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The airline business model is broken.
Back in late-2019, COVID-19 came along.
Borders worldwide closed down.
Businesses halted work travel.
All but a select few individuals cancelled their trips.
In the time when all but the most critical contact was to be avoided to hold the virus
at bay, travel was just not a risk worth taking for most.
One of the most major issues, for the airline industry, though, is not that demand is lower.
Lower demand is bad, it’s wreaking havoc on the industry, but it is a potentially surmountable
problem.
If an airline knew that 2020 would be 75% down, 2021 55% down, and 2022 30% down, the
solution would be simple.
They’d downsize their schedule, cut back on routes, unload assets, furlough employees,
and just become a smaller airline for the next few years.
These are, in fact, the tactics airlines are using regardless, but they don’t fully solve
the overall problem.
That’s because, even after airlines solve the problem of being too big for what the
travel market has become, the true issue, the one that will plague the industry for
the entirety of its period of recovery, is that even after they downsize, they just don’t
know how many people want to fly.
Now, think back to travel in the time before Coronavirus.
How full were the planes you were on?
How often was that seat next to you free?
The answer is almost certainly sometimes, but not that often.
On average, 85% of airplane seats in the US were filled in 2019—slightly higher than
the world average of 83%.
Some airlines achieved even better than this.
Ryanair, for example, filled 96% of its seats in 2019.
Now, simultaneously, when was the last time that you've gone to buy a ticket for a flight,
but there were none available?
This too certainly happens, but it's quite rare.
It might cost quite a lot, but if you want to buy a ticket to a flight, it’ll almost
always be available, no matter how close to departure it is.
This is very, very intentional.
Now, normally, the day-to-day job of adjusting the price of flights to extract every potential
dollar out of them is handled by computers—specifically, the complex revenue management software on
them.
Around early-March, 2020, though, the computers started to go haywire.
People weren’t booking like they were supposed to, so the computers lowered the price.
That should lead to bookings picking up, but they didn’t, which confused the computers
even more, which lowered the price further.
This went on and on and on until the point where you could book flights from New York
to London, for example, for just $100 or $200 round-trip, hours before departure.
It’d be easy to say prices were low because demand was, but that’s not exactly the truth.
It’s just a byproduct of the truth.
The computers and their revenue management software were tasked to extract the most revenue
possible, regardless of demand, but in this case, they failed.
Now, normally, businesses focus more on matching supply to demand, not demand to supply, because
regulating demand is very, very difficult.
Airlines, however, do both simultaneously.
This is because they just can’t regulate supply to match demand that well.
Demand for travel fluctuates depending on which hour of which day of which year it is—airlines
just can’t fluctuate their schedule to match that entirely.
For example, airlines in the US making their 2020 schedules knew that last year, 2,487,398
people travelled in the US on the second Thursday in April while 2,616,158 people did on the
third Thursday.
For the most part, though, airlines will operate the same schedules on both of those Thursdays.
That’s because the complexity of adjusting the schedule to such small changes just isn’t
worth the money it would save by operating slightly fuller flights.
Therefore, rather than rapidly adjusting supply to match demand, they rapidly adjust demand
to fit supply.
You can see this in action by looking at travel trends.
In the US, the least busy quarter in 2019 for air travel was the first—January, February,
and March—with 210 million passengers.
The most busy quarter, meanwhile, was the third—July, August, and September—with
243 million passengers.
Now, that’s not that much of a difference.
The third quarter, including the height of the summer travel season, was only 16% busier
than the first quarter, encompassing the depths of winter.
But is it truly that demand is only 16% higher in the summer than winter, or is it that airlines
are regulating demand to increase it in the winter and suppress it in the summer?
All evidence points to the latter.
So, airlines have this balancing act to perform.
They want to make sure that they can capture as much of the high demand in the summer as
possible, but they also don’t want to have a bunch of planes and staff sitting around
in the lower-demand winter, as that costs quite a lot.
Rather than flying a stable number of flights year-round or regulating demand to keep it
stable year-round, they do a little of both and meet in the middle.
The supply side is the easy bit.
There is a certain predictable ebb and flow to the yearly cycle of travel for a given
market for a given type of traveller.
For example, take the American business traveler.
Airlines know that the week with the lowest demand for travel from this demographic is
the last of the year, after Christmas, followed by the first of the year, the second to last
of the year, the week of the Fourth of July, then the week of Thanksgiving.
With this info, airlines can craft a schedule that, over those weeks, emphasizes leisure-focused
destinations over business-ones.
They might decide that, in the last week of the year, they’ll fly more flights to Florida
and fewer to Chicago.
Simultaneously, though, airlines know that September, October, and the week before Thanksgiving
are the strongest period of the year for business travel, so they might increase the number
of flights on common business routes over that time.
So, airlines tweak their schedule mostly on a month-to-month or week-to-week basis, but
for those day-to-day or hour-to-hour shifts in demand, they rely on regulating demand.
The number one tool used to do this is pricing, and we can see this in action.
Currently, if you want to book a ticket to fly United Airlines from Newark to Eagle County
Airport in Colorado on January 1st, 2021, it would cost you $353.
If you were to book United Airlines from Newark Airport to Denver International Airport on
January 1st, though, it would cost you $134.
