In this episode of the Greenbook Podcast, host Karen Lynch sits down with Tino Klähne, Director of Strategic Innovation and Intelligence at Lufthansa Innovation Hub, to explore how his team is shaping the future of travel. Tino discusses how they use strategic intelligence to spot emerging opportunities and threats, employing cutting-edge tools like AI, an innovative "indicator stack," and even Wikipedia edits to predict industry shifts. They also dive into the exciting potential of the metaverse for aviation and how Lufthansa’s open approach to sharing insights is transforming the travel ecosystem.

You can reach out to Tino on LinkedIn.

Many thanks to Tino for being our guest. Thanks also to our producer, Natalie Pusch; and our editor, Big Bad Audio.

Transcript

Karen: Hello everybody. Welcome to another episode of the Greenbook Podcast. It’s Karen Lynch, your host for today, and I’m excited to be having a conversation with somebody I met at our IIEX Europe event. One of our speakers on stage is Tino Klaehne, Director of Strategic Innovation and Intelligence at the Lufthansa Innovation Hub. Tino, thank you so much for joining me on the show today.

Tino: Thank you, Karen. Great to be here.

Karen: Good, good. Well, I’m glad. You know I’m always one to ask our guests to introduce themselves because while I’ve read your bio, and there’s a lot that I even want to get into with your bio, I’d rather from your own mouth, you explain to people a little bit about yourself, your background, your role.

Tino: My background actually lies in industrial design, and I worked in the design industry for a couple of years at different design agency, but then kind of found interest in the rules of strategy and innovation, and after an executive MBA, I kind of switched sides, and more or less softly, went into the corporate world—but not completely—by joining the Lufthansa Innovation Hub. And the Hub is, actually it’s a separate legal entity, so we’re our own company, a hundred percent owned by Lufthansa Group. So, that’s the big European airline group, around ten-plus different airlines, MRO, Technik, Loyalty businesses, catering business, so a lot of different aviation-focused businesses under that umbrella. And yeah, we are a unit that set out to drive innovation looking into the future, building businesses in the travel ecosystem. And my role in there is finding out what are the opportunities and threats that are ahead of us, trying to make sense of those, connecting the dots, really forming agenda-setting insights, and helping, yeah, to really capitalize on those opportunities, which we prefer to see change in the future. Yeah, and make it happen and move things.

Karen: You know, you mentioned that agenda-setting insights, and that’s actually one of the things I wanted to ask you about specifically. Explain what that means. Because I think there’s a lot of people that would be like, “Oh, agenda-setting insights. Tell me more.”

Tino: Yeah. So, as my title suggests, look, we have a team, or construct that is called strategic intelligence. So, that’s really about gathering data, turn that into information and insights about the future, and also about the rather further-looking future, right? We are not speaking about, like, 50 to 100 years of crazy scenarios, but really something, okay, what kind of decisions do we need to make today to be successful in a couple of years, right? It’s not about the next quarter because there is business intelligence trying to make sense out of that, right, to really short-term tactical things on, I don’t know, sales channels, operational channels, right? So, there are a lot of smart people trying to optimize those things. However, in not only Lufthansa Group, a lot of corporates, the question, “Okay, what happens in the next years,” right? “What happens, where should we invest in today from a strategic perspective, from a business model perspective?” And first of all, understanding is key before taking action, right—so because a lot of corporates, they just jump on the first hype they see, right? Or they have the typical corporate rhythm of, I don’t know, doing strategy every other year, right? And then they, I don’t know, ask a consultancy on, like, what should we do? And then they just blindly follow, without keeping track on where’s the world actually moving? Where is our strategy actually aligned to? Like, how do we move? Are others doing the same, right? And here a lot of different, let’s call it, like, classical disciplines actually come into play, right, and we try to really systematize that and provide those more future looking insights into our corporate model. 

Karen: So yeah, you talk about strategic intelligence as, like, a service, you know, that you’re providing. So, it’s interesting to think of it—you know, I think a lot of insights professionals—our audience is a lot of insights and analytics professionals, so traditionally, in the marketing research space. So, you know, some of them are very involved in innovation work, and some of them are, you know, just involved in marketing work or new product development. So anyway, the idea of strategic intelligence as a service is something that I’d love for you to expand on because I imagine some people are like, “Well, I am in service to our marketing team,” or, “I’m in service to our CX team.” But here, it’s different, right? You’re like an advisory board?

