{"id":25507845,"url":"https://github.com/lasyakonduru/e-commerce-analysis-using-advanced-sql","last_synced_at":"2026-01-24T06:03:56.157Z","repository":{"id":277668685,"uuid":"933150753","full_name":"lasyakonduru/E-Commerce-Analysis-Using-Advanced-SQL","owner":"lasyakonduru","description":"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.","archived":false,"fork":false,"pushed_at":"2025-02-15T09:23:24.000Z","size":10795,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-20T05:06:55.319Z","etag":null,"topics":["business-analytics","common-table-expressions","customer-segmentation","data-visualization","database-design","indexing","normalization-techniques","partitioning","window-functions-in-sql"],"latest_commit_sha":null,"homepage":"","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lasyakonduru.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-15T09:14:55.000Z","updated_at":"2025-02-15T09:26:11.000Z","dependencies_parsed_at":"2025-02-15T10:34:23.334Z","dependency_job_id":null,"html_url":"https://github.com/lasyakonduru/E-Commerce-Analysis-Using-Advanced-SQL","commit_stats":null,"previous_names":["lasyakonduru/e-commerce-analysis-using-advanced-sql"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/lasyakonduru/E-Commerce-Analysis-Using-Advanced-SQL","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lasyakonduru%2FE-Commerce-Analysis-Using-Advanced-SQL","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lasyakonduru%2FE-Commerce-Analysis-Using-Advanced-SQL/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lasyakonduru%2FE-Commerce-Analysis-Using-Advanced-SQL/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lasyakonduru%2FE-Commerce-Analysis-Using-Advanced-SQL/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lasyakonduru","download_url":"https://codeload.github.com/lasyakonduru/E-Commerce-Analysis-Using-Advanced-SQL/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lasyakonduru%2FE-Commerce-Analysis-Using-Advanced-SQL/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28715653,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-24T05:53:42.649Z","status":"ssl_error","status_checked_at":"2026-01-24T05:53:41.698Z","response_time":89,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["business-analytics","common-table-expressions","customer-segmentation","data-visualization","database-design","indexing","normalization-techniques","partitioning","window-functions-in-sql"],"created_at":"2025-02-19T07:54:45.494Z","updated_at":"2026-01-24T06:03:56.132Z","avatar_url":"https://github.com/lasyakonduru.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# **E-Commerce Order Fulfillment Analysis**  \n\n## 📖 **Project Overview**  \nThis 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**.  \n\nUsing **Advanced SQL Techniques** and **Python-based data visualization**, we extract insights to help businesses **improve logistics, boost sales, and enhance customer satisfaction**.  \n\n---\n\n## 🎯 **Objectives**  \n✔ **Optimize order processing efficiency** – Track delays and enhance fulfillment times.  \n✔ **Identify top-selling products** – Understand which products generate the highest revenue.  \n✔ **Segment high-value customers** – Analyze customer spending behavior and engagement.  \n✔ **Reduce shipping costs** – Evaluate cost variations based on order priority.  \n✔ **Identify preferred payment methods** – Determine customer payment preferences for better financial strategies.  \n\n---\n\n## 📂 **Dataset Overview**  \n- **Source**: E-Commerce transaction records with **51,290 rows** and **16 columns**  \n- **Data Transformation**: The raw dataset was **normalized into four tables** for efficient querying:\n  - `Orders` – Order details, including sales, profit, and shipping costs.  \n  - `Customers` – Customer demographics such as gender, login type, and device.  \n  - `Products` – List of all products and their categories.  \n  - `Categories` – Broader classification of product types.  \n\n---\n\n## 🚀 **Advanced SQL Techniques Used**  \nThis project incorporates **Advanced SQL Techniques** to improve query performance, simplify analysis, and generate powerful insights:  \n\n### **1️⃣ Window Functions**  \n   - Used to **rank top-selling products** and **calculate cumulative sales over time**.  \n   - Helps in **understanding product demand trends dynamically**.  \n\n### **2️⃣ Common Table Expressions (CTEs)**  \n   - Simplifies **customer segmentation analysis** by organizing complex queries.  \n   - Enhances readability and maintains **modular query execution**.  \n\n### **3️⃣ Ranking Functions (RANK() OVER)**  \n   - Assigns rankings to products based on total sales.  \n   - Useful for **identifying best-performing items efficiently**.  \n\n### **4️⃣ Partitioning \u0026 Indexing for Performance Optimization**  \n   - Used for **query optimization**, especially for large datasets.  \n   - Ensures **faster retrieval of insights** from orders and customer data.  \n\n---\n\n## 📊 **Business Insights \u0026 Findings**  \n\n### **📌 Sales \u0026 Revenue Analysis**  \n- The business generated **$7.8M in total sales**, with a **$3.6M profit margin**.  \n- **Sales peaked in May and November**, indicating strong **seasonal demand trends**.  \n\n### **📌 Top-Selling Products**  \n- The **highest-selling categories were Fashion and Footwear**, with **T-Shirts, Watches, and Running Shoes leading sales**.  \n- **Bundling slower-moving items with high-performing products** could increase sales.  \n\n### **📌 Customer Segmentation \u0026 Retention**  \n- **High-spending customers are primarily male**, highlighting an opportunity for **targeted promotions**.  \n- A **VIP loyalty program** can enhance customer retention and **increase repeat purchases**.  \n\n### **📌 Order Fulfillment \u0026 Shipping Cost Optimization**  \n- **High-priority orders have significantly higher shipping costs**.  \n- Encouraging **bulk orders and standard delivery options** can help reduce logistics expenses.  \n\n### **📌 Payment Method Preferences**  \n- **Credit cards dominate transactions (74% of total revenue)**, while **e-wallet adoption remains low**.  \n- **Promoting digital payment incentives** can increase checkout conversion rates.  \n\n---\n\n## 💡 **Business Recommendations**  \n📌 **Optimize Order Processing Efficiency**  \n   - Implement **automation in warehouses** to reduce the average processing time (currently 5.25 days).  \n   - Introduce **real-time order tracking** to enhance transparency and customer trust.  \n\n📌 **Increase Revenue with Targeted Promotions**  \n   - Leverage **seasonal sales trends** by launching exclusive discounts during peak months.  \n   - Promote **high-ranking products (T-Shirts, Watches, and Shoes)** through advertising.  \n\n📌 **Improve Customer Retention Strategies**  \n   - Create **personalized offers for repeat customers** based on purchase history.  \n   - Implement a **loyalty program** to encourage repeat spending.  \n\n📌 **Reduce Shipping Costs Without Affecting Delivery Time**  \n   - Offer **free standard shipping for bulk orders** to reduce per-item logistics costs.  \n   - Optimize **partnerships with shipping carriers** for discounted high-priority shipping rates.  \n\n📌 **Enhance Payment Flexibility \u0026 Checkout Experience**  \n   - Encourage **e-wallet and debit card transactions** by offering cashback incentives.  \n   - Introduce **Buy Now, Pay Later (BNPL) options** to reduce cart abandonment.  \n\n---\n\n## 🔧 **Technologies Used**  \n- **Database**: SQLite  \n- **Query Language**: SQL  \n- **Data Processing**: Pandas  \n- **Visualization**: Matplotlib, Seaborn  \n- **Development Environment**: Jupyter Notebook  \n\n---\n\n## 📂 **Project Files \u0026 Repository Structure**  \n📁 `Ecommerce_SQL_DATAProject.db` – SQLite database file  \n📁 `ecommerce_schema.sql` – Collection of SQL scripts used in analysis  \n📁 `E_Commerce_Analysis_using_Advanced_SQL.ipynb` – Jupyter Notebook for SQL execution \u0026 visualization  \n📁 `E_Commerce_Analysis_using_Advanced_SQL.html` – Summary of key insights \u0026 recommendations  \n📁 `README.md` – Documentation for project overview and findings  \n\n---\n\n## 🛠 **How to Run the Project**  \n1️⃣ **Clone the repository**  \n```sh\ngit clone https://github.com/yourusername/E-Commerce-Analysis-Using-Advanced-SQL.git\ncd ecommerce-sql-analysis\n```\n2️⃣ **Load the database (`Ecommerce_SQL_DATAProject.db`) into SQLite or DB Browser for SQLite.**  \n3️⃣ **Execute SQL queries from `ecommerce_schema.sql` to explore insights.**  \n4️⃣ **Run `E_Commerce_Analysis_using_Advanced_SQL.ipynb` in Jupyter Notebook to visualize trends using Python.**  \n\n---\n\n## 🔮 **Future Enhancements**  \n📌 **Use Machine Learning** to predict future sales trends.  \n📌 **Enhance customer segmentation with clustering algorithms.**  \n📌 **Develop an interactive dashboard using Power BI or Tableau.**  \n\n---\n\n## 👤 **Author**  \n🔹 **Lasya Priya Konduru**  \n📧 **konduru.lasya@gmail.com**  \n🔗 **LinkedIn: (https://www.linkedin.com/in/lasya-priya-k/)**  \n\nIf you found this project useful, **⭐ Star this repository** and feel free to contribute! 🚀\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flasyakonduru%2Fe-commerce-analysis-using-advanced-sql","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flasyakonduru%2Fe-commerce-analysis-using-advanced-sql","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flasyakonduru%2Fe-commerce-analysis-using-advanced-sql/lists"}