00:01.115 –> 00:08.103
Anthony, you know, I haven’t had a chance to travel too much out of the country in the last few years, but today I’m feeling like a global citizen.

00:09.425 –> 00:11.207
Well, if you, I got nothing.

00:12.048 –> 00:12.569
I’m Anthony.

00:12.749 –> 00:14.050
Welcome to No Vacancy Lives.

00:14.391 –> 00:15.252
That’s my friend, Glenn.

00:15.432 –> 00:17.595
You’re watching the number one show in hospitality.

00:31.237 –> 00:51.143
everybody thanks for tuning in to no vacancy live that’s anthony melchiorri i’m glenn hausman really appreciate you being here and if you’re not a subscriber like anthony is make sure you text the word hotel to six six eight six six that’s hotel to six six eight six six get our weekly newsletter yeah well we just found out that i’m not a subscriber

00:51.463 –> 00:52.103
Oh, I know.

00:52.724 –> 00:54.785
Yeah, I’m going to have to subscribe you to this.

00:54.805 –> 01:01.408
You’re missing a lot of great content, Anthony, kind of like how you’re going to miss today’s show where we have on Mike Rawson.

01:01.648 –> 01:04.809
He’s the CIO of Citizen M Hotels.

01:04.850 –> 01:06.410
I’m really looking forward to this conversation.

01:06.470 –> 01:08.351
Well, I’m not going to miss the show because I’m on the show.

01:08.431 –> 01:11.233
But yeah, and I had nothing for your upfront.

01:11.273 –> 01:11.613
That was –

01:12.513 –> 01:14.115
Very rarely do you leave me speechless.

01:14.455 –> 01:17.397
That makes me so happy when I’m able to do that, man.

01:17.657 –> 01:21.800
Really, this is what I live for at this point in my life.

01:22.181 –> 01:25.483
I love interviewing our guests, but making you speechless is my goal.

01:26.144 –> 01:26.684
All right, buddy.

01:26.724 –> 01:27.265
Well, you did it.

01:27.325 –> 01:28.065
Congratulations.

01:28.225 –> 01:29.827
Here we go.

01:29.847 –> 01:34.850
Let’s welcome to the show Mike Rawson, CIO of Citizen at Mike.

01:34.890 –> 01:36.532
Thank you so much for joining us today.

01:37.557 –> 01:37.797
Yeah.

01:37.858 –> 01:38.298
Hi, guys.

01:38.318 –> 01:38.959
Nice to meet you.

01:39.019 –> 01:44.047
And hopefully, Glenn, I won’t be too speechless because that won’t contribute much, but great to be on.

01:44.228 –> 01:52.480
Yeah, that would make for not such a good show if we just sat here speechless at each other, particularly for our audio listeners out there.

01:52.981 –> 01:54.842
Mike, thank you so much for being here.

01:55.682 –> 02:00.904
I’ve been a fan of Citizen M here in New York City.

02:01.664 –> 02:04.045
I’ve had a chance to see it and experience it.

02:04.145 –> 02:09.207
Y’all are ahead of a lot of trends that we’re seeing in day-to-day hotels now.

02:09.327 –> 02:13.308
So I just wanted to say that I’m a fan of the company before we get into it.

02:13.448 –> 02:15.649
Mike, where are you coming to us from today?

02:15.709 –> 02:17.410
Where in Europe or UK are you?

02:18.544 –> 02:35.622
Yeah, so I’m based in Amsterdam, so I’m in the Netherlands and originally from New Zealand, hence the strange accent, and came here when I was working for Heineken and that got me to the lovely city of Amsterdam and we’ve been here for seven years and enjoying Europe.

02:36.182 –> 02:42.626
Well, I will tell you, there are a lot of folks that are from New Zealand that are in the hospitality industry in the United States.

02:42.646 –> 02:47.908
We’ve had a lot of a lot of expats from there and from your neighboring country of Australia as well.

02:48.009 –> 02:50.030
So I’m very familiar that accent.

02:50.050 –> 02:50.450
I like it.

02:52.431 –> 02:53.231
I love it.

02:53.972 –> 02:57.294
I have a friend of mine that works with you guys as a third party contractor.

02:57.334 –> 03:01.236
And she’s very she has very high standards.

03:01.336 –> 03:05.138
And she says you are her favorite company and hotel to work with.

03:06.901 –> 03:13.750
That is high praise and the team works hard every day to make that happen.

03:14.050 –> 03:15.432
And we love it.

03:15.472 –> 03:20.459
We love having raving fans and it’s one of the big rewards of the job, to be honest.

03:21.220 –> 03:24.341
One of the things that I like is that you really focused on innovation.

