Professor Anthony Lucas – Part Two

Nick Hogan:

Okay, so let's move on to the free play study. So could you maybe just break that down for us

quickly, what you were looking at? Yeah.

Anthony Lucas:

That's a long road too, but first we started to look at free play just overall. For every dollar we

redeem in free play, what do we get back in incremental play? So we spent a dollar basically. Another thing

that bugs me, we're talking about, I think Dan mentioned earlier, things that bother me about things in the industry

people need to know, if someone says free play has no cost, just stop talking to them. It has a

cost called the payout, because when those bets hit, you got to pay out actual real money.

So we were trying to understand, okay, we know it has a cost, we could spend that money somewhere else,

so it'd be nice to know if that 40 million a year we're spending is actually doing anything for us.

And so there's two kinds of things. There's spend per trip and there's visitation, so we want to see an

uptick in one or both of those things. And so it was easier to measure spend per trip first, so

that's where we started, and at one point free play was doing pretty good. And again, free play is not

categorically bad. It's not. It can be done well. There's not some return on free play that applies to everyone.

That doesn't exist because it matters who you send it to, how much you send them, the offer protocol, frequency,

amounts, selection, all those things matter a lot and they will affect your return.

But what we kept finding was, and we did 20 something studies where we would just say, okay, what's a

dollar in free play produce? Now that includes everybody, high end, low end, middle, everybody, and that was the limitation

of the study. But we kept finding, we're giving away almost a dollar and we're getting back less. And so

we started to say, it doesn't look like... In the early 2000s, we saw success where we would see for

every dollar we redeem in free play, we get a dollar 35 in un-money spent, and that was pretty good,

35% return, and so things were good. And then they got bad really fast and so we started to see

75 cents was the norm. And shockingly, even though I just said everybody's program is different, but that 75 to

80 cents was very common. I would say 90% of the studies, we would find a dollar redeemed in free

play got us about 70 to 80 cents, somewhere in there in incremental play.

But then we started to say... We started broadly and we refined it. We started looking at that same model

but within segments of the loyalty program, like what did segment one do, segment two, all the way up to

the top players? And we didn't get very good results there either, and again, we're still looking to spend per

trip. And then skipping over a few studies, we got to the one I think you're talking about where we

did a randomized controlled trial, which is the gold standard of research design. I think any economist would agree with

that, including Nobel laureates like Richard Thaler, the guy I got it from.

So yeah, it's a pretty good design, and so we created... I think we took 600 players from the same

offer tier, not the same loyalty program tier but the same free play offer tier, and we split them up.

All 600 of those people I think got, I want to say it was $15 a week or something in

the pre-demotion period, and then we kept a hundred of them at that same level. And then I think we

decreased a hundred of them to $10 a week, and then we decreased another a hundred to $5 a week,

then we increased I think a hundred to $20 and we increased a hundred to 25. And so I don't

know if you've seen that study, but that's coming out in a British journal. It should be out in a

couple of weeks.

So the reason we did it was free play has ballooned into this tremendous expense, and it's a primary plan

centered for most casinos in the United States. And so I think it was Andrew Clemenow I want to say

that said we're in the golden age of free play. Everybody's doing it and they're doing it big, and he's

not wrong. And so yeah, we wanted to understand, is it increasing spend per trip? Is it increasing visitation? And

so we did this within groups study. We did it between groups one, two and Cornell, but we wanted to

say Nick was getting $15 a week in this six month period in what we call the pre-demotion period, and

then Nick got $5 a week in the post-demotion period, the same six-month period year over year, because we were

trying to address seasonality. How did Nick's behavior change? Because we changed his incentive, we dropped it by 67%. We

found that you actually gambled more. Let me see. I'll pull up the result. I think I have it right

here.

Of course, I don't, but... Yeah, actually I do. All right. So yeah, you actually had a significant increase with

outliers omitted. So your spend for that six month period actually went up, even though I dropped your incentive by

67%. The rest of them, I would say to summarize it, we don't really see a connection between the amount

of free play that you're giving people and their spend within that group, within that group. It could be different

for the high end, and so in the paper, we say, "Be careful, don't replicate this experiment with your best

players because there's too much risk."

