GAIN Momentum

GAIN Momentum episode #119: Public Reviews to Get on Top of AI Search Rankings | with Max Lüders
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Adam Mogelonsky: Welcome to another exciting episode of the GAIN Momentum podcast, focusing on timeless lessons from senior leaders in hospitality, food service, travel, and technology. I'm joined today by Max Lüders. He is the co-founder of MARA Solutions. Max, how are you?
Max Lüders: I am very good. Thank you. Thank you for having me.
Adam Mogelonsky: And of course, my co-host today is Vincent Somsen. Vincent is coming in from Valencia. How are you?
Vincent Somsen: Hey, Adam. Yeah, all good. Thanks for inviting me again and hosting it. So looking forward to exploring MARA and getting to know Max a little bit more.
Adam Mogelonsky: Cool. So to start off, MARA Solutions, give us the elevator pitch.
Max Lüders: Yeah, so MARA is an AI-powered reputation management tool, platform for individual hotels as much as hotel groups, focusing on providing the easiest way of handling guest feedback from collecting it to acting up on the feedback, I would say.
Adam Mogelonsky: And for context, before we started recording, we were talking about the importance of reviews and feedback now more than ever. Could you give us the Coles Notes on that?
Max Lüders: Yeah, I mean, we were talking about AI search tools coming up and becoming more and more important, right? And one, not the only, but one very important source of information for AI search tools like ChatGPT, Perplexity, and so on is reviews because it's honest guest feedback on topics that are subjective by nature, and that AI search tools can only get from that kind of source basically.
Adam Mogelonsky: Yeah, and I'd love to shift back into that during the middle of this, but first let's rewind and let's talk about the founding of MARA Solutions. So, what problem in hotel reputation management felt broken enough that you really felt that angst to build a company around what you're doing now?
Max Lüders: Yeah. So we were doing something different before coming to hospitality. We were basically doing market research based on product reviews. We are from a chair for marketing and sales. And that went well, but not so great how you want it as a founder, right? So we looked for other problems we could solve.
And one thing in the very beginning, we started before ChatGPT was a thing. And our AI solution, making use of large language models, of course, was providing genuinely individualized review replies to guest reviews, when most people didn't believe that such technology would exist.
And by accident, we learned that replying to reviews is a huge problem for hoteliers. And the feedback was so great that we said, "Okay, let's shift to that." So in the very beginning, that was what was broken. But now in hindsight, I'd say the real broken thing was that hotels treated reviews like a checkbox, really.
Respond, pull a KPI report, move on. And we think also with that background in market research that guests are already telling you what to fix, and the tools back then didn't really help anyone to act up on that feedback.
Adam Mogelonsky: Right. So you started in market research. What were some of the key findings from your market research around reviews? Any key insights to help hoteliers even today?
Max Lüders: Yeah, I think the much more important story here is that the PhD that my co-founder did that we based the kind of the first try for the company on, the finding was that you can gain the same level and depth of insights from product reviews, consumer feedback that's already there compared to actually asking them with traditional market research methodology, like doing surveys really.
You could gain the same insights from that, and that is also true in hospitality. So just use all the feedback, the public feedback that's there and focus on also generating public feedback instead of surveys that sit within the company. Learn from that feedback. You can do that with natural language processing technology.
And then use these insights instead of asking surveys that nobody wants to answer really.
Vincent Somsen: Yeah, and I guess those surveys, they might not refuel the bigger problem, right? And within the reviews, people maybe feel themselves a little bit more and they're not forced to do it, and they're not these biased questions. So that makes sense. And you mentioned before, Max, that before starting the company, you saw the general managers or maybe it's a concierge or a front desk manager that answers these reviews, they were treating it like a checkbox, right?
And why do you think that was? Why was it such a big problem? Why can't they just spend some time every day on answering these reviews?
Max Lüders: Well, I think that was even the first tester of our prototype back then. She said, "I'm doing that for two or three hours every day." And she was doing it every day. It feels like a dumb thing to do because it's so repetitive, but at the same time you cannot give it to anyone because, you know, you need to be able to write properly.
You need to handle that feedback in a very good way, and react to it in a good way. So she was not able to outsource or even delegate that to someone else. But then at the same time, it's not a very fulfilling task to do. And I think that is why people treated it like a checkbox. And then the other thing is, if you only look at KPIs, the reply rate, and then ratings in different areas, you don't learn a lot from it.
