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Leveraging Hotel Data with a Customer Data Platform | with Dr. Rik van Leeuwen · GAIN Momentum

• GAIN Momentum episode #85: Leveraging Hotel Data with a Customer Data Platform | with Dr. Rik van Leeuwen === Adam Mogelonsky: Welcome to the GAIN Momentum Podcast, focusing on timeless lessons from senior leaders in hospitality, travel, food service, and technology. Today we're focusing on technology with our guest here, who is Dr. Rik van Leeuwen, Head of Data at Ireckonu, Rick. How's it? Rik Van Leeuwen.mp4: Doing fantastic. Adam Mogelonsky: We're recording here, morning for me, afternoon for you on October 3rd. So Rick, start off elevator pitch. What is Ireckonu? Rik Van Leeuwen.mp4: Ireckonu is a middleware provider. Um, and in other terms is basically an integration hub to collect all data points around guests, basically from reservations to preferences, data consent. Survey venues such as Restaurant and Spa, and we basically create a 360 profile of the guests such that you can market towards them or serve them better when they are coming on property. Adam Mogelonsky: Cool. Let's start off here. You mentioned a term called middleware. Can you describe what that term actually is? Mm-hmm. Rik Van Leeuwen.mp4: Um, a middleware is a. A piece of software basically that is sitting in the middle of your IT infrastructure to orchestrate messages from one system to another. Um, and what we are doing predominantly, um, is with two-way integrations. So whatever is up, uh, updated within a PMS system, for example, will go towards a loyalty system. Um, and whatever comes from a loyalty system, we update them in the spa system, for example. And in the PMS again, so you can basically see it as a big postal office, basically that is taking messages from one system to another. Adam Mogelonsky: And that middleware is essentially replacing a lot of the one-to-one coating that hotels would have to do to set up these one way or two integrations. Rik Van Leeuwen.mp4: Yes, indeed, indeed. So, um, we are not only relaying messages from one system to another, we are also storing them within the middleware. Adam Mogelonsky: Yep. Okay. And you're storing them in something called a customer data platform or that sits above the actual warehouse, is that correct? Rik Van Leeuwen.mp4: Yes, indeed. That's basically a layer on top of middleware. So you can see a middleware as, um, really a foundational piece of software, um, where you can build other pieces of software on top of it that there are. Uh, able to make use of it. And CDPA customer data platform is, um, is one of those pieces of software that you can build on top of that. Adam Mogelonsky: So we have, essentially, we have all the various parts of a hotel that are collecting data or interacting as endpoints with the guest. It's going in to the middleware, which provides some form of no code way to bring all this data in. One other part of this is also the data cleansing or structuring Does, uh, I reckon you do that. And what is that? Rik Van Leeuwen.mp4: Yeah. Um, there are several ways that you are able to clean and check your data, right? So, um. Maybe a very basic example is already does an emoter, actually has an a sign in it. And do phone numbers have country codes in front of it, yes or no? Um, you can also go one layer deeper and checking if the domain of, um, the email. Um, actually exists, right? Um, and with this, basically we, uh, we are checking on a continuous basis because we are sitting in the middle of all these data points that are coming by. We are checking on the continuous basis, um, the quality of your data and, uh, and as well of how many fields are actually being filled in of your entire customer base. Adam Mogelonsky: It's very interesting. So we have middleware, CDP, and now one layer above would be the CRM. Is that correct? Rik Van Leeuwen.mp4: Yeah, you can, you can see as a CDP, um, definitely has overlap with CRM functionalities. Indeed, Adam Mogelonsky: Yeah. Okay. And one of the one, one of the features that I know hoteliers really care about is something called identity resolution. Can you unpack what that is and why it's so important for hotels? Rik Van Leeuwen.mp4: So basically identity resolution is, is for guests eventually to have like one way of logging in, right? And you can eventually log in, um, on an app, for example, that you might have in your IT infrastructure or on the website for example. Um, and basically what we are able to do here is providing a way of. For a guest to log in to see their own data as well. Because as a hotel company, you're collecting quite some data, um, for a guest to have a unified way of logging in, um, and be able to access their own data and the IT infrastructure. Adam Mogelonsky: Okay. And. There's obviously a lot of processes that underpin identity resolution because you're trying to automate a process of matching and merging profiles. Is that correct in