And by the way, loyalty program 101, when you start a loyalty program, we knew who our big players were

before we had loyalty programs because we had hosts and discretionary conflict, so we knew who they were. They were

already breaking their back for us. They're gambling a lot, they're visiting often. It's the low end and the middle

end, that's the people that have the most capacity for growth. So when you consider the enormous cost of loyalty

programs, when they are enormous, that's where we need to make the gains, is in the low end and the

middle end. Are these people betting more per trip? Are these people visiting more often? And that's why we select

those segments to do these studies, because they're the ones who need to get us across the finish line. And

so we don't really see a dramatic change in visitation or spend. We certainly don't see what we would want

to see, a result that would support the cost of the loyalty program. We haven't seen that.

And so I don't know what it is. Is it the environment? Is it the convenience, the location that makes

people patronize? But I don't think it's free play. Are there some people that are transactionally loyal? Of course, but

do you want to invest in them? Because they can be bought by the next operator for a dollar. If

they're that price prone or that promotion prone, why would you want to invest in a relationship with them? They'll

leave you for a better offer.

Nick Hogan:

Sure, and there are plenty of operators who really just do have the policy that they're not going to chase

free play programs, and I haven't heard a lot from them in terms of negative consequences because of that, to

be honest. But certainly, those conclusions do challenge this old adage that once you put out a free play level,

it's really, really difficult to take it back. And Dan, I'm just curious from the operational side, we know that

a lot of this free play stuff, it does boil down to a competitive set and the different operators chasing

one another, but have you played with these free play levels in your career? Do you see similar types of

things?

Dan Cherry:

Yeah, I think most operators have, and I think you touched on it, so much of it is market specific,

and in so many of these markets, you may be dealing with one rogue irrational operator who's... Not to say

you have to follow the leader, but to an extent, if you don't react to some extent if folks are

over-investing, you're going to lose a good chunk, at least in the short term. So yeah, I think you touched

on it, which is so much of it comes down to if we were to set up a brand new

market, it would be very easy, we could be much more rational. But in a lot of cases, the patterns

have been established over 20, 25 years, and it's a lot harder to unwind and make some of these changes.

But folks are always doing A-B in their database, or at least savvy operators are, to figure out where there's

opportunities to scale back effectively.

Nick Hogan:

For sure, for sure. Okay, so-

Anthony Lucas:

I agree, there's a legacy effect, and I have always... Gary Laubman said to me once, we're only as smart

as our dumbest competitor. It's what you were saying about if somebody's out there just killing it, we got to

get in line. But I would say I understand that and I definitely understand the short-term pressure because that's the

corporate world, the next quarter is long, but there's a lot of ways to get people in the door, and

if you had an extra 40 million a year to invest in the property, that might help too.

One of the common denominators that I see about successive markets is it's typically the nicest place, like Wynn, I

think they have a pretty good blueprint for that. And so there's a lot of ways to do it, but

I do understand all the things that Dan just said and they're very real. And if you're the CEO and

you got that big paycheck coming in, do you really want to rock the boat? Do you want to rock

it? And we got to say it because it's real. That's part of how decisions get made, and I totally

get it.

Nick Hogan:

And what we see is we do a lot of field trials and playing with various things, and one of

the conditions of our stuff is always that you don't monkey with free play during the study because it could

contaminate the results and whatnot, so they keep it constant. And we're just changing around inventory, looking more at demand,

fixating more on what it is that players want, and when we're putting that stuff out, again with no changes

at all to free play, we get these huge increments in spin, and they truly do not care about the

levels of free play. We don't see it impacting anything, so yeah, I-

Anthony Lucas:

That's interesting.

Nick Hogan:

Yeah. Rolling back to your original point, as we talk about so many topics in analytics, and especially get this

place in the AI, and I get so tired of this topic because where I land on so much stuff

is I say, okay, before we start getting into some really highfalutin long hair analytics on these things, let's first

make sure that we have a good solid grasp on the just basic economic model. What is it that makes

these people tick? What is it that's bringing them in? What is it that they demand and how do we

cater to it efficiently? Let's understand that before we go too high, and I think we still as an industry

have quite a ways to go on this stuff. We see it every day.

But now, this maybe slides us into a topic here that Anthony, you and I have discussed before, which is

cognitive dissonance in the industry, and that when you step forward and in your case, you're attacking arguments with a

scientific method, hard data, completely transparent methodology, and yet people still hammer on you about things that you write and

conclusions that you reach. So I have my theories on why this stuff is out there but I'm really curious

about your thoughts on this. So why do you think that these reactions are so strong and there's so much

cognitive distance out there on these things?