There is just this number, and you see they're performing good or not so good, and if you wanted to learn from it, you had to have the motivation to dive deep into the very reviews and the free texts, to be able to uncover what people actually complained about and take it from gut feeling to actual insights was also super hard.
Vincent Somsen: Yeah. Okay, that makes sense. Now, and what do you think about in-stay surveys, right? One of the tactics that hoteliers do nowadays in order to prevent a bad review or to learn from the experience during the stay already is they provide these in-stay surveys, right? Or feedback mechanisms. Do you agree with that tactic or what are your thoughts on that?
Max Lüders: Yeah, it's a great thing, a great way to try and not have bad reviews because you catch things while people are still there. The important part is you have to make sure that it lands with the right people at the right time because if someone's telling you their AC is broken and nobody comes to your room to take a look, it might even lead to worse reviews.
Adam Mogelonsky: You know, you mentioned a key word earlier that speaks to all of technology in general, not just hotel technology, not just review platforms, and that word is fulfilling. Everyone wants to do more fulfilling work, and therein I imagine that in designing MARA Solutions, you spent a lot of time and continue to spend a lot of time thinking about how you can help to automate the work around the work in order to make hoteliers' jobs more fulfilling insofar as answering those reviews.
Could you speak a little bit to the design of MARA Solutions with regards to how you are helping various team members have more fulfilling work at hotels?
Max Lüders: Yeah, I'd say we try to take the hard part out of answering and also out of doing all the analytical work that you need to do to be able to uncover actual insights that are also actionable. So we started with replying. We're doing that very good, having a brand voice, having smart snippets that is basically knowledge about the hotel so that the review replies look. All the drafts that we create, you can always edit, of course.
You have a very, very good starting point, and then you can give some informal feedback, so to say, and MARA will incorporate. That's easy. You can even automate if you want. I think what might even be a little more important is that things kind of magically happen. When we see something in the reviews, we try to push that to other systems and try to really operationalize the guest feedback.
And I think there are two things to say here. One is acting on feedback in daily operations, if you will, and then also making sure that guest feedback is used to offer better service in the longer run. And I think with your question, the first thing is a little more important.
So to give you two concrete examples, and again, we try to push the insights, and that's important, not the review, but the insight, the insight we uncover to the systems where they are actually needed and in which people not replying and analyzing reviews actually work. So two examples would be, we have a service ticket agent, and that's basically, we scan all the reviews and decide whether something warrants a ticket, and create it with a quote and a recommended action, and push that into the tool the team already uses.
So something like Mews Tasks, Flexkeeping, Sweeples, Netfix, housekeeping tools. And then maintenance or housekeeping, they never need a MARA login. But they just know there's mold in bathroom 205, and then that's taken care of automatically, so to say. So no one needs to scan reviews for things like that.
Or hopefully you caught that with in-stay surveys. A second thing is we identify guest preferences from reviews. So if you write in a review the pillow was too hard, then we put a preference for soft pillows in your guest profile in the PMS or CRM, and then when the guest returns, front desk knows they can say, "Hey, a soft pillow is already on the way to your room."
And that, you know, making that feedback available in daily operations without the heavy lifting of having to put something in the guest profile, I think people enjoy that when they have these magic moments. Also employees, when they see I can now give that guest a feeling of he has been heard the last time he gave us feedback, that's actually quite fulfilling.
And you do not need to do. You don't need to think of it, it just happens.
Vincent Somsen: I was just gonna ask Max if that's also something that you can track on a portfolio level. So let's say you have a brand of eight properties across Europe, and they stayed in your London property, and next time they go to your Amsterdam property and they do have the preference for a soft pillow or a firm pillow or whatsoever, is that something you can track within MARA?
Max Lüders: Yeah. So in this specific example, we try to. This is really cooperation with the partner. We can push these guest preferences into guest profiles like PMSs like Mews or in a CRM. And then identifying that two bookings are the same guest, this is mostly with the partner, but if that happened on their end, and with Mews, for example, I know that they do that, then this is shared across properties, yes.
