🧾 Balanced Tree Clothing Co Data Analysis

SQL Data Analysis & Business Insights Project

The Balanced Tree Clothing Co project focuses on understanding customer purchasing behavior, product performance, and revenue trends for a growing fashion retail company. Using advanced SQL techniques such as joins, subqueries, CTEs, and window functions, the project extracts valuable insights on customer segmentation, sales growth, and product profitability. The analysis helps the company optimize its inventory, marketing, and customer loyalty strategies.

Balanced Tree SQL Dashboard

Project Highlights

  • Customer Purchase Behavior

    Analyzed customer spending patterns and repeat purchase behavior using SQL window and aggregation functions.

  • Product Profitability

    Evaluated sales performance and profit margins across different product categories and styles using SQL CTEs.

  • Sales Growth Tracking

    Calculated monthly and quarterly growth trends with SQL date functions and analytical queries.

  • Customer Segmentation

    Classified customers into loyalty tiers (e.g., Bronze, Silver, Gold) using SQL case statements and ranking logic.

  • Revenue Optimization

    Discovered high-performing products and underperforming SKUs to optimize stock and marketing efforts.

About the Project

Balanced Tree Overview

Overview

The Balanced Tree Clothing Co project was designed to analyze customer purchasing and product performance data using SQL. It focuses on how data-driven strategies can improve revenue forecasting, inventory management, and customer retention. All data analysis was performed using structured queries optimized for scalability and performance.

SQL Process & Data Modeling

  • 📥 Extract: Loaded sales, product, and customer data from multiple tables into SQL environment.
  • 🧹 Transform: Cleaned and standardized transaction data using string and date functions.
  • 🧩 Analyze: Applied joins, CTEs, and window functions to derive key metrics and KPIs.
  • 📊 Model: Designed relational schemas with fact and dimension tables for scalable reporting.
Balanced Tree SQL Model
Balanced Tree Insights

Key Insights

  • 🛍️ 25% of customers contributed to 60% of total revenue — highlighting top-tier loyalty behavior.
  • 📈 Seasonal sales spikes identified during holiday months enabled better stock planning.
  • 💰 Top-performing categories were men’s jackets and women’s footwear with high profit margins.
  • 🎯 SQL queries revealed that customer retention increased with personalized offers.

When:
2025

Mode:
SQL Data Analysis

Dataset:
Retail Sales & Customer Transactions

Focus:
Customer, Product & Revenue Analytics

Project Snapshots

Business Impact

📊 Helped identify key customer segments responsible for the majority of sales revenue.

🧠 Improved decision-making by providing data-backed insights into product performance.

🚀 Enabled leadership to align sales, marketing, and stock planning using SQL-driven analytics.

Challenges & Learnings

⚙️ Handling complex joins between customer, product, and sales tables efficiently.

🧩 Optimizing SQL queries for better runtime performance on large datasets.

💡 Strengthened understanding of window functions, subqueries, and relational design principles.

← Back to Projects 🔗 View on GitHub