Latest / Host Planet: Airbnb, Holiday Let, and Vacation Rental Insights / This Vacation Rental Pricing Tool Thinks Differently

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This Vacation Rental Pricing Tool Thinks Differently · Host Planet: Airbnb, Holiday Let, and Vacation Rental Insights

0:00
Price is the most important thing in a short term method.
0:03
People reduce price too soon and this is largely driven by our
0:08
fear of being empty. If we tank the price last
0:12
minute, we're all kind of losing.
0:13
Is setting a base price for your vacation rental a mistake?
0:18
What are the common revenue management errors hosts are
0:20
making? And what can the short term
0:23
rental industry learn from airlines when it comes to
0:26
setting ideal rates? Neil Seer from Quibble is the
0:30
special guest on today's House Planner podcast.
0:32
He'll be answering all these questions and outlining how his
0:35
platform is changing the face of revenue management in the STR
0:39
sector. Neil, thank you so much for
0:46
joining us on the House Planet podcast.
0:48
When it comes to using a dynamic pricing tool, why is setting a
0:52
base price a waste of time? So when host or managers invest
0:59
in dynamic pricing software, they're normally hoping that the
1:03
the software will tell them what the price should be.
1:06
And instead they're spending a lot of time creating and
1:10
managing base prices. Because what the the model's
1:13
going to do is then take that base price and use it as the
1:17
foundation and just apply multipliers up and down to to
1:22
your base price. So in that regard, it's a waste
1:26
of time. But also the model can't really
1:28
find the optimal price for you in regards if you change the
1:32
base price, the whole prices are going to shift up or down.
1:35
So there's no optimal price point that it can find.
1:38
So how can you find the optimal price?
1:43
So I used to do pricing in the airlines and in the airlines we
1:47
used a different type of pricing model and it was called price
1:51
optimization. And in that regard, there's no
1:54
like you don't give it a starting point, it's going to
1:56
solve for what the price should be.
1:58
So in that regard, what we're doing is saying, OK, this is
2:02
what the market looks like, here's all the data inputs, try
2:05
every price and then tell me which one's best.
2:09
So in the event that you were to run an optimization model,
2:12
there's no base price because if say you give me a base price of
2:16
100, I'm going to try 100, but I'm also going to try
2:19
A101102103. And then you just rely on the
2:23
model to solve and say, hey, James, for next Saturday, the
2:27
optimal price is 105. And how do you do that with
2:33
vacation rentals? How do you kind of test the
2:36
different prices and then get that result of saying, look,
2:40
this is the best one? How does that work in practice?
2:43
OK. So you have to kind of zoom out
2:45
and rethink the way that we're going to set the price.
2:49
So basically what we're going to do first is try and predict if
2:52
you have a property, we're going to predict who your competitors
2:55
are. That's the first thing we're
2:57
going to do. And in pricing science, there's
2:59
pretty good data that says consumers can only hold about 8
3:04
to 12 options in their head at one time.
3:07
We're going to use 10 competitors that we think are
3:10
your best competitors and that's it.
3:13
So in optimization modeling, the way we do it, there's not a lot
3:17
of competitors. It's a very tight group that
3:19
we've just searched for immediately.
3:22
So then we're going to say, OK, what matters to the consumer
3:26
that's about to choose this property?
3:28
So you think about, you know, their their needs of, you know,
3:32
bedroom count, bathroom count, location, you're going to say,
3:35
OK, these are the properties that they might be looking at.
3:38
And then when we start to optimize price, we start to
3:41
think about, OK, what are the things in that listing that
3:45
would choose someone or, you know, 'cause someone to choose
3:47
your property versus another one.
3:50
And those turn out to be images, reviews, ratings and other
3:54
things. So all of those things play into
3:58
the probability that you get booked.
4:00
So what we're saying is, OK, if our property, it has the best
4:04
pictures, has the best reviews, has the best ratings, we're
4:07
going to have a really good chance if we show up on the
4:09
screen in front of a consumer. But if not, or if we have
4:12
really, really high quality competitors, it's going to
4:15
adjust where our price needs to be.