Tino: Mmm… somehow, yeah. And actually you could argue, we have three different audiences, right? So because, as the Lufthansa Innovation is a separate entity, and our goal is to capitalize on future opportunities, is that we are in service to ourselves, right? So, big part of the Lufthansa Innovation Hub is actually what we call a new business. So, it’s partnering, building and investing into opportunities. So, we are building startups ourselves, right, and therefore we should know the market and be smart about where we place our bets because our resources are not infinite, so we should know where we want to be in a couple of years, right? So, we need to place the bets, and therefore we bet on ourselves on knowing where those opportunities are. And therefore it’s in our own interest to be good here. Then obviously we have our corporate mother and the different companies that are part of that, that we’re also in service of. Because, yes, we are a hundred percent legal entity, we’re a hundred percent owned by them, so we obviously have a high interest in them succeeding, in them being future-proof, and helping them to navigate the future that gets more and more complex, right, and also making decisions that are looking a little bit further. Because they are very much in their own operations, right, so they are doing business intelligence on their operations, on their commercials, on like, how also their marketing is performing, right? So, they all have those abilities, however, they are lacking the resources to really make dedicated time for thinking, “Okay, what is the future? Like, how can we connect those things? And also, how can we share this across those different airlines?” Because otherwise ten airlines would try to look into the future, and probably would come up with ten different futures. And so here, yes, we some sort of provide strategic intelligence as a service. And the third audience that we’re serving is the entire industry because we want to drive innovation at scale, we want to inform the industry, and really put a [unintelligible 00:06:59] and universe together with them. Because we’ve seen our industry to be a quite slow innovator in the past decades, so that’s why we are very open with our research results, and share about 90% publicly, and therefore really provide this as a base fundament for industry exchange for fostering industry partnerships going forward because all that needs to rely on good insights.

Karen: Yeah, there’s so much to unpack with what you just said, so I’m going to go step by step, but I just want to call out that last part. I think it’s really unique that you are gathering intelligence and sharing it publicly for the betterment of the industry. I just don’t know that I know of any other category, or any other company in any other category, that is that generous.

Tino: Yeah, well, that generosity, there’s always a second part of the [unintelligible 00:07:54] is that, well, quite a while ago, especially when we were way smaller, we took the decision for thought leadership strategy to really going out there because it actually helped us to be perceived better inside, right? Because when we are around 10 people in 100,000 global company, it’s about, okay, you need to be heard. You need to be loud, right? And whatever we put out there and get recognized at good quality research and content, it get picked up by other leaders. And therefore we kind of took this outside-in strategy, on getting citations, on getting others actually quoting our work, and therefore our senior leadership taking recognition on, hey, this is actually what they’re doing. 

Karen: Interesting. Yeah, so actually, a very strategic move. So, [laugh] it’s really—it’s a cool to look at it. I want to, before we get into some of the methodologies—because you’re doing some great work—just want to go to one other thing because my brain is wrangling a thought, and this has to do with a way you’ve self-described in your bio as an omni-creative. And I’ll tell you why I’m hovering on that for a minute. I was just at a talk earlier this week where somebody referred to creative intuition as a big—this was a chief marketing officer—and he referred to creative intuition as a big part of his job. And here you are calling yourself as an omni-creative, yet you work in this very strategic space where critical thinking is at the heart of it, really. So, just talk to me a little bit about that, about why you kind of called yourself an omni-creative in this world, and the juxtaposition between creative thinking and critical thinking in strategic intelligence.

