Revenue Management is not just a technique to increase revenue through varying fares, but a complex field that plays with various levers to optimize revenue.
As each traveler has a different propensity to pay for a given service (eg. traveling on a given date at a certain hour), Revenue Management allows to differentiate those with a higher purchasing power, such as business travelers, who purchase their tickets less in advance than leisure travelers who have a lower purchasing power and prefer to secure the lowest fares through longer anticipation. This particular example of segmentation explains the most common observed impact of Revenue Management that fares are increasing when departure time gets closer.
When Revenue Management-based pricing replaces a unique-price system, the former unique price often remains available for sale either onboard or at the station (for travelers with a low price sensitivity), and is thus at the higher end of the price spectrum. Put differently, when operators introduce Revenue Management they usually start by offering discount fares in addition to the unique-price that also remains available, leading to decreasing average fares.
While demand for transport strongly varies between peak and off-peak hours, transport supply (eg. train capacity and frequency) can’t be adjusted as much. This mismatch leads operators to incentivize travelers to travel off-peak through lower fares, and compensate with higher fares on peak hours.
The impact of this Revenue Management lever on the average fare, compared to a unique-price system, can depend on operator-specific parameters such as the price difference between peak and off-peak, etc. It will however most of the time lead to decreasing average fares as:
In a competitive environment, Revenue Management allows an operator to adjust its fares to its competitors’ in order to maximize its market share. Thanks to fare-tracking solutions (like CAYZN Tracking), it is possible to automatically retrieve competitor prices from a ticket sales website and adjust fares accordingly.
As price-competition almost always lowers prices (compared to a monopolistic situation), Competition alignment leads to decreasing average fares.
As these 3 levers rely on very different optimization principles, all have in common that they rely on the ability to adjust fares dynamically. This is especially true of Capacity optimization and Competition alignement who strongly benefit from a real-time RM system that can react to changing trends and competition price adjustments very rapidly.
As we’ve set out, Revenue Management can lead to both an increase or a decrease in the average fare, depending on the operator’s strategy and how it uses each of the three levers described in part I.
A few operators, such as some legacy airlines, are using Revenue Management to create a very strong segmentation between business and leisure travelers, with a price ratio between the two extremes that can be higher than 10.
While they have long abandoned their former unique price, Revenue Management allows them to charge (much) more, on average, than they could if they would charge everyone the same unique price. As we illustrate on the graph below, the average price with Revenue Management is higher than the (hypothetical) unique price:
Other operators, however, apply Revenue Management principles that rely on discount fares to attract price-sensitive customer segments, to optimize off-peak load factors (Capacity optimization) or to fight competitors, as set out in part I.
As we illustrate on the graph below, the average price with Revenue Management is lower than the (hypothetical) unique price:
The average price being lower, this strategy can only be sustained if the volume of additional bookings achieved through lower prices more than compensates for the price decrease:
This means, for instance, that if prices decrease by -3%, bookings have to increase by more than +3% to allow revenue to increase.
Such a conclusion might seem quite straightforward, but achieving it is far from easy: an operator does control how much the price decreases, but it does not decide how many additional bookings it will get as a result.
This is where the power of a great Revenue Management solution comes into play.
On top of its award-winning revenue maximization algorithms, CAYZN now includes a new feature that allows the revenue analyst to maximize revenue and still force the system to reach a certain load factor threshold.
By pulling a simple slider, the user will require the algorithms to:
This new mechanism allows CAYZN users to make sure a high level of booking volume is reached (depending on the train/bus’ potential) and still maximizes revenue. Such a strategy therefore harnesses the full potential of Revenue Management, while still making an average price increase very unlikely.
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