4:17
So we put all that data in and for each of those things that
4:21
we're calculating, there's a like a coefficient or how much
4:25
does it matter. Price is the most important
4:28
thing in a short term rental. And if someone's going to pick,
4:30
that seems obvious, but it's the biggest impact.
4:34
So we have to model for that and say, OK, all these things
4:36
matter. And then as we start to evaluate
4:39
each price point, we can determine the probability if
4:42
you're going to get selected and then we can run the model and
4:44
it'll tell you what the price should be.
4:47
What is the difference between a dynamic pricing tool out there?
4:51
Maybe some of the industry leaders like Beyond for example
4:55
and quibble what is the difference in the final price
4:58
that gets spouted out as as this is what it should be.
5:03
If you think about the underlying technology of what
5:06
would happen if you put in a base price and then what you're
5:09
going to see the outcome of is they're trying to determine how
5:14
much to change your base price. They're going to put a
5:17
multiplying value on that and they use different metrics to do
5:21
that. And that's going to happen on
5:24
every day and they're going to say, OK, on these days, we
5:26
should take your base price and and move it up however much
5:29
percent. So in that regard, it's pretty
5:32
dependent on the base price that you're going to set.
5:35
You know, your final price has a really high dependency on
5:38
whatever base price you're setting and what you're relying
5:41
on quibble to do for you and the difference here.
5:43
And what we're doing is saying, OK, we just need your help
5:47
determining, you know, the market and the competitors and
5:50
we're going to automatically try and determine them for you, make
5:52
sure you're happy with who we're putting into the the model.
5:55
And then after that we're going to determine the price.
5:58
So it's kind of flipping the flipping the switch a bit and
6:01
saying, OK, your role here as the property manager host or,
6:06
you know, revenue managers, making sure that the, you know,
6:08
we got the right competitors in there.
6:09
You know, we're understanding the market well.
6:11
And then the price gets set. You're not managing a base price
6:15
like you would be in in other tools.
6:17
OK, real talk now for all you hosts out there, you know that
6:21
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7:36
What all the dynamic pricing platforms have in common is
7:39
setting a minimum rate. Obviously hosts say, look, this
7:43
is the minimum that I'm prepared to take.
7:46
Is there an argument to say that you shouldn't set a minimum
7:49
rate? Yes, if you're straight wanting
7:54
to maximize revenue, you don't constrain the model, but that's
7:58
not OK. When you talk to a host or
8:02
property manager, they've got owners that might say, hey, I
8:05
don't care if I'm unbooked, you know, don't don't take my price
8:09
below this price point. And to them, that's the
8:12
intrinsic value that they feel of the property and they would
8:15
rather not be booked. There's also an argument here of
8:20
reputation of the property in the sense of if you have an
8:23
extremely, you know, high end property and the model says I've
8:28
got 0 demand, I've got to take the price really far down here.
8:31
That might not be the best interest of the property long
8:34
term because the model is solving for the best revenue on
8:38
that single day, not like trying to determine, you know, all
8:41
future values. So there's constraints that, you
8:45
know, pure revenue management. No, no constraints apply in
8:49
terms of, you know managing host or or or owners kind of
8:53
expectations and limitations than restrictions apply.
8:58
Yeah, I guess with that kind of model you'd if you charge
9:01
anything, anything just to get a booking, your occupancy level is
9:06
going to be near to 100%. And there's revenue managers out
9:09
there who say you should never be close to 100%.
9:11
It means you're not charging enough.
9:13
They'll say, you know, you should be around 60%, maybe 70%.
9:17
Where is the sweet spot do you think in terms of occupancy for
9:21
vacation rentals? Yeah, it's going to depend on
9:25
the property. But when you're when you're
9:28
running an optimization model like what we were talking about
9:31
before, it's trying to find the point that's going to maximize
9:38
your earnings, not your ADR or your occupancy.