Anthony Lucas:

It doesn't really bother me and I understand it. I try to put myself in their shoes, and like you

said, science is an iterative process. And I read some of my own work from 25 years ago, and I

was saying some of the same things that my critics say to me today. That was my understanding of it,

but as I got new information, my understanding changes. And I think it was, I want to say it was

Alvin Toffler, the futurist. I think he was saying the illiterate person of the current century is the person who

can't learn, unlearn, and relearn. It's not the person who can't read and write, it's the person who can't learn,

and that's because technology is moving so fast and science is moving so fast.

And as Dan pointed out earlier, we haven't been in this intellectual hotbed for management theory in the gaming industry,

so science and this type of exploration is new to us, and so there's going to be some pushback and

I think there's going to be a lot of resistance and cognitive dissonance. And it's hard when you've been on

the job for 20 years and then somebody comes along and says, "Hey, your prior understanding is not the way

it works," or, "There are some problems with your prior understanding, or severe limitations." That's a tough pill to swallow.

And so I have cognitive dissonance, I'm no better, and I buy a stock, it goes down, I hang on.

Even though there's 27,000 better uses of that money, I don't sell it because I think it's going to come

back.

Nick Hogan:

Well, I would say your mistake starts with buying the individual security, because you're at such a huge disadvantage.

Anthony Lucas:

That's right. Yeah, there's a long list of mistakes. We could delve into that, but that's where good judgment comes

from, bad judgment, right?

Nick Hogan:

Yeah.

Anthony Lucas:

So there's a lot of cognitive dissonance. I think there's also something called the illusory truth effect, and I don't

know if you're familiar with that, but it's like if you hear a false message enough times from different sources,

even though you might know it's not true, even that extreme, you'll still believe it, accept it as the truth.

And so politicians have got a very firm grasp on-

Nick Hogan:

I was going to say, my God, is that a contemporary analogy?

Anthony Lucas:

But we're very political too. Corporations are organizations that are very political, gaming included, and so there's a lot of

that. It's not just cognitive dissonance. We've got these long legacy explanations of how things work, and a lot of

them are just crazy wrong, crazy wrong. I hear things about rebates. Well, I only get my checkbook out when

the player loses. No, that's not how rebates work. Not even close, and it's a super dangerous misunderstanding that's going

to cost you a lot of money, but that's the legacy. And so it's hard to change that well-established... I

don't think I should say that. It's hard to change the established thinking on something because you are battling this

psychological resistance to change and...

Anthony Lucas:

... resistance to change, and it doesn't feel good to know that you've been thinking about it in the wrong

way or not the right way. And plus it doesn't sit well when you say, "Well, I've had all these

people explain it to me and different people, and it's the same thing I've heard year over year. So how

could this other guy be right?" It doesn't land on our brain in a way that we're willing to accept

new ideas. That's not the way we've done it here before. We've always done it this other way. My experience

has shown different results, all this sort of resistance language that I hear. And I think those are some of

the reasons why. I had another theory I was going to invoke, but in my long answer, I actually forgot

it. This happens a lot.

Nick Hogan:

Well, one of my thoughts on this is I see more resistance on this front in the US than I

see it anywhere. We work all over the world, and I see more of this in the US than I

see anywhere else.

Anthony Lucas:

Me too.

Nick Hogan:

And I've always felt that legacy wise, it really came from, on the one hand, I mean, if we look

at our historical legacy, we're looking at the mob obviously was one of it. They were never big on info

share for one reason or another. Right? And the other, of course, is in the tribal gaming. And here we

have two separate paths, but both that are fairly insular and there's not a lot of trust outside influences and

things of this nature. So there's a lot of recruitment from within. And I think it has resulted in a

bit of tunnel vision throughout the industry that we've seen over the years. And that's been my dime store theory

on this for plenty of time there.

Dan Cherry:

Yeah, something that's related, you said something a while back that kind of struck a nerve with me that you

said when I asked you, "Well, when people come with their own studies, what are they saying? How they debate?"

And you're like, "Well, they don't come with their own studies." It was kind of disappointing to me. And maybe

you're going to tell me that, I hope you're going to tell me I'm way off base and every industry

is like this, but it seems like to me at least there's really this sense of territoriality in gaming, whether

it's because I think it's a zero-sum game, and if you win, that means I lose. But companies are not

only protected with their data as they should be, but they're very protective with the analytics, protective with the learnings,

right? Everybody feels like they have the secret sauce, and I can't share it.

