From Data Silos to Insights Intelligence with Thor Olof Philogène of Stravito

Speaker A: You could, uh, have the strategy, but not the money. Frankly, my belief is that if you have the right team, then you'll find the money and you'll kind of nail the strategy eventually, whereas just starting with the money won't necessarily get you there.

Speaker B: Guided by over 25 years in the data and research industry and assisting innovators with investment banking and advisory services, Sima Vasa brings you Data Gurus, a leading market research podcast that offers actionable insights for business acceleration and value creation. Join her as she speaks with key innovators in the space to bring you up to speed with the current state and the future of data analytics and data ecosystems. This is Data Gurus need support on your market research projects. Paradigm Sample is a full service market research solutions provider. Whether you need help with questionnaire design, survey programming or online data collection, we are ready to assist. Paradigm can do as little or as much as you need, saving you time so that you can focus on insights. Learn more@paradigmsample.com

Speaker C: welcome to another episode of Data Gurus. I'm Sima Vasu, your host. I'm excited to welcome my guests from Sweden. Thor Olaf Philogine, he is the CEO and co founder of Stravito. Welcome Thor.

Speaker A: Thank you so much Sima. Thank you for hosting.

Speaker C: So yeah, you're based in what part of, are you in Stockholm?

Speaker A: I am, I am based in uh, you know, the Nordic capital. Some would argue out Stockholm in Sweden and Scandinavia.

Speaker C: Yeah, there's a lot of um, what I've learned over the last two years, there's so much innovation in our space specifically that's headquartered in, in Stockholm. So it's quite exciting. I'm sure there's a great community of research, uh, and like minded people there.

Speaker A: I think, um, I'm quite proud to see that, that actually emerging to your point. But there's also a lot of innovation in the world of AI so it's, it's a really good spot to be in.

Speaker C: Fantastic. So, uh, you have an amazing background and journey to the point before you started the company and I just would love to the listeners to understand a little bit of your journey and how you got to the point of, you know, then we'll talk about your company and the pain points we're solving, but it's always nice to get a little bit of background.

Speaker A: Sure. Well, thank you so much, Seema. No, I mean in essence you could say I started out in a fairly traditional way for business school, but quite early on I realized that I wanted to be closer to technology. I wanted to Be closer to building stuff. And uh, that led me to start my first company, a uh, company that actually was acquired around 15 years ago and that acquisition brought me to San Francisco. So in San Francisco of course got exposure to the beautiful world of technology even more. And uh, I had some amazing years there before moving back to Europe and joining another early stage company, a uh, fintech company super early where I became the leader of the commercial function. So I became the chief revenue officer. Now that was a multi year journey but that eventually became a quite big company that was acquired by PayPal. And I reflect about those, say 15 and include the 12 years and add the eight years I've been doing Stravino. Across all those years I think it became clear that everything really depended on a couple of things. Uh, first of all people, uh, and the importance of getting the right people, creating an environment and culture where those same human beings can thrive. And second of all learning from data and insights. So at the fintech company I mentioned, a big part of our success was really building a commercial engine that was deeply data driven. Uh so decisions were grounded in evidence and not opinions.

Speaker C: I love those two things and both are you have to focus on it in terms of the people ensuring that they are in a safe environment to thrive, grow, fail fast, succeed. Right, so talk to me a little bit about your philosophy on people and then I want to get to the data and insights part as well.

Speaker A: Yeah, I think you touched on a couple of points there. Right. So I think uh, on the people first side I think that um, I mean this is ultimately a belief but I think that whereas there are several combinations. You could have the money, uh, but not the strategy and uh, the people, the team. You could have the strategy but not the money and the team. Effectively My belief is that if you have the right team then you'll find the money and you'll kind of nail the strategy eventually. Whereas just starting with the money won't necessarily get you there. So that's just like if you had to uh, you know, stack, crank the importance. And then you also touched on something seema which I think is super important. You know, I mean Google public made paper public. Some studies they made on this when they themselves try to build how can they build the best organization. And it's the importance of what you touched on which is psychological safety. And the way I try and typically talk about it is what it does is it unlocks capacity because the capacity that people would have to spend and dedicate to just thinking about where they stand in the political game. Political game, they can unlock and just focus on building something great and doing what the business needs to become even better. Um, which means that if you create an environment with great psychological safety, uh, you eliminate more politics and transparency, I think can kill silos. So there's a lot there, right?

