📦 Data Mart – Sales & Customer Analytics

SQL-Based Retail Data Analysis Project

The Data Mart project explores customer purchasing behavior and sales performance across multiple retail platforms. Using advanced SQL queries such as CTEs, joins, and window functions, this project identifies profitable segments, evaluates customer lifetime value, and measures store-level growth. The analysis supports retail teams in optimizing marketing campaigns, product pricing, and inventory planning.

Data Mart SQL Project

Project Highlights

  • Customer Segmentation

    Classified customers into loyalty tiers and purchase frequency groups using SQL CASE and grouping logic.

  • Revenue & Profit Analysis

    Calculated total sales, profit margins, and year-over-year growth using aggregation and window functions.

  • Product Performance

    Identified top-selling and underperforming products by category and region using ranking and joins.

  • Customer Retention

    Measured returning customer ratios and churn trends with SQL date logic and customer activity tracking.

  • Store-Wise Insights

    Compared store-level revenue and transaction volume to evaluate regional performance across outlets.

About the Project

Data Mart Overview

Overview

The Data Mart project represents a retail analytics solution designed to provide data-driven insights into customer spending patterns, revenue trends, and profitability metrics. By leveraging SQL, this project allows businesses to track performance across regions, customer segments, and time frames, transforming transactional data into strategic intelligence.

SQL Process & Data Modeling

  • 📥 Extract: Retrieved sales, customer, and product data from multiple normalized tables.
  • 🧹 Transform: Cleaned and standardized product details using SQL string and numeric functions.
  • 🔍 Analyze: Applied CTEs and ranking logic to compute store performance and loyalty-based segmentation.
  • 📊 Model: Built a reporting data mart combining fact_sales, dim_customers, and dim_products.
Data Mart SQL Model
Data Mart Insights

Key Insights

  • 🛒 Top 10% customers contributed to 45% of total revenue.
  • 📈 Seasonal sales growth of 30% identified in Q4 through time-based SQL queries.
  • 💡 Category “Electronics” delivered the highest profit margin among all departments.
  • 🌍 Regional sales variation showed stronger demand in metro cities compared to rural outlets.

When:
2025

Mode:
SQL Data Analysis

Dataset:
Retail Sales, Product & Customer Data

Focus:
Revenue, Segmentation & Store Analytics

Project Snapshots

Business Impact

📊 Enabled retail teams to identify key customer groups driving majority of revenue.

💬 Improved marketing and promotional efficiency with SQL-based segmentation insights.

🚀 Supported dynamic pricing and inventory optimization strategies using SQL analytics.

Challenges & Learnings

⚙️ Handling complex joins across large transactional datasets efficiently.

🧩 Designing multi-level customer segmentation using nested SQL queries and aggregations.

💡 Strengthened understanding of data warehousing concepts and SQL-based business reporting.

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