9:42
So on every single day, there is a maximum point.
9:45
And what we're trying to do behind the scenes is say, OK,
9:50
what's the total amount of people that are going to book on
9:53
this day? So we use historical demand kind
9:55
of like the airlines do. And we say, OK, we think there's
9:59
12 people that are going to book.
10:01
Well, we only have 10 properties, right?
10:03
So we're thinking up, there's no need to discount the price.
10:07
Now over time that those other properties start to get booked
10:11
and we might say, OK, we missed a few and the price continues to
10:14
adjust. But when you're, you know,
10:16
creating a revenue management strategy, it should be a Rev par
10:20
strategy, not like a true revenue management answer to the
10:24
question is I don't care what my occupancy is, I don't care what
10:29
my ADR is. All I care about is the final
10:32
revenue. And that's what your model
10:33
should be doing. There will be hosting property
10:37
managers out there who say my property is totally unique.
10:41
I can't compare it to anybody else because it's just not
10:44
similar. And it's in an area where we're
10:46
the only ones for 50 square miles or something like that.
10:49
And your model, as you were explaining at the start, you
10:52
know you're picking comparable properties to compare things to.
10:56
What do you say to those hosting property managers who say my
10:59
rentals are too unique? They might be right.
11:03
I mean, there's a chance that there are.
11:07
If someone is shopping and they are only considering that
11:11
property, then it's pretty hard to say, OK, the consumer's only
11:17
making one choice and it's basically assuming somewhat of a
11:20
monopolistic scenario where that's all that's going to
11:23
happen and it's pretty hard. So in that regard, you might
11:26
say, I know what the price should be and I don't want it to
11:29
be dynamic. And if that's the case, you can
11:31
lock it in. They may be right.
11:34
Generally speaking, most properties when people are
11:38
shopping, they're looking at other options.
11:42
But in those really unique cases, dynamic pricing is very
11:45
hard. So just run through as then what
11:48
are the most important data points to analyze when setting
11:53
rates for a property? All right, so there I put them
11:57
in two buckets. 1 is the things you can't change.
12:00
You can't really change the location.
12:02
You can't easily add bedrooms or other things.
12:06
And those are kind of the things that we're using when we're
12:08
trying to pick your competitors because the person that needs a
12:11
property needs either a four bedroom in that location, right.
12:15
So we're using a lot of that data for for competitor
12:17
selection. The other data that goes into
12:20
price optimization is the things that do change or you can
12:23
change. So we're talking about ratings,
12:25
reviews, images, amenities. All of those things play into
12:30
the probability that you're going to get booked.
12:32
As I said before, price, when it correlates to choice, if
12:36
someone's going to pick your property has the biggest impact.
12:39
But the other things like images have a huge impact if someone is
12:43
going to pick. And what we've done to try and
12:45
solve for that is we've run your images through an AI model that
12:49
tries to determine how much does a human like this image.
12:52
We do the same thing for your competitors.
12:54
Same with the reviews. You might have amazing reviews,
12:57
and that's awesome. It's going to help you get
12:58
booked. If your competitors have bad
13:00
reviews, that helps us keep your price higher.
13:03
But if they also have amazing reviews, it causes us to be more
13:06
competitive. So all of those attributes go
13:10
into the model. It's automated.
13:11
You don't have to do anything. We pull that data in and it, you
13:15
know, helps to optimize. There are things that we don't,
13:17
I don't think matter very much. So, you know, sometimes hosts
13:20
spend a lot of time trying to come up with the right name for
13:23
the property or the right description.
13:25
When we did survey data, those things didn't correlate very
13:31
high to if someone's going to pick your property or not.
13:33
So we actually took them out of the model because when we ran
13:36
it, it just didn't have much impact.
13:40
Interesting, very interesting. It's one of the things we hear
13:42
about quite a lot, isn't it? The the titles and the listing
13:45
descriptions. So, so just to explain to us
13:48
then how Quibbles approach differs from other dynamic
13:53
pricing tools that are out there?