Vincent Somsen: Yeah. Okay. Yeah, they would have to have a CDP or maybe a good CRM that actually does the matching and merging. Okay, that makes sense.
Adam Mogelonsky: Yeah, I mean, well, that's already an incredible use case because now we're talking about the reviews end to end and however much we're incorporating AI and automation to actually bring those preferences to make them actionable for future stays, which of course anyone in the finance department equates with actually doing better on the P&L, which is what's important, which is why we want an online review management system in the first place to help that, you know, to draw it end to end.
So Max, you've mentioned service agents, you've mentioned automated reviews, which I believe would be human on the loop. So they are, we're using AI to generate them and give them a little bit of personalization. It's not just a copy paste from a pre-generated draft, so to speak.
So those are two embedded AI use cases in your platform, and MARA Solutions is an AI-first online review management system. So what exactly does that mean to be AI-first?
Max Lüders: I think this is not really. It's more of an internal thing, I'd say. So most of what it means is more internal, and the customer only sees the output, right?
We were founded when AI, and AI as most people view it today, was barely a thing. So we have no legacy processes to retrofit for this new world, and with AI, it is a new world.
And companies that existed before that, they have to unlearn things, or they have to unlearn how they build stuff sometimes. And for example, our developers use AI to code throughout, so that means much smaller team. That's actually an effect of being AI-first or being founded when AI just became a thing.
Less communication overhead, no large management layer, and so on. So I think the result that customers actually feel is that we ship very fast, and that product quality and the results that the product delivers are much higher because it was built from the ground up with what's available now, right?
So we built in and for a world that's going to be AI-native, and we operate that way internally already. So I mean, yes, we did some things right, but I think the fact that we started building right when AI had its big breakthrough, if you will, isn't an achievement in itself, right?
The beauty of our position as an AI-first company is that we can question everything that people tell us they've always done it that way since forever, and that's a statement that really triggers my co-founder, Toby, and myself. And we would always say, "Okay, we're sure there is now a different way to do that today, and let's go find a way."
So I think using these tools, trying to also use them to our advantage helps us to try and rethink everything if that cannot be done in a better and more efficient way, and the main driver of that is AI, I'd say.
Adam Mogelonsky: Yeah. I mean, it's-- There's many other companies that are AI-first in that they're rethinking the business processes and the workflows in order to make one developer, one engineer account for four in a lot of ways. One salesperson count for three. It's tremendous what's happening. And it favors the companies like yourself that are using AI properly to rethink the entire structure all the way down from co-founder to account coordinator, everything.
Max Lüders: True. At the same time, I'd say you have to know what you're doing. So we're seeing a lot of vibe-coded companies, but then they lack the last 10 to 20%. And that will also get easier and easier, but there's still things that you need to take care of in order for everything to work securely, and so on.
So there is some heavy lifting that we need to do and hide the complexity of what we're actually doing for the customer.
Vincent Somsen: Yeah. And in terms of vibe coding, I think it's an interesting concept because nowadays you see a lot of, at least in our world, in the hotel tech space, there's a lot of people talking about vibe coding hotel SaaS, right? So what is your point of view? And I understand you might be a little biased, right?
But what is your point of view with hoteliers trying to build their own apps within their environment and
Max Lüders: Do it, do it, do it. Try to learn as much as possible. Don't be afraid of doing things or breaking things. I like when they do stuff like that. We sometimes learn stuff from it. Also, most of them at some point see, "Ah, okay, yeah, I understand why we need a software provider for something."
So I'm not afraid of that. I'm a fan of it, actually, and it also. You know, I vibe code a lot of stuff because that helps me to communicate to developers or my co-founder doing the product, "Here's something, you know, that's how I would imagine it," and I built it in 15 minutes just as a concept to kind of being able to communicate what I want.
So I think it gives great power to all of us. Still, to have a running, reliably working, affordable software product, you have to know what you're doing, and it'll take a while until we're there with only vibe coding.
Vincent Somsen: Yeah. Did you come across any hotelier-created projects that you thought could be interesting? Maybe from your customer base?
Max Lüders: Yeah, there's a lot of interesting stuff. I think at the moment the most interesting thing to do is to open up the platforms via MCP servers. We're also doing that now. And then, for example, we have a research assistant that's basically doing the heavy lifting when it comes to analytics.