I'm always a big believer of rising tide raises all ships. And not in all areas, but I think if

the industry becomes more knowledgeable in a lot of areas, delivers a better experience that benefits all operators because customers

don't just, for the most part, they spread the wealth. So the experience at one casino impacts my overall view

of the industry. Am I thinking about this wrong? Is there something to it or is this an industry really,

there's just a lot more opportunity to come together, potentially partner, public, private, do some studies together and maybe really

tackle some of these things as an industry? We do it with responsible gaming studies, but we don't really do

it with anything else.

Anthony Lucas:

Yeah, that's a good point. I think you're right about the secret sauce stuff, but just sort of is an

entertaining anecdote. I'll write a paper and some of these papers, I get calls from the governor's office of different

states and they start talking about policy changes, and that gets people really nervous. So then operators in that jurisdiction

might say, well, let's take a look at our program, or, I don't even want to go there because I'm

going to get myself in trouble. But a lot of times I'll hear, I read your study, I really liked

it, but our free play program, we don't do it that way. Our free play, and then I'll look at

their data.

So I mean, a lot of people think they have the secret sauce, but they don't. When I look at

their data, the sauce has gone bad, and so I don't eat it. But I don't know, it's always a

very awkward conversation when I say, let's look at your data and see what we got. And it's never good.

But I will say, I don't believe that free play is categorically bad. It can work, but what is sort

of almost categorically bad is we just do such a poor job of measuring it.

And you were talking about where are the studies? And even when I do see, a lot of times I'll

lecture about one of these studies and someone in the audience will say, "Well, we did that and we got

a different result." And then on the break or whatever, they'll come up to me and I'll ask them, "What

did you do?" And it's like, yeah, you didn't do that. That's not what I did, and what you did

is so flawed that I don't even have time to describe all the ways in which you went wrong. And

you can't even conclude anything of value from what you did. But I know that sounds really bad. It's also

very reflective of my actual experience. So sometimes we have to say things that aren't popular but need to be

said. And that's one of them.

I'm a doctor, you don't want me to take out your kidney, but I am pretty good at experimental design

statistics and analysis. I'm better than you. You don't have to be the best, you just have to be better

than the next guy. But no, I'm just kidding. I'm not saying I'm better than everybody, but what I am

saying is, again, back to Socrates, don't think that you know how to know things. It's not intuitive. Experimental design

is not intuitive. Statistics are not intuitive, at least for most people.

So a lot of these studies that we did, in quotes, are extremely poorly designed and they're relying on those

to make management decisions, which is really fraught with peril. So not only are the studies not done, but even

when they are done, they're very poorly done. And I think that varies by company. Some of these bigger companies

have people in their corporate office that are incredibly talented and they know how to do all the stuff that

I just said, but it's really hard to get... A lot of times the people in the corporate office, they're

doing some really good things. Like Rich Merman when he was with Gary Levi, incredibly smart guy. Great experimental designs,

but they had a lot of trouble getting the operators to accept the results just like I do. Even though

they had a hierarchical power, they still couldn't get them to do it.

Gary used to say, "We've designed all these beautiful jet airplanes, but we don't have any pilots to fly them."

So there's free play's biggest problem is not the incentive itself, it's the poor measurement. If we had better measurement,

we could incrementally refine it and we could find that sweet spot. It works. It works here. And I know

that that place exists. I know it does, but we got to have, like Dan was saying, we got to

have these public private.

We know how to know things, academics. We know how to design experiments. Industry people know what research questions to

ask and what and exactly where we need to look. So we need both sides have unique and valuable information

and input. We just got to get those sides together so that we can make some real progress because I

guarantee you, free play can work if you just do it right. And to do it right, we have to

have successive measurement studies and measurement models, results and measurement models to get there. But to get there, we have

to do it together. And until we get together, they have to stop thinking that they know how to set

up experiments and measure things. And we have to stop thinking we know everything about the gaming industry. So both

sides need to make concessions and both sides need to come together. But there is a happy marriage there, I

believe. Potential.

Dan Cherry:

Well, now's the time. So what's your next study and what's the next area of research that everybody could help

partner with you on?

