Net Revenue Analysis Across Warehouses -
SQL Project (DataCamp)

🧭 Context & Challenge:

A motorcycle parts distributor operating across three warehouses needed insight into monthly net revenue per product line, segmented by warehouse and client type.

The company accepts multiple payment methods (each with different fees), and leadership requested a clearer view of wholesale revenue trends across locations and time periods.

🧩 Objectives:

• Calculate net revenue by product line, month, and warehouse

• Ensure only wholesale transactions are included

• Account for payment method fees in revenue calculation

• Present data in a clean, decision-ready format

🛠️ Technical Approach (SQL):

• Used CASE logic to extract and format months from date

• Filtered dataset to only include "Wholesale" clients

• Grouped by product line, month, and warehouse

• Calculated net revenue: SUM(total) - SUM(payment_fee)

• Sorted results by product and revenue to highlight top categories

🔍 Strategic Learnings:

This project reinforced how well-structured SQL queries can surface powerful, revenue-critical insights.

By combining conditional logic, grouping, and business-specific calculations, I delivered a lean, scalable model for monthly reporting that could support decisions in pricing, product strategy, and operational focus.