{"id":23979336,"url":"https://github.com/nagar2nd/ecommerce-analysis---sql-power-bi","last_synced_at":"2026-02-04T08:01:45.648Z","repository":{"id":270946889,"uuid":"911929624","full_name":"Nagar2nd/Ecommerce-Analysis---SQL-Power-BI","owner":"Nagar2nd","description":"Conducted an in-depth analysis of Ecommerce data using SQL and Power BI. 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Sales Performance Analysis**\n- Calculate **total sales revenue** per category, sub-category, and region.\n- Identify the **top 5 best-selling products** by both sales revenue and quantity sold.\n\n#### **2. Customer Insights**\n- Find the **most loyal customers** based on purchase frequency and total spend.\n- Identify customers with the **highest average order value (AOV)**.\n\n#### **3. Operational Efficiency**\n- Analyze **average delivery time** by region.\n- Identify **regions or products** with the highest delivery success rates.\n\n#### **4. Date and Time Analytics**\n- Determine the **monthly sales trend** for the last two years.\n- Analyze **seasonality of sales** to identify peak months.\n\n#### **5. Advanced SQL Queries**\n- Use **window functions** to rank products based on sales within each category.\n- Calculate **month-to-date (MTD)** and **year-to-date (YTD)** sales metrics.\n\n---\n\n## **Phase 2: Data Visualization – Power BI**\n\n### **Objective:**\nTo create an interactive dashboard that visualizes key metrics derived from SQL analysis, enabling stakeholders to make data-driven decisions.\n\n### **Key Metrics to Visualize:**\n\n#### **1. Sales Performance**\n- **Total Sales Revenue:** Overall revenue generated.\n- **Average Order Value (AOV):** Average revenue per order.\n- **Sales by Category and Sub-Category:** Revenue breakdown.\n- **Top-Selling Products:** Top 5 products by sales revenue.\n\n#### **2. Customer Insights**\n- **Customer Lifetime Value (CLV):** Total revenue per customer.\n- **Top 10 Loyal Customers:** Customers with the highest purchase frequency and spend.\n- **Customer Segments:** Categorized by purchasing behavior (e.g., high spenders, one-time buyers).\n\n#### **3. Regional Analysis**\n- **Revenue by Region:** Regional sales comparisons.\n- **Return Rates by Region:** Percentage of canceled orders.\n- **Average Delivery Time by Region:** Operational performance.\n\n#### **4. Operational Metrics**\n- **Delivery Time Analysis:** Average, minimum, and maximum delivery times.\n- **Product Return Rates:** Percentage of returned products across categories.\n\n#### **5. Time Trends**\n- **Monthly Sales Trends:** Visualization of revenue trends over time.\n- **Seasonality Analysis:** Highlighting peak sales periods (e.g., festive months).\n\n---\n\n## **Resources in the Repository**\n\n1. **SQL_Queries Folder:** Contains all SQL scripts used for data analysis.\n2. **Executive Summary-Ecommerce.docx:** A detailed report summarizing insights and recommendations.\n\n---\n\n## **Dataset and Script Links**\n- **Dataset:** [Download Here](https://drive.google.com/file/d/1ePnRbauLEyaJMQEgH0GqvL49nReeeUVW/view)\n- **SQL to Upload Data:** [CSV to SQL Script](https://github.com/Ayushi0214/SQL-Python-Ecommerce-Project/blob/main/csv_to_sql.py)\n\n---\n\n## **Connect**\nFeel free to reach out for collaboration or feedback:\n- **Email:** shivaninagarofficial@gmail.com\n- **LinkedIn:** [https://www.linkedin.com/in/shivani-nagar12/](#)\n\n---\n\nThank you for exploring this analysis!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnagar2nd%2Fecommerce-analysis---sql-power-bi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnagar2nd%2Fecommerce-analysis---sql-power-bi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnagar2nd%2Fecommerce-analysis---sql-power-bi/lists"}