terms of what I'm saying? Rik Van Leeuwen.mp4: Yes, indeed. So when you collect indeed, like quite a number of profiles in, in. In such an ecosystem, basically you can continuously check if there are any duplicates there as well. Right? Um, most of the systems within hospitality do have some kind of match merch functionality, um, on first name, last name, and email. Um, but usually what you do not see really happening is that it does, that it is cascaded back into the ecosystem and that's where the two-way connectivity is such an important piece of middleware basically. So. You can only do match emerging basically in one place of your ecosystem and let that relay to all the other systems. And you can do it of course, with first name, last name, email, right? That's the most common use case. Um, but what you can do as well is you can add additional ruling on top of that to see if these, if these profiles might be the same. You can think of, for example, for net keying. So the way that you are. that you are saying a name, my name for example. Rick. You can spell it with a C or without a C. Right. And a front desk employee sometimes does not know if it is with a C or without a C or assumes it is something like that. Frenetically speaking, it is the same. It's not a 100% match, but if then as well the phone number is the same or the address is the same, right? Then you would still merge these profiles and then cascade that back to all the other systems where this profile exists as well. Mm-hmm. Adam Mogelonsky: And by cascade it back, do you mean overwrite the data? Like if we have, uh, my last name is Mogelonsky, spelled with a y and oftentimes. I'll see on my folio that it's sky with an I. So that means that I probably have within some systems, two different profiles. Adam Mogelonsky Y, Adam Mogelonsky I. And are you saying that your middleware can resolve the identity within it from those two profiles and then send that back to the pmms to overwrite and collapse those within the pmms? Rik Van Leeuwen.mp4: Um, it does depend on the functionality of the vendor, um, that we are, um, connected to. But if we do have the possibility to delete, for example, a profile, then we will do that. Or sometimes we also have the possibility to actually perform a merch event in the other system, basically. So it depends a little bit from, from every system, uh, what, what the possibilities are. But indeed, eventually what you would like to see, that there is only one A in the PMS indeed. Adam Mogelonsky: Right. So we have the basics covered for what I reckon you does, and I mean the basics. There's obviously a lot more. Let's take a step back and look at the why and the why behind having a product that's so powerful where you can store data in one place. You can clean that data and then you can act upon it. What is the why behind why hoteliers should look for recruiting A CDP and a middle middleware underneath that, Rik Van Leeuwen.mp4: I think what we ultimately want to do within hospitality is to provide an experience, right? An experience, not just on one touch point. but Basically over the entire timeline that you are interacting with your guests, and if we are not recognizing our guests that are coming back and providing value or showing loyalty towards the brand, it's not worth much at some moment in time if you're coming back into the hotel for the fifth time. and Still the question is being asked, is this your first time here? Or that you can actually say, welcome back. Those small details basically, uh, especially in luxury, um, is being appreciated. And that's what's actually building a relationship with the client, uh, or with the guest here, guest in this case, um, instead of looking at it just from a transactional point of view. So a lot of hotels really want to make this. Switch from having a transactional relationship with the guests really towards a relationship basically. Adam Mogelonsky: Okay. That makes a hundred percent, um, sense to me. Um, I'm wondering if we could now color this with some visual examples and. Essentially, the word that's being thrown out around a lot now is personalizations. And I'm wondering, could you describe one or two clear use cases that A CDP with a middleware underneath that can help hotels to act upon this idea of personalizations to help enhance the experiences of guests that are returning to a hotel? Rik Van Leeuwen.mp4: Definitely, um, maybe we can take preferences as an example here, right? So imagine that there are two atoms in the PMS system. You first stayed in London, for example, and your last name is spelled with a Y, and then you're staying in New York. Um, and your last name is spelled with an I. If you've mentioned already that you would like to have a newspaper in the morning and you would like to have additional hangers, um, you are expecting, uh, or you're hoping I think, uh, that those preferences are eventually being carried over from one property to