Following up on Monday’s episode, we go into a deeper dive on one of the areas that the airline industry is employing machine learning techniques.
Dynamic Pricing is a way to optimize ticket sales taking into account facts about the customer’s buying habits, flight information, weather conditions, and more.
Today’s episode takes into account the many variables that go into successfully developing a dynamic pricing strategy as well as how it may directly affect you!
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Welcome to the data couture. I’m your host Jordan. And on today’s data bytes, we’re going to be talking about dynamic pricing using artificial intelligence in the airline industry. Now before we get to that, following up on our Monday episode, I would like to tell you that today is the day Wednesday that I will be on the digital literacy panel for BP three Global’s driven conference located in beautiful Austin, Texas.
That’s our that’s our big Bry. Then, of course, if you haven’t had the chance to come down here, I urge you to do so. And again, I can still get you those reduced price tickets on the remaining days of the conference. So now let’s turn to the show.
All right now for today’s date bites, we’re going to be talking about dynamic pricing. And to remind the listeners, what that is dynamic pricing is a way to calculate fares based on the buying characteristics, and the various broader segmentation of customers looking to travel on airlines. Conceptually speaking, dynamic ticket pricing isn’t particularly a difficult topic to understand what happens an airline can identify a particular person making a flight inquiry, and then it can find its own data for that person’s flying history.
The person can be identified whether they are logged into their ticketing system, or whether or not the airline website has tracked them through a frequent flyer account. So this is very similar to what Amazon currently does today for tracking shopping histories to give particular prices on particular products in this case flights or flights for others, based on a person’s buying habits as well as their searches and various other various Of course, they are being very careful to divulge exactly how they are coming up with their their pricing tools. What’s interesting is then that the airline will use a particular person’s flight shopping history to then generate person specific fares.
So hyper personalized user experience to get them fairs that differ perhaps from offers that others might be getting other shoppers, even those that are looking at the exact same time perhaps even from a computer or a tablet located literally just beside the one being used by this person that they’re targeting. Of course, experts say that this kind of technology is most likely going to be used to offer discounts to customers with a kind of loyalty status, which can then be generated or can then generate a bundled fair offering that more or less fits the customer profile. Of course, though, in theory, the tech can also be used for different purposes, such as inducing a new customer with innovation, especially affordable ticket, or perhaps offer a higher price ticket to someone who’s likely to be undeterred by a particular up charge.
For those of you that don’t fly as often as some of us do, maybe this will be huge for you, you, you know, maybe only look at a flight website, or you don’t even go directly to the the airline’s websites, and then all of a sudden, you need to book the trip from LA to New York on the red eye, and boom, they come up with a super cheap ticket that you wouldn’t have otherwise gotten. Of course, these dynamic pricing algorithms and platforms will generate specialized offering based on the profile of the particular fair searched, even if they don’t have the specific identity of the shopper known.
So for example, if a person were to say query, single night midweek trip from New York, Chicago, that platform, that dynamic pricing platform might make the assumption that the Enquirer is traveling for business, and then it could prepare fair offerings that fit well, a business traveler profile would like. And so the ultimate aim of these sort of dynamic price offerings would be for the airlines to increase conversion rates, while also driving what they call incremental revenue increases. And they do that by showing that more travel or to more travelers that there is value in paying a bit extra for a bit more of a comfortable flights. So because these algorithms are built by humans, that means that there might be a little wiggle room and how they work, especially until they get precise enough to really know who you are, and to really give you that price that they know you can’t pass up.
So for example, maybe I have two frequent flyer travel accounts. And when I use for short flights that I get on off days, off times, weird bits of the year or whatever, to go fairly short distances, chances are, the dynamic pricing algorithm is going to give me a hell of a deal if I want to fly over to say, Paris or Berlin or something like this, right. So what you can do is one that’s for your business traveling, and it’ll know that you’re a business traveler have a second one for say, these short, maybe intermittent flights that you get, just to kind of game the system a little bit. So you can beat the dynamic pricing model and get fares that are much, much deeper. Of course, I don’t know how this will work in practice, or if it does work. I’m sure that people I think they’re employing a few MIT professors to build this. So they’ve probably already thought about this and built it out of it. But nevertheless, there’s a chance that you know, some good may come out of this.
So until next time, talk to you soon.
That’s it for the show. Thank you for listening. And if you liked what you’ve heard, then consider leaving a comment or like down below.
Stay up to date on everything data couture, be sure to follow us on Twitter at data couture pod, consider becoming a email@example.com forward slash data couture music for the podcast. It’s called foolish game. God don’t work on coming by the artist spin Meister used under the Creative Commons Attribution 3.0 license, writing, editing and production of the podcast is by your host Jordan.