Willingness-to-Pay (WTP) forecasting and optimization science is revolutionizing airline RM by helping airlines forecast and price based on customers’ price sensitivity, not class availability.
Willingness-to-Pay forecasting is an industry-first technology that helps airlines scientifically combat buy-down and drive incremental revenue. 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. In an environment with few competitors or fenced fare structures, the buy-down is automatically controlled through the implicit segmentation in the filed fares. However, as the airline industry has moved away from the traditional fencing, more input is required to control the availability to prevent buy-down. Taking this approach allows for a more scientific approach to something that was previously handled through manual intervention.
PROS has continued the evolution of this approach by implementing the PROS Willingness-to-Pay (WTP) Forecasting and Optimization methodology. This guide outlines the key steps to the WTP methodology:
In the early days of revenue management practices, the class codes (RBDs, classes, Fare Class, etc.) were the primary indicator of segmentation of a passenger’s willingness-to-pay, or price sensitivity. The class represented the “product” that the passenger wanted to purchase, and the price associated with the product. The airline filed fares for each of the classes based on the passenger’s price sensitivity and for the conditions associated with the fare. Those conditions made up the particular product that the passenger wanted to buy. These were tangible conditions like flexibility to cancel or refundability of the ticket. They were also conditions like the number of days before departure that the ticket was purchased. These conditions, and others, were designed to create segmentation amongst the passengers. In some cases, these practices are still being used. The passenger’s choice of a particular product could be a passive or active decision. In some cases, the passenger is actively choosing the conditions of the fare, while in other cases, the number of days before departure a passenger was buying made them unaware, they were choosing the product and thus a passive decision.
The traditional revenue management system forecasts the passenger demand at each of these classes and then optimizes under the assumption that the demand is purely interested in buying that class code due to the restrictions and conditions of the fare. The resulting optimization produces controls, typically bid prices, that set the lowest available class that should be sold based on the constraints to capacity imposed by the expected demand and value of that demand. This is done by comparing the fare for the class to the bid price, which serves as a hurdle rate.
However, with airlines moving to digital distribution of prices and low-cost carriers (LCCs) entering more markets, there has been a more targeted effort to remove the fare fences that were traditionally visible to the airlines. Removing this segmentation results in class codes having the same product association, but at different price points. Thus, the buy-down effect is observed as the passenger will only choose the lowest available class. As this problem has become more prevalent, airlines have begun using different phrases like price elasticity, price sensitivity, buy-up, buy-down, trade-up, yield-up, spiral down, class dependence problem, etc. All of these terms represent the situation that is being faced and highlight the need to address it. To combat this situation, airlines have employed different approaches. In most cases, airlines choose to use a rules-based approach in combination with the traditional forecasting and optimization. Using this combined approach, the bid price sets the lowest available class based purely on the capacity constraints, and then the analyst creates an action or rule that closes the lower classes as the departure date nears, forcing the demand to buy-up to those classes, preventing dilution.
In some cases, the analyst may also employ a load factor rule that will close the lower classes as the load factor increases. Airlines may also combine these approaches as well.
This approach has proven valuable at airlines, particularly those that take a method-driven approach to creating the rules. However, the application of these rules can often be too broadly or improperly defined, causing close-off of too much or too little. Further, this approach can also impact the network effect by overriding the result of the network optimization. And, because of the significant manual effort this approach requires, airlines leave themselves open to errors in inputs or missed opportunities.
Another approach airlines may employ is a methodology that increases the demand in the higher classes, closer to departure. This gives the traditional optimization the impression that higher-paying demand exists closer to departure, potentially setting controls at a higher availability level. The assumption with this approach is that there is enough demand for those classes willing to pay that fare. However, this approach still relies only on the bid price to force the buy-up, which is not the intended use for it. If a flight does not have enough demand to fill the plane, the bid price is likely to be quite low, indicating that any fare is sellable.
In this case, there is potentially still revenue opportunity by forcing demand to buy into the higher classes, which won’t be accomplished using just the bid price.
The remainder of this guide is a deep dive into the PROS recommended approach to forecasting and optimization with WTP methodology.
Figure 1 Outline of the key steps in the process along with PROS approach to solving those steps.
The lambda parameter estimates the volume component of the function and the alpha parameter represents the shape of the curve. Alpha also represents the amount of price sensitivity in the demand (figure 2). The p0 is a minimum price in the market.
Given this formulation, the next step is for the PROS Bayesian forecaster to generate a forecast of the alpha and lambda parameters. This is done by first generating historical observations of the alpha and lambda values. In a traditional bookings forecast, the observations are the bookings and a constraint probability, which is used to calculate an unconstrained bookings observation. These unconstrained bookings are used as an input into the model. In the WTP model, the alpha and lambda parameters are not directly observed, but instead are generated based on the historically observed bookings and the price paid for those bookings.