You can do that on your own in your platform; that's comparatively simple. But you can also have this agent do repetitive tasks for you when it comes to analytics or one-time analytics, stuff like that. But then opening that up, the same database through an MCP, sometimes results in things we didn't think of.
When you can connect MARA to Claude and then do something that you want to do, sometimes that triggers interesting ideas for us. I haven't yet seen something where someone is using something completely built from scratch in a productive environment, other than the very, very big ones.
But even there, I have to say, it's not very agile what they're doing. They're building something, and then it stays like that for years. I'd say they would sometimes still be better off with a vendor trying to update things regularly and push boundaries.
Vincent Somsen: Yeah, keep it secure. Okay.
Adam Mogelonsky: Yeah, and make sure that the data that's being put into whichever LLM frontier model you're using is actually accurate a lot of times because those things, even today, they can still hallucinate a lot. So this is great about vibe coding eventually becoming coding as smartphones became phones, AI-first company reorienting everything behind the scenes to go back to what the customer sees, the hotel that wants to use reviews to drive long-term and near-term returns, and therein we're talking about AI.
So let's lay the gavel down, so to speak, and say, how does a hotel look like to an AI if it has weak reviews?
Max Lüders: So most of the time, when it has weak reviews, and in the first place, that means weak rating, it will be disregarded by AI search.
That's generally speaking. There's always exceptions, right? So it really depends on what the guest is asking for, where the answer lives in reviews, like, I don't know, breakfast, friendliness, design, stuff like that.
Weak reviews hurt badly, but when it's more of a factual thing, they matter a little less. So, cheapest hotel near the station, I don't know, a hotel with a conference room for 40 people in ABC, ABC being a very small town, then if it's very narrow, you might get away even with not so great reviews.
But that's obviously not a strategy to build on, right? Because in the end, the answer engine is what they're trying to answer. ChatGPT and so on, they call themselves sometimes an answer engine. I think Perplexity brought that term up. What they basically tried to do is they tried to give you the same result that you would've come up with if you had unlimited time to do research.
So they try to emulate human search behavior. And that means they will check reviews anyway to stop you from making a bad decision. So, exciting, so to say, than not being found for bad reviews is potentially being found for the right things by the right people, and that is actually what AI search opens up, I think.
If you're very good at something, there will be people who care about this something. It can be vegan breakfast, it can be price, it can be location, whatever, and AI search lets guests search very, very narrowly and very narrowly defined needs. And oftentimes, the answer to that question lives in reviews.
Like, if you say as a hotel you have great vegan breakfast, that's marketing copy. It doesn't matter. But if we find that consistently in reviews, then you will likely be recommended if someone is looking for a hotel with a great vegan breakfast.
Adam Mogelonsky: Right, and you mentioned that it's ratings, but not mostly ratings, let's say. So what else factors into an AI's use of reviews?
Max Lüders: Yeah, into user reviews, it's basically really defined by the search prompt, right? So that's strongly query-specific, so to say. I don't know, a hotel can be highly visible for parking. You will maybe not find that in reviews, but vegan breakfast, and then be found through reviews, but not so much in a more generic search for good hotels in city ABC.
It's rating as we humans also look at rating first, and then we might dive deeper into what people actually write. And that ties back to replying. Obviously, there's also replies where a hotel can give you sometimes more information for the other potential guests disguised as an answer to the review.
Someone's complaining about a loud room, you can answer, "We have rooms with a garden view, and those are quiet." And then AI can consume that, and you can give knowledge to the AI even with answering reviews. So it's rating, content of the reviews, and then also content of your replies as a hotel.
Adam Mogelonsky: Wow. And, you know, you mentioned this in the face of being in a small town, let's say, is less dependent than a large town, which gets to the idea of competitive benchmarking. So can you talk about what MARA Solutions is doing with respect to competitive benchmarking of reviews and some of the other additional features you have therein?
Max Lüders: Yes. So I'd say there are two things here. You want to be able to compare to your... I think before we started recording this, I told you that we're now having a prototype developing together with customers, which is an AI visibility tracker. So tracking which position you are in when people look for different things through AI search tools.
In our core business, kind of the review and guest intelligence tool, you can look at headline KPIs against up to five competitors, and you define those competitors. You see the score, the response rate, review volume, and it gives you a very good understanding already of where you are compared to the five hotels that you consider your main competitors.
