Airlines have been early adopters of cutting-edge revenue-management (RM) technologies since the 1970s.
They were among the first companies to use dynamic inventory pricing, and some of the forecasting and inventory-management models they introduced in the 1980s and 1990s - including sequential upgrades to forecasting and optimization engines and the expanded use of fare restrictions, or fences - represented the vanguard of advanced analytics at the time.
These and other RM tactics successfully clustered customers according to their key attributes; for example, they distinguished the occasional leisure vacationers from the weekly business travelers.
This clustering generated significant additional revenue and contributed significantly to the growth and success of the airline industry.
To date, airlines have focused mostly on how to price core tickets.
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However, this approach ignores a recent, fundamental industry change: an increasing percentage of revenue now comes from ancillary items such as checked baggage, onboard food, premium seat selection, and extra legroom.
Given the growing importance of ancillary sales, airlines cannot continue simply to tweak their existing RM strategies and models expecting to optimize total revenue. Instead, airlines must optimize total revenue by taking attribute-level customization a step further.
They have an opportunity to adopt bundling tactics, product-suggestion analytics, and dynamic pricing to create customized recommendations for additional purchases, both at the original point of sale and over the course of the travel journey.
These tactics are already employed by other industries (notably online retail), and with the increasing power of advanced analytics, airlines can profile customers in ways not possible just a few years ago.
To move toward the next frontier of optimized total RM, airline industry leaders must overcome significant complicating organizational factors.
Precise and detailed forecasts would allow a near-perfect calculation of an acceptable minimum price for the core ticket to capture the rest of a customer’s spending on ancillaries.
McKinsey
For instance, RM departments at most airlines are siloed from other departments, such as sales and marketing, which hinders their ability to collect and wield the customer data needed to optimize total revenue.
In addition, few airlines employ data scientists, which prevents them from harnessing the latest advanced analytics tools to create cutting-edge predictive and prescriptive revenue-optimization models.
If airlines work to address these shortcomings, we estimate they could reap a 5 to 10 percent improvement in total revenue.
To achieve this goal, however, airlines must act soon, and they must develop these capabilities in-house.
If they wait along with all the other airlines for RM systems providers to innovate on their behalf, they will lose their chance at a competitive edge.
In an era when optimization of distinct processes and departments has met its ceiling, this opportunity is too big to pass up.
Opportunities for optimizing total revenue
Airline RM today is an exercise in setting prices and managing yield through inventory - how many seats are left, and what is the highest price we can sell them for?
But in the quest to optimize total revenue, inventory is just one input to the final price presented to a customer.
To realize the potential of total RM, airlines must adopt a bundled model that considers not only ticket price but also the probability that passengers will purchase other goods and services from the airline before, during, and after their journey.
But most airlines do not have the analytical capabilities that are essential to making those types of predictions.
In fact, the software necessary for total RM optimization does not yet exist, as most software providers are still focused on optimizing ticket revenue through increasingly advanced forecasting and optimization (for example, origin and destination forecasting and optimization).
Airlines will need specialists who can create these models from the ground up rather than operations researchers who can tweak existing models.
In short, no one in the airline industry is doing RM optimization well right now because the tools are not yet available, which means airlines will need to build them in-house.
The good news is they can draw on lessons from the pricing and bundling models used by leaders in other industries; online retailer Amazon is a solid example of how to rise above competitors and increase consumer spending.
Amazon: A leader in bundled pricing
Amazon’s total RM leadership is rooted in pricing models that rely heavily on automated algorithms to deliver real-time, customized pricing.
Amazon’s method, backed by a pricing team of 16 experts and 1,400 developers wielding two acres of underground servers operating with machine learning, generates custom prices based in part on an individual shopper’s attributes.
These custom prices are influenced by a multitude of factors, including supply, demand, the customer’s purchasing history, competitor pricing, and strategic initiatives.