03:24.361 –> 03:31.645
And as I said in the intro, you really brought a lot of concepts to light that we’re seeing now spread throughout the rest of the hospitality industry.

03:32.205 –> 03:40.309
How do you think about innovation and creating a culture of innovation so you can continue to do new things and not just copy what the other folks are doing?

03:42.247 –> 03:43.727
Yeah, it’s a good question, Glenn.

03:43.747 –> 04:03.734
So one of my roles is to really empower the team to, I guess, put them within a framework and wind them up and unleash them so that they all feel that they’re capable of being able to contribute and experiment and ultimately find things that our guests will love.

04:04.214 –> 04:07.915
And the way we do that in real terms is that we

04:10.000 –> 04:12.741
We have very easy mechanisms for doing proof of concepts.

04:14.341 –> 04:19.442
A lot of stuff will die there, but everyone has the right to try stuff.

04:19.742 –> 04:23.663
And then some of that stuff will go on to become a pilot.

04:23.743 –> 04:28.545
So we do it in one hotel, which is easy and low risk.

04:29.105 –> 04:33.546
And then obviously, if it goes really well, then we can take that out to all hotels.

04:34.346 –> 04:41.132
That framework is alive and well, and we have quite a few things running within it.

04:41.212 –> 04:41.432
Yeah.

04:41.512 –> 04:41.732
All right.

04:41.752 –> 04:43.414
Sorry, we lost Anthony there for a second.

04:43.594 –> 04:45.715
He is back right now.

04:45.735 –> 04:46.736
You threw me out, buddy.

04:47.357 –> 04:49.919
I wish.

04:50.019 –> 04:54.443
You know that that is my dream over here, but I would never do that to you.

04:55.964 –> 05:05.170
So, Mike, how are you thinking about some of the new things that are out there today compared to what we’re seeing in the wider hospitality industry?

05:05.190 –> 05:12.214
I guess it’s a long way of saying, oh, what’s out there that’s interesting to you right now when it comes to technology?

05:13.615 –> 05:14.436
Yep.

05:14.816 –> 05:17.638
It’s probably never been a better time to be in tech, to be honest.

05:17.738 –> 05:19.539
It’s a bit of a smorgasbord of technology.

05:21.098 –> 05:35.388
things obviously everyone is rushing around with ai and ai you know i i believe it’s as as big as the internet was whatever that was 10 or 15 years ago when it arrived i think it’s yeah i hate to break it it’s more like a 25 man

05:37.273 –> 05:43.797
Yeah, I’m trying not to, I’m trying carefully not to take us all down the rabbit hole.

05:43.837 –> 05:51.982
Listen, I still remember having to sign on to message boards between my college and my friend’s college in order to be able to talk to each other.

05:52.002 –> 05:53.363
And that was like 1991, right?

05:53.403 –> 05:54.844
So that’s crazy.

05:57.205 –> 05:57.646
All right.

05:57.766 –> 05:58.987
Anthony says he lost sound.

05:59.027 –> 06:00.528
We’ll see what happens there.

06:00.548 –> 06:04.211
So anyway, AI, that’s probably the biggest technology.

06:04.251 –> 06:11.878
But one of the problems that I see with AI, or at least describing AI, it’s really a catch-all phrase for absolutely everything, right, Mike?

06:11.978 –> 06:22.228
And everybody just thinks about, oh, my God, Skynet’s going to be here in about 20 minutes with AI, as opposed to all the myriad uses that you could find to it to –

06:22.848 –> 06:25.690
improve profitability in different sorts of ways.

06:25.750 –> 06:27.792
So how are you thinking about that?

06:29.854 –> 06:37.260
Yeah, so we see, if you think of it as two sort of big opportunities, probably personalization at scale.

06:37.280 –> 06:43.645
So being able to give you the guest all the benefits of that AI just when you need it.

06:44.505 –> 06:46.967
And then probably automation at scale.

06:47.007 –> 06:50.450
So being able to take that drudge work away from

06:51.731 –> 06:57.899
the employees so that we can do more value-added work and ideally get more time with the guests.

06:57.939 –> 07:07.370
So those are the two big tranches of how we want to cluster our work and then we want to start pulling that apart and using it carefully.

07:08.430 –> 07:13.952
There’s a lot of risk in the AI.

07:14.612 –> 07:15.892
We’ve been very careful.

07:15.932 –> 07:26.275
We are already doing quite a bit with it, but we’ve been very careful about adoption, about governance, security and privacy, so all the normal boring stuff.