13:56
So when we're, you know, to continue on the quality type of
14:00
the property, we're trying to determine when someone's looking
14:06
at 10 properties or you know, you're you're going through that
14:08
shopping experience, we're pulling that data.
14:12
And each of those attributes that we described has an impact
14:16
on if you get chosen or not. So imagine we pull all the data
14:21
from your property. We have all your images, we have
14:24
all your reviews, all your ratings, your amenities.
14:27
Do you have a pool? Do you have a hot tub?
14:29
Do you have a view, a Mountain View, Riverview, all those
14:31
things. And we can say, OK, we know kind
14:34
of the quality of your property now to set the price, we need to
14:38
do the same thing for what else is on the screen at the time.
14:41
So we're going to guess what those properties are going to
14:44
be. And we do that looking at all
14:46
your competitors. We do a big data collection poll
14:49
and we say, OK, this is everything in the market.
14:51
Who is the most likely to be compared or chosen instead of
14:55
me? So that's all we care about.
14:58
No one else is considered a competitor in that model.
15:00
The only other data that goes into it is someone or another
15:05
listing that we think may get chosen instead of everything
15:08
else is irrelevant. And then we say, OK, now we have
15:11
an idea of the market, we have an idea of what it looks like on
15:13
that day and then probability comes into play.
15:16
Probability based modeling is really hard to do.
15:19
It's what we used to do in the airlines, but we're going to try
15:21
now give you a market share probability.
15:25
So in the model, if you look at any of the days, you can say OK,
15:28
on this day it says I have a 20% probability of getting booked.
15:32
What that describes to the manager is if you're on the
15:36
screen with these properties, based on everything we know
15:39
about your listing and everything we know about your
15:41
competitors, that's a chance you get selected now, as you're
15:45
describing before. Well, do I want to do I want to
15:48
increase my chance? Like should I drop my price?
15:51
You know, like I could take that 20% chance of getting booked,
15:54
increase it to 90%, right? Yes, you could do that.
15:58
If you want to drop your price to $5 a night, we could do that,
16:02
right? Or you could say, oh, I have a
16:04
minimum price and I'm never going to go below that.
16:06
OK. That's the price we're going to
16:08
set and your probability may drop to 5% or 1%.
16:12
So what we're relying on the model to do for us is pick the
16:15
one that maximizes our expected earnings.
16:20
Is this true set and forget in terms of pricing?
16:25
All right. I would say right now where
16:28
we're at in pricing science and you know, I can talk about what
16:31
we do, it's probably 85 and 90% of the pricing choice can be
16:37
handled by the model. I'd say there's still room 10 to
16:41
15% for us, the human or the analyst or the revenue manager
16:45
to evaluate what the model's doing and try to say, oh, you
16:49
know, I think I know a little bit more here or maybe the
16:51
model's missing a little bit of information here.
16:54
And it might be, you know, helpful if I make some
16:58
adjustments or I can see some macro trends from, you know,
17:02
whatever's going on with tariffs or anything, you know, that
17:04
people are worried about that that's not that's not programmed
17:08
into the model. So you got these out external
17:11
layers. It's like, hey, you're not going
17:12
to be able to see this. So let's, you know, try and get
17:16
in front of it. So there's still room here for
17:20
for the human, but if you're, you know, a super busy property
17:25
manager and you say, OK, I'm not going to spend 10 or 15% trying
17:29
to figure out what those are. You're still going to do better
17:32
or you're going to make more money because you're going to be
17:34
priced really well. And interestingly, what you're
17:37
saying there is that there isn't a element of the pricing that
17:41
takes into account Donald Trump. Not yet, Not yet.
17:44
Not yet. That's a more expensive model.
17:46
We're going to have to work on it.