another. Um. That the shared within the hotel chain itself, right? And therefore you do not have to go back towards the front desk or towards the con shares and asking, Hey, can you please provide me a newspaper in the morning? Um, and that's already being given then the next time that you are actually coming towards that particular hotel brand, right? So that's really on property that you are caring about those particular details. Looking at more marketing efforts. For example, if you have seen that Adam is coming to New York and to London, for example, you can take these two stays into account to maybe recommend another hotel that is also close towards the experiences that you had within New York or in London. So you can, for example, as well take those preferences into account and cascade that towards a emo marketing effort here. Okay. Maybe you would also like Rome, because we've seen from all the guests that are also staying in New York and into London, that they also are likely to travel towards Rome, for example, and therefore you would not recommend the same hotel. And by taking into account the timeline and all the data points. Adam from Preferences to Sales to spend to all these, all these details, you're able actually to really understand what the guest wants and X upon that as a marketeer, for example. Adam Mogelonsky: And you mentioned two very specific things in your answer there, you mentioned that the person. Likes a newspaper, uh, in the morning, and then they like extra, um, extra hangers. I guess they're, they have suits and stuff like that. Those are very specific coloring points. And traditionally those would just be, uh, codified as something in the comments field, on a guest profile. And then it would come down to the actual hotel teams to brute force and read through the guest dossiers. Then act upon it during a morning lineup and express that when they're getting the rooms ready. How does I reckon you or a modern CDP slash CRM allow, uh, an, an enterprise level hotel to properly mark those off as different fields? That way we can act upon all those personalizations, but also not have them just be in the comments. Rik Van Leeuwen.mp4: Indeed. So comments can either be on a profile level or on a reservation level. Right? Um, usually the preferences that we just talked about are saved on a profile level and then being carried over from one property to another. and with this is there, you can sync this up with the property management system as well, right? What we can do as well is within the orchestration, if you know that this guest, um, is coming to a particular room, you can already create tickets basically for your housekeeping to mention eventually. Okay. Well, when, whenever you're preparing the room for, for Adam, for example, ensure that this is also being added towards the room, so we are able to take the preferences as well. And integrated with housekeeping to ensure that when this guest is coming towards this particular hotel room, so with the room numbered in there as well, we can already prepare the room in such a way that the, that this guest is expecting it or, um, or taking into account the preferences. Adam Mogelonsky: Wow, that's, uh, that's, that's very, uh, very powerful, right. To have that go directly into a, uh, any form of ticketing or, um, housekeeping or workforce optimization system. Um, I'm wondering, do you have another example to drill that home in terms of how we can actually help, uh, hotels with. Because that this to me represents the holy grail of what we're going after with personalization in 2020 5, 26, remembering preferences and actually acting upon them. How can you describe how the middleware can connect that directly into a service optimization system and from there, create these? Great experiences that people are expecting now, not only at luxury and ultra luxury, but now at upscale and uh, and soon midscale, I guess. Rik Van Leeuwen.mp4: and that also comes down to eventually have two way integrations, right? So it's not only creating the tickets eventually that, particular personnel is preparing the guest stay, but it's also understanding what is coming back eventually from personnel and property if they actually liked or disliked it. Right? Um, it's also quite important that. Personnel does not need to go into 15 different screens to update all these preferences and these kind of things, right? So we have to keep it in mind. They are so used to the property management, uh, uh, system screen that you would like to avoid basically that they need to have two or three other screens open eventually if something is changing. Um, and that's what we are taking care of, right? So we keep the ecosystem in sync with the two-way integrations that we are having. and that's so important here, what we are doing. Adam Mogelonsky: Right. So just to clarify here, 'cause the term I use for the too many screens is I call that dashboard fatigue. And uh, there's dashboard