07:26.775 –> 07:35.957
But it is important with AI, I think, because what I will say, Glen, is if you take it right now and you just chuck it into the company,

07:36.817 –> 08:02.251
um one of the problems with it is that it learns and and grows every single moment um if it starts doing something that you don’t like there’s no traditional way to pull that out so it’s not like you know today if we put something in the hotel and it’s rubbish yeah we can take it back out and tomorrow we learn from that if you put it on and it’s running and it’s busy doing stuff it starts doing stuff that you don’t like yeah you’ve got a problem

08:03.610 –> 08:06.753
Yeah, as a matter of fact, I just heard, I just saw the headline.

08:06.793 –> 08:07.554
I didn’t read the article.

08:07.594 –> 08:09.215
You could tell me if I’m way off base here.

08:09.715 –> 08:19.343
But it said something like chat GPT is now answering basic math questions less effectively than before.

08:19.624 –> 08:23.006
And the reason why I bring that up is because it kind of gets to what you’re saying.

08:23.387 –> 08:31.774
If it gets some sort of erroneous information, it can’t distinguish between what’s real in terms of factual information and reality and not.

08:32.094 –> 08:53.676
information or false information is that correct yeah i you know if it picks up on the fact that most people don’t like maths um then it’s probably going to adjust accordingly and it’s probably already still going to be a lot better than my maths but all of the all of the social inputs that you that that come from using it form part of

08:54.256 –> 09:08.552
the output so if you get a lot of people who think math is rubbish I’m sure that will affect the way that it operates and difficult to change it so it’s like at the moment if it starts doing that what then what because you know

09:09.513 –> 09:12.635
The thing has whatever it is, six trillion triggers.

09:12.815 –> 09:15.376
So it’s not that easy to change, I don’t think.

09:15.956 –> 09:17.156
All right.

09:17.176 –> 09:18.637
So that’s on the broad scale.

09:18.657 –> 09:27.561
But if you have individual programs that are geared towards very specific purposes, such as let’s take sustainability.

09:28.141 –> 09:44.937
If you could find ways to utilize AI in order to maximize sustainability and leverage your resources in a better way, that sounds like it could be more successful than this big umbrella type of thing that’s kind of everywhere.

09:45.237 –> 09:46.919
Is that an accurate statement?

09:48.433 –> 09:53.357
Yeah, so the use case would be that, and I think you’re right.

09:53.417 –> 09:57.460
There’s a huge guest focus on ESG these days.

09:57.500 –> 10:06.287
We’ve never seen more curiosity and, for example, not getting business because we don’t have the ESG credentials.

10:06.367 –> 10:09.850
So it’s now basically it’s here to stay.

10:11.050 –> 10:15.493
Yeah, we would obviously love to put it back to the guest.

10:16.033 –> 10:22.497
One of the things we’re aiming to deliver is that when you finish your stay, we will show you how much power you used.

10:22.597 –> 10:26.339
We will show you how much water you used for your particular stay.

10:27.199 –> 10:30.384
And we will probably need AI to help do that.

10:30.565 –> 10:33.489
So for me, that’s an enabler.

10:33.649 –> 10:35.813
It gives us the ability to do it per guest.

10:35.933 –> 10:41.822
And I can even probably start tracking it so that if you stay with us two or three times, I can start seeing a few

10:42.563 –> 11:05.462
if you’re efficient or not efficient compared to the other guests yep that’s pretty cool and you you could like gamify it right and if you use just a hypothetical example if you use less than x threshold maybe you get a free drink in the bar or something like that if you’re one of the top 10 10 guests on esg for the week we would give you you know a free drink or um

11:06.317 –> 11:09.180
maybe a cool little badge that, you know, you’re the ESG guy.

11:10.080 –> 11:18.187
So AI will allow us to do that kind of personalization and gamification at scale, right?

11:18.207 –> 11:21.711
Because we have to do it per guest and it has to work.

11:21.831 –> 11:32.380
So you’re putting a pretty big load on this thing and therefore it has to be mature and it has to be scalable and all the other tech stuff to make it work.

11:32.600 –> 11:32.720
Right.

11:32.740 –> 11:33.180
That makes sense.

11:33.220 –> 11:35.161
So Anthony had a little tech problem.

11:35.181 –> 11:35.781
He’s back now.

11:35.821 –> 11:42.742
And Anthony, we’ve been discussing AI in general and how it could be applied to specific programs, in this case, sustainability.

11:43.242 –> 11:50.004
And what’s interesting, Mike, is how you said you’re going to be looking at being able to reduce things through AI.

11:50.044 –> 12:01.506
But how are you thinking about sustainability now with technology in the more traditional sorts of ways as we get to the point where you can utilize AI in a more effective and efficient manner?

12:03.550 –> 12:13.572
Yeah, so we’ve spent the last few years basically doing a lot of foundational work and getting all of the data into our data warehouse and data lake.

12:13.653 –> 12:16.673
And basically, yeah, we love data.

