Origin and destination based demand of continuous pricing for airline revenue management

Abstract

This thesis presents an approach for estimating sell-up rates, which indicate a passenger's likelihood to upgrade to a higher fare class, based on historical booking data categorized by fare classes. Previous models explored in the literature, including Direct Observation (DO) and Inverse Cumulative (IC), have demonstrated limitations when applied to real-world historical booking data, as their outcomes may not align with the desired business expectations. To address this limitation, we enhance these models by incorporating data pre-processing techniques and devising solution strategies that provided to the needs of industry practitioners when dealing with historical booking data. By incorporating fare class availability data and adjusting past bookings accordingly, our proposed model offers a robust estimation of sell-up rates. To validate the effectiveness of our approach, we conduct an analysis using data from a major European airline. The numerical results demonstrate a significant decrease in the Mean Absolute Percentage Error (MAPE) when employing our proposed method, indicating its superior accuracy in estimating sell-up rates. This research not only fills the gap in the existing literature but also provides practical implications for revenue management practitioners. By refining the sell-up rate estimation process and addressing the shortcomings of traditional models, our approach offers a valuable tool for airlines to optimize their revenue management strategies. The utilization of historical booking data, combined with our model's enhancements, ensures more reliable and actionable insights, empowering practitioners to make informed decisions. Furthermore, our study contributes to the field by introducing data pre-processing techniques tailored specifically for historical booking data analysis. These techniques facilitate the extraction of relevant information and enhance the accuracy of sell-up rate estimations. As such, our research provides a comprehensive framework that encompasses both theoretical advancements and practical applications, thus offering a holistic approach to addressing the challenges of sell-up rate estimation in revenue management. In summary, the first chapter introduces a new method for estimating sell-up rates by leveraging fare class-based historical booking data. Through the refinement of existing models, along with the incorporation of data pre-processing techniques and solution strategies, our approach yields more accurate sell-up rate estimations. The analysis of data from a major European airline demonstrates the effectiveness of our proposed method in reducing the Mean Absolute Percentage Error (MAPE). By enhancing sell-up rate estimation accuracy, our research contributes to the advancement of revenue management practices and provides valuable insights for industry practitioners. In the second chapter, we present an innovative model for forecasting airline flight load factors specifically designed to account for the unique circumstances brought about by the Covid-19 pandemic. By incorporating various variables, including bookings, capacity, booking trends, and seasonal effects, our model aims to provide accurate load factor predictions. To validate its effectiveness, we conducted an extensive analysis using the dataset of one of Europe's largest network airlines, spanning the entirety of 2021. The findings of our study reveal that machine learning algorithms offer substantial improvements in load factor predictions compared to the traditional pickup method. Notably, the Covid-19 pandemic period introduces distinctive patterns and challenges to airline load factor data, leading to decreased performance of the pickup method. However, by leveraging advanced machine learning models, we were able to effectively capture the complexities and variations in load factors during this turbulent period, resulting in significantly enhanced accuracy. Our proposed model demonstrates a remarkable reduction in the Mean Absolute Error (MAE) score for load factor forecasts. When compared to the pickup method, the MAE score decreased from 7.94 to an impressive 1.99. These results underscore the potential of advanced machine learning techniques in accurately predicting load factors, particularly in the face of unprecedented disruptions like the Covid-19 pandemic. The incorporation of diverse variables into our model enables a comprehensive assessment of the factors influencing load factor dynamics. By considering variables such as bookings, capacity, booking trends, and seasonal effects, our model captures the intricate interplay between these factors and load factor performance. This complete approach enhances the accuracy and reliability of load factor forecasts, providing airlines with valuable insights for informed decision-making. The outcomes of this research highlight the significance of leveraging advanced machine learning techniques for load factor forecasting during challenging periods like the Covid-19 pandemic. The ability to effectively capture and analyze complex data patterns empowers airlines to adapt their strategies and optimize resource allocation in response to changing demand dynamics. By embracing the potential of machine learning, airlines can gain a competitive edge and make data-driven decisions to navigate through turbulent times successfully. In conclusion, this chapter introduces a different model for forecasting airline flight load factors, specifically tailored to the unique circumstances presented by the Covid-19 pandemic. Through the utilization of machine learning algorithms and the incorporation of various variables, our model surpasses the traditional pickup method in terms of accuracy. The significant reduction in the Mean Absolute Error (MAE) score demonstrates the efficacy of our proposed model in capturing the complexities and variations in load factor data during the pandemic. By providing more accurate load factor forecasts, our research equips airlines with valuable insights to optimize their operations and navigate through challenging times effectively.