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https://github.com/nagar2nd/ecommerce-analysis---sql-power-bi


https://github.com/nagar2nd/ecommerce-analysis---sql-power-bi

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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.



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## **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.



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## **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).



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## **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.



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## **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)



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## **Connect**

Feel free to reach out for collaboration or feedback:

- **Email:** [email protected]

- **LinkedIn:** [https://www.linkedin.com/in/shivani-nagar12/](#)



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Thank you for exploring this analysis!