12:16.973 –> 12:21.074
So even recently, we’ve got all the elevator data.

12:21.534 –> 12:23.055
Why would we have the elevator data?

12:24.636 –> 12:26.036
That’s also part of ESG.

12:26.096 –> 12:29.418
So if we can help optimize how the thing is basically working.

12:29.799 –> 12:29.999
Right.

12:30.199 –> 12:37.023
Also, you as the guest, you know, if you’re using our app, if we have the data, we can tell you and we know it’s your checkout day.

12:37.423 –> 12:40.725
We can tell you the average wait time for the elevator before you leave your room.

12:40.825 –> 12:40.965
So.

12:43.392 –> 12:53.175
Yeah, I see your eyes bugging because, Mike, in New York City, right, you’ve got these buildings that are super tall and they’ve only had two elevator bays, right?

12:53.335 –> 12:54.115
So you’re waiting.

12:54.135 –> 12:58.796
You could wait forever for that, especially at 730 or 8 o’clock in the morning.

12:59.297 –> 12:59.617
Anthony?

13:00.057 –> 13:07.679
I was talking to somebody yesterday about a project in Brooklyn we’re looking at, and he said, well, there’s only two elevators and it’s 43 floors.

13:08.379 –> 13:09.800
Something like that problem.

13:09.820 –> 13:11.120
It’s like an Uber for elevators.

13:13.513 –> 13:20.018
Well, at least you know that you don’t have to leave your room for the next 10 minutes and you’re not standing there.

13:20.078 –> 13:31.026
So I can’t help the fact that it’s 10 minutes, but I can at least stop you from actually leaving the comfort of your room and having to stand and wait and wonder how long it’s going to be.

13:31.366 –> 13:44.333
I’ll tell you what, and that, to compare it to New York City subways, they did surveys of subways that by having the time displayed by how long it’ll come, it kind of like relaxes everybody.

13:44.353 –> 13:46.114
It makes people less stressed.

13:46.435 –> 13:52.178
And to your point, Mike, all of this would improve that customer service and probably the brand likability.

13:53.840 –> 14:06.478
yeah so we try and we’re trying to make this so that it’s not just like one element of it we’re trying to make sure that it’s cohesive and that that the you know we need to get that value to the guest and so

14:09.022 –> 14:38.742
yeah we have a lot of building stuff we have bms systems and we we have a lot of building data but all of the data for us is just an enabler to to bring it to either the guest or the employee to either add value or to remove friction and part of my job is to be able to talk business to the business and tech to tech so um uh the more of that stuff we have these days the the to be honest the easier my job is in a way yeah

14:39.372 –> 14:41.514
Anthony, do you want to jump in?

14:41.694 –> 14:55.904
What do you think, not something you’re working on right now, but potentially something that you see coming up in the next five years or next few years that you really want to tackle, but maybe AI is not quite there yet?

14:57.935 –> 14:58.856
Yeah, so we’re working.

14:59.636 –> 15:03.699
So I guess the longer term plays, we see very much blockchain.

15:03.779 –> 15:06.802
So blockchain, and I’m being careful not to say crypto here.

15:06.822 –> 15:14.407
So just to be really- Yeah, everybody has become to think that blockchain just means crypto and NFTs, but it’s a lot more.

15:14.467 –> 15:18.170
It’s fundamentally could change everything about how transactions are done.

15:18.270 –> 15:19.351
Sorry, Mike, please continue.

15:19.611 –> 15:21.573
Yeah, I think it’s an important distinction.

15:21.593 –> 15:24.356
And obviously, we don’t want the SEC dropping in on this.

15:24.496 –> 15:25.737
I’m not talking about crypto.

15:25.817 –> 15:27.178
I’m only talking about blockchain.

15:27.198 –> 15:31.543
And blockchain is basically the technology to have a public ledger.

15:32.343 –> 15:36.487
We see that’s really got a lot of potential and it’s got a lot of potential for us to

15:37.568 –> 15:54.375
um help the guests basically have an easier easier journey through the system you guys have to you think about the planes trains automobiles and hotels that you have to go through we think blockchain would be a very elegant way to solve that and we also see very much that

15:55.175 –> 16:02.660
you know, your identity, your digital identity will be on your phone within the next, to be honest, you guys have already got it on your phone in the States.

16:02.740 –> 16:04.781
Apple’s already got your driver’s license on you.

16:04.821 –> 16:12.987
So the combination of the blockchain, the digital identity, I think it’s going to make things very difficult for the OTAs.

16:13.127 –> 16:22.493
And I think it’s going to make things a lot easier for guests and consumers to share their data and for us to easily help you.

16:23.741 –> 16:26.444
Give us an example of an application.

