Smart Occupancy: AI to Forecast Hotel Demand and Maximize Profits

Objectives

  • Improve the accuracy of hotel occupancy rate forecast and optimize resource allocation based on anticipated demand fluctuations.
  • Increase revenue by maximizing room bookings during peak seasons and minimizing lost revenue during low seasons.
  • Reduce operational costs by aligning staffing levels, housekeeping schedules, and inventory management with predicted occupancy rates.

Solution

We leveraged an AI model specifically designed for hotel demand forecasting. The model was trained on a comprehensive dataset including historical hotel bookings (occupancy rates, room types, booking lead times, cancellation rates), external market data (seasonality, holidays, local events, economic trends, competitor pricing), and weather data (potential impact on tourism)

Benefits / ROI ​The AI-driven demand forecasting model consistently outperformed human forecast by up to 50% for all room types, resulting in significant increase in revenue and a reduction in operational costs.

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