Tino: Yeah. So, as I mentioned before, my background is actually in design, and I worked in especially in industrial design, and I’ve worked across all different industries. And I always say, like, I designed everything from snowboard boot, over cell phones, refrigerators, to large industrial facilities, right? So, I’ve done a lot of different things, and as a designer, or maybe as a service provider, you get to know a lot of different industries, right? And my approach, I’ve always been quite strategic in trying to understand, okay, what’s the competition doing? Like, where’s the market going? Do customers actually come in, right? So, I know later, they called that design thinking at one point in time. So, I’ve always tried to look at it from all sorts of different perspectives than—well, when I was finished with my studies, the iPhone was released, right? And then there was, like a whole new of digital interaction, UX, UI, how they call it to there, right? So, there was then part of the job, and actually it was just a different medium where you apply more or less the same type of thinking. And when I went to business school later on, I saw startups and corporates just having different problems to solve, but still I could apply my critical and creative way of thinking, and create value, and really say, okay, this works. And I still do that to the very day and see the value created from that type of thinking because, just looking at, like, why do you solve it like that, right? So, like, what’s the reason? Why not doing, like, this and that is really being critical try to see from a different side, and then just play out and try out the things, right? Because, yeah, it might work, it might not work. And this is true still today, and it’s just using different tools. And I’m not a data analyst. I hate Axle, so I also still use different means, different tools to it. And I work with data analysts, they perform the analysis, I have idea on what’s worth looking into. I have a skill in creating narratives around those things because still the data and the insights, they might not speak for themselves because they still need the narrative. They need to be linked to problems, and they need to be probably differently addressed for the different stakeholders. And that’s also where sometimes the data analysts, they are missing the creative element, right, and therefore it’s great to link the different disciplines across.

Karen: So, are they, those folks, are they within your team, or are you tapping other areas of the organization to bring in that data analytical thinking or insights work? Or do they work for you, specifically on your projects?

Tino: So, we have a dedicated strategic intelligence team consisting of data analysts, and they also, they have also a research hat that is disciplinary, leading that team. That’s my colleague, Ivan. And he is also a doctorate in aviation and analytics, and he also worked with statistics, so he is really, like, into the data, and he works with them on a day-to-day basis. However, we then spar on, okay, how do we want to do research, right? So, developing that indicator stack, and our positioning on strategic intelligence. Together, we then develop the ideas, the service, and the formats that we want to or that we are providing to Lufthansa Group or to our internal employees. However, what then I do with the different analysis is bringing them to a wider context, spinning our overall narrative around that, bringing this into something that we call our innovation thesis, right? So, that’s really one big giant report storyline on this is how we see the indus—like, the change happening in the industry over the next ten to 20 years. And each and every analysis that we’re doing, one by one, is actually somewhat connected to that, right? And there, I also become the interface to different parts of the Lufthansa Group, but also to the ecosystem. So, we share not only our approach how we’re doing it, but we’re also speaking about those trends from a content perspective, right? So, I’m a lot on stage at our industry events internally at Lufthansa Group, while also visiting IIEX at Europe and Amsterdam for showing how we are also doing things.

Karen: And if I recall there, academia is also a part of that ecosystem that you have. So, how are you working with academia as well?

Tino: A lot of our research projects, we try to do together with different partners, and sometimes those partners come from—there are two providers, data providers, that are interested in seeing, like—or they see how we are using their tools and data, and they find that interesting, and very often want to showcase that. Otherwise, we are also working with academia or different think tanks on certain topics, like different technologies looking further into the future. And yeah, trying to publish. And yeah, because they also share our baseline on, okay, let’s make this open. Let’s make this public, have this for the greater good of the industry.

Karen: Cool. Very cool. I want to go back to something that you mentioned already, which is your indicator stack, which was one of my questions, actually, is, let’s… let’s talk about that. But also I want to imagine our listeners don’t really even know the context. They weren’t at Europe, and they’re like, “What even is an indicator stack?” So, can you start there as kind of just a primer: what is an indicator stack? And then we’ll talk about yours and what it blends [laugh].

