Hotel Revenue Management Analytics using Power BI for visualization

A comprehensive Power BI solution for hotel revenue management, offering detailed analysis of pricing, occupancy, market segmentation, and profitability.

Repository Structure

📂 PBI-PBI-hotel-revenue-analytics/
│
├── 📂 Asset/
│   ├── 📂 Images/
│   │   ├── 📄 0_Menu_2025-04-23 123745.jpg
│   │   ├── 📄 1_Executive_Summary_2025-04-23 123841.jpg
│   │   ├── 📄 1.2Executive_Report_2025-04-23 123921.jpg
│   │   ├── 📄 1.3_Other Executive_2025-04-23 123921.jpg
│   │   ├── 📄 2.Revenue_Analysis_2025-04-23 124051.jpg
│   │   ├── 📄 2.1_Other_Revenue_2025-04-23 124325.jpg
│   │   ├── 📄 3_Pricing_and_Occupancy_2025-04-23 124406.jpg
│   │   ├── 📄 4_market_segmentation_2025-04-23 124453.jpg
│   │   ├── 📄 5_Channel_Performance_2025-04-23 124533.jpg
│   │   ├── 📄 6_Profitability_Analysis_2025-04-23 124634.jpg
│   │   ├── 📄 7_Customer_Satisfaction_2025-04-23 124730.jpg
│   │   ├── 📄 8_Marketing_Performance_2025-04-23 124808.jpg
│   │   ├── 📄 9_Kpi_Tracking_2025-04-23 124844.jpg
│   │   ├── 📄 icons/
│   │   └── 📄 screenshots/
│   │
│   └── 📂 sample-data/
│       └── 📄 hotel_data_sample.csv
│
├── 📂 dashboards/
│   ├── 📄 hotel.pbix
│   ├── 📄 executive-summary.pdf
│   └── 📄 sample-reports.pdf
│
├── 📂 docs/
│   ├── 📄 implementation-guide.md
│   ├── 📄 metrics-dictionary.md
│   ├── 📄 analysis-guide.md
│   ├── 📄 dax-measures.md
│   ├── 📄 power-query-transformations.md
│   ├── 📄 visualization-specifications.md
│   ├── 📄 hotel-revenue-management-report.md
│   └── 📄 performance-optimization.md
│
├── 📂 scripts/
│   ├── 📄 power-query-transformations.pq
│   └── 📄 tabular-editor-script.csx
│
├── 📄 LICENSE
├── 📄 README.md
└── 📄 CONTRIBUTING.md

🏨 Hotel Revenue Management Analytics Dashboard

🚀 Overview

Hotel Revenue Analytics is a comprehensive business intelligence solution developed with Microsoft Power BI that transforms hotel operational data into actionable insights for revenue management. Designed for revenue managers, hotel directors, and commercial teams who need to optimize pricing, occupancy, and overall profitability in real-time.

✨ Key Features

• Comprehensive Revenue Analysis: Trends, seasonality, and behavior patterns by season and day of week
• Price Optimization (ADR): Analysis of the relationship between price and occupancy to maximize revenue
• Market Segmentation: Breakdown by guest type (Business vs. Leisure) and country of origin
• Distribution Channel Analysis: Comparison between direct bookings and OTAs
• Profitability Measurement: Analysis of margins, marketing efficiency, and cost structure
• Customer Satisfaction: Correlation between review scores and revenue

📊 Included Dashboards

Dashboard	Description
Executive Summary	Overview of key KPIs and main metrics
Revenue Analysis	Detailed revenue breakdowns by month, season, and day of week
Pricing & Occupancy	Analysis of the relationship between ADR and occupancy rate
Market Segmentation	Performance by guest type and country of origin
Distribution Channels	Comparison between booking channels
Profitability Analysis	Profit margins and cost structure
Customer Satisfaction	Correlation between satisfaction and revenue
Time Intelligence	MTD/QTD/YTD performance metrics and comparisons
Year-over-Year Analysis	Comparative performance analysis with previous periods
Marketing Performance	Marketing efficiency and ROI analysis
KPI Tracking	Performance visualization against targets

📋 Requirements

• Power BI Desktop (version 2.112.x or later)
• Hotel operations dataset (included as sample or connect to your own source)
• Basic knowledge of Power BI for customization

🔧 Installation and Usage

1. Clone this repository

git clone https://github.com/yourusername/PBI-hotel-revenue-analytics.git

2. Open the main PBIX file in Power BI Desktop

hotel.pbix

3. Connect to your own data sources or use the included sample data

4. Customize according to your property's specific needs

For detailed instructions, refer to our implementation guide.

📈 Key KPIs Analyzed

• RevPAR (Revenue Per Available Room)
• ADR (Average Daily Rate)
• Occupancy Rate
• TRevPAR (Total Revenue Per Available Room)
• GOPPAR (Gross Operating Profit Per Available Room)
• Marketing Efficiency: Revenue to marketing spend ratio
• Customer Satisfaction: Review scores and their impact on revenue

🔍 Dashboard Screenshots

0. Dashboard Menu

1. Executive Summary

1.2 Executive Report

1.3 Other Executive

2. Revenue Analysis

2.1 Other Revenue

3. Pricing and Occupancy

4. Market Segmentation

5. Channel Performance

6. Profitability Analysis

7. Customer Satisfaction

8. Marketing Performance

9. KPI Tracking

💡 Key Insights

The data analysis reveals important patterns that can drive strategic decisions:

• Strong positive correlation (0.98) between ADR and Occupancy Rate, suggesting opportunities for strategic price adjustments without significantly impacting occupancy
• Leisure guests generate the highest revenue, with visitors from the USA contributing most to overall revenue
• Direct booking channel produces the highest revenue, while the OTA channel commands a higher ADR
• Fridays consistently generate the highest daily revenue, while Thursdays show the lowest performance
• Strong correlation (0.89) between review scores and revenue, highlighting the importance of guest experience

🤝 Contributions

Contributions are welcome! If you'd like to improve this project:

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

📚 Additional Documentation

• Implementation Guide
• Metrics Dictionary
• Analysis Guide
• DAX Measures
• Visualization Specifications
• Power Query Transformations

📞 Support and Contact

Questions, issues, or suggestions? Open an issue or contact us at youremail@example.com.

📄 License

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

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