16:26.504 –> 16:31.890
Like you were talking about the trains and getting there, and you were talking about third parties.

16:31.930 –> 16:34.372
So tell me how that would be applicable.

16:35.894 –> 16:36.234
Yeah.

16:36.314 –> 16:41.780
So let’s say, like normal, you book your airfares and you book your accommodation.

16:43.841 –> 17:00.734
Later on, when we’ve got a bit more magic in the system, the ability will be there for you to firstly, even at the airport, you won’t have to show your passport every single stage of the journey.

17:01.114 –> 17:01.435
Why?

17:01.495 –> 17:03.076
Because basically already you’ll have…

17:03.636 –> 17:14.644
checked in once and effectively it will have taken those set of credentials with you so that you don’t have to show it and you already see now when you’re boarding, it just boards with your face.

17:14.884 –> 17:22.129
So your face will start to become basically your passport and you’ll have less times where you have to drag it out and do those things.

17:23.090 –> 17:26.552
Let’s say you board the plane with your face, it’s not difficult

17:28.994 –> 17:36.522
for you when you board the plane with your face to send us a notification that you’re coming to the hotel.

17:36.542 –> 17:42.728
So we would love to know that you boarded the plane because we’ve got a reservation for you.

17:42.808 –> 17:46.672
So good chance you’re coming to us because you’re going to land in New York shortly.

17:47.395 –> 17:50.376
Same time when you board the plane, you probably want an Uber.

17:50.836 –> 18:00.260
So all those downstream services will be able to be triggered in a much more, let’s say, public and transparent way.

18:00.360 –> 18:00.760
Right.

18:01.100 –> 18:01.900
Interesting, Mike.

18:01.960 –> 18:03.621
I think we’re starting to creep that way.

18:04.261 –> 18:13.625
Speaking about airports specifically, I noticed coming off my flight from Las Vegas at JFK, people boarding international flights were all standing up to do facial recognition.

18:14.105 –> 18:16.667
Now for that, it’s no longer optional.

18:17.267 –> 18:25.032
Secondly, I believe it’s, I’m forgetting which airport it is, but now Lyft and Uber are starting to implement a plan.

18:25.052 –> 18:26.313
You could order a car anytime.

18:26.594 –> 18:32.277
And when you start to get near the pickup zone, then it’ll get the car for you.

18:32.558 –> 18:36.200
So little, we’re moving closer, removing all this friction that you’re talking about.

18:37.358 –> 18:40.781
Yeah, so I think, you know, and then your luggage, for example.

18:40.841 –> 18:52.750
So we believe that you can have a tag on your luggage that’ll be good for all hotels and all airlines and make it so much easier to check things in and out.

18:53.370 –> 18:58.614
So I just have one tag that’s got some sort of RFID chip or a code or something like that.

18:58.654 –> 19:00.616
And they know this is Glenn’s bag.

19:01.456 –> 19:05.359
Sorry, Delta, he’s flying with American today, but we’re still going to get it to him.

19:05.620 –> 19:06.180
That kind of thing.

19:06.860 –> 19:19.549
So every time, instead of getting that barcode that’s a different barcode and a different everything from every airline for every trip, your signature for your luggage would be the same and basically belong to you.

19:19.689 –> 19:22.731
So to answer your question, we’re very…

19:23.451 –> 19:38.986
confident that the convergence of digital identity and the ability to have a whole journey that’s basically triggered from, I don’t know, boarding the plane or boarding the ship or boarding whatever it is, we think that’s going to arrive.

19:39.006 –> 19:40.528
And we want to be part of that.

19:41.068 –> 19:45.792
We want to be part of an easy part of that for you as a guest.

19:45.992 –> 19:59.361
The thing is, when you are ready to retire and done, you’re going to have the ability to know what it was like not even to have an iPhone and then the ability to basically check into everywhere with your face.

20:01.123 –> 20:02.684
You have a great job at a great time.

20:04.059 –> 20:07.503
Yeah, I’ve never been, you know, to be honest, we’re drowning in tech.

20:07.563 –> 20:16.853
We just need to be very careful about selecting, synthesizing the right stuff and understanding how, you know, what the best, what we should focus on.

20:16.893 –> 20:20.616
It’s very easy to get distracted, I think, these days with all of the tech.

20:20.657 –> 20:25.602
But I’d rather have that problem than basically not have the tech in front of us.

20:26.102 –> 20:30.064
Yeah, we had so many years with no meaningful changes in technology.

20:30.084 –> 20:37.488
I was watching 60 Minutes this week, and they were producing quantum computers, which I’m sure you know a lot more about than I do.

20:37.808 –> 20:41.370
But it sounds great, and it also sounds a little scary.

