BOUNCE - Charlie Butler (Co-CEO). Getting to the best decision at the right speed; Balancing data retrieval & fresh research; Why researchers still matter.

Delighted to have on Charlie Butler, co-founder and CEO of Bounce. Having started the business while still at university, Charlie and his team have built one of the fastest-growing companies in the insights sector, working with organisations including Coca-Cola, Diageo, Pernod Ricard and Tesco. We discuss everything from mental health and entrepreneurship through to AI, knowledge management and the future role of researchers. Among other areas, we cover:

• Founding a mental health charity after a career-ending rugby injury
• Building Bounce from a student startup into a global insights business
• Why a good enough decision made quickly often beats a perfect one delivered too late
• The challenge of turning research into faster business decisions
• AI-powered knowledge repositories and the future of organisational memory
• Citations, conflicting data sources and avoiding hallucinations
• Why researchers should be the conductors rather than the casualties of AI
• Synthetic data, digital twins and where reality diverges from the hype
• Venture capital, scaling a business and common fundraising mistakes
• Fiction books, optimism and building a company without becoming consumed by it

All episodes available at  https://www.insightplatforms.com/podcasts/

Suggestions, thoughts etc to futureviewpod@gmail.com

Transcript

SPEAKER_00 0:08

No other industry is like it when it comes to market research. When it comes to stubbornness and resistance to change and things being the way they are, you could spend all your life complaining about why things are the way they are, or you can put yourself in the shoes of the researcher and change, manage, and articulate a better future. And I think the reality on the ground is that humans make decisions with so many biases that all you're looking for is enough information to deliver enough confidence based on the speed required. The incentives for synthetic to or AI moderated call to work are so high that I think it skews the human dimension. So I think it skews humans' belief that it will be true. When you want something to be true, you are more likely to believe it will be true.

Meet Charlie Butler And Bounce

SPEAKER_01 0:58

Welcome to FutureView and a conversation I've been looking forward to sharing for a while. So Charlie Butler is the co-founder and co-CEO of Bout, a business that seems to have achieved the rather unusual feat of getting researchers, technologists, and AI enthusiasts all excited at the same time. Having started this company straight out of uni, Charlie and his team have built one of the most innovative businesses in the insight space, taking a very different view of how research should work. In effect, helping clients leverage existing knowledge and then filling in what you might think of as the gaps with automated research tools and partnerships with specialist providers. And given that Bounce works with organizations like DiAgio, Coca-Cola, Pono Rico, and Tesco, it clearly seems to be working. One of the other things I really liked about this conversation though is that Charlie doesn't really come across as the stereotypical technology founder. Before Bounce, he founded a mental health charity after a career-ending rugby injury. And throughout the interview here, there's a real emphasis on people, empathy, and helping researchers become influential rather than replacing them. As you'd expect, we do spend quite a lot of time talking about AI, knowledge repositories, synthetic data in the future of insights. But we also ended up discussing entrepreneurship, raising venture capital, leadership, optimism, fiction books, and why most business books could probably be a lot shorter than they are. So, Charlie, firstly, thanks so much for joining today. Really delighted to have you on the podcast. Thanks for having me. Not at all. Now I wanted to get going with the traditional icebreaker. So it's something that doesn't have to be deepest, darkest secret, anything like that. It's just something that people might not know about you unless they know you very well.

Building A Men’s Mental Health Charity

SPEAKER_00 2:36

I suppose, well, people in the industry won't know that the first entrepreneurial thing I did was when I was in university before setting up Bounds was actually set up a mental health charity, a men's mental health charity when I was 18 or 19 and really struggling myself and with a few friends who I opened up with, and we realized that was 2018 or so, and very little public support systems and networks and kind of peer-to-peer help, in particular in university. And it kind of came off the back of kind of a career-ending sports injury I had. And I kind of had this loss of identity and not really knowing what I wanted to do and struggled a lot, and then ended up spinning up a mental health kind of community called Tribe, which ended up raising 150,000 euro while I was in university for kind of suicide prevention and youth mental health services. And that was kind of the first taste of entrepreneurship in some way, but actually it was more around personal passion and things that I found difficult. Definitely something that I'd say most people don't know, but that was kind of my uh probably my first passion, and it's probably something that I will continue to spend a lot of time thinking about in uh doing nonprofit work in for the rest of my life.

