Episode 21 – Fair Fares? How Airlines Get Away with Dynamic Pricing

Episode 21 – Fair Fares? How Airlines Get Away with Dynamic Pricing


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more and better content now on with the show. Hello everybody and welcome back
to the data science ethics podcast. This is Marie Weber and Lexy Kassan and today we’re going to continue our
discussion on differential pricing and we’re going to focus a little bit more
on some industries other than retail today, like travel, including airlines and hotels and
maybe even a little bit of insurance. Last episode we mentioned an article
from Forbes that was talking about differential pricing and one of the
quotes in that article was differential pricing does take place in
markets where prices vary a lot, such as airlines and hotels and in
somewhat opaque market such as insurance prices that fluctuate sharply
can anger people. So Lexi, why do you think opaque markets
make consumers more comfortable with differential pricing? I’m not sure that it’s a level of comfort. I think it’s more the perception of it
being fair based on either risk or some sort of thought that it’s a supply and
demand type of issue so that there’s some other factor at play. For
example, with insurance, let’s say it’s car insurance, we tend to assume that prices will be
higher for younger drivers or for drivers who’ve been in accidents, things like that that raise the
risk of a potential problem, potential accident, something that the
insurance company would need to pay for. We’re sort of okay with the
idea that risk equals reward. We do it in investing,
we do it in insurance. The greater the risk the insurance
company is taking, the more reward, the more money they’re able
to get by taking on that risk, those types of risk to
reward considerations are
sort of accepted as fair, meaning it’s okay that the company gets
more for taking on that risk because the chances are they’re going to have to pay
out more in other markets like travel. It’s a perceived supply and demand even
though the fluctuations are not really based fully on supply or
demand. So for example, there are millions of
websites out there. Sure. Talking about what is the optimal
time to purchase a flight? Yep. It used to be, I think it was Tuesdays
in the morning or something like that. I recently read one that
said Sunday at 5:00 AM. Oh, you love being up at that time.
Oh yeah. That’s my favorite. See, back to episode on food,
daylight savings time, and my problem adjusting. However it was talking about the fact
that there are fewer people searching. There’s still the supply, the
supply of seats doesn’t change. They can’t produce more
seats overnight on a plane, so the supply doesn’t change, the demand goes up and down and they kind
of try to time how far out in advance people are booking travel and for what
purposes because they know that they can charge different amounts for people
based on different types of trips, how much they’re willing to pay, all
these different things. That’s fine. Why you could be sitting in a plane, and this is actually something that I’ve
looked at because I’ll use flightaware to track the flights that I’m on and
it’ll tell you the range of prices that people have paid for that flight
and you’re like, all right, so where do I fall in this continuum? I’m usually not the bottom
and I’m usually not the top, but there’s definitely a
range of different prices
that people are paying to get on that flight. Absolutely, yeah. At the end of the day, you’re still
taking the same plane to the same place. The perception is when I was searching, when I made my decision
to purchase that fair, it seemed fair to have that
fair. That’s a tongue twister. One thing that occurs to me that
differentiates between retail and other industries, more black box industries. In retail you see a retail price. You have an anchor set of
this is what full price is. How do you know what full
price is for an airline ticket? I feel like for most of people it’s based off of their
experience buying plane tickets before, like if you bought that route before, if you know that a flight from Houston
to Chicago normally costs about this much, then when you look for your
next flight, you would expect it to be about that much generally, but then
you think about the timing of the year. Did you go when there was an event?
Did you go in there as a holiday? Is it in the middle of the day?
Is it an early morning flight? Is Everything comparable? How do you really know what
full price is for that ticket? Just like in the insurance industry,
how do you know what full price is? For an insurance policy,
you have to find it Tuesday to Thursday, the exact
same week of every year and do it. I done this, I’ve done this. I can tell you right
now, I don’t remember, but again, in retail you have comparable items. You have a price that’s shown as your
anchor. We’ve talked about anchoring bias, so your anchor prices $100
for an item and you know, based on that anchor, if there’s a line through it and
now so $75 you’re getting a deal, so you’re at 25 percent off. But that also is an anchoring bias
that can be manipulated. Absolutely. But at least there’s something
in your head that says, okay, I should be expecting roughly this
and if I go on another retail website, I can compare their price and if I see
that this one says that the retail price is 120 and this one says
it’s 100, which one’s lying. There’s no similar comparison to be made. If you don’t have that same anchor saying, well if you book now you get 20 percent
off because it’s Sunday morning at 5:00 AM and doesn’t tell you that this
will ever be some other price. It doesn’t, so I think that’s part of the reason why
there are so many sites that are trying to provide advice about when to book
flights to get the best deal and why there are different tools that not only for
flights but even on Amazon will tell you if a price is trending up or trending
down so you know if a price is trending down, maybe you’ll wait. If it
looks like a price is trending up, then you’ll buy now versus wait until
later where you could be setting yourself up to pay a higher price. The other thing that I think helps
with airlines is that there are so many places where you can compare flight
data and you can also manipulate prices based off of like if you can
be flexible with your flight, you can do things where you say, okay, well what if I left two days earlier or
got back two days later and it helps you see where prices are higher versus where
prices are lower so there is a lot of data available, but you’re still travel.