17:50
Let's kind of talk a few like pricing and revenue management
17:54
tactics. Now, how far in advance should
17:58
hosts be adjusting their rates and how frequently should they
18:02
be tweaking them? Let's imagine that they're they
18:05
have static rates or they don't use dynamic pricing or even if
18:09
they do use dynamic pricing, how often should they be looking at
18:12
stuff all? Right.
18:14
So it depends a bit on like the size of the property.
18:17
So if it's really big, you know, imagine a 5 or 6 bedroom, the
18:21
family that might book that of the group is going to book that
18:23
further in advance, right? Because they got to schedule all
18:26
those people and get that done. So those bigger properties of
18:30
book out further, smaller properties closer.
18:32
And so you kind of understand the booking curve, right?
18:35
And when, you know, revenue managers say booking curve,
18:37
they're kind of watching the occupancy of that property fill
18:40
up. And normally there's a point in
18:43
that curve where you see the see acceleration or it start to pick
18:49
up, which means that's when most people are interested in booking
18:52
that one bedroom in that city, right?
18:54
So as a revenue manager, generally you want to be right
18:58
before that period checking the price because you want to go
19:01
into the peak of that curve or the steep part of that curve,
19:06
making sure your prices are ready.
19:08
And that can change depending on, you know, the the type of
19:11
property and you know, if you have a lot of listings or a lot
19:16
of different markets, you'll have a lot of different time
19:19
periods that that occurs. When it comes to revenue
19:23
management and pricing rentals, what are the most common
19:26
mistakes that you see houses and property managers making?
19:29
OK, in the airlines, there's research that gets done.
19:34
I know more about airlines because I spent a lot of time
19:36
there that tries to look back at the decisions that got made in
19:41
most of the time what we see is the errors happen that people
19:47
reduce price too soon. And this is largely driven by
19:52
our fear of being empty. And I guess if you think about
19:56
the layer, sometimes it's the host of this your property, or
19:59
sometimes you have the property manager and they have to go to
20:04
their owners and say, hey, you know, the price or the the
20:07
calendar was empty. And that's a hard conversation.
20:10
And I think it drives a lot of fear and drives a lot of last
20:12
minute discounting. So the biggest mistake I think
20:16
in all revenue management is aggressive discounting.
20:19
And I think in our industry particular, we have a lot of
20:22
last minute discounting. And you know, that is really
20:26
pervasive where it's just like, OK, I got to, you know, tank the
20:29
rate here. And I think as an industry it
20:33
could be corrected, but we kind of have to come together as
20:35
like, hey, you know this, you know, if if we tank the price
20:39
last minute, we're all kind of losing.
20:42
Yeah, and I guess you know that that will feed into guest
20:46
behaviour as well if they're just going to think, well, I'm,
20:48
I'm going to book last minute because I know I'm going to get
20:51
20% off off the price in in terms of the airlines, I'm quite
20:54
interested. Do airlines have minimum rates?
20:59
Because, you know, sometimes you get on a plane and there's a few
21:01
empty seats and you think, well, maybe they could have sold them
21:03
if they'd have discounted them heavily.
21:06
Yeah. So I was in the airlines during
21:10
the transition to like low cost carriers or ultra low cost
21:13
carriers. So I guess you'd be more
21:15
familiar with Ryanair. I was used to work at an airline
21:17
called Frontier and these type of airlines basically got rid of
21:22
a lot of restrictions. But what you generally see in
21:26
the airlines are advanced purchase rules and those if you
21:31
ever are shopping and you get within a certain period of time,
21:35
what they will do is close out all the discount rates or the
21:39
discount price points and say hey, at this point in time, if
21:44
I'm seven days in, meaning there's only seven days left to
21:46
book, you know, the major carriers are expecting that.
21:49
That traffic is largely business traffic.
21:53
You know, there might be, imagine 5 business people and
21:57
two leisure travellers. They're going to allow the
22:00
leisure travellers to stay home and they're going to try and
22:03
capture the five business passengers because to them
22:05
that's worth a lot more. So the trend in the airlines is
22:09
180° opposite where as you get closer, generally those price
22:14
points start to close and they say, hey, if you need to go to
22:18
New York right now and it's tomorrow or whatever, they're
22:21
like, you know, you have this really inelastic demand.
