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Three business analytics case studies were undertaken, encompassing market basket analysis, customer segmentation, and campaign management. SAS Visual Data Mining and Machine Learning on SAS Viya was utilized to explore data and provide insights. A comprehensive report addressing both technical and business aspects was delivered.
This project uses RFM (Recency, Frequency, and Monetary) segmentation to analyze customer behavior and provide insights for targeted marketing campaigns. By classifying customers based on their purchasing patterns, strategies can be tailored to improve customer retention, drive growth, and maximize the lifetime value of each customer.
RFM customer segmentation on 496 e-commerce orders (₹14.28 Cr revenue) to allocate a ₹15L Q3 re-engagement budget across 6 tiers. Python · Power BI · DAX. End-to-end portfolio project.
SQL-powered customer behavior analysis using Instacart’s market order dataset. Includes RFM segmentation, market basket analysis, and reorder pattern discovery.
Companies often struggle to answer key questions: Which products generate the most revenue? When do customers buy the most? Which customers are most valuable? Which customers are likely to stop buying? This analysis uses SQL to transform raw sales data into insights that support better strategy, marketing, and customer retention.
End-to-end retail analytics dashboard built with Power BI. Features star schema data modeling, 37 DAX measures (Time Intelligence, RFM segmentation, Pareto/ABC analysis), What-if discount simulation, and drillthrough store-level analytics.
Explore Superstore sales data with MySQL database setup, data insertion, and cleaning. Perform EDA and RFM customer segmentation using Excel & SQL. #DataAnalysis #CustomerSegmentation #MySQL #EDA