Looking at it right now. True. You would have to be able to simulate
what it will look like tomorrow and the next day at all different times of day
and the next day and the next day and so forth. There are sites that now do this
and they’re absent now do this as well. There’s one called hopper
that I’ve been on for awhile. That’s very interesting in that it looks
at how far in advance to book something and whether that price is likely to
go up or down in the next few weeks or months or what have you, and it tells you kind of the optimal time
it thinks you should buy and on which airline and all kinds of things you have
to be able to simulate like that does to say, all right, what are all the algorithmic inputs
that would potentially contribute to a different flight price and
even then there’s no baseline. There’s nothing that says
full price Houston to Chicago
from Tuesday to Thursday of next week is $1,000 round trip. There’s nothing that gives you that price
point and says this is the most that you would have to pay. There’s absolutely the expectation that
if you try to do it day of there’ll be more than that. There’s an expectation that if you do
that in advance it will be much less, but there’s nothing that gives you
a baseline like you have in retail. Correct, and I would say that
insurance, it’s even more so that way. I do feel like at least
with I plane ticket, when you search from one destination to
another for a specific date range and you get all those different comparisons, that helps provide you with
an idea of what the market is. Maybe it doesn’t tell you
what the anchor should be, but at least you get a sense of what the
range is and where you’re comfortable pain based off of the time that the
flight we’ll take the number of stops, etc. There is some opaqueness to it, but not as much as like
insurance. Like an insurance. I have no idea whaT my price
will be until I get a quote. Sure, but that quote is based on inputs.
Just like you were talking about, what time is that flight,
how many stops and so forth. You could look at it the same way
and say, what’s the total coverage? What’s the per incident coverage? What’s the amount of deductible
that I have on that policy? And you can play with those inputs to
try and see what your quote would be, and there are comparisons
sites, many, many, many comparison sites for
insurance quotes for that reason, because each company
will price differently, even given the same set of inputs and
that gives you the ability to see which of these is likely to be the best
value. On the flip side of that, you can think about it as which of these
companies do I trust to actually pay out? So it’s a matter of your risk,
right? So if you want lower risk, you want a company that’s more stable, you don’t want some fly by
night company that says, oh, I’ll give you a great deal on that
insurance, it’ll be $20 a month. And then the moment you have a
problem, they disappear. Again. Risk versus reward. Are you suggesting that
there should be baseline? That’d be nice, but I’m
not going to say that I don’t think it’s realistic and insurance
because there are so many inputs. Companies have different aLgorithms
for the risk that they’re taking on. They have different actuaries
working on the problem to say, what is the risk based
on all of these factors? If we look at this person’s
driving record, their age, the type of vehicle and so
forth, the area that they’re in, we have a different set of weightings
essentially for the algorithm that we use versus the next company uses. Based
on how we’ve evaluated that data. I don’t think that it’s likely we
would see a baseline for all of that. I think it could somewhat more
likely in an airline situation, just like we have for hotel rooms, there’s are kind of rack rate that
nobody pays for what it’s worth, nobody pays the rack rated hotel. If you ever look on the back
of the door in a hotel room, there’s usually an info
sheet about the room. It tells you the escape routes and
all that good stuff. There’s fire. It’ll sometimes lift the rack rate and
it’ll say something like $350 and you know that you just spent
$160 a night for that room, but there’s your baseline. It’s present. I think that something like that could
be possible for airlines even given all of the seasonal factors, even given a
number of other things, they could say, well, right now we’re running a
special and this happens a ton. when you think about the sales that get
promoted all the time for southwest, for instance, of $99
fares and all this stuff, you presumed that the
rate normally is higher. It’s more than $99 per
leg of your journey, but they can promote it because now
they’ve given you a price to compare to. You know, maybe it’s worth something for people
to know that they’re getting a deal and they should book. The other piece of it is
you do want to feel like, especially If you’re going to a site
that’s telling you I can help you get the best price that you are getting the best
price possible and some of the things that we found researching this episode
is that those sites don’t always show the best price possible. Sometimes they
are doing differential pricing, which is different than showing you
the best price possible. Absolutely. So there were a couple of
examples that we came upon. One was from 2012 and it was orbits that
had been showing more expensive hotel options to people who browse their
site from an apple computer, a mac, ios versus those who came
in from a windows computer. Let’s say there were 10 hotels. They ranged in price from
$150 a night to $400 a night. They might rearrange them so that they
showed the ones that were more expensive, maybe $300 a night instead of the ones
that were maybe 2:50 a night or something like that. So that it would
