Retail Performance Analysis Using Power BI & SQL
Retail Performance Analysis Using Power BI & SQL
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Developed a 3-page interactive Retail Performance Dashboard in Power BI with cross-page navigation and dynamic date slicers, transforming raw transactional data into executive-ready insights
Analyzed 5.34M+ in sales revenue across 4 regions and 20 products, surfacing KPIs like 28.77% profit margin, 23.48% YoY growth, and average order value of 44.53K to support data-driven decisions
Segmented 60+ customers by age, gender, and geography using advanced DAX measures, uncovering repeat purchase patterns across 120 orders to drive targeted marketing strategies
Built a real-time inventory control module with automated low-stock alerts, supplier performance benchmarking, and order fulfillment tracking (65% delivered, 9.17% returned) — reducing stockout risk
Swiggy Sales & Market Performance Analysis Using Power BI & SQL
Swiggy Sales & Market Performance Analysis Using Power BI & SQL
Built an end-to-end analytics project analyzing 198K Swiggy orders using SQL and Power BI. The project explores sales trends, city-level demand, restaurant performance, and food category preferences.
Key findings revealed ₹52.99M in total sales, strong demand in major cities like Bengaluru and Hyderabad, and that Non-Veg food accounts for ~63% of total orders. The analysis also identified weekend sales peaks and top-performing restaurants, helping highlight opportunities for marketing campaigns and partnership optimization.
Telecom Customer Churn Analysis Using Power BI
Telecom Customer Churn Analysis Using Power BI
Built a Telecom Customer Churn Analysis Dashboard in Power BI across 20K customers, uncovering a 34.22% churn rate and quantifying 18.59M in charges lost to churned customers
Identified tenure as the strongest churn predictor — new customers (0–10 months) churned at 60%+, while long-tenure customers stabilized below 25%, guiding early engagement strategy
Compared churn behavior across contract types, internet services, tech support availability, and payment modes to pinpoint the highest-risk customer segments for proactive retention
Delivered business-ready insights showing month-to-month contract holders churn at 2x the rate of annual subscribers, directly supporting recommendations to incentivize long-term plan adoption
Daily Household Transaction Analysis Using Power BI, SQL & Python
Daily Household Transaction Analysis Using Power BI, SQL & Python
Developed a comprehensive Personal Finance Intelligence Dashboard in Power BI tracking 7M in household transactions across 4 years, with drill-through pages for Overview, Spending Behavior, Transaction Trends, and Transaction-Level Details
Applied DAX measures to surface a 35.66% savings rate, 1.09M net savings, and YoY expense trends — giving a clear picture of financial health over time
Analyzed 25+ spending categories and payment mode behavior to identify where money leaks, with Money Transfers (606K) and Investments (271K) as top outflow drivers
Visualized cumulative wealth growth from 0 → 6.8M alongside daily and weekly transaction patterns, enabling data-backed decisions on budgeting and expense control
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