📊 Marketing Analysis Project

📌 Objectives

The objective of this project is to provide an interactive and detailed analysis of key marketing metrics to support strategic decision-making. This includes leveraging both Power BI for interactive dashboards and Python for data analysis, cleaning, and visualization.

🚀 Functionality

This project includes:

• 📈 Interactive visualizations: Pivot charts and tables in Power BI to explore data.
• 📊 Key metrics: Analysis of important KPIs such as sales, channels, products, complaints, campaign performance, and more.
• 📅 Temporal analysis: Trends over time to identify patterns and opportunities.
• 🗂️ Five tabs in Power BI:
  • 🌍 Global: General view of all metrics.
  • 📦 Orders: Detailed analysis of orders.
  • 🛍️ Products: Monitoring and analysis of different products.
  • 📢 Campaigns: Evaluation of marketing campaigns.
  • 👥 Customers: Customer information and analysis.
• 🐍 Python Analysis:
  • 🧹 Data Cleaning: Using pandas for data cleaning and preprocessing.
  • 📊 Visualization: Using matplotlib and seaborn for data visualization.
  • 🌐 Interactive App: A Streamlit app to present the results interactively.

🛠️ Tools Used

• 🖥️ Power BI: For creating the interactive dashboard.
• 🐍 Python: For data analysis.
  • 🐼 pandas: Data cleaning and preprocessing.
  • 📊 matplotlib and seaborn: Data visualization.
  • 🌐 Streamlit: Interactive app to present the results.

🔄 Development Process

1. 📥 Extraction: Data obtained from CSV files.
2. 🔄 Transformation:
   • 🖥️ Power BI:
     • 🔗 Combining tables using Power Query.
     • 🧹 Data cleaning: Removing duplicates, handling null values, and normalizing data.
     • 📈 Data enrichment: Adding calculated columns and transforming data to improve analysis.
   • 🐍 Python:
     • 🧹 Data cleaning with pandas: Removing duplicates, handling null values, and normalizing data.
     • 📈 Data enrichment: Adding calculated columns and transforming data to improve analysis.
3. 📤 Load:
   • 🖥️ Power BI: Integrating transformed data into Power BI for analysis and visualization.
   • 🐍 Python: Preparing data for visualization and analysis in Jupyter notebooks and Streamlit.

📈 Results

Various metrics have been created using DAX (Data Analysis Expressions) in Power BI to provide detailed and customized analysis:

• 📊 KPIs calculation.
• 📏 Calculated measures for specific analyses.
• ➕ Calculated columns to enrich the data.
• 🔍 Filtering and dynamic segmentation of data.

In Python, the analysis includes:

• 🧹 Detailed data cleaning processes.
• 📊 Creation of visualizations to explore and present data insights.
• 🌐 Development of an interactive Streamlit app to dynamically explore the results.

📊 Power BI Dashboard

Here are some screenshots of the Power BI dashboard:

📊 Visualizations

In addition to the Power BI dashboard, a complete analysis has been performed using Python, including:

• 🧹 Data Cleaning with Pandas:

  • Removing Duplicates: Ensuring that the dataset is free from duplicate entries to maintain data integrity.
  • Handling Missing Values: Addressing missing data points through imputation or removal to ensure a complete dataset.

• 📊 Data Visualization with Matplotlib and Seaborn:

  • Histograms and Bar Charts: Visualizing the distribution of data and comparing different categories.
  • Line Charts: Analyzing trends over time to identify patterns and opportunities.
  • Scatter Plots: Exploring relationships between different variables to uncover correlations.
  • Heatmaps: Providing a visual representation of data density and relationships between variables.
  • Box Plots: Summarizing the distribution of data and identifying outliers.

📂 Project Structure

• 📁 app/: Streamlit app to present the results.
  • 📝 main.py: Main script for the Streamlit app.
  • 📄 marketing_campaign_cleaned.csv: Cleaned marketing campaign data.
• 📁 assets/: Directory for app assets like images and logos.
  • 🖼️ menu.png: Menu image.
  • 🖼️ portada.png: Cover image.
• 📁 data/: Directory for raw and processed data.
  • 📄 marketing_campaign.csv: Raw marketing campaign data.
  • 📄 marketing_campaign_cleaned.csv: Cleaned marketing campaign data.
• 📁 images/: Directory for Power BI screenshots.
  • 🖼️ screenshot_1.png: Power BI screenshot 1.
  • 🖼️ screenshot_2.png: Power BI screenshot 2.
  • 🖼️ screenshot_3.png: Power BI screenshot 3.
  • 🖼️ screenshot_4.png: Power BI screenshot 4.
  • 🖼️ screenshot_5.png: Power BI screenshot 5.
• 📁 notebooks/: Jupyter notebooks with the Python analysis.
  • 🧹 data_cleaning.ipynb: Notebook for data cleaning.
  • 📊 data_visualization.ipynb: Notebook for data visualization.
  • 📄 marketing_campaign.csv: Raw marketing campaign data.
  • 📄 marketing_campaign_cleaned.csv: Cleaned marketing campaign data.
• 📁 powerbi/: Directory for Power BI files.
  • 📄 dashboard.pbix: Main file of the Power BI dashboard.
• 🚫 .gitignore: Git ignore file.
• 📜 LICENSE: License file.
• 📄 README.md: Readme file.
• 📋 requirements.txt: Python dependencies file.

🌐 Web App

The interactive app created with Streamlit allows exploring the analysis results dynamically and accessibly. It includes features such as:

• 📊 Interactive charts and graphs: Visualize data through various types of charts and graphs that update in real-time based on user interactions.
• 🔍 Filters to dynamically segment data: Apply filters to the data to focus on specific segments, such as time periods, product categories, or customer demographics.
• 📈 Detailed views of key metrics and trends: Drill down into specific metrics to see detailed trends and insights, helping to identify patterns and opportunities.
• 🖥️ User-friendly interface: The app is designed to be intuitive and easy to use, making it accessible to users with varying levels of technical expertise.
• 🛠️ Customizable dashboards: Users can customize the dashboards to suit their needs, adding or removing widgets and adjusting the layout as required.

You can access the web app here.

📧 Contact

For any questions, you can contact me at:

• 📧 Email: [email protected]
• 💼 LinkedIn: Juan Duran Bon

💡 Suggestions and Contributions

Suggestions and contributions are welcome. Please open an issue or submit a pull request to discuss any changes you would like to make. Here are some ways you can contribute:

• 🐛 Report Bugs: If you find any bugs, please report them by opening an issue.
• 🌟 Feature Requests: If you have ideas for new features, feel free to suggest them.
• 💻 Code Contributions: You can contribute by fixing bugs, adding new features, or improving the documentation.
• 📝 Feedback: Any feedback to improve the project is highly appreciated.

📜 License

This project is licensed under the MIT License. See the LICENSE file for more details.