Speaker C: Yeah, you're absolutely right. And the amount of time that people have to kind of COVID their authenticity in order to play the political game, that's an energy drain versus that energy to build and innovate and grow personally as well as professionally. So I fully support that and I think, I hope more companies focus on that. Let's talk about data and insights. I think that, um, I'm a person who loves data. It drives so many decisions. But, um, there's also a place where gut instinct, uh, tends to be the norm. And so how did data and insights manifest its importance in your journey to date?

Speaker A: Yeah, no, I think, um, there's the part that kind of the observation that I have just looking back and then there's kind of the observations that we made when founding Stravito. But if I start with the, uh, format, I'd say that it became clear to me in hindsight when I look back at that first business that we built, uh, the company I joined in the US and then the FinTech I joined in Europe that eventually went on to be acquired by PayPal. Throughout those journeys, I realized that the successes I had were really about me spending time on uh, the one hand kind of understanding, uh, spending time acquiring understanding, analyzing data and using that as almost, you know, to build a flywheel, commercial flywheel or product flywheel in some cases where you effectively infuse that at the right level so you would, you know, effectively, you know, and. Sounds like a cliche. Build a learning organization. Right. So it's, uh. I guess the core point is you need to. It needs to be part of a system. It cannot live on its own. So it's not. The data is not for the data team or the insight is not for the insight scheme alone. It needs to be part of a system where. For you to benefit from that flywheel effect.

Speaker C: Yeah. It's cultural. Right. And it's curious.

Speaker A: Yes. And to your point, seem. I'm actually really happy that you mentioned that. That was actually another insight when from one of those journeys is just. I realized that it was getting the data and kind of understanding what to do and coming with recommendation was like not even half the job. The bigger part of the job was getting people to want to use it. And to get excited and feeling confident about taking that data and actually betting sometimes very large amounts of money. And that is, to your point, very cultural. It's cultural transformation that you need to be engaged with. Apart from of course the foundational stuff that still needs to happen.

Speaker C: Yeah, absolutely. So talk to me about when you started Estravina. What did you see in the market that said there's a pain point here that we can help with?

Speaker A: Yeah, no, so the interesting thing is, so I just left that Fintech company I mentioned. During those years I met one of my co founders, Sarah Sara Lee. Uh, we worked a good five years together, uh, there and um, we were exploring the idea of building something together. And uh, we had just decided, we decided to join forces with two other entrepreneurs. So Stravito has four co founders, Anderson, uh, Andreas, and they had just built and sold a shopper research, uh, company currently owned by Ipsos. And what they had discovered, which was I think fascinating, uh, was that these organizations that they would sell to, and even we were talking about Unilevers, you know, of the world type companies, they were commissioning enormous amounts of research, but there were actually very few employees who knew where anything was nor how to use it. And I think it's one of those things. And I've been at many dinner table conversations when you say, it's like, do you know when you get this email and there are 20 people recipients on it, it's like do you know where X and X file is? Et cetera. And most people can relate to that. If you've been in a big organization, you know what that is. And we discovered that, well that was actually a massive problem. So we did some research of our own and we realized that that was not only a very big problem, but it was actually something we could very much solve. So we started kind of thinking about what that would be. And um, when we decided to found the company, our goal was quite simple at the time. So what we said was like, we need to simplify. This is what we called it, knowledge discovery. So how do we make sure that knowledge discovery at very large organizations like Fortune 2000 sized 500 companies is actually a solved problem? It wasn't at the time. Uh, and since then we've grown to serve many of the world's largest companies across sectors in both the US and North America and Europe.

Speaker C: That's amazing. And I love the word that you've used in our conversations is that companies have a corpus of data and give us an idea of the volume and the magnitude of Data that your platform ingests to kind of provide knowledge discovery.