fatigue and then there's also something I call zombie systems, which are. These systems that you're deployed, but you're only really using them 5% of the time. And the reason for that is not out of, uh, a dislike for the system, it's just there's too many other screens going on that people don't have time or the me, uh, the energy to look at yet. One more screen. So what you're saying is that you allow the team members to continue to look at the PMS. That is the system that the guest service agents are front facing with the guests, but you are helping to, with your continuous syncing process, to normalize the data so that way it can be acted upon. Rik Van Leeuwen.mp4: Exactly, and maybe a very simple example here. Is that, uh, a common use case is as I reckon you, we are populating user defined fields in a PMS system. Um, and what we can do, for example, if we eventually have a clean profile, we can already populate in A UDF, in the PMS, the way that the front desk personnel should greet the guest. If we've seen that this is the first reservation you say. New guest, for example, and therefore procedures are there to explain something about the property. For example, if the guest is recurring or repeat guest, right? Then you would like to say, welcome back. And there is no need right now for the front desk, uh, employee or agent to go to another screen basically to understand if this is a recurring guest, it's already populated in the field, right? And in this way you are already. Nil, the user or the guest experience, basically from the first touch point. Uh, if a guest is arriving in the hotel right. Adam Mogelonsky: Okay. And just, just for clarity, UDF. Rik Van Leeuwen.mp4: A user defined field. So this is, this is something that, um, opera or Muse or appo or infor, these are all different PMS systems, right? Um, it all has a first name and a last name, for example. But as well, you can populate certain fields that is, that are different or configurable. For every different hotel chain, and this is also how a hotel chain would like to operate to have some flexibility within the system. And the hotels are using basically the UDFs. They use defined fields to make some of the screens configurable. Adam Mogelonsky: Very cool. Now, one other thing, we've largely been talking about the guest through the implied assumption of a leisure guest. I wanna switch things over to groups and, uh, and sales or, or corporate subscriptions. How does a middleware or CDP help with the group, uh, group sales process and also personalization at scale for group guests? Rik Van Leeuwen.mp4: Um, what we are able to do here as well is to streamline transactional communication, for example. So because we are receiving. Reservations objects and person objects. You can also understand that a certain reservation is part of a group indeed, right? Um, if this is the case, then you can change your transactional communication, for example, to maybe display different information, um, but still, um, very related towards the event, for example, that is happening on that particular day. So. With this foundation, basically, you are also able to change the way that you're communicating in transactional communication or marketing communication efforts here. Adam Mogelonsky: Wow. Okay. Now going back to the leisure side of things, just that we've clarified groups. One term that I am a big gung-ho person for, advocate for is total revenue. So looking at how we can take a guest, learn their preferences, and then maximize revenue per guest by way of food and beverage. Spa activities, et cetera. How does, I reckon you help in that regard? Rik Van Leeuwen.mp4: Um, we've mentioned of course already a couple times the PMS system because it's also. One of the most important systems in a hotel it, uh, ecosystem, right? Um, but what you also typically see is that there are restaurants, indeed, spa or golf, uh, possibilities. And we will also connect, uh, towards those systems. Um, some of them one way, some of them two way depends a little bit on the possibilities. But what you then can see is that. You are staying on property, but you also, for example, have two reservations, um, in the restaurant and maybe in the spa, for example. And because we're connecting that towards the same profile, the same Golden gas profile we are able to understand as well is what are the, what are the interests, right? Preferences? Do you like to go to the spa? Do you like to go to the restaurant, but also to get the folios in? From those systems as well. And therefore you can really create this, um, this complete view of what the guest is actually spending. Adam Mogelonsky: Right. Can you give us an example? Uh, there, uh, you know, you're able to identify a Spago or, and then, uh, give them a marketing journey that gets more revenues, pre-arrival on site or more loyalty. Gets them to take a, take you up on an offer to come back. Rik Van Leeuwen.mp4: Yeah. So there are, there