This project provides a comprehensive SQL-based reporting framework for analyzing Sales, Customers, and Products within a modern data warehouse environment.

It utilizes a combination of fact and dimension tables (Kimball-style star schema) to deliver key business intelligence metrics such as:

• Change-over-time sales trends (daily, monthly, yearly)
• Cumulative sales analysis
• Year-over-year product performance comparison
• Customer segmentation (VIP, Regular, New)
• Product segmentation (High-Performer, Mid-Range, Low-Performer)
• Part-to-whole category contribution
• Detailed customer and product reports with key KPIs

The SQL scripts are modular, well-documented, and designed to support both operational reporting and strategic decision-making. They can easily be integrated into BI tools such as Power BI, Tableau, or Looker.

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📂 Data Sources

Table	Description
gold.fact_sales	Sales transaction fact table
gold.dim_customers	Customer dimension table
gold.dim_products	Product dimension table

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🔥 SQL Modules & Analytics

1️⃣ Change Over Time Analysis

• Daily Sales Trend
• Monthly Sales Aggregation
• New Customers by Year

2️⃣ Cumulative Sales Analysis

• Running total sales over time

3️⃣ Year-Over-Year Product Performance

• Current sales vs historical average sales per product
• Year-over-year growth comparisons

4️⃣ Part-to-Whole Category Contribution

• Category-level contribution to total revenue

5️⃣ Data Segmentation

• Product Cost Segmentation
• Customer Spending Segmentation

6️⃣ Customer Report View

• Create view gold.report_customers for customer-level analytics and segmentation

7️⃣ Product Report View

• Create view gold.report_products for product-level analytics and segmentation

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📊 Example Use Cases

• Executive dashboards for leadership teams
• Marketing campaign segmentation
• Product portfolio management
• Inventory optimization
• Customer lifetime value modeling (CLV)
• Yearly performance reports

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