22:25
So it's actually a lot the opposite in the airlines.
22:28
I wonder if there are property managers out there who put the
22:31
price up if there's only if it's a short booking window to the to
22:36
the guests arriving. And just following on from that,
22:39
I mean that this might be one of the key lessons, but what are
22:42
the key things that vacation rental managers could learn from
22:46
the airline industry when it comes to pricing?
22:50
That is it. I mean pricing discipline in
22:53
terms of creating a strategy and, and, and sticking to it in
22:58
the sense of is this is going to sound counter intuitive, but
23:04
dynamic pricing is generally an admission that we can't get the
23:08
price right the first time, like we can't perfectly predict the
23:11
market. So when I was in the airlines,
23:13
we were trying to change the price as few times as possible.
23:17
That sounds weird, right? But if say we do it really early
23:20
and we get the price set correctly 100 days out or 90
23:24
days out, the price would be less dynamic because we've
23:27
actually solved the correct price early in that window.
23:31
And that's the advantage of trying to use optimization or
23:35
forecast based modeling where it's OK, we're going to try and
23:38
get to the right price as soon as we can.
23:40
And if we get it right, we're not actually going to change it
23:42
because it's the correct price. So it's actually counter
23:45
intuitive and the better we get at forecasting, the better we
23:50
can get at, you know, not having to dynamically adjust the rate
23:53
as much. And it sounds counterintuitive,
23:55
but as our goal as a revenue manager has just set the right
23:58
price and then we got it. We've all heard of posts and
24:02
property managers out there who maybe they've got a few rentals
24:06
and they've heard about dynamic pricing and they want to give it
24:09
a try And then they log in and maybe just find it too
24:12
complicated. And, and there are other people
24:14
out there who say, yes, you can use one of these brilliant
24:17
dynamic pricing tools, but you need somebody to to manage it,
24:20
to drive it for you because it is, it is difficult.
24:24
Is it possible to manage a dynamic pricing tool effectively
24:28
if you're a pretty non techie host?
24:34
I'd say yes. It's kind of back to the 10%
24:38
thing where it's like, OK, 90% of the work is in setting it up.
24:44
And from our perspective, if we get it set up properly, which
24:48
takes, you know, time to put the data in, evaluate and make sure
24:51
that the everyone's on board with the price, the minimum stay
24:54
strategies, all that most of the work should be done upfront.
24:58
And you can either use a consultant, we provide some
25:01
consultancy if you want help setting up and then it should be
25:07
well managed. Now the the 10 or 15% can come
25:11
in events that you might that you know, the model doesn't know
25:15
when certain events are going to be launched or holidays might be
25:22
managed better by using some controls.
25:25
So inside the application we have controls for holidays,
25:27
controls for events that you can create and manage.
25:30
So I think yes, you can set it up and it will work very
25:36
effectively for you. If you manage it and you want to
25:39
put in the effort to try and capture that 10 to 15%, then
25:43
it'll work better. Stands to reason.
25:48
Tell us about Quibble then a little bit and tell us about any
25:52
success stories of people who've maybe moved over from a
25:55
different software and they've joined Quibble.
25:58
What has the the uplift been? Maybe after a year or so?
26:02
Yeah. So most of our clients as you
26:04
imagine have come over from using a competitive tool.
26:09
We were just doing for our website some customer stories
26:15
and we were reviewing some data for a client of ours in New
26:18
Orleans. It's book NOLA, they have a
26:21
really nice portfolio properties and managed by a guy named Mike
26:25
Springer and they had seen over probably nine months consistent
26:31
unit revenue or Rev par growth about 15 to 20%.
26:35
So just by getting a better understanding and use using this
26:39
type of modeling, you know he saw really good results.
26:42
Most of our clients see something like that where
26:45
they'll come over and you know by adding in these quality based
26:51
metrics and making decisions you know more at the nightly level,
26:56
you'll see a pretty good improvement.
