Using a machine learning-powered tool to forecast no-shows, automate overbooking strategies, and increase profitability while improving the passenger experience.
Context
Viva Aerobus needed to optimize its overbooking strategy to maximize efficiency, reduce revenue loss from passenger no-shows, and protect customer satisfaction.
The challenge was balancing optimal seat occupancy with the risk of denied boarding, while minimizing operational and financial impact.
Approach
We deployed OVBO, our proprietary machine learning tool that predicts passenger no-shows and calculates optimal overbooking levels per route and market.
The system automates overbooking strategies, integrates with major PSS platforms such as Navitaire and Radixx, and adapts in real time to market conditions and historical patterns.
Results
- Significant reduction in revenue loss from no-shows.
- More accurate, automated overbooking decisions.
- Lower risk of denied boarding, improving passenger satisfaction.
Key Technologies
OVBO | Machine Learning | Navitaire | Radixx | PSS Integration