20:43.444 –> 20:44.485
Yeah, it’s pretty terrifying.

20:45.265 –> 20:53.010
It’s terrifying because the AI convergence, when AI becomes much smarter than us, is probably due to quantum computing.

20:53.310 –> 20:56.932
And there’s a lot of unknowns with it, right?

20:57.252 –> 21:09.280
But I’m also pretty confident that it’s not as Terminator 2 specified as everyone thinks it is.

21:09.420 –> 21:11.081
It’s just going to be like Terminator 1.

21:11.841 –> 21:35.989
so not yeah probably just this one glenn if you were in charge of it like you’d be somewhere in the woods with a little button and you’d be like let’s see if what happens if i push this button but mike what i’d like to know is i want to talk a little bit about uh the role of technology and data collection right improving customer satisfaction there

21:36.429 –> 21:50.735
One of the problems that we’ve faced throughout our entire careers is it seems like everybody collects information, but isn’t able to utilize that in a meaningful way to make me more loyal or have a better experience, et cetera, et cetera.

21:51.015 –> 21:55.738
How are you thinking about that conundrum and making it much more of a reality for people on a day-to-day basis?

21:57.438 –> 22:05.742
Yeah, so I think coming from outside of hospitality, it took me a while to get my head around a few of the challenges that were

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seem to be driving everybody crazy.

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And the data is fragmented and the data can be quite dirty.

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And hospitality has a special challenge that the OTAs, the booking.coms of these worlds also want to make that traveler data as anonymous as possible, right?

22:25.412 –> 22:34.294
So what we did is we looked at all those requirements and we’ve come up, we needed a customer data platform.

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So that’s different from a CRM.

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Customer data platform will clean data, will produce a golden profile.

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So from the three or four different fragmented profiles that you give us, your Gmail address and your Expedia account and stuff, we will figure out who you really are.

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And then obviously a single customer view.

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So the ability to see what you’re up to in total.

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So it is challenging and the data is dirty.

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And the customer data platform is quite a big piece of kit.

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So it’s taken us a while to get into a position where, in fact, I was just showing someone today.

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So starting literally a few weeks ago, all our ambassadors can see our champions report for tomorrow.

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It will show them that this champion is arriving.

23:30.436 –> 23:41.225
what hotel they stayed at previously, what room they’re in, and basically what your favorite drink is, and what your temperature, your average temperature of your room was in that previous day.

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So that allows us to be smart with the room.

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So talking about ESG.

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Finally.

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We can be smart with the room allocation.

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The guys can get a cold room.

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The girls can get a hot room to be an old dinosaur.

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And then maybe you can sell that technology to the big brands because they have all the resources and they’re not doing any of this.

24:01.755 –> 24:03.277
That’s what’s great about being small.

24:04.298 –> 24:04.558
Yeah.

24:04.678 –> 24:17.829
What really surprises me, Mike, I’m not going to throw anybody under the bus here, but I stay very frequently with a lot of major brands and they’re very aware of every penny I’m spending in the hotel and where I am and all of that.

24:17.969 –> 24:26.555
Yet, I still haven’t seen any actionable insights on their part come my way, either through the booking mechanism or anything happening on site.

24:26.635 –> 24:28.537
I still have to ask for my free bottle of water.

24:29.423 –> 24:30.244
It’s annoying, right?

24:31.465 –> 24:33.226
So I think that there’s two answers to that.

24:33.266 –> 24:39.051
So one of the reasons I love Citizen M is that we effectively, we’re property developers.

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So we own 90% of the buildings that we have, and that makes us an unusual hotel company.

24:44.875 –> 24:46.717
And secondly, you guys know,

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that there’s a very fragmented ownership model, right?

24:49.966 –> 25:00.953
So you’ve got an owner, you’ve got an operator, you’ve got a franchisor and all those guys, they have their own budgets and they have their own stakeholders and they have their own fricking everything.

25:01.193 –> 25:03.415
And who owns that information?

25:03.895 –> 25:23.166
that’s curious i’m always curious about that because if uh it is a great question and i have to say i have a soft brand hotel right and i booked somebody in does that mean somebody with a hard brand on you know halfway across the country has access to that data i don’t know i’m just asking questions but it seems like a bigger challenge than what you all have to face

25:24.184 –> 25:40.925
yeah so we have a and because we have a centralized model um we turn something on and like i said that report that we’ve just enabled everyone gets it um and that’s a huge advantage for us in terms of how we bring our usps to the market i guess and how we

25:41.545 –> 25:44.166
really ensure that the guests get the same experience.

25:44.186 –> 25:51.329
So I can guarantee you, whether you’re in Glasgow or whether you’re in New York, you’re going to get the same experience.

