* This is the second part of our look at the challenges and opportunities facing airlines as they attempt to increase their ancillary revenue. Part One is here.
A new paradigm for total revenue management
Taking attribute-level customization and applying it across all revenue categories to create a smarter RM model will enable airlines to create a potentially infinite number of price points.
The core ticket price will become just one input into the pricing engine alongside other factors such as a traveler’s typical book-to-flight time, arrival time at airport, number of checked bags, size of party, and past ancillary purchases.
All of these components will combine to create custom offers reflecting what individual travelers value most and are willing to pay for.
We estimate that within three to five years this type of RM model could become table stakes. Some airlines, mainly low-cost carriers and start-ups, are already building these solutions.
Legacy airlines, however, are in danger of falling behind.
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Significantly, this paradigm shift does not mean giving up on the advancements in core ticket pricing and the systems, processes, and products achieved in recent years.
As noted above, these advancements have produced significant value—but they are unlikely to provide similar returns for the future.
We have identified three steps that airlines should take to move toward a total RM model:
- adjust the organizational chart and break down silos to improve data visibility
- hire the necessary data science and analytics expertise
- build a pilot project
The first two steps in particular will entail potentially dramatic cultural changes.
Many airlines will need to focus on developing a more innovative, test-and-learn mind-set with a willingness to fail and an increased appetite and pace for development.
We estimate that airlines committed to making the necessary changes can begin testing and enjoying the direct impact of revenue improvement within a year.
1. Adjust the organizational chart and break down silos to improve data visibility
Because many of the tasks performed by various departments require deep technical expertise, airlines have historically divided operating functions into silos.
As a result, data is siloed as well. RM may oversee core ticket pricing and steering; marketing may manage onboard ancillary pricing; and sales may handle pricing with rental car, hotel, and other partners.
A total RM model—one that brings together RM authority and then optimizes the entire process—will require vast amounts of input data from across the airline.
In short, the current state of fractured data and authority is not conducive to total RM optimization.
Airline leadership must begin by revising the organizational chart to allow for communication and collaboration among the various departments—and likely reallocating authority of pricing for all products to a single leader.
Of course, integrating teams that have historically worked as distinctly separate units will require a significant shift in mind-set.
2. Hire the necessary data science and analytics expertise
Once you have the data, you need someone who knows what to do with it.
While no airlines are employing data scientists and developers at Amazon’s level, most start-up and legacy airlines have hired technical talent for their RM functions.
However, as mentioned above, no solution currently exists for optimizing total revenue in an airline.
We estimate that within three to five years this type of revenue management model could become table stakes. Some airlines, mainly low-cost carriers and start-ups, are already building these solutions. Legacy airlines, however, are in danger of falling behind.
McKinsey
Airlines will need to find talent capable of building total RM functions from scratch, bringing the data together, and using that data to make better decisions.
The airline also must have enough confidence in the new RM functions to override automated pricing decisions.
A concerted effort will be required for the airlines to gain a competitive advantage by developing expertise in-house.
To succeed, airlines will need to look beyond employees who think in terms of traditional airline operations research; hiring data scientists from other industries (such as trading and retail) will be the key to unlocking the full potential of technology-enabled total RM optimization.
While many airlines have the necessary talent to take the first step—building more heuristics that adjust prices outside the existing RM systems, then incorporating them into subsequent analyses—most will need to add more advanced analytical skills to their teams.
People with expertise in writing bundled product algorithms, needs-based segmentations, and price-elasticity models, for example, will be critical to informing the right price decisions.
Indeed, one major US airline recently hired its first machine-learning data scientist.
Furthermore, relatively new analytical concepts such as artificial intelligence and machine learning could enable opportunities that were not possible even a few years ago.
Models are now more capable of identifying patterns in data and predicting the future better than ever before.
3. Build a pilot project
Airlines will need to conduct testing and learn what works for their business model by measure of attribute-level customization, which will allow them to determine both the optimal price (that is, the highest price a customer with certain attributes is willing to pay) and how to offer it.
For example, ancillary products can be offered as a bundle or a subscription.
Using existing information on a customer’s attributes—including travel persona (for example, business or leisure), purchase channel, advance purchase window, origin and destination, and number of passengers in a booking—allows airlines to create high-level segments and can go a long way toward differentiating their offers and optimizing total revenue.
One potential approach is to test a concept on a single market that carries a relatively low risk of diluting revenue. An airline might choose an off-peak period of a seasonal route to South America, for example, with a good mix of business and leisure passengers. To further minimize risk, they might only apply the test prices to a select number of flights.
In short, the current state of fractured data and authority is not conducive to total RM optimization.
McKinsey
A second approach might be for airlines to perform A/B testing, similar to what an online retailer might undertake. By performing tests in one part of a network and not in another, it’s possible to compare results of the pilot against the control group to understand the impact and to continue to improve model performance.
The next step—collecting passenger-level data—will be a long-term process.
While airline loyalty programs are indeed a rich source of passenger data, as mentioned above, the majority of revenue comes from infrequent travelers who are unlikely to participate.
Eventually, airlines will need to engage the important subset of less-frequent travelers, rethink their loyalty programs, and broaden them to offer incentives tailored to specific traveler types.
For this and other reasons, airlines should first focus on building up their understanding of attribute-level data.
Summing up
To gain and hold a competitive edge, airlines must change the way they approach and manage their revenue.
Piecemeal surface changes will no longer suffice.
While online retailers and the like offer a model for contemporary and efficient RM, airlines need to make some fundamental structural, software, and talent adjustments before this model is useful—and they need to run a pilot project to help work out the kinks.
The fact is, airlines can no longer avoid focusing a critical eye on their revenue management.
* Part One of this investigation is here.
* This article was originally published by McKinsey & Company. It was written by Riccardo Boin, David Delfassy, Giacomo Palombo. McKinsey Copyright 2017. All rights reserved. Reprinted by permission.