Then there's also a deeper piece, that's the comp and sentiment analytics, and it gives you the same qualitative topic-level, very deep analysis that we run for your own reviews, but applied to your competitors. And you see exactly where those five properties are strong and where they're weak by topic, right?
Not just that they score higher or whatever. Then with the AI visibility tracker, I think it becomes a little more interesting, and it adds a different layer because the comp set there is auto-generated. And why is that? Because we track which properties actually get named for relevant search prompts, and you never pick your competitors.
And the reason is that which sources are cited, and why a competitor gets recommended for a given prompt, that's highly dependent on search intent. And again, if it's about the vegan breakfast, there will be other hotels that the AI will compare you against, as opposed to looking for a hotel with parking space or whatever, then the comp set will be different.
So it really depends on search intent, what's your relevant competitive set in this AI search world, I would say.
Vincent Somsen: Yeah.
Max, regarding that, is there any advantage for independent hoteliers that have an independent website over brand.com websites? And does MARA work with both of these clients?
Max Lüders: Yeah. So we work with both of them. We actually currently, in the testing of the visibility tracker, see there is a great advantage of being owner of your own website because you can change things and you can experiment a lot. And something we see in early testing is that long-tail landing pages, you know, family-friendly hotel, dog-friendly parking, stuff like that.
The parking example comes up again and again because we saw that one of our customers is very successful in this very specific search intent because they have a landing page on parking with the hotel. And if you're able to add something like that, edit, which is sometimes harder when you're part of a bigger chain, you definitely have a chance for quick wins at the moment.
Adam Mogelonsky: Yeah. You know, that brings up a very interesting insight, because what I'm seeing is that AI is sort of rewriting how website hierarchy is designed. Where before you wanted those really long landing pages that had all the information buried in there in multiple sections. But AI really wants very clearly defined, structured, and crawlable facts.
So therefore, instead of parking being its own little row or section at the bottom of every page, it gets its own page. Is that sort of what you're saying in terms of how that niche of pet-friendly hotels, family hotels drive to, they're looking specifically for parking, and then the AI is saying, "Well, aha, you have a specific page on parking, therefore I'm gonna favor you in the search." Is that what you're getting at here?
Max Lüders: More or less, yeah. It's knowing what people are looking for when people should find you. Like if they are looking for parking and you know you're strong at providing parking space or be it a co-working thingy where people can do workation. So these searches get highly, highly individualized, and you have to know your niche, and then you have to build the website and the information that you provide, not only on your website, but also on OTAs and review websites.
They need to be consistent and up to date. You need to add FAQs to your website that answer such questions. You have to have those long-tail landing pages for real search intent, stuff like that. So yeah. You need to know your strength and what your niche is, and your niche can...
It doesn't need to be I'm a workation hotel. Your niche can also be, I offer good value for money. Great price. You sleep for a great price with us, and you know what you get, but whatever. But knowing that and then trying to make AI tools see it, that's basically the task now.
Adam Mogelonsky: And within that, this parking lot example, parking space example, your platform would then help to diagnose and identify the insight, let's say, that will identify to say, well, certain guests are searching and they're finding your hotel specifically because you have a parking space. You should really lean into that as a feature and update your website and all the other channels where you're displayed to bring that to the forefront.
Max Lüders: Yeah.
Adam Mogelonsky: Bring that to the forefront.
Max Lüders: Yeah, I think there are two ways to tackle that with us. So what we do with AI search tracker is first we scan your website and reviews, and from that we kind of define what we think people should find you for. That's one part. The other part is taking it from the review insights that we have.
If we see in reviews that people find it very convenient to park with you, and you didn't even know maybe, then that might be one of your strengths that people should be able to find you for. That's one thing, and then this is how we kind of define the search intent that you would want to be found for.
And then, secondly, what we do is we search for these things, we track replies, look at the sources that the AI tools cite, and then from the sources that are cited and the content of these sources, we try to give you recommendations on how you can try to improve your visibility, because we see that those three hotels have dedicated landing pages for that, for example.
Stuff like that. But this, again, is early-phase experimentation. This is now a manual service at the moment, together with some customers, and we will then productize that afterwards.