Tino: Sure. Also, I will try to make it more tangible. So, the indicator stack is a concept of our research approach. Actually, it’s the central concept that we have there. And people might have heard from a technology stack, right? So, companies choose different technologies and tool providers as their main suppliers or as their resources, and that’s what they call their stack. And we have that kind of stack for what we call indicators. So, that’s a group of data sources, but also perspectives that we try to bring together to understand what is happening in the future, right? And therefore, we are looking first of all at two different groups, which you can divide into lagging indicators, into leading indicators, right? So, lagging indicators means those are indicators that show you something—where you did something and then that has a result, right? So, you’re always looking at the results of different things. And those indicators that classical market and competitor research would look into because those are the ones, like, you’re trying to make sense of results, right? So, that’s consumer sentiments, where you look into reviews, where you look into MPS scores of customer satisfaction, where you might do social listening, right? Some of those are already quite advanced, and a lot of companies are not even there yet. And another example for lagging indicators might be looking at what we call market dynamics, right? So, those are stock performance, and those are market shares, but also sales data, or growth rates of different products or services, right? So, this is really, okay, how is the market performance in that sense. Then we kind of look a little bit more into what’s called, like, the unknown or the future, you look into outcome-based indicators, so trying to make sense of local resonance. So, media sentiment is a very big one here, right, where you can distinguish between mainstream and expert publications, right? So, what are they talking about, different companies, but also different areas and technologies, right? So that’s, like, I don’t know, blockchain in aviation, right? So, are all the aviation websites speaking about blockchain lately or not, right? And do they see from a positive or from negative side? So, you can really do a lot of different types of analyzes here. We are looking at earnings call transcripts, right? So, what are the analysts, but also senior or the executive boards talking about in their analysts calls, so you can see some strategic priorities. And, for example, we have done an analysis here, like, when did they start talking about sustainability measures in our industry, right? Which is actually not that long ago, sustainability, yeah, became a topic, and earnings calls was like, well, this could have been, or rather, should have been, a topic for quite a while. It’s good that it is now. And in there, you can really quantify how much of a topic and how positive or negative the sentiment is around this. And then another outcome-based indicator is user behavior, right? So really, okay, how are they using my application? Like, what’s their search behavior, like Google Trends, right? So, that’s another, also very easy to obtain indicator. And then, if we rather, go into the leading indicator. So, it’s like, okay, you measure something, you look at something that might lead to something; that’s why it’s the leading indicator. Those here we are looking at product launches or announced partnerships of, like, for example, we are constantly tracking which airline is partnering with which type of startup, right? So knowing, like, okay, this is an early experiment. We call this a proof of concept, and therefore we try to re-engineer their strategies, right? Because you see, those are the topics, they form early partnerships. It might be that they’re interested in scaling this out. How does that relate to our own strategy? Have we also talked to that startup? And new means of technology enable you to do that at scale, right? So, there is not a single person trying to read the news and then fit this? No, we have systems and tools in place that do that for us automatically, and we just need to review those things. And then there’s other, sometimes even classical approaches, as well as patent analysis, right? So, it’s technology dynamics we look into. There, we have also one more fancy or own approach in there, which is Wikipedia edits that we’re looking into, which is, again, okay, where are technologies leading, right? And technologies is a very interesting topic for itself because very often you have a topic, and then you push it, and you look for use cases and things right? So, it’s sometimes the other way around.

Karen: I remember when you shared this in Europe, and Natalie and I talked about this. Wikipedia editing, that was really interesting. That was one of those a lot of people might be hearing, you know, what you’re saying, and thinking, “Yes, that makes sense. Yes, that makes sense.” But Wikipedia, that was a big aha for me, this idea that edits, tracking the edits—and correct me if I’m wrong—but kind of how many edits are happening, or like, editing activity means there’s something going on there. So, can you just pause there and tell me more about that? Because that seems to be something that might be accessible to others who are trying to tap into some trend work in their world, right? Just, anyway. 

Tino: Yeah, it’s definitely repeatable for everyone interested in that. Because how did we came to the idea of at least, like, trying that was, as an analyst or a researcher, you’re always looking for high quality sources, right? So, because you rely on the quality of the data and the media that you have. However, as a desk researcher, a lot of your research actually starts at Wikipedia, right? So, it’s the default for definition and well-researched and documented data and information, so it’s first start. And Wikipedia as an institution, it also shares this open approach to knowledge, right? They want to make all the knowledge available to the world, and so they also share publicly the amount and the type of edits that each and every article has, right? So, this is accessible for everyone. And there you also can more or less re-engineer, or see what type of edit is it like. Is it a formatting added? Is it like, do they just take out some typos? Do they just renew some of the sources? So, you could really see, okay, hey, there is additional knowledge to a topic being added. And we did an analysis looking at a couple of different technologies where—because you can see, oh, there is patterns, or there’s research projects on certain technologies. I know, like, quantum computing, and that’s something that’s still very much out there, right, because we don’t, I don’t know, have a lot of applications in our daily lives, right? So, this is nothing that our local newspaper would write about, right? So, however, looking at patterns might still be too early. And at that time, we were also doing our version of Gartner Hype Cycle, and then looked into okay, how can we get that even further, and look into where technology or knowledge about technology kind of enters into the mainstream? And [unintelligible 00:22:37] found at least an interesting correlation of Wikipedia edits being even earlier than the older news and media hyping about a certain technology, right? And we repeated that for a handful of different technologies, and always saw that, oh, it’s around a year earlier, you could see it in Wikipedia edits. Obviously, that’s not a statistical, profound analysis yet, however it’s worth repeating, looking into. And yeah, and we saw at least that for technology-related cases, that was an interesting enough indicator for us to repeat it in the future. 

