https://github.com/aydan-moon/marketing-campaign-analysis

A Streamlit app for analyzing marketing campaign data.
https://github.com/aydan-moon/marketing-campaign-analysis

Last synced: about 1 month ago
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A Streamlit app for analyzing marketing campaign data.

• Host: GitHub
• URL: https://github.com/aydan-moon/marketing-campaign-analysis
• Owner: Aydan-moon
• Created: 2024-12-07T10:39:00.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-12-07T10:52:03.000Z (about 1 month ago)
• Last Synced: 2024-12-07T11:25:30.823Z (about 1 month ago)
• Language: Jupyter Notebook
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

        

# Marketing Campaign Analysis App 📊

This project is an interactive **Streamlit app** designed to analyze marketing campaign data. It provides insights into **Conversion Rates**, **Retention Rates**, and **A/B Testing** outcomes through dynamic visualizations and metrics.

## 🔑 Key Features

1. **Conversion Rate Analysis**  

   - Calculate conversion rates within a specified time window.  

   - Analyze conversion rates across categories like gender, region, or other campaign metrics.

2. **Retention Rate Analysis**  

   - Determine user retention over a defined period.  

   - Group results by single or multiple categories for deeper insights.

3. **A/B Testing**  

   - Compare the performance of control and personalized marketing variants.  

   - Perform statistical tests to determine significant differences.

## 🛠️ Tech Stack

- **Python Libraries**: 

  - Data Manipulation: `pandas`

  - Statistical Analysis: `scipy`

  - Visualization: `matplotlib`, `seaborn`

- **Streamlit**: Interactive front-end for real-time data analysis.

## 🚀 How to Run the App Locally

🚀 How to Run the App Locally

1. Clone this repository:

   ```bash

   git clone https://github.com/Aydan-moon/marketing-campaign-analysis.git

   cd marketing-campaign-analysis

2. Install dependencies:

   ```bash

   pip install -r requirements.txt

3. Run the Streamlit app:

   ```bash

   streamlit run app_04.py

4. Open your browser at http://localhost:8501.

## 🤝 Contribution

Contributions are welcome! Feel free to fork this repository, submit pull requests, or open issues for feature suggestions and bug fixes.

## 🌟 Acknowledgments

Special thanks to the open-source Python community and Streamlit for their fantastic tools!

## 📝 License

This project is licensed under the MIT License.