Speaker A: Oh, I mean that's a really good question. And um, we do talk about it as a corpus and I think we have one big benefit. And this is like our role is a neutral role. Um, we are the Switzerland of sorts. We are not a research producer. And as such what we have enabled is, uh, we can in a very agnostic way, effectively make sure that all the relevant sources that are both internal and external can actually be searched for from one single place. So trusted answers from that large company knowledge corpus can be surfaced. Now your question was give me a sense on how large it is. And I think it, it will depend. Uh, and you know, but like we have, many of our clients are present in more than a hundred markets and local markets have their own, you know, before we arrived, would have their own silos and they would have their own produced materials and they would also buy, you know, they would commission kind of ad hoc research, but they also have syndicated data, they would have their own dashboards. And if you think about it, what that means is you have hundreds of markets that produce internal stuff, you have hundreds of vendors, you have hundreds of dashboards and all of this, these are multipliers. Right. So you effectively end up with quite a lot. Uh, so yeah, it is quite vast. Uh, but we now consider that that's something we've been able to solve quite nicely for many, uh, of our clients and are eager to take a step forward.

Speaker C: Yeah, I mean I would think that the clients that you have again also have, um, a curiosity, a cultural belief that data can be part of the system. Um, and it's really democratizing access to knowledge. Right. I think a lot of times Consumer Insights tends to, you know, you go there to get the answer, but not necessarily are empowered to get the answer yourself. So tell me, how does that work from your perspective when you work with clients? Is it truly saying, look, we have all this data and now I want, I don't know, X number of hundred of people to have access to this knowledge?

Speaker A: Yeah, no, I think the goal from the very beginning was to uh, democratize access and to make sure it wouldn't be for the few, but for the many. Uh, but in a way that would respect whatever governance requirements you have and whatever secrecy requirements you have, while at the same time respecting everything from PII data to all kinds of compliance and regulations that companies need to follow. But I think we've really had a customer and kind of end user first approach. Uh, and I think ever since the very first brick we kind of used to build the street reader house, if you will, had infuse in it. That's from the very, very beginning. And uh, we had the bar extremely high on end user experience. Because when you start talking about kind of making available to a lot of people, then you also, I mean, we are in a, uh, attention, I mean attention is a very, it's a currency that's scarcely available right now. People don't have time, people are time starved and you know, kind of attention pouring, attention pouring. So those are tools we've used to lower that, make it more accessible. And I think we, we believe that it's, it's ultimately about reducing the friction on the end user size, whether it's how you design that user experience, but also how you apply AI. And um, what we've been able to do is to go from just hosting and serving that data to become what we call like an insights intelligence platforms. And if we go back to the, I mean you used the word corpus before and yes, we do sit on the company's trusted knowledge corpus. Yes, we're neutral and no, we're not conducting any research. But, um, the fact that we can aggregate internal and external sources, whether it's Euromonitor Mintel or whatever it might be, uh, that neutrality really matters, you know, because it's, it means that whoever is using it feels that they can actually take their insight slab decisions with greater confidence.

Speaker C: I'm curious. There's, there's a vast amount of data that the platform holds. There's a question or a query that you, you go into the platform and you want to get answers. How do you, how do, how do you prioritize what fuels the answers? Right. If you have multiple studies that might be targeting the same questions or they're tangential to the topic of interest, how does the system prioritize and produce the results for the end user?

Speaker A: I think there are two parts to answer your question, which is like, how do we know what to surface when somebody asks something Better way of saying, yeah, so I think we need to start with kind of how AI stands because, uh, everybody talks about AI and AI is the big thing and it drives markets. Some people talk about it's overvalued, undervalued. I mean, there's no consensus. However, our viewpoint is that the solution is AI plus human expertise. Ah. So humans cannot be removed. And I think the reason I'm saying so is we actually have insights experts in house. When we build technology, we do it from the vantage point, we know who we are serving and we know what they care about. So when we build what was originally kind of like the natural language processing systems and when we surface, uh, when we do what you call source selection on what is it that you utilize? Kind of in our AI assistant, all of that has been informed by industry expertise, by human beings that actually understand. So whereas you know, I'm sure there are a ton of great AI systems out there that are everything for everyone, we are not, we're pre focused and it's that expertise that allows us to build something that gradually becomes better at surfacing what you care about, but we also give you the opportunity of understanding what we surface. So you alluded to our AI assistant, uh, which is uh, really what kind of your users utilize. And um, with that teams can ask strategic questions and our AI assistant will answer, you know, relevant, verified insights with reference materials. You'll see exactly where it comes from and you can, and you can see where those sources are. But that's the short answer, the non technical answer.