So, flying to Eagle County is two and half times more expensive than flying to Denver,
even though it is only 120 miles further from New York.
To United, the costs of operating these two flights are nearly identical—it’s the
same aircraft type and roughly the same flight length.
There might be slight economies of scale by flying to Denver as it’s a bigger airport,
and there’s a greater potential of a flight diversion at Eagle County airport since it’s
in the mountains, but overall, the costs are very similar.
What accounts for the difference in price is demand, not cost.
United Airlines thinks it knows that four months prior to the flight, it can charge
$353 for the flight to Eagle County and still end up with a full plane.
Meanwhile, to end up with a full plane to Denver, it has to price it at $134.
These prices will increase as it gets closer to departure, but just because of the way
people book their travel to Eagle County Airport, $353 is the right price right now.
So, how does United know this?
Well, because it’s what it did last year, and it worked.
Airlines’ greatest asset truly is their data.
United Airlines knows exactly how much they need to price their flights at a certain level
of fullness and a certain number of days before departure, and that means they can eke out
every single bit of potential revenue from a given flight.
If United priced the Eagle County flight too high, they might end up with empty seats,
which is terrible for an airline.
Every time a plane takes off with an empty seat, the airline is loosing out on potential
revenue.
Aside from taxes, airport fees, and catering costs, there's essentially no extra cost to
carrying an additional passenger so selling a cheap ticket over no ticket is always worth
it.
Simultaneously, though, if an airline prices a flight too low, they might sell out of seats
too early—loosing out on the potential to sell high-priced tickets to last-minute travelers,
who generally are those traveling for work who aren’t paying for themselves.
So, airlines rely on these price fluctuations to end up with a plane exactly full, exactly
at departure, therefore, in theory, extracting the exact highest potential level of revenue
from the flight This makes the problem rather obvious.
Airlines simply don’t know what the recovery will look like.
Specifically, the computers don’t know how people will respond to changes in pricing,
so they don’t know how to extract the most possible revenue from a flight by ending up
with an exactly full flight, exactly at departure.
The airline industry has had sharp drop offs in demand before, but typically only in response
to financial crises or terrorist attacks.
In this case, in a pandemic, the closest equivalent happened one hundred years ago, in 1918, when
the commercial aviation industry was only in its infancy.
Therefore, the aviation industry as a whole, and especially individual airlines, have no
data and no idea what the demand patterns during and after a pandemic will be like.
After the initial drop-off in travel, and the plummet in pricing in response, airlines
essentially turned off the computers—shut down the revenue management algorithms.
They’ve gone back to relying on the humans who traditionally are only there to tweak
what the computer thinks.
That’s because the humans were able to realize something that the computers could not.
Traditionally, airline demand is quite elastic—people will often decide whether or not to book based
on the price.
It is exactly this, in fact, that makes it possible for the airlines to regulate demand
by changing prices.
In the midst of the initial stay-at-home orders, though, the only type of person who would
fly was someone who needed to fly—someone who would pay whatever it cost to get that
ticket.
Therefore, there was no sense in lowering prices because that wouldn’t stimulate demand—it
would only reduce revenue.
They also didn’t jack them up tremendously—cautious not to get into the public relations nightmare
of price gouging—but they more or less just kept them stable to what a normal April would
look like.
As May came about, and there was a small, initial restart to discretionary travel, most
every American airline lowered prices by about 10% to 20% to stimulate demand slightly.
The problem, though, is that there are still way too many variables impacting travel to
accurately predict and stimulate demand.
American Airlines, for example, took a big gamble in May when they were crafting their
revised summer schedule.
They accurately surmised that people who were vacationing in summer 2020 would be particularly
interested in destinations where the focus was the outdoors, such as at the beach.
They also knew that many of the traditional beach destinations that Americans travel to
in the Caribbean would be shut behind closed borders, so demand for this type of vacation
would naturally shift domestically to Florida.
This hypothesis was further supported by the fact that, in May, when they were working
on this schedule, Florida had one of the lowest rates of infection in the US.
Therefore, the airline decided to add loads of capacity into Florida to account for the
relatively high demand it expected.
Of course, starting in early-June, there began a steep spike in the state’s Covid cases,
leading to increased wariness among consumers to travel to the state.
Also, on a more general level, much of the summer travel season in the US did not live
up to airlines’ expectations due to a second spike in nationwide case numbers and increasing
domestic travel restrictions.
Passenger demand trends returned slightly closer to the April, 2020 scenario where it
was less and less the cost of travel that stimulated demand, and rather external conditions.
COVID has turned the job of those in revenue management at airlines closer to an art than
a science.
With decades upon decades of data, airlines got good at modeling the predictable patterns
of travelers, but failed to consider the possibility for the unimaginable.
Now, not only is overall demand lower, but airlines can’t even extract the same level
of revenue from the same number of travelers on a given flight.
This unpredictability has a quite literal, monetary cost for airlines, so that means
that, even when the travelers come back, even when the airports are full again, true recovery
will not happen until passengers are predictable again.
Uncertainty is not just a word.
It’s a force of nature that we can never get rid of, which is why mathematics has set
up a system of tools to address it.
The mathematics of uncertainty is fascinating and an incredibly powerful analytical tool,
but it’s an area of math that most people don’t know a lot about.
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After watching this video, taking the course on uncertainty will be even more interesting
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