I'm really excited to be here this afternoon to talk to you about dynamic
pricing for ancillaries. So if we go back in time, and a few presentations
already covered this, to the 90s, PROS Sabre, Ideas, other companies brought
technology straight out of the computer lab into this industry. And it had a huge
impact. Some of you were there. I've heard the stories, read the case studies about
how big of an impact dynamic pricing had, and all the fares, and all the things
we've been talking about today back then. But something else changed in the 90s.
This is the first website where you could book a ticket on direct
from an airline and it really changed how a traveler interacted with
travel. So today travel is, 44% of travel transactions are digital, and a
part of the the point of this is that there is so much data in that. As Amit
was saying earlier, 50 times more data than there was even 10 years ago. What
are we doing with that data and shouldn't that data be involved in how
we price? Well, it turns out we're doing pricing in a lot the same way, so
similar tools, but it's evolved incrementally. And so
if you think about how would you do this today, if you're starting fresh, but if
you didn't have the constraints you had before what would this look like? And you
don't have to think about it or imagine it because it's been done, right? Uber or
Amazon, other companies, how to freshly start from. And these companies adopted
innovations like artificial intelligence and machine learning in order to price more
accurately, more rapidly, and deliver on that continuous dynamic pricing promise
that you've heard talked about today. Well, that's where Perfect Price comes in.
So Perfect Price is a dynamic pricing company. We use artificial
intelligence and machine learning to deliver on that promise. And you might
ask, well, how do we know that you do it right? It's one thing to say you work
like Uber and Amazon; it's another to actually deliver on that promise. Well, we
happen to be advised by the guy who wrote the algorithms at Uber and
Amazon, Bob Phillips, who many of you probably know and at least read his
book in the past. And so by using that we've been able to
really address the complexity issue and I think many of the presentations today
talked about how complex this world is going to be. I have a newfound
appreciation for how difficult it is to actually deliver that upgrade or that
lounge access when you bundle something together and now have to work with
legacy systems to deliver it, but in a few years we're gonna be past that. It's
gonna be easy to deliver it. That's going to be an everyday thing and now you're
going to be dealing with billions of prices, right? And dealing with billions
of prices is a different problem than dealing with filed fares because it's
continuous, it's infinite, it's an infinite set. It's just much harder to
get a grasp on that and you move from managing prices to managing models and
how one prices. And so we've done this before, we've got a lot of experience in
this space. The example that I think is most relevant from a science standpoint
is Getaround. So Getaround is a peer-to-peer car rental company and the
depth of this problem is incredibly complex because you have
different cars, and by different I mean like Tesla to Porsche to Honda Civic on
the same street corner, in cities around the world. And they need to set prices
every 15 minutes. And so solving that problem with anything other than machine
learning and artificial intelligence just is impossible. They came to us
because they tried and and they weren't able to do it. One of the things that was
amazing to us is how big of an impact getting pricing right has. I don't have
to tell anybody in the room how big of an impact it can have, although I think
many of you are kind of rolling your eyes right now because 3.7 times revenue
is a big number, right? You think, well that couldn't possibly happen. We run
things way too efficiently for that to happen. And yet I've heard stories today
from the channels about how just adding photos to shopping results can increase
conversion rate by 20%. And you would think this is an efficient market, how
could you possibly get 20% higher conversion in such an efficient market?
But it turns out that's happening. So I think one of the things we'd like to
work with more of you on is testing and proving out the promise. So I'm happy to
be here today and thank ATPCO for their support because we couldn't do
this without them. I think when you talk about billions of prices obviously
nobody's going to enter them by hand, so being able to work with the
industry leader to actually deliver on that value, not to mention the data that
it takes to power that, so thank you very much for listening. If you'd like to know
more about us, I'm happy to talk to you and answer questions later. And right now
I'm happy to answer questions from Sean.
Thank you, yeah. I'm impressed! Great job, Alex. So I think it was fairly
self-explanatory what Perfect Price is doing and you're in other sectors
of travel to, like the vacation short-term rental space. So one of the
things that some of the people at the beginning of this program said is that
we want to try to get ideas outside of travel. So is there any kind of metaphor,
analogy from something outside of travel that's kind of like a an equivalent
impact of what it is that you're talking about being able to use
artificial intelligence to analyze pricing? Is there's something from outside?
Yeah, so raise your hand if you used Netflix. If you've ever used Netflix.
So most of you, or at least all that are paying attention. So a few years ago,
about a decade ago, they did what was called the Netflix prize. A friend of
mine ran it, was the prize master, and it was a competition that improved
recommendations. And so teams from all over the world applied different
techniques to to forecast what you would want to watch next. And the winning team
took a totally different approach. They used supervised machine learning and
they looked at what people like you watched, right? So instead of looking at
what actors were in it, what genre is it, you know, is it a rom-com? Is Harrison
Ford in it? But they looked at what did people like you watch and they
dramatically outperformed any other solution. And I think when you think
about everything in the conversations we are having today, whether it's pricing or
bundling or recommendations, it's the same, right? You're not managing the
different features and the factors and having a human understanding, you're
just letting the algorithm understand it.
So traditional revenue management sort
of looked at certain sets of factors, and now you're talking about how you need to
look at new factors, new approaches to solving that problem, and sort of similar
to how Netflix. I remember back in the original you could actually go online
and, like, fill out the ...
Yeah and I think that that really is it.
I mean, if you look at all the work the revenue managers do today, like it's a
tremendous amount of work, and not, as you know one of the slides
talked about earlier, that's only on a hundred and fifty million prices a day.
When you up that to 150 billion prices a day, it just starts to
be too tough to keep up with and you need to look at a different way of managing.
Because you finished early, I want to add a little extra time. So just, when you
have all this data that then creates a problem. If you want to be able to
figure out for benchmarking what else is out there? All these dynamically
generated offers are now outside of the typical fare filing. Do you
somehow connect back in to help people, like, sort of plan and understand what
the big benchmarked prices are?
Yeah, so that, I mean, that is definitely getting
into unexplored territory because before using software like PROS and the other
software that's out there, that's a major part of it--do my filed
fares line up with the rest of the market? And if you look at Uber and
Lyft, there's no way you could possibly, as Uber, go look at every Lyft price in
every market every five minutes because you can open your phone and watch them
change, you know, literally in real time, right? And so by using AI
machine learning we're able to adapt to different strategies so you're not
saying, you know, what percentage of my fares were misaligned
with the market, or where am i under priced or overpriced, or what price premium
am I at? You're looking at, what is the consumer doing, or
whatever channel I'm selling into, are they buying? Are they not buying? And that
becomes a much more scalable way of managing your pricing strategy.
Okay, thank you, Alex, for your presentation. Appreciate it. Alex Shartsis from Perfect Price.