Speaker C: Does that mean that your teams work really closely with clients to ensure that the way that you kind of um, prioritize data and insights is in sync with how the client would see it as well?

Speaker A: Definitely. I mean there, I mean it's, there is definitely. So so far I've only talked about the traceability. So where does the answer come from? But like I think what you're alluding to is like how does that manifest, you know, in terms of your cooperation with the different companies you serve. One of the many things we do, uh, when we onboard a client is we learn about their internal vocabulary. We learn, we effectively learn because like if you might be at a, say a consumer packaged good company or you know, consumer electronics company, you might have your own internal lingo, you might have acronym that designate certain products. Uh, you might. And then maybe you like you're big on kind of oncology studies. If like you're a pharmaceutical company, et cetera, we make sure to that whatever system we bring to the customer actually has the relevant knowledge like embedded, populated. And some of it is done like you know, really us spending time understanding what is it, what is it that's real for this company. So I would say that there's at this level, I would say of companies we serve, meaning Fortune 500 sized companies. That's an expectation. You know, there needs to be some level of system, the system needs to adapt to the reality to some degree. And that's, that's something we do.

Speaker C: And it seems like Once you're embedded in an organization, it's very difficult to switch because there's such a huge investment in making sure that there's alignment in the vocabulary, in the prioritization of sources that fuel the answers. It's almost like a consultative engagement that could spawn for many years, if you will.

Speaker A: That might be true, Seema, but some wise human beings said only the paranoid survive. So I think, I don't really see it that way. You know, I think honestly we, our clients expect us to be ahead of the curve. They expect us to see where the puck is, you know, figuratively going. And so we need to be several steps ahead and effectively anticipate. So I don't really think that we can in any way relax and just benefit from, you know, that level of. Because honestly, it doesn't matter. We need to be several steps ahead to earn their trust over time. So, um, I wouldn't say that we see it that way.

Speaker C: I wanted to clarify. I guess the point is when a client brings you guys on, it is a huge investment of time and effort to make sure that it serves the organization. Um, and obviously you have to keep up with the innovation cycle and keep up with meeting client needs. But this is not a plug and play here. Buy a license. See you later. That's the distinction I was trying to make.

Speaker A: Yes, absolutely. Uh, absolutely. But I think, you know, we've been. Become much better over time at kind of onboarding clients. You know, it's something we've done hundreds of times, so it's something we, we feel very confident in because I think the reality is whoever we bring on, they actually have full time jobs. You know, this is so, you know, they are not interested in something that gives them a second full time job. Right. So it's like we, ye. It's our job to really make that transition super smooth. And I think I'm actually quite proud of like what the type of responses we get and the type of level of feedback we get from how smooth that is. And many of our clients are surprised to see that it's something because we've done it so many times and because we have ourselves a very strong customer centric approach, we're actually quite good at it. Uh, but I mean, to your point, it definitely requires effort.

Speaker C: Yeah, yeah, you have a strong playbook. It sounds like to encourage clients you kind of need to. Right. In terms of, to be able to sell to multiple large companies. So let's switch gears a little bit. I know everybody is always concerned about the customer relationship. You Know, you're working with these large data sets that clients have purchased or commissioned research studies, either syndicated tracking, ad hoc studies. And all of a sudden now we have this middle layer that's kind of taking and ingesting the data and, um, in some ways owning the customer relationship. How's that relationship work, um, in terms Nielsen's or any of these companies, um, that provide data to clients?

Speaker A: Yeah, no, I think that's a fair question. Right. The way I tend to think about it is as having spoken about neutrality. We don't compete with research providers. But if anything, and this is something we are clearly seeing in the numbers, we actually increase utilization of their work. Uh, so when companies lean in, research actually gets used more. Yeah, because suddenly, you know, it's the typical, you know, it's like it's accessible, it's embedded. You don't need to go to 10 different web pages to ask the same query. And that typically, you know, leads to repurchase and leads to deeper partnerships. And you know, in many cases we actually, you know, our, the data that we host will be embedded in other places. Right. Because so, for instance, some of our clients have their own, you know, internal tooling. Uh, and, uh, so we don't necessarily, or we're not trying to be at a certain point kind of in the user experience. We're really trying to reduce the amount of friction the end user needs to go through to do what they need to do. Right. Because ultimately they have responsibilities and they need to use multiple data sources to address those responsibilities.

