This book chronicles airline revenue management from its early origins to the last frontier. Since its inception revenue management has now become an integral part of the airline business process for competitive advantage. The field has progressed from inventory control of the base fare, to managing bundles of base fare and air ancillaries, to the precise inventory control at the individual seat level. The author provides an end-to-end view of pricing and revenue management in the airline industry covering airline pricing, advances in revenue management, availability, and air shopping, offer management and product distribution, agency revenue management, impact of revenue management across airline planning and operations, and emerging technologies is travel. The target audience of this book is practitioners who want to understand the basics and have an end-to-end view of revenue management.
(Cont.) We propose several approaches for dynamic picing and resource allocation. Numerical experiments suggest that coordination of pricing and resource allocation policies in a network while taking into account the uncertainty of demand can lead to significant revenue gains. Finally, in our conclusions we propose an integrated approach to airline revenue management that combines all four aspects that we studied here, and suggest directions for future research.
“There is no strategic investment that has a higher return than investing in good pricing, and the text by Gallego and Topaloghu provides the best technical treatment of pricing strategy and tactics available.” Preston McAfee, the J. Stanley Johnson Professor, California Institute of Technology and Chief Economist and Corp VP, Microsoft. “The book by Gallego and Topaloglu provides a fresh, up-to-date and in depth treatment of revenue management and pricing. It fills an important gap as it covers not only traditional revenue management topics also new and important topics such as revenue management under customer choice as well as pricing under competition and online learning. The book can be used for different audiences that range from advanced undergraduate students to masters and PhD students. It provides an in-depth treatment covering recent state of the art topics in an interesting and innovative way. I highly recommend it." Professor Georgia Perakis, the William F. Pounds Professor of Operations Research and Operations Management at the Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts. “This book is an important and timely addition to the pricing analytics literature by two authors who have made major contributions to the field. It covers traditional revenue management as well as assortment optimization and dynamic pricing. The comprehensive treatment of choice models in each application is particularly welcome. It is mathematically rigorous but accessible to students at the advanced undergraduate or graduate levels with a rich set of exercises at the end of each chapter. This book is highly recommended for Masters or PhD level courses on the topic and is a necessity for researchers with an interest in the field.” Robert L. Phillips, Director of Pricing Research at Amazon “At last, a serious and comprehensive treatment of modern revenue management and assortment optimization integrated with choice modeling. In this book, Gallego and Topaloglu provide the underlying model derivations together with a wide range of applications and examples; all of these facets will better equip students for handling real-world problems. For mathematically inclined researchers and practitioners, it will doubtless prove to be thought-provoking and an invaluable reference.” Richard Ratliff, Research Scientist at Sabre “This book, written by two of the leading researchers in the area, brings together in one place most of the recent research on revenue management and pricing analytics. New industries (ride sharing, cloud computing, restaurants) and new developments in the airline and hotel industries make this book very timely and relevant, and will serve as a critical reference for researchers.” Professor Kalyan Talluri, the Munjal Chair in Global Business and Operations, Imperial College, London, UK.
In a world of changing lifestyles brought about by new services, technology and e-commerce, this book enters the arena of contemporary research with particular topicality. Integrating both theory and real world practices, Ng advances the latest concepts in pricing and revenue management for services in a language that is useful, prescriptive and ye
The book provides a comprehensive overview of current practices and future directions in airline revenue management. It explains state-of-the-art revenue management approaches and outlines how these will be augmented and enhanced through modern data science and machine learning methods in the future. Several practical examples and applications will make the reader familiar with the relevance of the corresponding ideas and concepts for an airline commercial organization. The book is ideal for both students in the field of airline and tourism management as well as for practitioners and industry experts seeking to refresh their knowledge about current and future revenue management approaches, as well as to get an introductory understanding of data science and machine learning methods. Each chapter closes with a checkpoint, allowing the reader to deepen the understanding of the contents covered.This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.
This is the first comprehensive introduction to the concepts, theories, and applications of pricing and revenue optimization. From the initial success of "yield management" in the commercial airline industry down to more recent successes of markdown management and dynamic pricing, the application of mathematical analysis to optimize pricing has become increasingly important across many different industries. But, since pricing and revenue optimization has involved the use of sophisticated mathematical techniques, the topic has remained largely inaccessible to students and the typical manager. With methods proven in the MBA courses taught by the author at Columbia and Stanford Business Schools, this book presents the basic concepts of pricing and revenue optimization in a form accessible to MBA students, MS students, and advanced undergraduates. In addition, managers will find the practical approach to the issue of pricing and revenue optimization invaluable. Solutions to the end-of-chapter exercises are available to instructors who are using this book in their courses. For access to the solutions manual, please contact [email protected].