are different ways here of doing so, um, one of the, the ways that is possible, but in, but in, I reckon you is, um, via incorporating pay recommender engine into your booking engine. So. A regular booking flow is you select a date, you select a room, uh, the amount of people, and then eventually you would pick, the room that you, that you would like, right? And then you are continuing towards the screen where you can see multiple possibilities of amenities or activities, right? But if you know that this guest is already locked in, for example, you can then take the historical data points of this guest. And present them with, uh, with an activity that they are maybe able to do there. So to really personalize here the experience during the booking engine, and as I reckon you, uh, we provide APIs that you are able to integrate, um, within your booking engine. So that's one way of basically driving revenue during booking, um, during, during the booking process. Another way of, for example. Um, trying to get guests back towards your hotel is with the marketing efforts that are also already briefly touched upon, right? So if you know that the guest is interested and maybe a local guest as well, um, if you then have a new menu within the restaurant, you're able to reach out towards the guests here that are local. Because we know where, what the address is. We know how many times this guest actually stayed or dined in the restaurant or in the hotel, for example. And therefore when you have a new menu and you want to celebrate a certain event, you can target those guests the first that are in a 50 miles or 50 kilometer radius. So this is then from a pre-arrival point of view, or actually during the booking process, till. Post, post stay, you can also trying to get additional revenue in by understanding what the activities were that the guest is actually doing within, within a certain hotel. Adam Mogelonsky: Yeah, almost, uh, I was thinking like, uh, sort of a first look deal, and then it allows really marketers to get very creative and granular with their offers or, you know, uh, one. One to many sort of marketing, uh, or one-to-one, whichever term you wanna throw around. Um, within all this, there is a fear that, um, there's, there's gonna be, mistakes are gonna be made, made you, you think someone's a spago, but they're actually not. And then you turn 'em away. And the phrase I've used to encapsulate all this, and the one, it's not mine, I've heard it, is garbage in, garbage out. Can you describe what that phrase means on a data level? Rik Van Leeuwen.mp4: Yeah. it's indeed often used within training machine learning models, um, as well as indeed doing some, some kind of prediction here. Um, If you not clean your data or you have a lot of duplicates, then you do not actually see on a data level what a guest actually likes or wants. Um, and because your data is basically unclean or not a good basis to train your machine learning model upon, you'll also, of course, um, will not predict the right thing. So it's basically teaching a kid. What a dog is or what a cat is or these kind of things. And if you are saying, oh, well you are pointing towards a dog, what is actually a cow, for example, right? Then you are training or teaching basically a kit. Um, not what is a dog or what is a cat or what is a cow. And that's also basically the same thing within data points within a hotel IT ecosystem that are not. Clean or duplicated or not connected towards the same profile. If you do not have this Golden Gas profile, then a lot of the AI and machine learning initiatives, um, will probably not get the desired result. If you don't have your ducks in order with your data management, then it's, it's, it's partial. Information that you are able to push into your AI or machine learning initiatives instead of where the most gain is from a more complete view of the guest here. Adam Mogelonsky: Yeah. Now you mentioned, uh, the buzzword of all buzzwords right now, which is ai and, um, I think that. Not to stroke your ego too much, but you're probably one of the few people that actually understands what AI really is and what it really means in hospitality. Can you describe what AI features are embedded within? Uh, I reckon you and should be standard for a good CDP going forward. Rik Van Leeuwen.mp4: Yeah. Um, I think if we're talking indeed specifically around the customer data platform, um, we, as I reckon you have spent a tremendous amount of effort in clustering guests. So we all know the word term, um, segmentation, right? And segmentation is nothing more or less, if we really numb it down, is. Having a couple selection criteria to go through your active customer base. And with the selection criteria, we are saying, okay, has more than three stays, stayed in the last seven or eight months and has spent more than a thousand. And that is basically how I wanna get a group of guests, a segment, um, to target to. Then you will always