Figure 2 Representation of the parameters of the exponential demand curve.
Figure 3 Price Demand curve after discretization to the class level.
This approach provides several key characteristics that make it a robust model for estimation of the price sensitivity. Those are:
This forecast is then sent to the optimization process for fare transformation.
PROS RM Advantage uses the properties of the network optimization and dynamic program where the transformed fare and demand can be used, without any changes to the traditional formulation. This means that the primary focus on the WTP Optimization is the calculation of the transformed fares and demand.
The transformation process is a marginal revenue calculation called the Concave Envelope Data Transformation (CEDT). The algorithm assesses those fare classes that are on the efficient frontier. An efficient class, one on the efficient frontier, is one that should be open under at least one available capacity scenario to maximize revenue. A class lying below the efficient frontier, called inefficient, is a class that should never be open for sale under any available capacity scenario and thus the class is closed or inherits the availability from the class below it. The intention of this process is to identify classes where the revenue is diluted so much that it results in a negative margin, thus in a revenue maximization process, it is optimal to close the class. Once this step of closing classes has occurred, the system next performs a demand and fare transformation. The purpose of this step is to provide an estimation of the actual amount of demand expected at each class and the relative value of that class, given the incremental revenue achieved in that class. The transformed demand is calculated as the incremental demand if one additional class is open for sale. The transformed fare represents the incremental revenue per passenger if one additional class is open for sale. Once the transformed fare and demand are calculated, they are sent to the Linear Program for network optimization and the dynamic program for leg optimization. The resulting outputs from the optimization are the flight/leg/compartment bid prices and the ODIF/ POS level transformed fares. Figures 4-6 show an example of this calculation of transformed fares and demand.
Figure 4 The demand (as calculated from the forecast), daily fare, and calculated cumulative revenue. These represent the input into CEDT.
Figure 5 Visual representation of the efficient frontier. Class V lies below the efficient frontier and thus is inefficient.
Figure 6 Transformed demand and fares calculated. Since V class is inefficient, the transformed fare is $0.
Since the transformed fare is the representation of the incremental revenue for a given class, this is now used when comparing a fare to the bid price for the purposes of determining availability. PROS RM Advantage outputs the necessary data for consumption by the availability calculator. PROS RTDP Advantage is seamlessly integrated with RM Advantage, allowing the system to send real-time updates of new bid prices to RTDP. Once this data is in RTDP, the availability is calculated by comparing the transformed fare to the bid price. If RTDP does not find the transformed fare, the system falls back to the daily fare provided by RM Advantage.
As the airline industry continues to evolve to keep up with market shifts and competitive environments, airline revenue management systems must evolve and modernize with it. One of the biggest evolutions related to revenue management is the way the price and product are presented in the market and how the price sensitivity of the passenger is considered. Airlines have approached this with unscientific, rules-based methods, which at best prevent significant revenue dilution and at worst cause more revenue dilution.
The solution to this is to perform scientific forecasting and optimization steps to best capture the price-sensitive demand and adjust availability accordingly. With PROS WTP methodology, PROS has built upon previous experiences from traditional and hybrid forecasting and optimization to build the next stage in modernizing airline revenue management. The WTP model uses the observed bookings and the fare of those bookings to estimate a continuous price/demand curve. These forecasts are then discretized to the class level and sent to optimization where optimal controls can be generated. This data is seamlessly integrated with PROS RTDP to distribute accurate and timely availability.
The benefits of PROS WTP Forecasting and Optimization include both revenue improvement as well as more flexibility in the model for the analyst in the changing business environment. The revenue improvements have been studied using both simulation methods as well as with real data.
This solution has proven benefits in both simulation and with real airline data. The results consistently show an improvement of between 1% and 3% over traditional revenue management controls where the fare classes are assumed independent.
The improvement is on top of the benefits already achieved by moving to O&D-level forecasting to take into account the network effect.
The revenue benefits will be dependent on the market characteristics, including the competitive environment, fare fences/conditions, and the price sensitivity of passengers. Regardless, Willingness-to-Pay methodology unveils great revenue potential for airlines to exploit on the path to class-free dynamic pricing and modern airline retailing.
Justin Jander is a Senior Director of Product Management, focusing on the Revenue Management products at PROS. Justin has been with PROS for 14 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. These improvements have been through new features and functionality that improve the industry-leading science as well as enhancements to the way analysts use the system. 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. His most recent accomplishment includes the successful launch of the new PROS RM Editions product, which includes launching RM Essentials and RM Advantage.
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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