A story about science, technology, and people, The Future of Pricing provides an inside look at how airlines price tickets and how practices developed in the airline industry are now revolutionizing the world of pricing. This book is written for business professionals and students wanting to better understand the rapid growth of scientific pricing.
Even as the distribution and sale of commercial airline tickets has shifted in recent years from physical reservation offices to the Internet, many airline commercial processes remain highly reliant on pre-Internet technologies and standards. This legacy infrastructure compels airlines to publish a discrete set of prices in each market they serve, and to select prices for each itinerary from among only this limited set of possible price points. Recent advancements in distribution technology, such as the New Distribution Capability (NDC), offer airlines the chance to break away from these constraints. These new standards enable the creation of customized offers with prices that could be generated dynamically in real time. While airlines have shown interest in these new technologies, practical methods for integrating dynamic pricing into existing airline revenue management (RM) and distribution systems have yet to be defined and evaluated by academics or practitioners. In this work, we propose the first mechanisms for dynamic pricing designed specifically for use in the airline industry. By selectively providing increments or discounts based on demand segmentation and estimates of willingness-to-pay (WTP), our mechanisms can increase airline revenues by stimulating new bookings from price-sensitive travelers while encouraging more price-inelastic travelers to buy up to higher price points. Moreover, the methods are compatible with the pricing, RM, and distribution systems currently used by airlines today. Our dynamic pricing heuristics emerge from the development of a novel theoretical model of customer choice. Using the model, we introduce a new concept called "conditional WTP" to describe how a customer's willingness-to-pay for an itinerary can change depending on the other alternatives available in his choice set. We show how assuming an unchanging maximum WTP for air travel, as in past work on dynamic pricing, can lead to overestimation of WTP in competitive environments, and describe how an airline's estimates of conditional WTP play an integral role in our dynamic pricing mechanisms. We test our dynamic pricing methods in the Passenger Origin-Destination Simulator (PODS): a robust agent-based booking simulation that models the interactions between passengers and airlines. In a complex, competitive network, we find that our heuristics can increase airline revenues by up to 1 - 4% from traditional pricing and RM alone. Incrementing prices can result in revenue gains through an increase in yield, and discounting can lead to higher revenues through demand stimulation and share shift from other airlines. In both cases, we identify a phenomenon we call "forecast spiral-up" which increases yield by protecting more seats for higher-value fare classes. We also develop a variant of the heuristic in which multiple substitutable flights are priced simultaneously, leading to additional revenue gains. Finally, we provide the first in-depth assessment of the practical implications of dynamic pricing for the airline industry. We focus on airline concerns that dynamic pricing could lead to price wars, excessive discounting, and a race to the bottom. We also evaluate some of the potential legal implications and customer reactions that could emerge as dynamic pricing becomes more commonplace. These analyses provide new insight on how airline competition could potentially change as dynamic pricing is integrated into traditional airline processes.
Revenue management (RM) has emerged as one of the most important new business practices in recent times. This book is the first comprehensive reference book to be published in the field of RM. It unifies the field, drawing from industry sources as well as relevant research from disparate disciplines, as well as documenting industry practices and implementation details. Successful hardcover version published in April 2004.
The airline industry has come a long way since deregulation in 1978. Yet its ROI to investors is lowest among all industries. This book provides the secrets of how advanced analytics helps profit optimization. It is written with candor to show the limitations of today's most widely used systems and how new futuristic systems are needed for optimized decision making. It describes the systems, process and governance that align the entire management from the CEO down to the managers in making profitable decisions every time. Using numerous cases from the commercial functions of sales, revenue management, marketing and network planning, the book provides an insider look into the sub-optimal decision processes today. It shows how embedding advanced analytics from enterprise perspective optimizes the decisions. It provides a playbook for the CIO and C-Suite to build and ensure governance of the models, data and process for enterprise analytics. Commercial airlines that adopt the advanced analytics paradigm shift will see direct competitive advantage and lasting change in profits. This is the type of change recommended by Gopal Ranganathan, industry consultant and founder of Quad Optima Analytics, a company dedicated to helping airlines have this advantage.
