🚀 Optimizing Price Using SQL + Revenue Modeling
I recently completed a project where I analyzed a real demand-curve dataset using DuckDB SQL to answer a key business question:
👉 What price maximizes total revenue?
Using regression functions like REGR_SLOPE and REGR_INTERCEPT, I built a revenue model, calculated the optimal price point, and compared it with real transaction data to validate the result.
This project strengthened my skills in:
📌 SQL (DuckDB)
📌 Regression modeling
📌 Demand curve analysis
📌 Revenue optimization
📌 Business-focused analytics
It’s a great example of how technical SQL skills + business thinking help companies set smarter prices and increase revenue.
💡 Key Insights from the Analysis
Built a demand curve using price vs. quantity sold
Applied log-linear regression directly in SQL
Used REGR_SLOPE and REGR_INTERCEPT to model demand
Derived the optimal price using calculus-based SQL expressions
Calculated expected maximum revenue
Validated the model by comparing against real revenue records 📈 Results
Optimal Price: $20,106.44
Expected Max Revenue: $733,106.95
Highest Observed Revenue: $840,703.51 (used for model validation)