Why do airlines sell too many tickets Nina Klietsch

Have you ever sat in a doctor’s
office for hours

despite having an appointment
at a specific time?

Has a hotel turned down
your reservation because it’s full?

Or have you been bumped off a flight
that you paid for?

These are all symptoms of overbooking,

a practice where businesses
and institutions

sell or book more
than their full capacity.

While often infuriating for the customer,

overbooking happens because
it increases profits

while also letting businesses
optimize their resources.

They know that not everyone
will show up to their appointments,

reservations,

and flights,

so they make more available
than they actually have to offer.

Airlines are the classical example,
partially because it happens so often.

About 50,000 people get bumped
off their flights each year.

That figure comes at little surprise
to the airlines themselves,

which use statistics to determine
exactly how many tickets to sell.

It’s a delicate operation.

Sell too few, and they’re wasting seats.

Sell too many, and they pay penalties -

money, free flights, hotel stays,
and annoyed customers.

So here’s a simplified version
of how their calculations work.

Airlines have collected years worth
of information

about who does and doesn’t show up
for certain flights.

They know, for example,
that on a particular route,

the probability that each individual
customer will show up on time is 90%.

For the sake of simplicity,

we’ll assume that every customer
is traveling individually

rather than as families or groups.

Then, if there are 180 seats on the plane
and they sell 180 tickets,

the most likely result is that 162
passengers will board.

But, of course, you could also
end up with more passengers,

or fewer.

The probability for each value
is given by what’s called

a binomial distribution,

which peaks at the most likely outcome.

Now let’s look at the revenue.

The airline makes money from each
ticket buyer

and loses money for each person
who gets bumped.

Let’s say a ticket costs $250
and isn’t exchangeable for a later flight.

And the cost of bumping
a passenger is $800.

These numbers are just for the sake
of example.

Actual amounts vary considerably.

So here, if you don’t sell
any extra tickets, you make $45,000.

If you sell 15 extras
and at least 15 people are no shows,

you make $48,750.

That’s the best case.

In the worst case, everyone shows up.

15 unlucky passengers get bumped,
and the revenue will only be $36,750,

even less than if you only sold 180
tickets in the first place.

But what matters isn’t just how
good or bad a scenario is financially,

but how likely it is to happen.

So how likely is each scenario?

We can find out by using
the binomial distribution.

In this example, the probability
of exactly 195 passengers boarding

is almost 0%.

The probability of exactly 184 passengers
boarding is 1.11%, and so on.

Multiply these probabilities
by the revenue for each case,

add them all up,

and subtract the sum from the earnings
by 195 sold tickets,

and you get the expected revenue
for selling 195 tickets.

By repeating this calculation
for various numbers of extra tickets,

the airline can find the one likely
to yield the highest revenue.

In this example, that’s 198 tickets,

from which the airline will probably
make $48,774,

almost 4,000 more than without
overbooking.

And that’s just for one flight.

Multiply that by a million flights
per airline per year,

and overbooking adds up fast.

Of course, the actual calculation
is much more complicated.

Airlines apply many factors
to create even more accurate models.

But should they?

Some argue that overbooking is unethical.

You’re charging two people
for the same resource.

Of course, if you’re 100% sure
someone won’t show up,

it’s fine to sell their seat.

But what if you’re only 95% sure?

75%?

Is there a number that separates being
unethical from being practical?

尽管在特定时间预约,您是否曾经在医生办公室坐
了几个小时

酒店是否
因为订满而拒绝了您的预订?

或者你是否被
你支付的航班撞了?

这些都是超额预订的症状

,企业
和机构

销售或预订
超过其全部容量的做法。

虽然经常激怒客户,但

超额预订的发生是因为
它增加了利润,

同时也让企业
优化了他们的资源。

他们知道不是每个人
都会出席他们的约会、

预订

和航班,

因此他们提供的服务
比实际提供的多。

航空公司是典型的例子,
部分原因是它经常发生。 每年

约有 50,000 人被撞
下航班。

这个数字
对航空公司本身来说并不令人惊讶,

它们使用统计数据来确定
要售出多少张机票。

这是一个微妙的操作。

卖得太少,他们在浪费座位。

卖得太多,他们会付出代价——

金钱、免费航班、酒店住宿
和恼怒的顾客。

所以这里有一个简化版本
的计算如何工作。

航空公司已经收集了多年
的信息,

关于哪些人会出现在某些航班上,哪些人不会出现

例如,他们知道,
在特定路线上

,每个
客户准时出现的概率为 90%。

为简单起见,

我们假设每个客户
都是单独旅行,

而不是作为家庭或团体旅行。

那么,如果飞机上有 180 个座位
,他们卖 180 张机票

,最可能的结果是 162
名乘客将登机。

但是,当然,您也可能
最终获得更多

或更少的乘客。

每个值
的概率由所谓的二项分布给出,该

分布

在最可能的结果处达到峰值。

现在让我们看看收入。

航空公司从每个
购票

者那里赚钱,并为每个
被撞的人赔钱。

假设一张机票价格为 250 美元
,并且不可兑换为以后的航班。

撞到
一名乘客的费用是 800 美元。

这些数字只是
为了举例。

实际金额差异很大。

所以在这里,如果你不出售
任何额外的门票,你可以赚到 45,000 美元。

如果你卖了 15 个临时演员
并且至少有 15 人没有出现,

你赚了 48,750 美元。

那是最好的情况。

在最坏的情况下,每个人都会出现。

15 名倒霉的乘客被撞
,收入只有 36,750 美元,

甚至比你一开始只卖 180 张票还要少

但重要的不仅仅是
一个场景在财务上的好坏,

而是它发生的可能性有多大。

那么每种情况的可能性有多大?

我们可以通过使用二项分布来找出
答案。

在这个例子中,
正好有 195 名乘客登机的

概率几乎是 0%。

正好有 184 名乘客登机的概率
是 1.11%,以此类推。

将这些概率
乘以每个案例的收入,

将它们全部

相加,然后从收入中减去
195 张已售门票的总和

,您将
获得出售 195 张门票的预期收入。

通过
对不同数量的额外机票重复此计算

,航空公司可以找到
可能产生最高收入的机票。

在这个例子中,这是 198 张机票,

航空公司可能
从中赚取 48,774 美元,

比没有超额预订的情况下多出近 4,000 美元

这只是一次飞行。

将其乘以每家航空公司每年 100 万个航班

,超额预订就会迅速增加。

当然,实际计算
要复杂得多。

航空公司应用许多因素
来创建更准确的模型。

但他们应该吗?

一些人认为超额预订是不道德的。


为相同的资源向两个人收费。

当然,如果你 100% 确定
某人不会出现,

那么卖掉他们的席位也没问题。

但如果你只有 95% 的把握呢?

75%?

是否有一个数字可以区分
不道德与实际?