The retail giant also excels at understanding the psychology of pricing when assembling bundles, and it uses A/B testing exceptionally well to test price points.
The advanced tactical and psychological techniques in Amazon’s pricing practices allow it to beat competitor pricing while making a higher margin on complementary products that are often bought together; this is a bundled-pricing model.
The algorithm knows whether two or several items are often bought together, and it offers consumers the option to purchase these in addition to the original item they searched for.
In this way, even if Amazon offers a lower price than competitors for the initial item, it is more likely to sell complementary items at a higher margin.
For example, consider that Amazon may charge $6.72 for a cube of orange sticky notes. Many consumers searching for orange sticky notes will be interested in other colors as well, so its algorithm suggests purchasing alternative colors at the same time.
In this specific example, Amazon offers the a cube of aqua sticky notes for $9.03, a 34 percent markup (exhibit). This combination of pricing, bundling, and recommendation is a powerful approach to pricing psychology.
/mckinsey sticky pricing.jpg?tr=w-320%2Ch-240%2Cfo-auto "Click the image to enlarge")
Through bundled pricing, Amazon achieves a higher margin on related products
Click the image to enlarge
Now imagine the same concept applied to airlines.
Precise and detailed forecasts would allow a near-perfect calculation of an acceptable minimum price for the core ticket to capture the rest of a customer’s spending on ancillaries.
A proactive scrape of individual markets and testing of new price points would pinpoint the optimum market equilibrium.
And targeted, customized offers generated with the help of advanced analytics would create additional revenue streams.
Indeed, this concept is the future of airline RM.
Customizing the travel customer offer
Maximizing total revenue depends on connecting a customer’s total spending data with that customer’s profile in the airline’s system.
Doing this allows for attribute-level customization, where airlines can segment customers into known groupings by location, demographics, and so forth.
For example, if an airline knows that passengers flying from Miami, Florida, to Denver, Colorado, are more likely, on average, to check 1.9 bags at $35 apiece, it can offer them a lower ticket price than it would offer to passengers flying from Denver to Miami, as they may be less likely to check baggage.
In theory, making these connections would also allow airlines to undertake passenger-level customization, which depends on even more granular data.
By linking a passenger’s frequent-flyer number to all purchases made before, during, and after the flight, for example, airlines can calculate exact profitability and the likelihood that the passenger will purchase ancillary products.
This insight may then be used to offer personalized discounts on base fares.
However, several complications with passenger-level personalization suggest that airlines may not yet reap an adequate return on such an investment.
To move toward the next frontier of optimized total RM, airline industry leaders must overcome significant complicating organizational factors.
McKinsey
The same person may behave very differently when flying for business than when traveling with family or friends, for instance.
And most airline customers are not frequent travelers; in 2015, the president of one major US airline revealed that 87 percent of the airline’s passengers only flew on that airline once that year - but those passengers accounted for more than half of its revenue.
This reality suggests that airlines may not be capturing all necessary information on these travelers, and the utility of the information they do capture is inconsistent.
In other words, at this point in the customer journey, personalization may be useless. Customization, however, can be priceless.
Take the cruise industry, which is already on the path to optimizing total revenue.
Carnival Corporation has a portfolio of ten brands and has built a global database of customer wants and needs based on prior behavior, allowing the company to microsegment customers and build profiles for “lookalike” customers.
This database enables each brand to target its marketing dollars and promotional dollars for those passengers who have the highest total revenue potential (both ticket and onboard sales).
For instance, say “casino lovers” spend upward of $120 a day in casino gambling during full days at sea, while “spa lovers” spend $200 a day in spa services; marketers can create targeted offers to each segment based on total expected value.
The company has also built into its pricing systems more aggregated estimates of total customer revenue by major customer source market.
Airlines that can create similar tools and databases of attribute-level profitability could reap huge dividends.
* In the second part of this article, we look at a new paradigm for total revenue management for airlines.
* 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.