📊 SQL Business Analysis using Classic Models Database

Overview

Performed business analytics on the Classic Models database using SQL to analyze customer behavior, credit risk, sales activity, and operational metrics. Applied JOINs, CTEs, Subqueries, CASE statements, Window Functions, and Data Cleaning techniques to extract actionable business insights and support data-driven decision-making.

Business Problems Solved

Customer Analysis 👥

• Analyzed customer distribution across countries and cities.
• Segmented customers into Platinum, Gold, and Silver tiers based on credit limits.
• Identified high-value customers for targeted business strategies.

Sales & Order Analysis 📈

• Identified customers who never placed orders.
• Analyzed customer purchase patterns and order frequency.
• Connected customer and order data for business reporting.

Customer Ranking & Segmentation 📊

• Ranked customers within each country based on credit limits.
• Identified premium accounts and high-value customer segments.

Data Quality & Preparation 💡

• Handled missing values using COALESCE.
• Standardized customer location and address information.

SQL Concepts Used

• SELECT, WHERE, ORDER BY
• GROUP BY, HAVING
• Aggregate Functions
• INNER JOIN, LEFT JOIN, RIGHT JOIN
• Subqueries
• Common Table Expressions (CTEs)
• CASE WHEN
• Window Functions (DENSE_RANK, LEAD)
• Data Cleaning (COALESCE)

Key Insights

• Identified customer concentration across countries and cities.
• Segmented customers based on purchasing capacity and credit risk.
• Discovered inactive customers with no order history.
• Ranked high-value customers to support relationship management.
• Analyzed customer purchase behavior and ordering trends.