Paper Title
RULE BASED ANALYTICS: ENHANCING E-COMMERCE CUSTOMER INSIGHTS

Abstract
Customer segmentation is central to targeted marketing, retention, and lifetime value optimization. Traditional approaches such as the classical Recency–Frequency–Monetary (RFM) model provide interpretability and ease of implementation [1], but they often overlook behavioral richness, especially in digital contexts. Enhancements to RFM incorporating session time and visit depth have been proposed to improve identification of loyal customers [2], while clusteringbased segmentation methods reveal latent groups but suffer from high computational cost and limited transparency [3]. Temporal dimensions, such as recency decay and lifetime value, further capture evolving customer relationships [5], and hybrid RFM–ML frameworks offer marginal accuracy gains at the expense of explainability [4]. Engagement-focused segmentation using web analytics signals, including staying-rate proxies, has shown effectiveness in differentiating casual from committed users [6]. Rule-based scoring systems implemented in SQL and operationalized via BI dashboards strike a balance between interpretability and practicality [7]. Building on these insights, this project proposes a fully explainable, rule-based segmentation framework using LRFS: Length, Recency, Frequency, and Staying Rate. LRFS features are engineered directly in SQL from transaction and session logs; each metric is binned and scored (1–5), and a simple additive scoring rule assigns customers to Gold, Silver, or Bronze tiers. Decision-tree style conditional logic supplements scoring for business rules (e.g., “High Priority” or “New but Engaged”). Visual analytics are delivered via Power BI dashboards presenting segment distribution, LRFS profiles, and R vs S quadrants for action. The approach is low-cost, scalable, transparent, and readily deployable in enterprise BI stacks. Evaluation focuses on interpretability, business utility (retention lift, campaign targeting), and dashboard responsiveness rather than black-box accuracy metrics. Deliverables include SQL scripts for LRFS engineering, scoring rules, and a Power BI report template that together demonstrate the model’s operational readiness. Keywords - Customer Segmentation, LRFS, Rule-Based Scoring, SQL Feature Engineering, Power Bi, Explainable Analytics