https://github.com/mmenock/northwind-analysis
This project leverages the well-known Northwind dataset from Microsoft to build a comprehensive dashboard that provides deep insights into sales and revenue trends. Whether you're a data analyst, business strategist, or just a data enthusiast, this dashboard helps uncover key sales patterns, customer behavior, and revenue distribution across differ
https://github.com/mmenock/northwind-analysis
powerbi python
Last synced: about 1 year ago
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This project leverages the well-known Northwind dataset from Microsoft to build a comprehensive dashboard that provides deep insights into sales and revenue trends. Whether you're a data analyst, business strategist, or just a data enthusiast, this dashboard helps uncover key sales patterns, customer behavior, and revenue distribution across differ
- Host: GitHub
- URL: https://github.com/mmenock/northwind-analysis
- Owner: MMEnock
- Created: 2025-02-12T20:35:23.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-12T20:43:48.000Z (over 1 year ago)
- Last Synced: 2025-02-12T21:32:33.428Z (over 1 year ago)
- Topics: powerbi, python
- Homepage:
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- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 📊 Northwind Sales & Revenue Analysis Dashboard
## 🚀 Overview
Welcome to the **Northwind Sales & Revenue Analysis Dashboard**! This project leverages the well-known **Northwind dataset** from Microsoft to build a comprehensive dashboard that provides deep insights into sales and revenue trends. Whether you're a data analyst, business strategist, or just a data enthusiast, this dashboard helps uncover key sales patterns, customer behavior, and revenue distribution across different dimensions.
## 🎯 Key Objectives
This dashboard aims to answer critical business questions, such as:
1. 📈 **Sales Trends**: How do total sales and revenue fluctuate over time (monthly, quarterly, yearly)?
2. 🏆 **Top Products**: Which products generate the highest revenue, and how have their sales evolved?
3. 💰 **Average Order Value (AOV)**: What is the AOV per customer, and how does it vary by region?
4. 👑 **Top Customers**: Which customers contribute the most revenue, and what are their purchase patterns?
5. 🎖 **Sales Representatives' Impact**: How much revenue does each sales representative contribute?
6. 🏷 **Category Contribution**: What percentage of total revenue comes from each product category?
7. 🌍 **Geographical Insights**: How does revenue distribution vary across countries and regions?
8. 📉 **Customer Retention**: Which customers have increased or decreased their purchases over time?
9. 📊 **Profitability**: What is the profit margin per product and per category?
10. 🔄 **New vs. Returning Customers**: What are the revenue trends for new versus returning customers?
## 🗂 Dataset
The Northwind dataset consists of:
- **Orders**: Contains order details, including customer, order date, and total price.
- **Order Details**: Contains granular product-level data for each order.
- **Products**: Information on product names, categories, and suppliers.
- **Customers**: Details of customers, including country and company name.
- **Employees**: Sales representatives responsible for handling orders.
- **Suppliers**: Information about product suppliers.
## 🚦 Features of the Dashboard
✔ **Interactive Sales Trends Visualization** 📊
✔ **Top Products & Categories Performance** 🔍
✔ **Customer Segmentation & Purchase Patterns** 🎯
✔ **Sales Representative Contribution Analysis** 📢
✔ **Profitability Breakdown by Product & Category** 💹
✔ **Regional & Country-wise Revenue Distribution** 🌎
✔ **New vs. Returning Customer Revenue Insights** 🔄
✔ **Customizable Time Filters for Deep Analysis** ⏳
## 🛠 Installation & Setup
1. **Clone the repository:**
```bash
git clone https://github.com/yourusername/northwind-sales-dashboard.git
cd northwind-sales-dashboard
```
2. **Install dependencies:**
```bash
pip install -r requirements.txt
```
3. **Run the dashboard:**
```bash
streamlit run app.py
```
## 📌 Usage
- Use the interactive filters to analyze different time periods and sales patterns.
- Drill down into customer and product performance to identify high-value segments.
- Compare new and returning customers to understand retention and growth strategies.
## 🤝 Contributing
We welcome contributions! To contribute:
1. Fork the repository 🍴
2. Create a new branch: `git checkout -b feature-name` 🌿
3. Commit your changes: `git commit -m "Added new feature"` 📝
4. Push to the branch: `git push origin feature-name` 🚀
5. Submit a Pull Request! 🎉
## 📜 License
This project is open-source and available under the [MIT License](LICENSE).
## 🙌 Acknowledgments
- **Microsoft** for the Northwind dataset 🏢
- Open-source community for inspiring data visualization solutions 💡
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🔥 **Let's turn raw data into actionable insights!** 🔥