Feature Analysis and Evaluation of Airline Passenger Satisfaction Based on Decision Tree

The global airline industry is recovering from the COVID-19 pandemic. Passenger satisfaction is on a downward trend despite rising demand. For the airline industry to prosper during this recovery period, it is crucial to identify the critical variables determining passenger satisfaction. This study utilises a decision tree algorithm for data analysis, mathematical modelling and feature selection to reveal the determinants of passenger satisfaction. The model is further refined and simplified by selecting the most significant features. The study is successful in identifying the most vital elements influencing passenger satisfaction. Notably, online boarding, in-flight internet services and personalised services for different passenger types emerged as key drivers. Knowledge of these factors enables airlines to focus on specific areas of improvement, thereby increasing overall passenger satisfaction. The implications of these findings for airlines are practical as they provide valuable insights for improving service quality and passenger satisfaction. Addressing the factors identified allows airlines to optimise efficiency, strengthen their competitive advantage and ensure long-term success in the post-pandemic recovery phase. Furthermore, airlines can safeguard their brand reputation and foster customer loyalty by understanding customer preferences and enhancing satisfaction.