show them as though they. Those were the best options
available to a mac user. Theoretically because
since they purchased a mac, they were willing to pay more for
a computer. So why not for a hotel? And they had done this based
on data they collected. They looked at what types of
computer somebody had come in on, what they ended up booking, and then used it to set the experience
going forward for other people who would come in on the same type of computer. Now there are legitimate reasons to
know what kind of computer someone has. So marie, give us some examples. Why do we need to know what kind
of computer or somebody is on? We go to a website. I’m a user experience standpoint. We you are managing a website. You want to know are people using a
mobile device, are they using a computer? What type of browser they’re using,
what type of computer they’re using, the operating system, and that helps you make sure that your
site runs properly in all those different environments. So you definitely as a company that’s
writing a website as somebody in the marketing department that wants to make
sure that you’re providing a good user experience for your customers, you
want to have access to that data. So the question becomes should that data
then be used to make decisions on what type of pricing does show? Yeah, that was customers or what
options to show that customer. Exactly. The other example that
we Saw was from expedia. I think you had looked into this one, so tell us a little bit about the
expedia example. Yeah, so we’re going to link to this in the show notes. It’s actually a video that we saw from
the canadian broadcasting corporation. Cvc, and they did it. They did a test where they brought three
different consumers into their offices and they had them browse basically
using the regular browser setting, using an incognito browser setting, and then based on what they
typed in, what results came up. The idea was user experience, user behavior could potentially influence
what offers they were being shown. What was interesting is as they
were going through the test, they noticed that one person, instead of going to expedia.com
went to expedia dots, see a, the canadian version of the website
and that actually produced different results, so even the geography could change the
results and that was one of the things that they followed up in terms of
the reporting and the interviewing. They went back and they did a question
where they said to basically expedient why would the country of origin for
the website make a difference in the pricing, and so it was the fact that you could
have control over these different components as a consumer searching out
the best price to do it in incognito mode, do it in your regular mode,
see where you get the best offer, and then use that to move
forward with your purchase. What I found interesting in the interview
is the fact that expedia couldn’t really defend it and it kind of goes
back to what we were talking about in the forbes article from our previous episode
where when people find out about price discrimination, that
usually not very happy. Yeah. And it still. It also goes
back to training transparently. Have to be able to explain what’s going
on and be transparent about what’s happening. So if they had come up
with an answer that said, well, the canadian government has different
rules about taxation of flights or something like that. And we showed the and all inclusive fair
like we’re supposed to and it showed a lower fare. It’s because we assumed
this was a canadian resident. Therefore they would have gotten
a lower price. Seems reasonable, could have been an explanation, obviously
wasn’t, but could’ve been. Again, be transparent, Just tell it like it is. The problem becomes when you create an
algorithm you can’t explain for sure. what we’ve seen is that when
you have a black box algorithm, those who have to be in
front of a broadcasting
corporation or a pr release or something like that, investigative,
investigative reporters, whomever can’t explain it, they are not prepared to defend the
algorithms that are being used and that becomes a problem for publicity for the
company at some point down the road for the data scientists to try and figure
out and then provide them with a reasonable explanation. And I think it also goes back to when
you want your customers to have a certain type of experience with you. That’s not just what
they see on the website. It’s not just in the
service that you provide. It’s also with the perception of that
service in the end and that perception could be changed based
on reporting like this, even if they had a great experience, if they feel like they were taken
advantage of in terms of pricing, then they might be less likely to
come back to you in the future. So even if it was successful, it could end up having negative
consequences down the road. What I find really interesting about this
example is that in one of the articles we read talking about the orbits issue, they were asking other companies that
have aggregators of airfares about their use of differential pricing,
including expedia and travelocity, and at the time travelocity,
expedia said, oh, we won’t do that. We don’t do that. Be interesting to know exactly how long
they didn’t do that until expedia.ca had different pricing. That is expedia.com.
Yeah, that would be very interesting. So what do you think, is it
fair to pay different fairs? Let us know in the comments below. We hope that you enjoyed this episode
of the data science at this podcast. Again, this is Marie Weber
and Lexy KassanLexy Kassand Thanks for much for joining us. See You next time. We hope you’ve enjoyed listening to
this episode of the data science ethics podcast. If you have, please like and
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