Speaker C: Do you report on, um, data usage back to the companies that are. Yeah, that's. I missed that one. That's really, really helpful in terms of the value you provide to even the companies where the research is commissioned. Very powerful and you have objective data, then to say your, your data is actually being used a lot more and it's increasing over time.

Speaker A: I mean, we've actually had this discussion with some of our partners and when we've looked at it, we've been able to highlight. Take a look at. Well, this is what we're seeing and the trend is quite clear. Way to think about it is, um, I mean, if we in any given day will have, you know, uh, thousands of queries being asked and you really want to make sure that if you as a research provider sit on data that is relevant to any of those queries, you make sure to appear in those queries.

Speaker C: Right? That's the. Yes.

Speaker A: And you can either choose to be part of it or not. And when we've Looked at it, we've seen that it's not only something I say, it's something we've actually seen happen when data integrations become complete or not.

Speaker C: Yes. Uh, it's analogous to, uh, not exactly. But I know a lot of companies are trying to figure out. In the beginning they were trying to figure out how do I increase my Google search metrics. And now they're saying, um, how do I increase my visibility in ChatGPT or any other LLM? Because I need to be relevant. I mean, you guys are in a private ecosystem, but still it's like I want to make sure if I'm a data provider that I'm relevant and I'm where I need to be in terms of utilization for the questions that are being asked. That's it.

Speaker A: Yeah, and I think we're, I mean whether it's research provider or Stravito, I think we all have a duty to try and provide our customers more value over time. Which means that like gradually, as time passes, the service needs to become more valuable. And that's true for us. It's true for any. Nobody's escaping it.

Speaker C: Yeah, true. All right, so that's a good way to segue into the future. Technology is moving so rapidly. As you know, you guys are working on different features, uh, leveraging AI. Where do you see the future of your platform, um, and your solutions going as technology continues to accelerate?

Speaker A: Yeah, well, I actually think the future started a while back ago.

Speaker C: Um, that's a nice introduction and a really nice phrase. I thought the future started a long time ago,

Speaker A: but if I kind of repeat some of the things I said, I mean, what we do is we're in the business of building AI that is purpose built, but we're in the business of doing so in a way that combines it with human expertise, uh, which is where we benefit from having folks that come from the industry. Now this year we uh, launched AI Personas. What that does is it really addresses the question of relevance. Now when we take a look at the many companies, huh, we're fortunate to serve, many of them already have segmentation studies. And often each of those studies will be hundreds of pages of long. And if you think about the world of before, in order to apply it, you required kind of some skill in memorization. So the way to think about Stability's AI Personas is that they are built from those individuals brand segmentation studies that allow them to simulate consumer perspectives. Uh, and what that simulation means that anyone using Stravito's AI Personas can stress test product ideas, campaign ideas, strategy ideas in real time. So the way to think about the actual software is like we bring those Personas to life in an interactive interface. You can ask questions, you can get answers that are grounded in real data. So it's not chatgpt open Internet data. And you get source references, you have traceability, so you see where it comes from. And for example, with Lavazza, the coffee brand, we published a case study, came out in Ad Age. In their case they had uh, perspectives and answers that were condensed perspectives that I think were based of thousands of survey respondents with full traceability back to the source. And that's quite exciting. So you give end users the ability to convert static documents into real time interactive conversations without being a black box. So it's like, it's like trust the source. You actually see where it comes from. And as an end user, you benefit from much earlier warning, much earlier stress testing to help you evaluate ideas before you commit to what you still need to do, which is like some level of, you know, many times, many cases, expensive primary research. Now, since we're talking about the future, I just want to go a bit further. AI Personas is the first of several agentic AI capabilities we're building now. Our general goal is how can we help the companies we serve to make smarter bets and identify growth opportunities. This is ultimately, and I'm saying this because I've hosted m, our own kind of consumer insights podcast. I've had multiple conversations with Insights leaders. This is about helping Insights leaders earning a bigger seat at the table and helping them drive top line growth for the companies where they work. That's what it's about.

