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being one of the first 200 to sign up at brilliant.org/Wendover. 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. I certainly didn’t until the new Brilliant
course on “Knowledge and Uncertainty” came around. This and all of Brilliant’s courses are
designed so they take these big, complex subjects and teach them in an intuitive way. With visuals, interactive elements, and clear
explanations, all you need to do to learn about uncertainty or computer science or math
or science is commit to working through the course regularly. After watching this video, taking the course
on uncertainty will be even more interesting for you as you’ll be learning the exact
sort of math that those working in revenue management at airlines are using to try to
work their way through their massive uncertainty problem. So, commit to learning today and be one of
the first 200 to sign up at brilliant.org/Wendover so you get a 20% discount.
Newark was mentioned without being insulted?
"Airlines failed to consider the possibility for the unimaginable"
As failures go, maybe failing to foresee the unimaginable is not such a bad one :).
Does someone knows the soundtrack used in this video.
It says: musicbed syncid mb01eidfy1caflh