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https://github.com/data-edd/e-commercestore_analysis

This project analyzes e-commerce data to provide insights into sales performance, profitability, and customer behavior using Power BI.
https://github.com/data-edd/e-commercestore_analysis

data-analysis powerbi powerbidashboard

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This project analyzes e-commerce data to provide insights into sales performance, profitability, and customer behavior using Power BI.

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# E-Commerce Power BI Analysis

## Description
This project analyzes e-commerce data to provide insights into sales performance, profitability, and customer behavior using Power BI. The dashboard includes key performance indicators (KPIs) that help in decision-making and business strategy optimization.

## Dataset Information
- **Source:** E-commerce transaction database
- **Format:** Excel / CSV
- **Key Attributes:**
- **Order ID:** Unique identifier for each transaction
- **Product Name:** Name of the purchased product
- **Category Name:** Product category
- **Sales:** Total sales amount
- **Profit:** Profit earned per transaction
- **Quantity:** Number of items sold
- **State & Customer Region:** Geographic details of customers
- **Shipping Type:** Delivery method used
- **Order Date:** Date of transaction

## Installation & Requirements
To set up and explore the Power BI analysis, ensure you have the following tools installed:

### Required Tools:
- Power BI Desktop
- Data source connections (Excel, CSV)

## Usage
To analyze the data and explore insights:

1. Clone the repository:
```bash
git clone https://github.com/data-edd/ecommerce-powerbi-dashboard.git
```
2. Open the `E-COMMERCE-DASHBOARD.pbix` file in Power BI Desktop.
3. Refresh the dataset to load the latest data.
4. Explore the interactive dashboards and reports.

## Key Metrics & Insights
This Power BI dashboard focuses on the following key performance indicators:

- **YTD Sales:** Year-to-date total revenue from sales.
- **YTD Profits:** Year-to-date total profit.
- **YTD Quantity:** Total number of products sold year-to-date.
- **YTD Profit Margin:** Profit margin percentage calculated on YTD sales.
- **YTD Sales & Total Quantity by State and Customer Region:** Geographical analysis of sales distribution.
- **Sales by Category Name:** Breakdown of revenue by product categories.
- **YTD Sales by Customer Region:** Regional sales trends.
- **YTD Sales by Shipping Type:** Impact of shipping methods on sales performance.
- **Top-5 Products by YTD Sales:** Best-performing products based on revenue.
- **Bottom-5 Products by YTD Sales:** Least-performing products by revenue.
- **Year-on-Year Sales Growth by Product Category:** Trends in sales growth across different product categories.

## Methodology
The analysis follows a structured approach:

1. **Data Import & Transformation:** Data cleaning and preparation using Power Query.
2. **Exploratory Data Analysis (EDA):** Identifying trends, patterns, and relationships.
3. **Data Modeling:** Creating relationships and DAX measures for custom calculations.
4. **Visualization:** Presenting insights through Power BI dashboards.

## Contributions
Contributions are welcome! If you’d like to contribute:

1. Fork the repository.
2. Create a new branch (`feature-branch-name`).
3. Commit your changes and push to the branch.
4. Submit a pull request.

## Contact
For any inquiries or support, feel free to open an issue or contact [mr.armstrongedward@gmail.com].