Wholesale Revenue Analysis by Product Line**

📊 Project Overview

This project analyzes wholesale sales data to determine how much net revenue each product line generated per month per warehouse. The dataset contains sales transactions from June to August 2021, including details such as payment methods, warehouses, and client types.

The insights from this analysis can help business stakeholders understand revenue trends across product categories, warehouses, and time periods to optimize supply chain and sales strategies.

🎯 Objectives

✅ Filter sales data to include only wholesale orders
✅ Calculate net revenue as SUM(total - payment_fee)
✅ Group results by product line, month, and warehouse
✅ Format month names as 'June', 'July', and 'August'
✅ Sort results by product line, month, and net revenue (descending order)

🛠 Tech Stack

• SQL (PostgreSQL)
• Data Aggregation & Query Optimization

📝 SQL Query Breakdown

The main query performs the following operations:

1. Filters only "Wholesale" orders using WHERE client_type = 'Wholesale'
2. Extracts the month name using TO_CHAR(date, 'Month')
3. Calculates net revenue as SUM(total - payment_fee)
4. Groups results by product_line, month, warehouse
5. Sorts the final output

📌 SQL Query

SELECT 
    product_line,
    TO_CHAR(date, 'Month') AS month,
    warehouse,
    SUM(total - payment_fee) AS net_revenue
FROM sales
WHERE client_type = 'Wholesale'
GROUP BY product_line, month, warehouse
ORDER BY product_line, month, net_revenue DESC;

📊 Expected Output

product_line	month	warehouse	net_revenue
Product A	June	North	$XX,XXX
Product B	June	West	$XX,XXX
...	...	...	...

📌 Key Learnings

• Using TO_CHAR() to format date values
• Applying SUM() for revenue aggregation
• Structuring SQL queries for performance optimization
• Sorting results for better insights