Adam Mogelonsky: But you know you're an AI-first tech company, so that'll ship next week, right?
Max Lüders: Well, actually, we are very, very, very fast at shipping. Because the world moves so fast and AI allows us to move fast, we deliberately moved away from, I don't know, one-year product roadmap. We obviously know the strategic direction, and AI search is very much a strategic direction. But other than that, we only have six-week development cycles.
We decide every six weeks what it is that we're going to build next. So the AI visibility tracker is now a prototype that has been built in six weeks. We're using it with customers now, learning from that. And then in the next cycle, we will build the recommendation engine based on that, collect feedback again, and so on.
So this really allows us to move very, very quickly. Sometimes, I have to say, we're even a little too quick for our customers. They sometimes can't keep up, or if someone looked at MARA a year ago and is looking at it now, they consistently are impressed by where we are now, because given AI tools, we can move very, very quickly.
Vincent Somsen: Yeah. And for other founders in the hotel technology space that are listening, or perhaps innovative hoteliers, how do you get those pilot hotels? You have a large customer base already. How do you currently find those innovative hoteliers that would like to participate in the beta testing?
Max Lüders: Yeah. At the moment, I'd say we have a very high share of innovative hoteliers being our customers just because we brought something new to a category. So this is the benefit of being this young but already big enough company.
In the very beginning, the difference between the market research product we had, and we made some good money with it, but the difference between the first product that was pre-hospitality and then coming into hospitality in the sense of how people were talking to us and that we were able to talk to them revealed that we were working on an actual real pressing problem.
Replying to reviews was a pain in the ass, forgive the language. So people wanted... We were contacting people, having no network in hospitality. We were contacting people on LinkedIn, and I'd say 50% of them wanted to talk to us because we told them we're providing individualized replies to reviews, and it was such a problem for them and such a pain again that they wanted to talk to us.
And then we... The prototype that we had back then was really, compared to now, nothing. But they were like, the first tester really was, I think the test was on Friday, and she said, "Can I have that on Monday?" We didn't even have payment, nothing. And then if the problem is big enough, people are motivated enough to inform you what your next step needs to be.
They were telling us brand voice. They were telling us smart snippets, having knowledge about the hotel. If someone says small bathroom, that's our standard reply. I want to have that, you know, not as a template, but la-di-la-di, and that is how we built everything.
And then honestly, with the AI visibility tracker, this is not... We test a lot of things, and we ask customers, "Do you need something like that?" Maybe with a prototype. And there were things in the past couple of months and years where people said, "Yeah, interesting," but now, AI visibility tracker, that's something else. People are like, "Yeah, I want to be part of that. I want to build that with you because it's interesting to me, and it's a real problem that I'm seeing at the moment."
So you really feel it when you get the feedback from telling people what you're doing. You feel when you're onto something. And currently, the hypothesis with AI visibility, we feel we're onto something again.
Vincent Somsen: Yeah, makes sense. And in terms of your competition, what do customers say that really sets you apart? Why do they think you are different from some of the other tools that are out there?
Max Lüders: Yeah, it's a little hard to tell because it's very much soft facts. One could take a look at the reviews on Hotel Tech Report. I would say there is something: ease of use is one. The product just works. You don't need any... People can just start using it, and on day one they will understand it.
Quality of results, be it the replies or the insights, is very good. That's also the benefit of a young company. We don't have 10-year-old legacy tech, right? So this is also somehow given by nature.
At the same time, I have to say we're hearing since two years at least, the legacy players, they can now have the same when it comes to reply quality and so on. We, from customer feedback, don't see it as much.
And then one thing that regularly surprises me is being approachable. When you write something, when we get an email from a customer, they will hear back in at latest two days, and most of the time things are solved within that time.
So being able to approach us, getting service, and we're lucky enough to have a not-so-service-heavy product. We have only one person taking care of 1,500 hotels.
And then listening to customers, developing with customers, and moving quickly. Something happens with MARA every month, I'd say.
Yeah, but this is very soft, right? It's a...
Vincent Somsen: Sure. Yeah, but it's important. Yeah, people care about these things.
Adam Mogelonsky: So, you talked about a six-week roadmap versus a one-year roadmap and some features. Where are online review management systems going?