25:51.569 –> 25:55.431
And I realize now that’s a good trick in hospitality.

25:56.111 –> 26:03.534
I don’t know if you can share this, but what percentage of your guests, if they use New York, they use your other cities?

26:03.834 –> 26:05.375
Do you track that information?

26:07.734 –> 26:29.950
yeah you mean so so how international is the is the new york that’s a good question um i know it roughly so i know for example that interestingly there is a big distinction between the new york times square hotel and bowery and bowery tends to get a huge amount of u.s travelers so your u.s citizens

26:30.911 –> 26:35.272
basically don’t want to be a tourist in Times Square and they all love being in Bowery.

26:35.772 –> 26:38.612
Times Square gets a huge amount of tourists.

26:38.672 –> 26:41.373
So we get a ton of international travelers.

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And I’m going to say it’s at least twice as many in Times Square in terms of international travelers compared to Bowery, which sometimes is, I don’t know, I looked and sometimes 70, 80% just US travelers.

26:56.356 –> 26:56.976
That makes sense.

26:57.876 –> 27:09.101
Are you able to determine that I’m an American and going to Zurich and that way I may not speak the country’s native language and have someone aware of that, maybe?

27:09.121 –> 27:14.364
Yeah, so if you’ve basically…

27:15.262 –> 27:32.498
become on got into a champion segment so you’ve done enough business with us then then um we do have enough information to be so if you go to taipei for example right we would be able to understand that that’s not going to be your native language so um

27:33.519 –> 27:57.852
but i’ll also say that you know right now the you know the ambassadors are our secret source um they’re amazing and and the way that we hire them is i think kind of special so they will also do a lot of that brokering right away and and um and help you out just because they’ll know sure anthony’s got questions on that yeah right the way you hire the ambassadors

27:59.798 –> 28:08.888
Yeah, so I think, I believe that, you know, very much that the ambassadors are our secret source, more so than the tech and all the other stuff.

28:09.228 –> 28:11.211
And why do I believe that?

28:11.271 –> 28:16.557
Because the casting process, so how we hire our ambassadors, I think is unique.

28:17.497 –> 28:19.618
and how we go about doing that.

28:19.638 –> 28:35.086
And that’s courtesy of Michael Levy, who was basically a bit of a legend in the hospitality area and how he went about that process of making sure we get the ambassadors who are our secret sources is a really interesting process.

28:35.126 –> 28:45.752
And so the casting day is pretty much like nothing you’ve ever seen if you’re thinking about job interviews and a traditional hiring process for

28:47.193 –> 28:55.439
So not to give too much away on the secret sauce, but we have casting days and they really are quite different.

28:55.780 –> 29:08.049
And the net result is you get some ambassadors who I guarantee you wouldn’t normally fit into a hotel ambassador profile and end up being just fantastic at looking after our guests.

29:09.150 –> 29:13.594
Well, you know what I’m going to do is I’m going to apply for a job just so I can find out how you do it.

29:14.475 –> 29:20.917
What we really encourage on those casting days is, yeah, we don’t care about your background.

29:21.137 –> 29:22.858
We care about your attitude.

29:23.178 –> 29:32.221
And the casting days are there to basically show that attitude and allow us to see it and help select it.

29:33.973 –> 29:36.294
Yeah, and we’ve said that a thousand times before.

29:36.374 –> 29:40.295
You can train skills, but you can’t train attitude.

29:40.315 –> 29:42.055
I’ll probably get shot up by telling more.

29:42.415 –> 29:46.136
Hey, listen, Mike, I’m willing to risk it, so feel free to share.

29:46.156 –> 29:47.296
I’m just kidding.

29:47.996 –> 29:48.957
We’re not going to do that to you.

29:49.877 –> 29:56.318
All right, it looks like your internet starts to have a little trouble being that you’re an ocean away over there.

29:56.358 –> 29:58.779
How about some good final thoughts for us, and then we’ll let you get out of here.

30:04.022 –> 30:05.764
Yeah, delaying the internet.

30:05.944 –> 30:06.845
He’s out of here already.

30:07.385 –> 30:08.326
No, no, he’s not.

30:08.346 –> 30:09.007
There he goes.

30:09.027 –> 30:09.527
He’s catching up.

30:11.329 –> 30:13.792
Final thoughts are we’re really well.

30:14.512 –> 30:15.333
Oh, am I gone?

30:16.234 –> 30:16.895
Nope, you’re there.

30:17.295 –> 30:18.396
See, your internet’s slow.

30:22.880 –> 30:24.622
Yep.

30:24.742 –> 30:26.043
I’m just checking the connection.

30:26.064 –> 30:28.286
But anyway, sorry about that.

30:30.163 –> 30:30.784
No, it’s all good.