SPEAKER_01 3:53

Yeah, I mean it's it's a fantastic story. And and if you don't mind me sort of asking, digging a little bit further into it. So I I'd imagine, I don't know what sports it was, but I'd imagine piecing it together, it's probably quite a macho type of world, and you're not meant to admit any weakness and all that type of thing. And so how does it work? Is it more like peer groups? Is it's the likes of you starts to open up?

SPEAKER_00 4:16

Yeah, yeah, it was rugby that I played when I was a teenager, so yeah, quite um macho and in in every sense of the world, I suppose. And I my whole identity was wrapped in that thing, and then when I had to give it up, I had you know, my right leg is effectively half metal, I had to have six surgeries from the age of 14 to 18. What ended up happening was I realized that like once I was not in that team, I had this idea of like, well, what am I then? Like I have no friends now because my friends were the rugby team, or my friends were can they only liked me because of my sport or whatever it was. So that was that's a hard thing to go through when you're a teenager at the best of times. And then I mean, like most cathartic vulnerability experiences, my best friends uh, you know, I think we were in the bar, a pub or something, and we ended up kind of going, Wow, that period in school was really tough, and for a very different sport, a different reason, we both had a went through a similar struggle and ended up opening up to one another, and then we kind of laughed and at how crazy it was that we were walking beside each other in the corridors for five years, going through a very similar bad experience and never spoke to one another about it, even though we were friendly and similar groups, and that kind of inspired a very simple message, which was to kind of your first display of vulnerability having such a positive response to it, encouraged us to try and like get more people opening up and speaking about it in a very public sense. And funnily enough, I got invited on like a podcast before podcasts were really a thing in university, and I remember after doing that, there might have been four or five hundred listeners, and I might have gotten four or five hundred messages straight off the back of it. And I think what that does is it gets you angry in some sense because you're saying, How is this causing such a reaction for such a normal thing? You know, being in a bad place mentally is such a normal thing uh for everyone. So that kind of inspired us to do something about it, even on a very small scale, and a very at a local community level. And I think now it's you know just the world has come on leaps and bounds around that sort of thing. But it told me a lot about myself and you know what I was capable of and what I could get through. And I think also the type of business I wanted and life I wanted to then have. Yeah, definitely a kind of a I mean an unknown, but a very central part of my and our story.

SPEAKER_01 6:36

Yes, as I said, we might digress a little bit from the planned focus that I mean, I also think it's an incredibly meritorious initiative, and even though things have moved on, I mean I think men in particular are notoriously bad and doing anything more than a kind of token check-in. Of going, you know, are you alright, mate? And they go, Yeah. And then they don't go, are you really? Well right. And and as you say, I think that idea of actually sharing something about yourself leads to.

SPEAKER_00 7:02

Yeah, and also like the reason why I think I was I had the confidence to do so was because I was just surrounded by exceptional women. You know, my mum, my sister, I've been with my partner for eight years, Ellen, like those people might have easily been the key that unlocked the door, and also kind of without even telling you subconsciously, like educated you on how to pull things out of your male counterparts. Like, even things like I remember my mum saying whenever I was upset as a kid she would take us on a drive, because whenever we were looking at each other, I was more likely to open up. You know, boys like looking in the eye is harder to open up. And that's my theory for like why saunas have become really popular in Ireland. And I think most of it, because in saunas, if I'm sitting beside my friend, we're usually looking out, not at one another. And I think it's funny, like that learning is why. Like, if I knew my friend I'd be struggling and I I wanted to try and speak to him, sitting on a bench where you're looking out or going on a walk where you don't have to look in each other's eye, is like a learning I got from my mum on how to get people to feel more comfortable opening up or displaying vulnerability first, then allows people to then be vulnerable after. So, again, it's kind of the learnings from the exceptional women in my life, and then that kind of is a probably a through line through my whole life, but and how you carry that into your male relationships.

SPEAKER_01 8:15

Yeah, yeah. I mean, your mum's totally right about it. It was actually a point that was raised. I had a book I read about it's like bringing up boys, boys, and actually the uh that was one of the points. They said going for a drive, exactly that thing where you're not slightly distracted, you're not looking at each other is one of the tricks to do it. So

How Vulnerability Works Better Sideways

SPEAKER_01 8:33

going back to Bouse, I mean, there's an interesting kind of origin story there as well, which I'm sure you've told many times before, but let's do it again. Do you want

The Dragon’s Den Loss That Sparked Research

SPEAKER_01 8:41

to play the dragon's den type scenario?

