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6 Breaking Down Willingness-to-Pay in RM www.pros.com 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. In PROS RM Advantage, the system associates every booking observation with the price paid. In order to accomplish this in RM Advantage, the system will use the daily fare that was in place at the time the booking occurred, as well as the daily fare offered during periods where no bookings occurred. Using the bookings with the associated fare allows the system to focus on the expected demand at different price points, rather than being associated with the fare class itself. This is the first step in the process where the RM system is breaking away from the barriers of the class codes. Once the PROS Bayesian model has generated the alpha and lambda forecasts, the system estimates the cumulative price-sensitive demand at each of the current class codes that make up the class block by discretizing the curve at the current fare value (figure 3). FIGURE 2 Representation of the parameters of the exponential demand curve. FIGURE 3 Price Demand curve after discretization to the class level. 100 80 60 40 20 0 $0 $100 $200 $300 $400 $500 $600 $700 $800 $900 $1,000

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