Karen: Yeah. That’s cool. Thank you for that. Because, again, I just kept thinking to myself, that’s also part of that, you know, tapping into your creative intuition, if you will, to use this gentleman’s [face 00:23:28] about, you know, what you’re seeing. It may not be a statistical correlation, but there is something there that is leading to some hype that you should be paying attention to. So, anyway. You could, you know, I didn’t mean to interrupt you, but I just was like, I need to stay with the Wikipedia before you shared more about the indicator stack. Was there more in that kind of what you’re doing that we can learn from? 

Tino: Yeah, I think that there’s only one category missing in the leading indicators, which is the input-oriented one, where we try to make sense of interest. And here we are looking into funding dynamics, so where is venture capital and corporate venture capital, or even R&D expenditure being allocated, right? So, where those companies spend their money on researching themselves? But also, where do venture capitalists, seemingly smart people, trying to place bets in the future, right? And yes, there’s all discussion about inflated valuations, and now we see [winter 00:24:24], those kind of things. However, we still find that quite interesting because yes, our companies that we are building also try to raise this type of money, right, because they need that for scale. And money is an awesome industry indicator because, yes, it shows the interest, like, where did it place the bet, and at the same time, it accelerates development in there, right? Because you could always argue, when there’s a lot of money, there will going to be a lot of progress, theoretically.

Karen: Yeah, and we say—you know, we have a weekly live stream, and my co-host on that, we talk all the time about, you know, when there’s investments, we’re always, like, follow the money because if some venture capitalists, are investing in this company because they have this technology, either it’s because they are, you know, putting their money on it, they’re gambling or risking because they believe in it. That’s one outcome, right, that more people will do similarly, but also it just shows excitement and energy around that, and other people will start to copy that. Other people will try to innovate in that space. So, that one feels a little more manageable to me.

Tino: Yeah, and also what you just said, like, looking at this perspective, per se, is interesting enough in itself, however, we then believe that’s definitely not enough because as a senior executive, I do not want to place my important strategic decisions of the future of my company, solely looking into where do VCs put their money, right? Because I would want to have a very diverse set of view information on, okay, how does that look from a technology perspective, like, are there patterns, right, are there already other competitors doing this and that? Because I might not be an innovation leader, but I might be the best fast-follower, right, which is a different type of innovation strategy. However, I just want to have this broad idea on different data points, information, and insights that I can make a good and nice enough or informed decision that is, yeah, better informed than just relying on to one perspective. 

Karen: Yeah, yeah. Super cool. Super cool. So, you mentioned, you know, that you have a some assist in the systems that you do this, and I’m imagining artificial intelligence is a part of some of the work [laugh] you must be leveraging machines to help you take all of this in. So, let’s just talk a little bit about how AI is either helping streamline your work or, you know, kind of giving your team an advantage with efficiencies. Talk to me a little bit about the role it’s playing. It’s a big deal.

Tino: Yeah, it’s a deal. It’s also big deal in terms of efficiency, right? And a lot of the tools that we use, they release AI features. And we love playing and exploring those because, yes, they make the work much, much faster. I wouldn’t say that they give us additional, different insights, but it’s just—right now, it’s a lot about the efficiency, it’s about summarizing things, it’s about cleaning data, but it’s also helping us to, I don’t know, lay out content, create content faster because, yes, we need to, or we translate this content into written content pieces. And while we are not there yet in having AI fully write that, but it helps us setting a baseline, and then really going into editing it, and then going forward. So, it’s a huge support, and we’re also playing on it on a constant base, trying different features, but also then looking into okay, where do we stand now with the AI doing the data analysis, right? What comes out of it, then kind of comparing it to some of the manual things. So, it’s definitely a space that we are closely observing, actively testing, and also sometimes building our own tools by connecting, via API, to all those providers because it all got so easy, right? Because you can just get an API from OpenAI, so you can call your own GPT chatbot from another tool, right, so those are all integratable to, I don’t know, your own CRM or your own systems. And there you can really build nice little add-ons for researchers.

