https://github.com/lasyakonduru/e-commerce-analysis-using-advanced-sql

This project analyzes e-commerce order fulfillment using Advanced SQL Techniques and Python-based visualization to uncover insights on sales trends, customer segmentation, shipping cost optimization, and payment preferences.
https://github.com/lasyakonduru/e-commerce-analysis-using-advanced-sql

business-analytics common-table-expressions customer-segmentation data-visualization database-design indexing normalization-techniques partitioning window-functions-in-sql

Last synced: 19 days ago
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This project analyzes e-commerce order fulfillment using Advanced SQL Techniques and Python-based visualization to uncover insights on sales trends, customer segmentation, shipping cost optimization, and payment preferences.

• Host: GitHub
• URL: https://github.com/lasyakonduru/e-commerce-analysis-using-advanced-sql
• Owner: lasyakonduru
• Created: 2025-02-15T09:14:55.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2025-02-15T09:23:24.000Z (12 months ago)
• Last Synced: 2025-05-20T05:06:55.319Z (9 months ago)
• Topics: business-analytics, common-table-expressions, customer-segmentation, data-visualization, database-design, indexing, normalization-techniques, partitioning, window-functions-in-sql
• Language: HTML
• Homepage:
• Size: 10.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# **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**  

```sh

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! 🚀