Maximize hotel chain revenue with AI-driven dynamic pricing.

Static pricing strategies hinder hotels from maximizing revenue by failing to adapt to fluctuating market demand. Traditional fixed pricing overlooks opportunities to capitalize on peak demand and struggles to maintain optimal occupancy during low seasons, resulting in significant revenue loss.

Roomify's AI-powered dynamic pricing engine leverages machine learning to predict demand and recommend optimal prices, simultaneously maximizing revenue and occupancy. It intelligently analyzes historical patterns, competitor pricing, seasonal trends, and broader market conditions to deliver real-time, data-driven recommendations.

• KPI Monitoring: Monitor key performance indicators (KPIs) such as occupancy rates, Average Daily Rate (ADR), Revenue Per Available Room (RevPAR), and overall revenue growth.
• Occupancy Trends: Visualize seasonal patterns and demand fluctuations for actionable insights.
• Price Elasticity Analysis: Analyze the impact of price changes on demand across different seasons.
• Competitive Positioning: Benchmark pricing against competitors and broader market trends.

• Real-time Optimization: Instantly calculate optimal prices for diverse market scenarios.
• Revenue Maximization: Identify the ideal balance between price and occupancy to maximize total revenue.
• Business Insights: Generate actionable recommendations supported by detailed revenue impact analysis.
• Price Sensitivity Testing: Evaluate demand responsiveness to various price adjustments.

• Competitor Analysis: Simulate the impact of competitor price adjustments on your hotel's strategy.
• Seasonal Strategies: Compare pricing performance across various seasons and identify optimal approaches.
• Holiday Pricing: Optimize pricing specifically for special events and holidays.
• Weekend vs. Weekday: Analyze and optimize day-of-week pricing strategies for maximum impact.

• Machine Learning Metrics: Monitor model accuracy and key predictive performance indicators.
• Feature Importance: Gain insights into the key factors influencing demand predictions and their relative importance.
• Data Quality: Track data coverage and quality metrics to ensure model reliability.

The core technologies powering Roomify include:

• Python 3.10+: Core programming language for backend logic and ML model development.
• Streamlit: Interactive web application framework for the dashboard interface.
• Scikit-learn: Machine learning library for demand prediction models.
• Pandas & NumPy: Libraries for efficient data manipulation and analysis.
• Plotly: For creating interactive and dynamic data visualizations.
• Matplotlib & Seaborn: For static and statistical plotting in data exploration.

Roomify leverages a meticulously crafted, synthetic yet realistic dataset designed to simulate real-world hotel pricing scenarios. It includes:

• Two years of daily data (2022-2023).
• 730 data points, reflecting realistic seasonal patterns and market dynamics over two years.
• Key metrics: Date, season, competitor price, Roomify's price, demand, occupancy, and revenue.

• Seasonal Variations: Higher demand during summer and holiday periods.
• Weekend Peaks: Increased leisure travel demand during weekends.
• Price Elasticity: Realistic modeling of demand response to price changes.
• Competitor Influence: Market-driven pricing dynamics, reflecting competitor strategies.

Get Roomify up and running quickly with these steps:

• Python 3.10 or higher
• pip package manager

1. Clone the repository

   git clone <repository-url>
   cd roomify-pricing-dashboard

2. Install dependencies

   pip install -r requirements.txt

3. Run the application

   streamlit run app.py

4. Access the dashboard

   • Access the dashboard via your web browser at http://localhost:8501.
   • The dashboard will automatically load with pre-generated sample data.

roomify-pricing-dashboard/
├── app.py                    # Main Streamlit application
├── requirements.txt          # Python dependencies
├── README.md                 # Project documentation
├── utils/                    # Utility modules
│   ├── data_prep.py          # Dataset generation and processing
│   ├── pricing_model.py      # ML models and optimization logic
│   └── visualization.py      # Chart creation and insights
├── sample_data/              # Generated datasets
│   └── pricing_data.csv      # 2-year synthetic pricing data
└── reports/                  # Analysis reports
    └── example_report.pdf    # Sample business report

Navigate Roomify effectively with this guide:

Start with the Global Insights page to gain a comprehensive understanding of overall hotel performance:

• Review key performance indicators (KPIs) and core metrics.
• Analyze occupancy trends and seasonal patterns.
• Examine price-demand relationships.
• Assess competitive positioning.

