge grounding, while the segmentation module effectively distinguishes high-value and churn-risk customers. The average response time remained within a range perceived as acceptable for real-time mobile conversations, and user testing confirms that the keyboard-based interface does not disrupt normal communication practices. The findings indicate that embedding retrieval-grounded generation and lightweight customer analytics directly into daily messaging tools can significantly improve the operational efficiency of small enterprises. This integrated approach reduces the burden of manual response handling while enabling data-driven promotional decision making. The framework offers a practical pathway for adopting artificial intelligence in small business environments and provides a foundation for future enhancements such as temporal behavior modeling and multilingual support.