Karen: You know, you mentioned, I think in your presentation, the idea of creating and distributing frameworks. And anybody who knows me knows I really love a good framework. Like, I love when people [laugh] put information into something that helps me make sense of it. So, I think it’s a unique skill. I think it is easier said than done, as somebody who has tried to come up with a framework, and I’m like, my brain doesn’t always go there, but I really appreciate when I see it. I’m like, “Oh, excellent framework.” Talk to me a little bit about that, why that’s a part of your work, what you’ve noticed about it?

Tino: Yeah, it’s based on the sheer recognition that, oh, we don’t do analysis work for other analysts, right? Our audience is very diverse. It’s from our industry, it’s also from other industries, and they have different backgrounds, and they need something they need to understand, right? It needs to fit their context, it needs to get them also emotionally, right? So, that’s why we are still, like, successful frameworks for good narratives that really resonate with people, they’re like, “Ah yeah, this is how you could see the world, how you could describe it,” and then all those things make sense. And with a lot of models, like, yes, they might be wrong, but they’re helpful. Yes, you can always argue on, like, “Yeah, but what about this? You have missed this element, that element,” but that’s not the sheer case to have something a hundred percent working and scientifically correct, but it’s something that gets you into discussion, that gets people attention, also, yes? Because we are in an attention game because there is content information, it’s always out there, right? So, it’s a matter of quality that you get in there, and also in a good framework, it also it… it withstands time, right? So, you can always come back to it, and you can put new things that happen in industry in the same framework and still, like, use it to make sense of the new things. So, that’s why we have some frameworks that we’ve actually introduced in our company seven, eight years ago, that we’re still using, we’re coming back to. Whereas I can—also by now, people know and understand what we’re doing and what we’re speaking about, and sometimes they just realize, “Ah, that’s actually coming from you. Good to know.”

Karen: Cool, cool, yeah. Back to that credibility that you’ve been building through the work that you do, so or positioning within the industry as an authority. So, nice work. Cool. One of the things that came up in some of your work is around the metaverse, and I think that it came up in the context of, kind of, the future of aviation and, you know, kind of a possible scenario. So, I’d love to talk a little bit about that, but giving you the spoiler alert that part of my intrigue about it is, are you also doing think—like, so there may be some sort of a role that aviation plays in the metaverse in the future, but also, are you taking the metaverse into some of your analytical work? Like, are you actively playing in it now in the work that you do? [laugh]. So, I have two questions around there, I’m hoping you can go there for me [laugh].

Tino: Yeah, definitely. And yes, you’re right. So, one of our recent projects—I think was also part of our newsletter that went out today—is where we publicized our last part of a series of scenario analysis they were actually together with the Airbus, so the airplane OEM, and Bauhaus Luftfahrt, which is one of the leading think tanks on the topic, where we dived into, okay, what could the metaverse actually mean? Because, as I mentioned before, it’s a little bit of a mindset thing of, do you see things as an opportunity or as a threat, right? And as an airplane manufacturer, and as an airplane operator, like, an airline, you probably want to see that as an opportunity, and see, like, what is possible, right? So, will virtual travel replace real world travel, or will it be complementary? Or do we see other scenarios where the concept of the metaverse has other use cases, other business cases around that? And interestingly enough, the metaverse and travel navigation, it’s actually one of the biggest use cases. And coincidentally, also in today’s newsletter, we had a chart that showed that over the past ten years, we have tracked 80-plus virtual reality projects, only from airlines, right? And this also shows sometimes how slow technologies progress because ten years ago, like, VR has already been around, right? So, the first goggles have been around. And yes, we have been testing and playing around with them at the Lufthansa Innovation Hub ourselves, and yes, we now also have Apple Vision Pro. And yes, we might also be working on use cases and project potential companies in that space were in there. And interestingly, like, the industry has a lot of use cases. Pilots are trained, cabin training, simulations. Repair shops, they’re using augmented reality goggles and glasses. I think there, every major hotel chain has a digital replica in the metaverse, maybe Fortnite, or like any other of the current—

Karen: Well, you’d need a place to stay when you can’t be out there in Fortnite and not get tired, right? [laugh].

