$1.5M in additional revenue generated from six targeted routes within three test markets
Company: International airline
Industry: Airlines
Revenue: >$5 billion
Seeking improved profitability, a major global airline needed a solution to counter buy-down – a revenue-dilution problem in which higher-fare passengers are induced to buy at lower prices because classes below their willingness-to-pay remain open and available for purchase.
Carriers use various methods to combat increasing demand uncertainty and buy-down behavior – seat-parking, forced class closure and artificial demand influences. Unfortunately, these methods often harm, rather than improve, revenue performance.
The international carrier adopted PROS Willingness-to-Pay (WTP) forecasting and optimization and started testing revenue performance of six routes in three key markets. The airline saw a 4.7% revenue increase for these markets, with the six routes generating an additional $1.5 million in fare revenue.
Challenge
This global airline faced significant threats to revenue performance from low-cost competitors disrupting the industry with reduced fares and restriction-free purchase options. With easy access to price deals, high-yield business travelers were increasingly buying down.
Typical rule-based constraint and load factor approaches, while valuable, were causing over-allocation of low-fare seats and under-allocation of high-fare seats, impacting overall network optimization and revenue dilution.
Breaking this cycle required a sophisticated willingness-to-pay forecast model that utilizes both product-sensitive and price-sensitive demand to optimize seat availability and revenue.
Solution
The international carrier adopted PROS Willingness-to-Pay (WTP) forecasting and optimization as part of their revenue management solution. The airline focused on testing revenue performance of six routes in three key markets.
The WTP module encompasses both yieldable demand (late-booking, schedule-sensitive, brand-loyal) and airline-priceable demand (price sensitive). To ensure accurate forecasting and understanding of passenger willingness-to-pay, the solution incorporates various inputs: last-seat availability data, rich historical data, comprehensive competitive information, sophisticated customer segmentation, seasonality variations, and other factors as well.
As a result, the airline was able to:
- Adopt a scientific approach to capturing price-sensitive demand and gain confidence in their decision-making.
- Accurately forecast demand, anticipate buy-down, determine WTP, and employ real-time dynamic availability and pricing.
- Start solving their revenue dilution problem due to buy-down and demand uncertainty by accepting more high-yield bookings and move lower-yield bookings to more profitable options.
Learn more about willingness-to-pay in airline RM here.
Results
PROS Revenue Management with Willingness-to-Pay capabilities improves passenger revenue by enabling the airline to confidently accept more high-yield bookings and move lower-yield bookings to more profitable options.
Within months of its implementation, this major global airline significantly increased revenues in three initial test markets, serving predominantly business travelers, and generated incremental revenue on six targeted routes to/from those markets.
With WTP, the airline achieved significant cost optimization and ROI, while reaching key milestones on its retail transformation journey.
- 4.7% increase in revenue: The airline increased passenger revenue in three test markets within months.
- $1.5M in revenue: Additional revenue was generated from six targeted routes within the three test markets.
- 7% to 10%: The carrier was projected to show significant gains using Willingness-to-pay for leisure travel markets.
- 1% to 4%: Notable gains for business travel markets were projected as well.
Willingness-to-Pay forecasting and optimization helps airlines break the traditional pricing paradigm by removing the barriers of RBDs and fixed price points. It enables carriers to price based on customer price sensitivity, not class availability, and speeds up the transition to class-free dynamic pricing.
Interested in learning more about PROS Willingness-to-Pay forecasting and optimization? Contact us today.
