Contemporary e-commerce platforms generate voluminous transactional and behavioral data streams that, absent a structured analytical framework, remain commercially inert. ShopMind 360 is proposed as a comprehensive, end-to-end behavioral analytics and personalization engine that synthesizes four complementary machine learning and data mining methodologies within a single, automated pipeline. The system operationalizes Recency-Frequency-Monetary (RFM) analysis for multidimensional customer value quantification, K-Means clustering for behavioral segmentation into four actionable customer archetypes (Champions, Loyal Customers, At-Risk, and Hibernating), Random Forest ensemble classification for continuous purchase-probability scoring, and the Apriori algorithm for market-basket association rule mining to generate confidence-ranked product recommendations. The analytical pipeline ingests a synthetic yet realistically parameterized dataset comprising 500 customer profiles, 50 product records, 2,000 transactional orders, and 5,000 browsing interaction logs, structured within a multi-sheet Excel workbook and subsequently persisted to a normalized MySQL relational database. All computed analytical results are surfaced through an interactive Power BI dashboard, providing business stakeholders with filterable, real-time-refreshable visualizations of customer segments, revenue contributions, purchase probability distributions, and product affinity rules. Experimental evaluation demonstrates that the four-segment K-Means model achieves stable cluster centroids with well-separated RFM profiles, the Random Forest classifier attains high discriminative accuracy in identifying high-value customer segments, and Apriori mining yields statistically significant association rules with lift values substantially exceeding unity. The system architecture adheres to modular design principles, enabling independent maintenance and extensibility of each analytical component without pipeline restructuring. ShopMind 360 establishes a replicable, open-source blueprint for data-driven customer engagement in small-to-medium-scale e-commerce operations.
Kumar et al. (Thu,) studied this question.