Use the Dynamic Pricing Simulator for real-time optimization:

1. Set current pricing parameters (e.g., your hotel's price, competitor price, season, day of week).
2. Click "Calculate Optimal Pricing".
3. Review the optimal price recommendations and their projected revenue impact in detail.
4. Analyze price sensitivity and elasticity.

Test different scenarios with the Scenario Explorer:

• Competitor Changes: Observe how competitor pricing adjustments impact your pricing strategy.
• Seasonal Shifts: Compare performance across various seasons.
• Holiday Impact: Optimize pricing for special events and holidays.
• Day-of-Week: Analyze and optimize weekend versus weekday pricing strategies for varied demand.

Monitor model accuracy and feature importance:

• Review R² score and other prediction accuracy metrics.
• Understand which factors most significantly influence demand predictions and their relative importance.
• Assess data quality and coverage.

Roomify delivers significant and tangible business benefits:

• Dynamic Pricing: Dynamically adjust prices based on real-time demand and market conditions to unlock maximum revenue potential.
• Revenue Maximization: Achieve the optimal balance between price and occupancy for peak revenue.
• Competitive Advantage: Gain a significant competitive edge by swiftly responding to market trends and competitor actions.

• Automated Recommendations: Reduce reliance on manual pricing decisions with intelligent, automated recommendations.
• Data-Driven Strategies: Formulate robust, data-driven strategies based on historical patterns and comprehensive market insights.
• Scenario Planning: Test and validate pricing strategies before real-world implementation.

• Average Daily Rate (ADR): Optimize room rates for maximum profitability.
• Revenue per Available Room (RevPAR): Maximize revenue efficiency per available room through intelligent allocation.
• Occupancy Rate: Maintain optimal occupancy levels consistently.
• Total Revenue: Significantly increase overall revenue through intelligent pricing strategies.

• Algorithm: Random Forest Regressor (a powerful Machine Learning algorithm).
• Features: Key features include Roomify's price, competitor price, season, day of week, holidays, and various other date-derived attributes.
• Training: Utilizes an 80/20 train-test split, validated through cross-validation.
• Performance: Achieves strong R² scores, typically ranging from 0.7 to 0.9 on synthetic data.

• Method: Grid search optimization across predefined price ranges.
• Objective: Maximize total revenue (calculated as price × predicted demand).
• Constraints: Demand is capped at the hotel's predefined capacity.
• Output: Provides optimal price recommendations along with expected revenue and occupancy.

Early implementations of Roomify's dynamic pricing engine consistently demonstrate:

• A potential 15-25% increase in revenue through intelligently optimized pricing strategies.
• Improved occupancy rates, especially during traditionally low-demand periods.
• Enhanced competitive positioning through market-responsive pricing.
• Significantly enhanced overall profitability through data-driven decision-making.

Roomify is continuously evolving, with planned enhancements including:

• Real-time Data Integration: Seamless integration with live booking and property management systems (PMS).
• Advanced ML Models: Implementation of advanced machine learning models, including deep learning, for even more precise demand prediction.
• Multi-property Support: Scalable support for multi-property hotel chains and portfolios.
• Market Intelligence: Integration of external market intelligence data and trends.
• A/B Testing Framework: Development of an A/B testing framework for controlled experimentation of pricing strategies.

This project serves as a demonstration, utilizing synthetic data purely for educational and consulting purposes. The models and insights provided are simplified for clarity and should not be directly applied to actual business decisions without thorough validation and customization tailored to specific hotel operations.

We welcome contributions to improve the Roomify pricing dashboard:

1. Fork this 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.

For questions, issues, or feature requests:

• Create an issue on the GitHub repository.
• Contact the development team for direct assistance.
• Review the existing documentation and examples.

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

Roomify Dynamic Pricing Dashboard — Transforming hotel revenue management with intelligent, data-driven strategies for a more profitable future. 🏨💰