Speaker C: Love it. I love it. I almost can see these Personas coming to life in a boardroom. Right. In terms of really bringing the voice of the customer fully front and center as it relates to driving, uh, and making decisions.

Speaker A: I think there are some great companies, I think many of you know, will know them that have either kind of empty chairs at kind of the boardroom or they have token, they have token. They basically have token representation of their customers. What if that chair would talk? You know, that's, that's, that's quite powerful.

Speaker C: That's right. Yeah. That's amazing. And I, I don't doubt that that's going to happen one day.

Speaker A: It's happening, it's happening.

Speaker C: Uh, uh, let's switch gears though. You've done a lot of cool things in your career and you know, we have lots of listeners that are in the beginning stages of their Journey. Um, um, you have people who are contemplating if they should be entrepreneurs or not. What are kind of key pieces of advice or what's kind of the one thing that you keep central as you lead your team and grow the business are there and it might go back to the people in the data and insights. M. But are there other kind of softer things that you think about as it uh, relates to leadership?

Speaker A: Well, I think to, to I'll get back to leadership. I think there's, there's one that you and I, I think you and I talked about, uh, although casually. But I think there's something that is true for, you know, so for context, I've lived through several technology shifts. You know, I've been part as an entrepreneur and kind of Web 2.0 social media. I've seen Metaverse and Crypto from very close. But I believe that from our vantage point, this is assuming that people that listen to you seema have an affinity for insights and affinity for data. I think that what we're seeing now is actually bigger for us. And the reason why is the best Insights and data professionals are great at asking the right questions and that kind of what differs the good from the great. They're not order takers, they challenge us. And the beauty of kind of this technology shift is that LLMs or you know, kind of large language models, they reward exactly that skill. The better the context, the better the question, the better the output. So in that sense I think we so kind of data professionals, insights teams, we've been training this, we've been training and preparing for this moment our entire career. Uh, and it's game changing. So I think we need to lean in.

Speaker C: That's so true. That's so true. I take for granted actually that everyone is curious. Um, that's not necessarily the case. You refine it over time if you're not as curious as another person. But those questions really drive the results that you're trying to get to through all these technologies.

Speaker A: But I want to go back to your leadership question because I didn't fully answer it, but I think leadership always comes back to people. In my view, the right people can fix strategy and money problems and the wrong people can't. And I think my approach and our approach has been to focus heavily on psychological safety, on transparency. Uh, when people don't have to watch their backs, you can really unlock their full capacity. Uh, transparency removes politics. If, uh, you don't want silos or games, leadership need to model that behavior. And then lastly on AI specifically I really think it's a moment for senior leaders in our kind of area of the world to step up. Uh, and I think you and I talked about this. Um, but there are studies that confirm this, that this is not something that we can delegate. This is where kind of, uh, experience, judgment and guardrails matter. So, yeah, it's really that opportunity.

Speaker C: I completely agree with you. And I think that sometimes we undervalue all those experiences, regardless of what technology shift is happening, to say, oh, I don't know that as well. I'll get some young person to deal with it and I'm going to just not necessarily use the same critical principles of leadership and kind of creating parameters that we've used in the past. It's no different. Probably a little bit more learning on our part, but it's something that's quite exciting in my mind in terms of the next shift in our industry. Thor, thank you so much for joining me. I thoroughly have enjoyed our conversation. Uh, look forward to keeping in touch with and, uh, when you're stateside, please do let me know.

Speaker A: I look forward to it. I was actually thinking about that very same thing. Thank you so much, uh, for having me seem. I really appreciate it.

Speaker C: Take care. Thank you.

Speaker B: Thank you for listening to the Data Gurus podcast, brought to you by Infinity Square. If you enjoyed this episode, please leave a five star review and be sure to subscribe so you never miss an episode. Tired of market research solutions that put your project in a box? At Paradigm Sample, we approach market research support with customized and consultative solutions. Whether you need help with questionnaire design, survey programming, or online data collection, we're ready to assist. Let us know your needs and we can customize a solution just for you. Learn more@paradigmsample.com.