Anthony Lucas:

Well, I can't really talk about it. There's a few. I did talk about one, which is where we're repackaging

the pay table, but the other ones I can't really talk too much about because what happens is if there's

public record of it, it interferes with the double-blind review process for journals. So I have to be really, really

careful about what I say. I can't go to academic conferences anymore because they always want to publish my presentations,

and that interferes with the journal publication process. And if I don't publish in journals, I have to teach and

I don't want to do that. So that's part of the reason why I can't talk too much about it.

But I am doing some stuff statistically on the interpretation of P-values and the use of survey data measured on

a Likert scale used as a continuous variable and structural equation modeling and things like that. So I'm writing papers

on the limitations of those things, but they're not really related to the gaming industry. So I could talk about

that. But the gaming stuff, I got to be careful. We have done some work-

Nick Hogan:

That's some hardcore statistics there. Looking at these... So you're talking just generally. Yeah.

Anthony Lucas:

Yeah. We're targeting the journal Nature.

Nick Hogan:

I see.

Anthony Lucas:

Which is a pretty big scientific journal. What we're doing is really, I think it has a lot of gravity.

It's going to affect scientific inquiry, I think more broadly than gaming stuff. But believe me, if you think that

the negative feedback we get from these studies is bad, wait until this one comes out. I mean, we're going

to get it from every angle.

But I'm looking at is it better, and Nick, you might have some thoughts on this, but is it better

to put a popular game in a good location and a bad game in a poor location, or the opposite?

Which is kind of an industry question that, because as you talked about earlier, we know location dramatically affects performance,

like the locational characteristics of the game. It's not just a theme, it's not just a pay table, but location

on the floor. I mean, any game can look better if you put it in a really good spot. So

we're trying to understand which combination is actually better for operators because we have to make decisions. We all have

bad games and we all have good games. Where do we put each? Which is better? Which combination is better?

So that's something that we're looking at at different places around the world.

Nick Hogan:

And that is, it's an area that we found is very tough to guide on this. I mean, we look

at it very broadly and we grade locations and groupings across the floor and stuff to deal with that. But

that's one of those areas where it just always seems to be layout is really more of an art form

in many ways. And there's so much in the venue that's environmentally specific. And it's a fascinating topic for sure.

So we have some basic coefficiency [inaudible 01:28:19].

Anthony Lucas:

Oh, yeah. Environmental psychology, I don't know if you've read Moravian and Russell and those guys from MIT, but I

couldn't put that stuff down. I mean, I was so fascinated by it when I was doing my dissertation. And

Mary Jo Bitner, I think she's the Arizona State, did a lot of work on the service scape and just

really cool stuff about approach and avoidance behavior. And it applies to gaming natural elements. But I'd like to know

if I can ask you a question.

Nick Hogan:

Absolutely.

Anthony Lucas:

What kind of locational profiles are you finding are successful?

Nick Hogan:

Again, it's almost, well, it's game specific a lot of times. So we find that we look title, we find

that certain games will resonate in... We define everything. We assign value to an area in a casino. So we

started with breaking it up by 5,000 square foot parcels and giving an A grade through F, etc, etc. This

became then deeply algorithmic, now it's quite complicated, all the values that go into location grading. But what we find

is that some machines just, they'll have a sweet spot in let's say a C grade location, whereas others will

have a thing in an A grade location. So we're constantly swapping inventory to grab incremental yield from that. So

it's, again, it's just one of these weird voodoo things that we look at. And we don't have great explanations

for what we see there, but there's a lot of variability in data there. And I will say, Anthony, my

offer is always open. If you need the data, you have my number. So we'd love to work with you

on some of these things for sure.

Anthony Lucas:

But have you looked at performance potential analysis or creaking analysis or anything?

Nick Hogan:

Yes, indeed. However, going, yeah, I think that would be something we would need to do over the phone opposed

to a podcast. Yeah, it's something we're into here and there for sure.

Anthony Lucas:

But could you talk about things that, and if you can, it's okay, but could you talk about specific things

that, I know it changes from title to title, but are there any broad-based specific location parameters that jump out

at you as these kinds of things, it's next to the pit, it's on a major aisle, it's under a

high ceiling? Is there a basic profile that makes a good location? Is it remote? What is it?

Nick Hogan:

The short answer really is no. I don't think we could get into that much environmental specificity. And in the

early days, we attempted to accumulate these types of details. We had an input form that we wanted each operator

to complete during the onboarding process, and we just found that they could not be bothered to do that. And

it was asking these types of things, adjacencies and this kind of stuff. So we do look at things like

level of the building and things of this nature, but very, very, very broad. And the one conclusion there is

the further away you get from the floor, the ground level, it's generally decaying as you go up. But it

really depends on where the amenities are, where the areas of ingress are, all these types of things [inaudible 01:32:04].

