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https://github.com/lut-ful/e-commerce-sales-report

This dashboard provides a visual analysis of e-commerce sales data
https://github.com/lut-ful/e-commerce-sales-report

data data-analytics data-science data-visualization power-bi statics

Last synced: about 2 months ago
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This dashboard provides a visual analysis of e-commerce sales data

Host: GitHub
URL: https://github.com/lut-ful/e-commerce-sales-report
Owner: lut-ful
Created: 2024-06-13T07:12:40.000Z (6 months ago)
Default Branch: main
Last Pushed: 2024-06-23T18:22:13.000Z (6 months ago)
Last Synced: 2024-06-23T19:37:08.437Z (6 months ago)
Topics: data, data-analytics, data-science, data-visualization, power-bi, statics
Language: Jupyter Notebook
Homepage:
Size: 183 KB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

# E-commerce Sales Report
This dashboard provides a comprehensive visual analysis of e-commerce sales data using Power BI.

## Metrics Included

- **Profit by Month**: Visualizes profit trends over time.
- **Sum of Amount**: Total sales amount aggregated.
- **Sum of Profit**: Total profit generated.
- **Sum of Quantity**: Total units sold.
- **Sum of Avo_order_val**: Average order value.
- **Sales by Categorical Quantity (State)**: Breakdown of sales by state.
- **Sales by Categorical Quantity (Category)**: Breakdown of sales by product category.
- **Payment Mode Quantity**: Analysis of sales volume by payment method.
- **Top Profit by Subcategory**: Highlights the most profitable product subcategories.

## Tools Used

This dashboard was created using Power BI.

## How to Use This Report

This report is designed to help stakeholders in understanding and leveraging e-commerce sales data effectively. Here are some key insights you can derive:

- **Identify Sales Trends**: Analyze profit trends by month to understand seasonal variations.
- **Top Performing States and Categories**: Determine which states contribute the most to sales volume and which product categories are most profitable.
- **Optimize Payment Methods**: Identify popular payment modes to optimize checkout processes.
- **Subcategory Insights**: Explore top-performing product subcategories to focus marketing and inventory strategies.

## Example Applications

- **Business Strategy**: Use insights to develop targeted marketing campaigns.
- **Operational Efficiency**: Optimize inventory management based on sales trends.
- **Financial Planning**: Forecast revenue and profit based on historical data.

## Dashboard Screenshot

![Ecommerce Sales](https://github.com/lut-ful/E-commerce-sales-Report/assets/108027559/5ae1971e-28f2-4cf8-ae7e-4fa932bd946f)

## Getting Started

To view the dashboard and interact with the data:
1. Clone the repository.
2. Open the `.pbix` file in Power BI Desktop or view the dashboard online.

## 📫 Connect

- Email: [email protected]
- LinkedIn: [Md. Lutful Kabir](https://www.linkedin.com/in/mdlutfulkabir/)
- Portfolio: [Md. Lutful Kabir](https://www.datascienceportfol.io/mdlutfulkabir)