Customer Shopping Behavior Analysis
Problem: Businesses lacked visibility into which customer segments, categories, and discount strategies actually drove revenue vs. just activity Built: An end-to-end pipeline performed data manipulation on 3,900 transactions in Python/pandas, answered 10 business questions in PostgreSQL, and designed data visualizations in Power BI with custom DAX KPIs and multi-filter slicers Result: Uncovered that male customers generated 2x the revenue of female customers, with 3,116 loyal customers vs. only 83 new
Walmart Sales Data Analysis
Problem: Retail leadership lacked clear visibility into true category profitability, peak staffing needs, and which branches were losing revenue year-over-year Built: Performed data manipulation on 10,000+ transactions in Python/pandas, then wrote 9 SQL queries using window functions and CTEs including a custom year-over-year branch comparison Result: Identified Fashion Accessories as the most profitable category and flagged the top 5 branches with the steepest revenue decline