Changing the Game: Inside the New Strategy Redefining Airline Pricing

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Modern airline retailing faces numerous challenges as it adapts to changing consumer expectations and technological advancements. A key challenge is delivering a seamless, personalized shopping experience across multiple channels. However, many airlines still rely on class-based pricing models, which can limit their ability to offer truly customer-driven pricing that reflects individual preferences and behaviors. 

Balancing this legacy approach with the demand for personalization complicates the customer experience, as travelers increasingly expect tailored offers and flexible pricing that aligns with the customized shopping experiences they encounter in other sectors. At the same time, airlines must manage complex inventory and pricing strategies to stay competitive and maximize revenue. 

To address these challenges, airlines are increasingly turning to innovative solutions that leverage new technologies. One such approach is the PROS Willingness-to-Pay (WTP) model, a revenue management strategy that leverages AI and machine learning to forecast demand and elasticity based on the maximum price a customer is willing to pay for a product or service. By analyzing historical data, market trends, and customer behavior, the model provides dynamic pricing recommendations that align with customer demand and help airlines maximize revenue. 

“Some vendors claim that revenue management is either dead or becoming obsolete, especially with the airline industry’s shift toward offers and orders,” said Justin Jander, senior director of product management at PROS. “However, we stand by the idea that revenue management is far from dead. In fact, it’s the backbone of creating offers and a key driver of value creation and growth.” 

SkiftX recently sat down with Jander for insights into this revenue management evolution and to explore how the WTP model is helping airlines optimize profitability, improve customer experiences, and navigate the complexities of modern retailing through more accurate pricing and demand forecasting.

Challenges Within the Airline Retailing Landscape

Data is essential for understanding customers’ needs, preferences, and buying behaviors, yet converting that information into tangible benefits can be difficult. While airlines have access to an immense volume of data, they often struggle to harness its full potential to create meaningful, personalized customer experiences. 

“Legacy systems limit the ability to provide dynamic and personalized offers, as outdated infrastructure often hinders real-time adjustments,” Jander said. “Airlines also face pressure to maximize revenue while operating within razor-thin margins. Personalization aims to improve customer satisfaction, but it also serves the airline’s bottom line — happy customers return, spend more, and contribute to profitability. However, airlines need to make the most revenue possible on each transaction, making it a high-stakes decision.” 

This process is further complicated by the need to predict future demand. For example, if a passenger is booking a flight 100 days in advance, the airline must anticipate whether a higher-paying customer will book later or whether to offer a competitive price now. Airlines must navigate these complex challenges to fine-tune a dynamic pricing strategy that balances customer satisfaction with financial survival.

How Segmentation Data Powers Dynamic Pricing 

According to Jander, dynamic pricing is not about creating individualized offers but about segmenting passengers into groups and tailoring offers based on their general characteristics. 

“Segmentation helps identify patterns, such as business travelers who fly on Monday mornings in high-demand markets and are more willing to pay versus leisure travelers who fly on Wednesdays when demand is lower and are typically more price-sensitive,” Jander said. “The challenge lies in predicting how much each group is willing to pay for a particular product, such as the flight itself or ancillaries like checked bags, over time.”

While this may sound straightforward, airlines manage thousands of flights daily, often booking up to 360 days in advance. This adds significant complexity to the task. 

“In simple terms, airlines use passenger segments — including high versus low willingness-to-pay groups — along with historical data and forecasts to predict demand at different price points,” Jander said. “Many passengers think of the product as simply their seat on the plane, but in reality, the product also includes the timing of when the ticket was bought. A business traveler who needs to fly tomorrow to close a $10 million deal is more willing to pay a higher price than someone visiting their grandmother for her 50th anniversary, whose willingness to pay is much lower.” 

Currently, pricing segmentation is effectively managed by using dynamic availability, adjusting the number of seats offered at each price point based on demand forecasts. However, the future of airline pricing may lie in moving away from rigid class codes toward more flexible dynamic pricing models. This shift would allow airlines to adjust prices in real time, based on a broader set of factors like individual purchase history and market conditions, making pricing even more responsive to customer behaviors and market fluctuations.

Inside the PROS Willingness-to-Pay Model

Across the travel industry, AI and machine learning are helping companies effectively navigate vast amounts of data, enabling more accurate forecasting and decision-making. 

“At PROS, our foundation has always been in data science,” Jander said. “When we started back in 1999, AI wasn’t the buzzword it is today, but we were already using data science to solve complex problems. Now, with more advanced algorithms through AI, we’re helping airlines make even better decisions.”

The PROS WTP model enables better decision making by using AI to forecast the relationship between price and demand. By analyzing price sensitivity — how much demand changes with price variations — WTP pushes demand into higher fare classes and reduces revenue loss from lower fares. 

“Willingness-to-Pay is not a pricing algorithm,” Jander said. “It’s not about pricing individuals differently — it’s a method to understand broader market segments and forecast demand based on price. Traditional revenue management forecasted demand based on fare classes, like how many Q- or M-class tickets would be sold. Now, we focus more on the relationship between price and demand. Instead of looking at class codes, we analyze how many people are likely to buy at various price points, ultimately helping airlines capture more revenue by offering the right prices at the right time.”

Achieving Success With PROS WTP

Hawaiian Airlines has been a customer of PROS for the past two years, partnering closely to enhance their revenue management system. The implementation of the PROS WTP model has played a critical role in helping Hawaiian Airlines drive revenue, minimize buydown, and stay competitive in the challenging leisure travel market.

In running the PROS WTP model without manual intervention, Hawaiian Airlines saw better revenue performance by holding out for higher-paying customers. 

“This automated approach outperformed the manual rules, giving the airline confidence in the solution and helping maximize revenue,” Jander said. “This also meant that by holding seats for higher-paying customers, the airline avoided the risk of having to turn away passengers or send them to a competitor.” 

While passengers may not love paying more for those last seats, the result is that higher-paying customers received the premium product they expected, and the airline benefited from having them onboard.

“The success of our relationship with PROS really comes down to an approach we mutually have of it being less about being a customer and a vendor and more about a continued partnership,” said Justin Matthew, manager of revenue management systems at Hawaiian Airlines. “We’re at the forefront of a lot of change when it comes to the integration of AI in offer and order management, and we know we need to be part of this change.” 

While some vendors claim that revenue management is becoming obsolete as the airline industry shifts toward offers and orders, PROS believes it remains a crucial part of the process. As Jander emphasized, revenue management continues to be the foundation of offer optimization, evolving alongside new technologies like willingness-to-pay models. 

“You can’t abandon the principles of revenue management,” Jander said. “It is the backbone of the offer, helping airlines strike a balance between maximizing revenue and delivering a great customer experience.”

To learn more about the PROS Willingness-to-Pay model, click here. 

This content was created collaboratively by PROS and Skift’s branded content studio, SkiftX.