Max Lüders: I think that online reputation management needs to be redefined. We said earlier that reputation and review management was basically treated as a checkbox: reply to review, move on, maybe pull a report containing your reply rate.
I think we already added this layer of true guest intelligence, being able to uncover insights very, very quickly, very individualized to the hotel, also scalable across a portfolio of several hotels in your portfolio, operationalizing the feedback data.
So this is the guest intelligence layer, I'd say. But then most importantly, I think AI visibility and the recommendations and so on, that gives meaning back to this term of reputation management. You have to try to actively manage your reputation and your visibility in AI search tools. And I think this is where the category is heading more or less or has to go.
We're trying to be at the forefront of that.
Adam Mogelonsky: Awesome. So, whether it involves MARA or not, could you give hoteliers listening to this some tips for getting more online reviews, and to help service the volume and the recency of those reviews to help with AI search?
Max Lüders: Yes, I hope so. I mean, you cannot do a lot about the reviews on Booking because Booking will ask people who booked through Booking or Expedia and so on, the OTAs. But there are platforms like Google or TripAdvisor that you can try to prompt people to post a review there, right?
And it all comes down to asking people. Be nice to them and ask them, be it really personally asking them during checkout. Maybe you have a little QR code, and front office asks if everything was great, and then tells you it would be great if you would leave a review because that's important for us.
You can do post-stay emails asking for reviews. That's also one thing that works. Just, you know, again, if you liked it, here's the link to our Google review page. It would help us a lot.
And one other thing, and that involves MARA now, one other thing that you can do is, we also deliberately, again, we try to do things a little differently.
People were constantly asking us for surveys, being able to send surveys to guests, and we were like, "Honestly, we think it's a tool of the past." And I always ask people, "Do you fill out a survey when you get one?" And most of the people say, "No, I don't. I don't like that. I don't have time for that," and so on.
But people were asking us, and then we said, "Okay, then, if they want it, let's build it, but let's try to build it a little different."
So you can send a traditional survey, but this is internal data. You want to be visible to the AI tools, right? So public feedback and reviews is the gold standard in the future.
What we recommend is using what we call the conversational review booster, and that is you send a survey or you ask for feedback. It's kind of an internal review. You give a star rating as a guest. There is free text.
Then if you, my most liked example, the vegan breakfast, if you said, "Breakfast was great" in that free text, we will ask you a follow-up question, "What specifically about the breakfast did you like?"
And then people might say, "The vegan options were great." And you say other things and so on, and we create a review for you using your input, not leaving out negatives. And you can, as a hotelier, set up if you only want to ask people that gave four or five stars.
So we can leave negatives in there. If the bed was a little too hard for you but you still gave five stars, that's okay. We leave that in the proposed review, so it's authentic. And we link to TripAdvisor and Google, and then you can post that review there.
And that way we try to make it very simple for guests to leave their feedback publicly without putting words in their mouth.
Adam Mogelonsky: That's a very cool feature.
Adam Mogelonsky: Max... Yeah, no, no, that's incredible because you're getting the feedback and then you can post it and make it simple for people to copy-paste more or less. Max, it's been a very, very helpful conversation regarding reviews and where it's going with AI search.
Is there anything else you want to mention before we close out?
Max Lüders: Well, there's a lot. I think we talked about a lot of important things. I think it's an interesting time to maybe rethink how you're approaching reviews and feedback and how you want to use it as a hotel, right?
That ties a little back to the opening, that the public reviews are the most honest signal that a hotel has.
And I think you should try to use that. Yeah, I think it's interesting times to start working on that, and there's a lot of things we all do not know yet, and we can learn. And if you try to learn them now, there's a good chance that you will be a winner of that development.
And I think that's a cool thing to do. And maybe one thing I would suggest to hotels, because many have not done that, just ask ChatGPT what it says about your hotel and what it recommends, instead of you trying three prompts that you think people should know you for. Just ask ChatGPT. It might hallucinate a little, but it costs nothing, and most hoteliers haven't done it yet. So try to just do that and learn something from it.
Adam Mogelonsky: Yeah, that's a great way to finish off. Max, it's been a very delightful conversation. Thank you so much for coming on the show.
Max Lüders: Thank you very much for having me.
Vincent Somsen: Thank you, Max. Good to see you.