26:58
And tell us what's the cost of using Quibble and where should
27:01
people go to find out more? We charge by listing, so it's
27:05
$10 per property per month and they can sign up at
27:09
quibblerm.com. All right, check it out.
27:12
We'll put that link in the show notes as well just before we get
27:16
to our questions to finish then, Neil, what trends are you seeing
27:21
in the STR space in 2025 that house and property managers
27:24
should know about? All right.
27:26
We, it's a trend that started probably before 2025, but
27:30
there's a lot of conversation right now about AI and I think
27:35
there is confusion about what that means.
27:41
And I think what we see really is 90% marketing, 10% product
27:47
when it comes to AI and what's available.
27:49
And host or managers probably have a feeling that they might
27:52
be, you know, missing the train on AI.
27:55
I'm not saying they shouldn't invest or they shouldn't try and
27:58
uncover great opportunities. But when people say everything
28:01
is AI, then nothing is AI. And for us, we see two really
28:05
good use cases for true artificial intelligence to help
28:08
you price. It's in the images that we were
28:10
talking about before with computer vision and then
28:13
understanding language. So we also extract text from
28:16
reviews and try and help understand how that might impact
28:19
the next booking or not. So I think the trend in AI is
28:23
really important, but it's also a lot of marketing.
28:28
So pulling that apart I think is really critical for for
28:31
management companies. Great advice, I think.
28:35
All right then, Neil, a few questions to finish.
28:38
What's your favorite brand in the STR space?
28:41
Apart from Quibble, of course. Apart from cool, you know, I
28:45
think it's really interesting what Kasago is doing with Picasa
28:49
that I didn't. I think it tells an interesting
28:52
story about where property managers, you know, are are
28:56
going to be focused and kind of what the next couple years of
29:00
the industry might look like in terms of the importance of the
29:04
local connection to the market, I think is really interesting.
29:09
All right. 22 great brands there and well purged, haven't they?
29:13
Now it's, it's all sorted it's all done now so we'll yeah, I'm
29:18
sure we'll we'll find out more about that in the near future.
29:21
An individual in the STR space that you'd like to give a shout
29:24
out to and why? So we have an advisor to our
29:28
company. He's in the travel tech space
29:31
and he works at really high level in price and price
29:35
optimization and guided us a lot through understanding how
29:40
consumers make choices and then understanding how to build a
29:44
model that that will optimize for that is Jim Barlow.
29:48
He's, you know, high level revenue management operator,
29:51
worked at Sabre, currently at Amadeus, a big travel tech
29:54
company. He's been really critical in
29:56
forming price modelling for us. Excellent.
30:00
Nice shout out there for Jim. Finally, then, your number one
30:03
lesson learned since founding Quibble.
30:06
I think there's a really interesting opportunity for us
30:10
in this space for pricing. I think as you see, there's a
30:13
lot of new consultants coming up that'll do price revenue
30:16
management for you. And I think we have a really
30:18
good opportunity to, you know, drive pricing science and the
30:24
underlying technology that makes better pricing because what we
30:29
have that's different than the airlines, that's different than
30:32
the hotels is extremely unique products and we should be able
30:36
to price accordingly. Airlines are pricing seats on
30:40
180, you know, seat plane, some hotels have, you know a hundred
30:44
300 rooms and they're similar. We have really interesting
30:47
opportunity to pull out the uniqueness of all these listings
30:51
and incorporate it into our pricing.
30:54
The future is pricing. We'll wait to see what what
30:57
comes out and but the other science behind it is certainly
31:00
fascinating. Neil, thank you so much.
31:02
I feel like I've learned a lot about pricing and revenue
31:05
Management Today. Thanks for joining us on the
31:07
Host Planet podcast. Thank you, James.
31:10
Fascinating to chat to Neil Sia there from Quibble about all
31:13
aspects of revenue management. If you're looking for more free
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