E-Commerce Order Fulfillment Analysis

📖 Project Overview

This project analyzes the order fulfillment process of an e-commerce business, covering sales trends, product performance, customer segmentation, shipping cost optimization, and payment method analysis.

Using Advanced SQL Techniques and Python-based data visualization, we extract insights to help businesses improve logistics, boost sales, and enhance customer satisfaction.

---

🎯 Objectives

✔ Optimize order processing efficiency – Track delays and enhance fulfillment times.
✔ Identify top-selling products – Understand which products generate the highest revenue.
✔ Segment high-value customers – Analyze customer spending behavior and engagement.
✔ Reduce shipping costs – Evaluate cost variations based on order priority.
✔ Identify preferred payment methods – Determine customer payment preferences for better financial strategies.

---

📂 Dataset Overview

• Source: E-Commerce transaction records with 51,290 rows and 16 columns
• Data Transformation: The raw dataset was normalized into four tables for efficient querying:
  • Orders – Order details, including sales, profit, and shipping costs.
  • Customers – Customer demographics such as gender, login type, and device.
  • Products – List of all products and their categories.
  • Categories – Broader classification of product types.

---

🚀 Advanced SQL Techniques Used

This project incorporates Advanced SQL Techniques to improve query performance, simplify analysis, and generate powerful insights:

1️⃣ Window Functions

• Used to rank top-selling products and calculate cumulative sales over time.
• Helps in understanding product demand trends dynamically.

2️⃣ Common Table Expressions (CTEs)

• Simplifies customer segmentation analysis by organizing complex queries.
• Enhances readability and maintains modular query execution.

3️⃣ Ranking Functions (RANK() OVER)

• Assigns rankings to products based on total sales.
• Useful for identifying best-performing items efficiently.

4️⃣ Partitioning & Indexing for Performance Optimization

• Used for query optimization, especially for large datasets.
• Ensures faster retrieval of insights from orders and customer data.

---

📊 Business Insights & Findings

📌 Sales & Revenue Analysis

• The business generated $7.8M in total sales, with a $3.6M profit margin.
• Sales peaked in May and November, indicating strong seasonal demand trends.

📌 Top-Selling Products

• The highest-selling categories were Fashion and Footwear, with T-Shirts, Watches, and Running Shoes leading sales.
• Bundling slower-moving items with high-performing products could increase sales.

📌 Customer Segmentation & Retention

• High-spending customers are primarily male, highlighting an opportunity for targeted promotions.
• A VIP loyalty program can enhance customer retention and increase repeat purchases.

📌 Order Fulfillment & Shipping Cost Optimization

• High-priority orders have significantly higher shipping costs.
• Encouraging bulk orders and standard delivery options can help reduce logistics expenses.

📌 Payment Method Preferences

• Credit cards dominate transactions (74% of total revenue), while e-wallet adoption remains low.
• Promoting digital payment incentives can increase checkout conversion rates.

---

💡 Business Recommendations

📌 Optimize Order Processing Efficiency

• Implement automation in warehouses to reduce the average processing time (currently 5.25 days).
• Introduce real-time order tracking to enhance transparency and customer trust.

📌 Increase Revenue with Targeted Promotions

• Leverage seasonal sales trends by launching exclusive discounts during peak months.
• Promote high-ranking products (T-Shirts, Watches, and Shoes) through advertising.

📌 Improve Customer Retention Strategies

• Create personalized offers for repeat customers based on purchase history.
• Implement a loyalty program to encourage repeat spending.

📌 Reduce Shipping Costs Without Affecting Delivery Time

• Offer free standard shipping for bulk orders to reduce per-item logistics costs.
• Optimize partnerships with shipping carriers for discounted high-priority shipping rates.

📌 Enhance Payment Flexibility & Checkout Experience

• Encourage e-wallet and debit card transactions by offering cashback incentives.
• Introduce Buy Now, Pay Later (BNPL) options to reduce cart abandonment.

---

🔧 Technologies Used

• Database: SQLite
• Query Language: SQL
• Data Processing: Pandas
• Visualization: Matplotlib, Seaborn
• Development Environment: Jupyter Notebook

---

📂 Project Files & Repository Structure

📁 Ecommerce_SQL_DATAProject.db – SQLite database file
📁 ecommerce_schema.sql – Collection of SQL scripts used in analysis
📁 E_Commerce_Analysis_using_Advanced_SQL.ipynb – Jupyter Notebook for SQL execution & visualization
📁 E_Commerce_Analysis_using_Advanced_SQL.html – Summary of key insights & recommendations
📁 README.md – Documentation for project overview and findings

---

🛠 How to Run the Project

1️⃣ Clone the repository

git clone https://github.com/yourusername/E-Commerce-Analysis-Using-Advanced-SQL.git
cd ecommerce-sql-analysis

2️⃣ Load the database (Ecommerce_SQL_DATAProject.db) into SQLite or DB Browser for SQLite.
3️⃣ Execute SQL queries from ecommerce_schema.sql to explore insights.
4️⃣ Run E_Commerce_Analysis_using_Advanced_SQL.ipynb in Jupyter Notebook to visualize trends using Python.

---

🔮 Future Enhancements

📌 Use Machine Learning to predict future sales trends.
📌 Enhance customer segmentation with clustering algorithms.
📌 Develop an interactive dashboard using Power BI or Tableau.

---

👤 Author

🔹 Lasya Priya Konduru
📧 konduru.lasya@gmail.com
🔗 LinkedIn: (https://www.linkedin.com/in/lasya-priya-k/)

If you found this project useful, ⭐ Star this repository and feel free to contribute! 🚀