Anthony Lucas:

I've found the same thing on riverboats. All else held constant, as you go up each deck, performance goes down.

Nick Hogan:

It just decays almost linearly. Yeah.

Anthony Lucas:

And same thing, it's tough to get action on the second floor of a casino. There are some exceptions.

Nick Hogan:

For sure.

Anthony Lucas:

I think Sequan might be one, but yeah, you're right. I found that too. I have done some specific things.

I've found that accessibility and visibility really helped performance of a game. Ceiling height, I found some interesting stuff there.

I'm trying to think of what else, L course, standard deviation. But I've looked at, when I worked at Stations,

one of Frank III's big things was can we explain why games do what they do? So we had to

develop these performance potential models that were incorporating location and game characteristic variables to explain performance. And we got some

of them, like we did, I think it was Barona's floor. We explained 95% of the game to game variation

in performance with one model. But we had to use some really advanced math to do it.

So they had hired this girl with a PhD in math, but we couldn't explain it. So that's the problem.

That's why I'm doing, in my research now, I'm trying to just do paired samples, independent samples T-tests, because I

really want to help the industry. I have to publish or they'll make me teach. So I have to do

that. But my real aim is I want to help the industry do better. I really do. I don't have

a dog. I don't need consulting income. I don't own a casino. It's just pure curiosity for me. I don't

have a dog in the hunt. So yeah, I just want to know. I really just want to understand how

to make it better.

Nick Hogan:

Well, in my view, that is the most noble motive of all, honestly. Truly, truly.

Anthony Lucas:

I've painted myself in a good picture.

Nick Hogan:

Well, so Anthony, any upcoming events or initiatives that you'd like to promote before we sign off today? You have

any speaking engagements or anything of this nature?

Anthony Lucas:

Yeah, you're talking to the worst self-promoter on the planet Earth and worse networker. So I've combined those two things

into a huge limitation. But there is the gaming, I think isn't the Gaming and Wagering Conference? I want to

say it's like late May of '26, but they have it every three years. It's the old Bill Eddington conference,

and it's the best academic conference I've ever been to. And I think it's in Las Vegas this year. But

that's a really great conference. You get a lot of really, it's a great mix between industry and academia, and

I think a lot of productive stuff. Conversations I think come out of that and offer start positions for research

in a collaborative sense, which is what Dan and I were talking about. It's important. And I think that conference

is worth plugging. I want to say it's like May 26 through the 28th. I think it's like three days

in 2026.

Nick Hogan:

And it's called the Gaming and Wagering Conference. Is that right?

Anthony Lucas:

Yeah, that's not right, but it's close. It's close.

Nick Hogan:

I'm not familiar with it. Okay. Okay.

Anthony Lucas:

It's a huge conference. I think there's risk-taking in there somewhere. Gaming and risk-taking, but it's the old Bill Eddington

conference and it's been taken over by UNLV and UNR. And Dr. Bo Bernhard did it for years, but they

have incredible speakers. It's just such, it's so well done. And now I think Brett Abarbanel is doing it, and

she's really talented. She's heading up the IGI right now and she's taken it over and she's... Brett is the

person you want doing anything that's important to you. She's just really good. So yeah, I'm sure she'll do a

great job with it. There's no academic conference that's even close to this. I mean, it's just that good.

Nick Hogan:

Okay.

Anthony Lucas:

But I'm going to be in Tahoe in a couple months to do that, was it EDP?

Nick Hogan:

Yeah. Executive development program for UNR.

Anthony Lucas:

Yeah. And aside from that, I don't know. I got to travel around and give talks, but no need to

plug them.

Nick Hogan:

Okay. Okay. Very good. Very good. Well, gentlemen, I think we've reached the end of the allotted time for today.

I just wanted to thank you both for the great discussion today, Anthony and Dan. It was really, it's lovely.

Always enjoy speaking with both of you. And yeah. Well, thanks so much guys for your time today. Hugely appreciated.

Anthony Lucas:

Well, thank you. Appreciate you having me on.

Dan Cherry:

Thank you both. Have a great holiday.

Nick Hogan:

You too. Thanks.

Anthony Lucas:

All right. Take care.