Advertising Dashboard on Power BI

Table of Contents

• Project Overview
• Demo
• Technologies Used
• Features
• Data Sources
• Getting Started
• Usage
• Best Practices
• Contributing
• License
• Contact

Project Overview

The Advertising Dashboard is an advanced Power BI project aimed at providing clear insights into advertising campaigns. By leveraging data analysis and visual storytelling, this dashboard allows stakeholders to monitor key performance metrics like Total Spend, Clicks, Leads, Impressions, and Cost Per Lead (CPL). The interactive nature of the visuals empowers users to make informed decisions that optimize advertising strategies and improve Return on Investment (ROI).

Demo

Click to view dashboard via Power BI web Link

Easily accessible visuals provide insights at a glance, enhancing decision-making for advertising strategies.

Technologies Used

• Power BI Desktop: For data visualization and dashboard creation.
• DAX (Data Analysis Expressions): To create custom calculations and measures.
• Power Query: For data extraction, transformation, and loading (ETL).
• GitHub: For version control and project documentation.

Features

• Interactive Dashboard: Engaging visuals with drill-down capabilities allow users to explore data insights seamlessly.
• Dynamic Visualizations: Combines line charts, column charts, and matrix tables to compare key metrics in real-time.
• Comprehensive Metrics Tracking: Offers detailed analysis of Total Spend, Total Clicks, Total Leads, Total Impressions, CPC, CTR, and CPL.
• User-Friendly Design: Intuitive layout that prioritizes visibility and ease of navigation, ensuring insights are easily digestible.

Data Sources

This project utilizes multiple data sources to provide a comprehensive overview of advertising performance. These may include:

• Marketing databases
• Customer Relationship Management (CRM) systems
• Excel spreadsheets
• Any other relevant datasets

Getting Started

1. Clone the Repository:

   git clone https://github.com/yourusername/advertising-dashboard.git

2. Open in Power BI Desktop:

   • Launch Power BI Desktop and open the .pbix file located in the cloned directory.

3. Review Data Sources:

   • Ensure that the data connections are properly configured based on your local environment to maintain data integrity.

Usage

• Explore the Dashboard: Interact with slicers to filter data based on date ranges, campaign targets, or other relevant dimensions.
• Analyze Trends: Use the running total visuals to assess spending vs. lead generation over time.
• Optimize Campaigns: Identify high-performing and underperforming campaigns using matrix comparisons of key metrics.

Best Practices

• Data Governance: Follow best practices for data validation and transformation to ensure accuracy and reliability.
• Documentation: Maintain thorough documentation of data models, calculations, and visualizations for ease of use and understanding.
• User Feedback: Engage stakeholders for testing and gather feedback to refine dashboard usability and performance.
• Performance Optimization: Regularly monitor and optimize performance to ensure a smooth user experience.

Contributing

Contributions are welcome! If you have suggestions or improvements, please create an issue or submit a pull request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For inquiries or feedback regarding this project, feel free to reach out:

• Your Name
• Email: israeljvow@gmail.com
• LinkedIn: LinkedIn Profile
• GitHub: GitHub Profile

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