Revenue management (RM) is the science behind offering the right product at the right price to the right customer at the right time. By finding ways to evaluate a customer’s willingness to pay, revenue management scientists have developed algorithms to adjust a product’s price based on its demand.
Originally created by PROS to optimize airline ticket pricing, airline revenue management has changed tremendously over the past decade and, driven by the Offer and Order industry initiative, is set for its next (r)evolution.
How do industries use revenue management?
Now back to what revenue management is. Today, revenue management concepts can apply to any industry that deals with limited capacity. The fundamentals of revenue management revolve around capturing the value of remaining capacity, particularly in situations where the capacity is constrained, and then combining that value with an understanding of the willingness-to-pay of the buyer (read how airlines can forecast customer willingness-to-pay). These two components combine to form the price that the buyer should pay, with the goal of maximize revenue for the seller. The strategies associated with revenue management span across many industries including hotels, cruise lines, air cargo, rental cars, ocean freight, ride sharing apps, and others. Of course, the foundations of revenue management started in the airline industry. Many of these industries rely on fare/price designators to help with booking and downstream systems. A big next step in the revenue management field is the move to dynamic pricing science, an area PROS has been pioneering.
What are the two main drivers of revenue management?
When it comes to revenue management pricing solutions, there are many dynamic factors at play. However, there are two major variables that consistently influence revenue management: inventory (capacity) and willingness-to-pay.
Capacity constraints
The first driver of revenue management is determining capacity constraints based on the remaining inventory. To accomplish this, the revenue management system is aware of how many seats, rooms, cars, etc. are left to purchase. In the typical RM industry, the remaining capacity has a fixed shelf life once the flight (or equivalent) departs. This means the window for capitalizing on the revenue opportunity is fixed and once it has passed, there is no way to recover. Based on this, the system relies on an accurate forecast of volume to understand how many people are expecting to use the remaining resources. For each of those passengers, the system must also understand how much they are worth, so you can assess which set of passengers is best to take.
Now that you are holding this information, you need to perform the optimization. In modern airline revenue management, the result of this optimization is a bid price. It’s the value of the next resource that is available. It isn’t the price that you will pay though. It’s the value the next passenger must be worth to take that next resource. In its simplest form, a bid price value of zero indicates that there is not enough demand to satisfy capacity. This doesn’t mean the price should be zero, of course.
Instead, it means that the pricing strategy for this entity should be based on willingness-to-pay alone, not a capacity constraint consideration. If a bid price is greater than zero, then it means that there is a positive value associated with the next available resource. The larger the value, the more valuable that next resource is. This value says that the next passenger must be willing to pay at least the value of the bid price. While it may seem like a complicated concept, mathematically it holds as an excellent way to represent the value of the next seat. As technology has evolved, the importance of the bid price has remained and refined. As you can see from the formulation, it requires an accurate forecast of volume and willingness-to-pay to get high quality bid prices.
Willingness-to-Pay
The second driver of revenue management is capturing passenger willingness-to-pay. Already in this post, the phrase willingness-to-pay has been used. In general, it’s mostly a self-explanatory concept. Just as the name describes, the goal is to capture how much a passenger is willing to pay for a given product. The details behind this are for the revenue management system to understand the relationship between demand and price for a given product. This is the classic concept of a demand curve. In general, as price goes up, demand decreases. Understanding this relationship between price and demand is critical to solving the revenue management problem.
This is where high-quality forecasting comes into play. The revenue management system has a mechanism that predicts the volume component of the price/demand curve at various prices. Once the system understands this prediction, a simulation is possible that determines the optimal point at which revenue is maximized based on the relationship of price and demand.
In addition to the relationship of price and demand itself is the proper segmentation of the passenger. This step is critical as different segments of passengers will have different willingness-to-pay. This is really where it becomes important to understand the behaviors of higher paying demand, such as traveling on Monday mornings.
Once you have these two components in place, the system can generate the optimal price for a given passenger. In today’s world for most airlines, this means selecting the right fare/price designator, also called availability. But the future holds much more for these industries as they start to break away from these designators and focus on generating the optimal price directly. This is what is meant by dynamic pricing. We’re ready for the future today, so now is the time to join PROS and understand how your airline can be part of that journey.
PROS Revenue Management technology
PROS is an industry leader in revenue management technology. Not only does PROS identify key factors in optimizing revenue, but we also leverage over thirty years of data science, analytics and machine learning in a robust system that can predict market trends using advanced forecasting methodology.
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