Tino: Yeah, yeah, yeah, yeah, yeah. And it’s about branding, right? And a lot of the incumbents care about their brands because that is what they have, right, and they want to bring it into the virtual world. Because you see consumption patterns, like, people pay real money for digital clothing, right? They still care about the brand, so they might still care about where they spend their stays in the metaverse. So, there’s actually real world use cases, and we believe there’s financial opportunity in there, or at least there is, again, efficiency use cases for airlines, airplane manufacturers out there.

Karen: So, are your team members—you know, talk about the headsets and your teams going in there. Are they going in there, also, though, and seeing what is trending in the metaverse, in those different realities, and pulling some of that? Like, I imagine that’d be a pretty sweet job if your job was, you know, spend a couple hours in the metaverse and tell me what’s happening. Go see what’s new in Fortnite, go see what’s new in [laugh] Minecraft, or Roblox, or whatever, and report back in. Like, again, that might be a very simplistic way for me to be looking at it, but I’m thinking that’s a pretty cool aspect of somebody’s job, perhaps.

Tino: Yeah. It’s part of my colleague’s Anna job, actually. So, she covers this topic, also out of personal interest. So, she regularly spends time in the metaverse, as you want to say so, by at least testing all the travel and airline-related products and services out there, right? So say, okay, where do we stand? What’s the new things? Are they really that good, right? But also testing other stuff on, like, okay, what does the technology of now, Apple Vision Pro actually allows us to do right? And, yes, there is meetings happening in the metaverse, like, just to see, okay, is this something also from a… not from a travel perspective, but, like, from a general technology progress perspective that we believe could be something as the future of work? So, yeah, it’s part of someone’s job.

Karen: So, cool. Yeah, that’s cool. Anna, good job, Anna. Way to get that one [laugh]. I’m sure there’s a lot of people that are like, “Oh, I think that would be cool.” Tino, question I have for you before we wrap. Are there any other kind of emerging methodologies or things that, you know, an insights or an analytics professional might want to look into, sort of like what we just talked about with the metaverse, sort of what we talked about Wikipedia. But is there anything else that, you know, could be a really cool playing field for people who do the work that we do in insights and analytics?

Tino: Yeah well, one of the next experiments that we actually plan to do, or actually wanted already to do, but didn’t find the time yet, is looking into podcast transcripts because we believe, or we have the base assumption, those are much more emotional, a lot less edited than an industry report, or a blog post whatsoever because those are always edited, right? Always the good information is taken out, and they lack any sort of information or emotion. So, we believe, well, if you look at industry podcast, and you can transcribe that at scale, and you can then put sentiment analysis, but also keyword thematic analysis on top of it, you might find something. So, we have, already, a plan on how we would want to do that in terms of tooling and stuff. We haven’t found anyone ever doing that before. And yeah, if someone finds time to that before us, reach out to me, or would like to know if this is something worth trying going forward. But yeah, it’s something on our list to at least test, and then see if this is something we want to put into our indicator stack for the future.

Karen: I love that so much, and it’s just an absolutely perfect way to wrap this up. Is there anything you wish I had asked you that I didn’t get to, Tino? Because we’ve really talked for so long, and I want to be mindful of your time.

Tino: What we haven’t spoken about, but another thing that we are trying at the same time is, all the things around the indicator stack, we’re also trying to build a tool out of that, right, that at some point in time we might also want to make accessible for other industries out there because we see interest in things how we’re doing it, and we see a demand in innovation being done in the future.

Karen: Very nice. Very nice. And I think that’s really great. You know, you mentioned your newsletter. I feel like we should, you know, after the show, like, be able to give people a link in our [show notes 00:38:34] on how can they subscribe to that to follow the work that you’re doing. It seems like you are just the type of person and organization that can teach people a lot. So, thank you so much for that. Any other way people can follow the work that you’re doing?

Tino: As you said, our website, tnmt.com. We’re very active on LinkedIn, from a company perspective. Myself also, I’m quite active on LinkedIn, so just connect and reach out to us.

Karen: I love it. Thank you so much for joining me on the show today.

Tino: Yeah, thank you for having me, Karen. It was a pleasure.

Karen: My pleasure as well. And thank you, Natalie, for what you do as our podcast producer. So, appreciate you. Our editor, Big Bad Audio, thank you so much. And of course, our listeners, we do this week after week to hopefully inform and educate and inspire you, and I hope this episode did the same. I’m certainly walking away with a lot of inspiration myself. So, we’ll see you next time. Take care.