https://github.com/nagar2nd/ecommerce-analysis---sql-power-bi
Conducted an in-depth analysis of Ecommerce data using SQL and Power BI. The project highlights sales performance, customer behavior, and operational efficiency, offering insights for strategy optimization.
https://github.com/nagar2nd/ecommerce-analysis---sql-power-bi
dax powerbi sql
Last synced: 5 months ago
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Conducted an in-depth analysis of Ecommerce data using SQL and Power BI. The project highlights sales performance, customer behavior, and operational efficiency, offering insights for strategy optimization.
- Host: GitHub
- URL: https://github.com/nagar2nd/ecommerce-analysis---sql-power-bi
- Owner: Nagar2nd
- Created: 2025-01-04T08:11:19.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-04T18:38:34.000Z (over 1 year ago)
- Last Synced: 2025-03-03T21:24:21.576Z (over 1 year ago)
- Topics: dax, powerbi, sql
- Homepage:
- Size: 5.02 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Ecommerce Sales Analysis
#### NOTE : The .pbix(Power BI file) and the Original Presentation file are too big to be uploaded here, So attaching the links to access them.
1. **[[Dashboard_file.pbix:]](https://drive.google.com/file/d/1qg100YR1Otk0u4yJRci5TbssgsC2wT3U/view?usp=sharing)** Power BI file with interactive dashboards visualizing the analysis.
2. **[[Presentation - Ecommerce PDF:]](https://drive.google.com/file/d/1EGWXmA52t6y60a--MH_J6AWcvRMmCpPT/view?usp=sharing)** A professional presentation of the entire analysis.
This repository contains an in-depth analysis of an Ecommerce dataset, focusing on sales performance, customer behavior, operational efficiency, and time trends.
The project is divided into two phases: **SQL Analysis** and **Data Visualization**, providing actionable insights to drive strategic decision-making.
---
## **Phase 1: SQL Analysis – Advanced Queries**
### **Objective:**
To perform advanced data analysis using MySQL queries to answer key business questions and extract meaningful insights related to sales, customers, operations, and time trends.
### **Tasks and Questions:**
#### **1. Sales Performance Analysis**
- Calculate **total sales revenue** per category, sub-category, and region.
- Identify the **top 5 best-selling products** by both sales revenue and quantity sold.
#### **2. Customer Insights**
- Find the **most loyal customers** based on purchase frequency and total spend.
- Identify customers with the **highest average order value (AOV)**.
#### **3. Operational Efficiency**
- Analyze **average delivery time** by region.
- Identify **regions or products** with the highest delivery success rates.
#### **4. Date and Time Analytics**
- Determine the **monthly sales trend** for the last two years.
- Analyze **seasonality of sales** to identify peak months.
#### **5. Advanced SQL Queries**
- Use **window functions** to rank products based on sales within each category.
- Calculate **month-to-date (MTD)** and **year-to-date (YTD)** sales metrics.
---
## **Phase 2: Data Visualization – Power BI**
### **Objective:**
To create an interactive dashboard that visualizes key metrics derived from SQL analysis, enabling stakeholders to make data-driven decisions.
### **Key Metrics to Visualize:**
#### **1. Sales Performance**
- **Total Sales Revenue:** Overall revenue generated.
- **Average Order Value (AOV):** Average revenue per order.
- **Sales by Category and Sub-Category:** Revenue breakdown.
- **Top-Selling Products:** Top 5 products by sales revenue.
#### **2. Customer Insights**
- **Customer Lifetime Value (CLV):** Total revenue per customer.
- **Top 10 Loyal Customers:** Customers with the highest purchase frequency and spend.
- **Customer Segments:** Categorized by purchasing behavior (e.g., high spenders, one-time buyers).
#### **3. Regional Analysis**
- **Revenue by Region:** Regional sales comparisons.
- **Return Rates by Region:** Percentage of canceled orders.
- **Average Delivery Time by Region:** Operational performance.
#### **4. Operational Metrics**
- **Delivery Time Analysis:** Average, minimum, and maximum delivery times.
- **Product Return Rates:** Percentage of returned products across categories.
#### **5. Time Trends**
- **Monthly Sales Trends:** Visualization of revenue trends over time.
- **Seasonality Analysis:** Highlighting peak sales periods (e.g., festive months).
---
## **Resources in the Repository**
1. **SQL_Queries Folder:** Contains all SQL scripts used for data analysis.
2. **Executive Summary-Ecommerce.docx:** A detailed report summarizing insights and recommendations.
---
## **Dataset and Script Links**
- **Dataset:** [Download Here](https://drive.google.com/file/d/1ePnRbauLEyaJMQEgH0GqvL49nReeeUVW/view)
- **SQL to Upload Data:** [CSV to SQL Script](https://github.com/Ayushi0214/SQL-Python-Ecommerce-Project/blob/main/csv_to_sql.py)
---
## **Connect**
Feel free to reach out for collaboration or feedback:
- **Email:** shivaninagarofficial@gmail.com
- **LinkedIn:** [https://www.linkedin.com/in/shivani-nagar12/](#)
---
Thank you for exploring this analysis!