The Path to Maximizing Overbooking Profits for Low Cost Airlines

Abstract

Low-cost airlines commonly face revenue losses from passenger no-shows, typically ranging from 5% to 10%. To mitigate these losses, overbooking has become a core strategy. This paper uses AirAsia as a case study to explore how it maximizes revenue while balancing passenger experience through data-driven dynamic overbooking. The research method utilizes a case study, focusing on AirAsia's use of the Rokki platform for real-time data collection and the Kambr machine learning system for predictive modeling and dynamic adjustments. This platform can fully integrate multi-dimensional information such as flight booking data, passengers’ historical travel records, and route passenger flow fluctuations, providing real-time data support for overbooking decisions. The study found that iterative optimization based on feedback mechanisms significantly improved load factors and financial performance while effectively reducing the risk of denied boarding. The conclusion is that intelligent overbooking strategies leveraging digital platforms and machine learning are crucial for low-cost airlines to maintain operational efficiency and market competitiveness.