30:31.064 –> 30:31.925
It’s just your final thoughts.

30:31.945 –> 30:34.388
I wouldn’t believe it, but we are actually on a fiber connection.

30:35.970 –> 30:37.051
Yeah, well, I mean.

30:37.332 –> 30:38.593
Final thoughts.

30:38.613 –> 30:40.536
Yeah, we look forward to continuing.

30:45.785 –> 30:48.085
We look forward to growing massively in the US.

30:48.206 –> 30:57.207
It’s been really well accepted by all our guests and it’s a really exciting time to be in hospitality and have the Citizen M brand.

30:57.427 –> 31:00.108
So hopefully we’ll see you guys at a hotel soon.

31:01.468 –> 31:02.728
So how many hotels do you have?

31:04.848 –> 31:05.769
We have 33.

31:06.009 –> 31:13.570
We have 15 in the US and there’s at least another three or four, five, six in the pipeline.

31:15.066 –> 31:43.679
so um if you went up in boston are you thinking about the franchise model at all or no um we i think uh we kind of got our yeah we gave it a go i would that would be the way to talk about it and probably asia we gave it a bit of a go and it didn’t work out very well and i believe that we will stay very much an ownership model and the only time that that would change is if

31:44.965 –> 31:53.748
it’s just too complex or we just can’t do a great location because we can’t own the land, then I believe that we would be flexible.

31:55.609 –> 32:10.454
So companies like yours, I’d imagine, and I’m probably asking a question you can’t answer, that you get looked at by the big brands pretty often to potentially purchase.

32:10.474 –> 32:10.534
Yes.

32:12.233 –> 32:17.977
Yeah, so we have two main shareholders, which is basically the Dutch pension scheme and the Singapore pension scheme.

32:17.997 –> 32:21.760
So you couldn’t probably do any better than that if you tried.

32:21.880 –> 32:27.624
And you kind of have to because a hotel is going to be, I don’t know, 100 million for hotels.

32:27.725 –> 32:30.387
So it’s a very capital intensive exercise.

32:30.447 –> 32:34.009
So you’re not going to have just any partner for that.

32:34.069 –> 32:38.453
You’ve got to have someone who actually likes throwing big chunks of money around.

32:39.713 –> 32:54.629
yeah i don’t know if um if hotels traditional hotel chains would actually be that interested in us because we’re so we’re so capital intensive so um it might scare them to be honest so um uh yeah that makes us a bit a bit different but

32:56.469 –> 33:05.911
we know that we do things differently and we know that some of the stuff we’ve been up to has been very closely monitored, let’s put it that way.

33:05.951 –> 33:16.754
When we did the recent subscription program for the CitizenM subscription monthly, that really has been, and it’s already starting to be copied and we expected that.

33:16.994 –> 33:22.075
So lots of people were checking that out and we like that.

33:22.175 –> 33:22.495
It’s good.

33:22.755 –> 33:23.415
I think it’s healthy.

33:24.497 –> 33:24.737
Great.

33:25.517 –> 33:25.798
Awesome.

33:26.318 –> 33:28.058
Mike Rawson, thank you so much for being here.

33:28.198 –> 33:29.119
We really appreciate it.

33:29.879 –> 33:31.000
Guys, it’s been a pleasure.

33:31.060 –> 33:32.420
Sorry about the internet.

33:32.460 –> 33:34.161
Clearly, I failed my own test.

33:34.201 –> 33:36.342
No, it’s not your fault.

33:36.362 –> 33:38.402
Something definitely happened in between.

33:38.422 –> 33:40.783
I’m telling you, you’re thousands of miles away, man.

33:40.823 –> 33:41.884
It’s all good.

33:41.904 –> 33:43.044
Thanks so much.

33:43.124 –> 33:44.745
We really, really appreciate you.

33:45.085 –> 33:50.967
And hey, we really appreciate all of you guys being here week after week, year after year with us.

33:51.127 –> 33:51.788
And be sure to…

33:52.748 –> 33:54.989
Follow us if you’re not already.

33:55.009 –> 33:57.171
Follow us on Facebook, on LinkedIn.

33:57.191 –> 34:00.593
NoVacancyNews.com is where you can get all of the back shows.

34:00.653 –> 34:05.676
And if you happen to be watching us, why not download our shows wherever you get your podcasts?

34:05.756 –> 34:08.598
Remember, everybody, we’ve got one live, so blaze on, Ed.

34:10.239 –> 34:10.999
Be kind to yourself.

34:11.199 –> 34:12.960
We’ll be back with new shows all next week.

34:13.180 –> 34:13.661
See you later.

34:13.781 –> 34:13.921
Bye.

34:13.941 –> 34:15.422
Great technical issues today.