confirm your gut feeling because you'll always find a group of guests that would probably fulfill this selection criteria. And with clustering, we are basically looking at behavior instead of selecting guests based on, uh, on a couple rules. And with behavior, you usually. Look at more than just two or three attributes. You are looking at 30, 40, or sometimes even 50 different attributes that define behavior on a, on a guest level. And we are also looking at if you stay in the weekend, yes or no, or if you indeed made use of the spy. If you book far in advance or short in advance, if you are coming with a company or a group or a travel agent. And By creating all these different attributes, you are, you're able to let a machine learning algorithm or an AI algorithm find certain groups of guests that are showing similar behavior, and by presenting this back towards the hotels, then by analyzing the attributes that define these groups of guests, you actually attach a label towards them. So it's kind of reversed engineering almost, where you say, okay, this is a group of guests. The AI or machine learning algorithm has no idea what this group of guests specifically entails. However, on attributes level, they are showing very similar behavior and only staying in the weekend or book best available rate and these kind of things. So, and then you get dense groups of guests that. You are able then to market to, right? Because that's eventually the goal that you're trying to achieve here. You have your entire customer base and you wanna have groups of guests that you're able to market towards on bringing them back because they show the most value or the most potential, for example. Adam Mogelonsky: You can get very specific. Now, you mentioned clustering in terms of 50 or so attributes. There's another AI term we should probably touch upon here, which is supervision. Can you unpack what that means in terms of a super supervised learning versus unsupervised? Rik Van Leeuwen.mp4: So the initiative that we just talked about with the clustering, that was unsupervised learning. We are looking at a group of guests, but we don't know exactly what they're doing. So we need to look at the features basically, or the attributes to understand, um, what this group actually entails. And a recommended engine is supervised learning. So we have a guest. It's picked certain, um, certain activities during the booking engine process, and therefore we know what we presented and what was picked. If we are doing this a hundred thousand times, for example, you can say, well, if I see a similar guest around this and I present these options, and this will probably be the likelihood of this guest buying it, for example, and because you know what was presented and what was bought. That is what's called supervised learning because you have the label or you have the decision that was made by the guest, for example. Adam Mogelonsky: Right. Makes sense to me. And, uh, it's very cool 'cause the, the tools you're giving marketers are just incredible to really get granular and really just analyze and then cluster and, and really do defined. Personalized offers to get people in and just more, more importantly, give guests the bespoke information they need or want to learn more about the hotel and the products according to what they're actually interested in. But I wanna circle back now, uh, to the tail end of our conversation, to something you said at the very beginning, the storage of data consent. Can you describe why that's important? Rik Van Leeuwen.mp4: Yes, definitely. So, um. It is quite important to understand from a guest level if they opted in or opted out on certain consent that are available within your hotel chain. Some of them you cannot opt out, such as the general terms and agreement, for example, but. Where you are able to opt out is if you would like to receive a survey, yes or no. After, after you state or uh, marketing efforts for your spa or for your restaurant, for example. Um, usually these are stored in different systems, right? And if a guest wants to opt out out of all marketing efforts, for example. it is quite useful that if that it is also centralized. So as I reckon you, we built the product over several years, right? And, and we started with a reservation object and then later on we added the profile object. Uh, that's right now, uh, 12, 12 ish or 13 years ago, basically since I reckon you started. And as we built this rights foundation, uh, we've also added the data consents as a field basically towards. Um, towards the person object, and this consent can be updated as well from different sources. So when a guest is at the front desk and says, I want to opt out of marketing efforts, then whenever. The guest is logged in on the website, for example, and you, they are seeing their consents. They are expecting them that you're opted out or opted in on these different consents levels. So there needs to be one central place