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How Airlines Can Forecast Customer Willingness-to-Pay

Combating buy-down is a hot topic in the revenue management space. In fact, it has been a hot topic for a long time! Buy-down is the concept where passengers have a certain willingness-to-pay for a product but will buy down to the lowest available class. This effect dilutes the airline’s revenue, unless protections are in place to prevent the buy-down. Aditi laid out three ways that airlines can solve this business problem.  Incorporating willingness-to-pay into the actual revenue management decision was one of her solutions we’ll dive into.  

This specific approach relies on scientifically forecasting the price sensitivity of the airline passenger, incorporating that price sensitivity into an optimization decision, performing the network and leg optimizations, and then adjusting availability based on that price sensitivity. As easy as that may sound, there are a lot of details to the process. Before moving forward, it’s important to understand exactly what price sensitivity means.  

Price sensitivity indicates how much demand change there will be given a change in price.  Highly price sensitive demand means that a small change in price can result in a large change in demand. Less price sensitive demand means that a change in price does not result in a large change in demand.   

The algorithm that you choose for determining the price sensitivity of the passenger is critical, and of course, hotly debated. PROS was a leader in the industry when Hybrid Forecasting was introduced. The hybrid approach was based on availability data, which determined which portion of the demand was price sensitive and which was product sensitive. Hybrid opened the door for airlines to begin addressing this problem and has since been successful. Building on that success, PROS Product Management and Science teams set out to explore an option that could improve on the results of hybrid and create our path towards the future of revenue management, continuous dynamic pricing. Our solution: Willingness-to-Pay Forecasting and Optimization. The solution focuses on using the value of the passengers that have booked rather than using the class code of the booking.  

This is a huge opportunity for moving ahead to the future of dynamic pricing where we’re less focused on class codes. The PROS approach to willingness-to-pay lays the groundwork for class-free revenue management and dynamic pricing – in other words – it’s one step closer to continuous pricing based on science. Airlines are looking to proven and innovative ways to keep up with market shifts and competitive environments. More flexible pricing means that airlines can shape and price products based on what they know about the passenger through science. The benefits of PROS Willingness-to-Pay Forecasting and Optimization include both revenue improvement as well as greater flexibility for analysts operating in a changing business environment.

Sounds interesting, right? We certainly think so! I think it’s so game-changing that I wrote a whitepaper to give you the details of forecasting and optimization components of our new solution. In this whitepaper, we review in greater detail the purpose of using willingness-to-pay methodology, the mechanics of how it works within forecasting and optimization, and also how this process impacts availability calculations in PROS Real-Time Dynamic Pricing. To top it all off, PROS Willingness-to-Pay is now available in PROS RM Advantage. When you’re done reading the whitepaper, you’ll have the information you need to get started. What are you waiting for?

About the Author

Justin Jander is a Director of Product Management, focusing on the Revenue Management products at PROS. Justin has been with PROS for 11 years, all within the Product Management group, focusing on the travel products. During that time, he has overseen the continuous improvement of the PROS revenue management products. In order to understand the needs of the always-changing industry, he has worked with airlines across the world, which allows him to understand the business problem and translate that into features that can improve the RM system. Justin earned a Bachelor of Science degree in Mathematics from Stephen F. Austin State University and a Master of Science degree in Statistical Science from Southern Methodist University.

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