Client Overview
A hotel chain sought to enhance profitability by improving the accuracy of demand forecasting. Faced with fluctuating occupancy rates and operational inefficiencies, the client implemented an AI-powered model to predict demand using historical bookings, market trends, and weather data.
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.
Challenges
The hotel faced fluctuating occupancy rates and operational inefficiencies, impacting profitability. Traditional forecasting methods struggled to account for seasonal trends, local events, and other market fluctuations, leading to inaccurate predictions. The hotel needed a more precise, data-driven solution to optimise resource allocation, align staffing, and improve revenue management.
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.