basically where the consents are being pulled from, and I reckon you, is a, is a, is, is quite a good centralized, uh, place to actually store these consents. Adam Mogelonsky: Right, and I brought that up because the learning that's being. That's transpiring on your system is based upon data that has been opted in. Is that correct? Right. Rik Van Leeuwen.mp4: So, um, you can use this as well to. Either sent an email or not, or deliberately not sent an email towards a guest, right? So what you can analyze as well is you can analyze the behavior of opting in and opting out. Sometimes you are receiving, for example, too many emails or a particular brand, and that is. The whole reason why you are opting out because it gets annoying, for example. I think we've all encountered that once or twice. Right. And if you can see, and this is also the data that we're capturing within the IRE You platform, we are sending out the email and we are looking at behavior of the emails as well, if they are opening or clicking even within the email, if you've seen that your last three emails were not opened. Why doing the fourth newsletter again, that you're ascending on a monthly effort, right? We can better send another marketing effort, for example, instead of the regular marketing updates that the marketing team is thinking of. So then you can actually say, because the last three, for example, uh, um, email efforts were unopened, please exclude this person from. The newsletter group, for example. And in that way you are still able, whenever it's really important or crucial that you are gonna reach out towards your guests and you're still able to, right? And otherwise you can be losing, for example, marketable audience here. Adam Mogelonsky: Yeah, I, I see that all the time is, uh, you, you opt into something like an, an email form and then they just spam you and then it really turns you off. And then once they're opted out, they're gone forever. So this is, uh, it's a very important feature and it's almost a defensive mechanism to keep. To keep the newsletter system alive so that way when it is important and when the system recognizes it, you actually have the opt-ins to properly communicate that very important message. Little bit like, uh, I guess a little bit like the boy who cried wolf, right? If you, if you, if you are, the classic story, right? If you're doing it too often, then you're turning people off and then when the message actually is important, they're already gone. So that's a very cool feature. Um. On that note, are there any other important features that you wanna mention at this time that you think are very important for A CDP right now and are gonna be critical for hotels going into the next year? Rik Van Leeuwen.mp4: I think the ability to, to segment the guests, right? I think that's the. The most important feature, basically of a CDP, um, and preferably on all the data points that are available within, within, within the system. Um, so really think indeed of consents that are, that are available, um, if you've stayed in the restaurant or at the hotel. Um, I, I really think those should be the features that. Um, hotels should be looking out for, if that's, if that's actually a possibility by, uh, by looking for a vendor basically, of elevating the way that they are reaching out towards their guests to really have a 360 engagement overview of a, of a, of a guest. And are you able to, or to market towards these guests as well? Adam Mogelonsky: Yeah, I think it's important that everyone understands through the guest mind. They don't differentiate between room's, product, F&B spa, to them it's all one experience on property. So if somebody has a great experience at the spa, and then you're talking about to them about your new wine selection. What, what, what good is that? That's not the way a friend would talk to a friend. A friend would start off a conversation, hey, saying, Hey, we got a new spot treatment in with these great new, um, you know, uh, skincare products. You gotta come try it. And then, oh yeah, by the way, we have it packaged with some room nights. You should come sometime. We have a limited time offer, et cetera. Right. Rik Van Leeuwen.mp4: Exactly Adam Mogelonsky: Yeah, it's, it's just fantastic what you said about how you can, you can have a system that gives hotels more clarity on the data to then do those types of very creative offers to help grow incremental business, both more bookings as well as more revenue per guest. Rik Van Leeuwen.mp4: indeed. Adam Mogelonsky: Rik, is there anything else that we haven't talked about that you think is important that we should mention to close out? Rik Van Leeuwen.mp4: No, I think, um, I think we covered the product as it is, like CDP, um, and what the platform should be doing. Uh, I think we covered that pretty, pretty, pretty neatly. Thank you. Adam Mogelonsky: Awesome. Thanks Rick. Thanks for coming. Rik Van Leeuwen.mp4: Thank you so much. Cheers.