SPEAKER_00 8:44

Yeah, I mean, so I went to Trinity College in Dublin, which is like the number one university for entrepreneurship in Europe, and it's why I wanted to go there. My sister had gone, and she's a doctor, but I I'm the youngest of four, about my parents entrepreneurial. I kind of went in this pursuit of entrepreneurship and that that university, and not many people from my school going there, and that was like a really I just like enjoyed a lot of those elements. But very quickly, what I loved about college or university was very quickly I was going to these talks from Michael O'Leary, the CEO of Ryanair, and Isolt Ward, the founder of Food Cloud, an amazing business in Ireland, and very quickly got like the bug for wanting to set something up. And luckily, there was lots of outlets for both finding like-minded people, although that sounds kind of like university jargon. In reality, I was in the college bar and you know, drinking pints with people who also had that ambition to set stuff up. And I was incredibly fortunate to meet my now three co-founders, Ronan, Josh, and Brandon, who were studying computer science. And there was this Dragon's Den competition coming up, or Shark Tank for US listeners, kind of a very similar idea. And we applied effectively with a business, or we're going to apply with a business that was just really bad. And in that Dragon's Den competition, we got a huge amount of feedback, being like, You did no research, you spoke to no customers, you validated nothing. And we then kind of had the bug of, well, we love working on this idea, but now that idea is really bad, we need to go do research. And very, very long story short, I ended up meeting the CEOs of a lot of the big research agencies in Ireland as a student looking for advice and help on how it works. I learned a lot through that process. I met with the likes of Qualtrics and Taluna and some of these uh kind of software companies disrupting the space. And then I met with you know the marketing directors of Diaggio and Coca-Cola and Tesco in Ireland and said, hey, you know, I'm trying to do research. How I got meetings with all of them, I still don't know. I was just maybe persistent and didn't take no for an answer, and I was a student, so maybe they were just willing to help. But what I learned through that kind of cycle and through looking at all the other startups in this space was just a lot of the core problems that still persist within research. It felt like a the agencies had amazing people, but lacked core competencies in technology, so we're never really gonna innovate in this new machine learning world that was coming. So far, companies were obsessed over the like tool they were building and giving, you know, giving someone a fishing rod instead of teaching them how to fish, that kind of idea. So we felt that was going to hit a natural end because so much research is custom and complex and starts at the end and moves backwards. So the more we became engulfed in this industry, we effectively scrapped the other idea and started on this kind of five, six, seven-year journey of building what we would foresee as the future of how technology and agencies would evolve, all rooted in the same objective, which for market research is simply the understanding of the person you're trying to sell to so you can build better products, sell more stuff to them. You know, that's all research is is the pursuit of a greater understanding to make greater decisions. And we felt there was a lot of flaws from a technology and a people and a process point of view that with me and my co-founders, we could try and iterate and build a solution around. And that that's effectively how we end up falling into the research phase was trying to do research ourselves and realizing there was no good. If there was no good solution for us, then maybe there was no good solution for everyone, and there goes a lot of room for growth.

SPEAKER_01 12:20

And I I totally I totally see it. It's fascinating coming at it from a sort of an external perspective. And I guess I won't get into the detail of the company. One of the companies I work with is somebody I used to work closely with who came in and became the chief data scientist and wasn't out of the research background, out of the research world conventionally. And he was like, why on earth is it done like this? And that's the other business he's he's set up is to is to reconcile everything.

SPEAKER_00 12:48

So, yeah, really. To a certain point, though, it's interesting you say that because I think that's what enabled us to uh to do our like zero to one, like the software we built and the way we pitched and the way we understand the problem really gave us an advantage. That kind of lateral thinking, you know, taking first principles, putting it into an industry, you don't have the like baggage of experience in in a kind of a funny way, but definitely the kind of one to ten journey we've been on since has been completely tied to the quality of researchers and industry experts that can almost help explain why things are the way they are. You know, there's this amazing book, Same as Ever by Morgan Housel, the financial author, which is like you should really focus on the things that don't change in order to try and predict the future instead of trying to predict the things that will change, because I think it no other industry is like it when it comes to market research, when it comes to stubbornness and resistance to change and things being the way they are, you could spend all your life complaining about why things are the way they are, or you can put yourself in the shoes of the researcher and change, manage, and articulate a better future. And I think when we combined our like technical superiority with first principles thinking and you know, progressive industry experts, it was a very potent mix that has definitely accelerated our growth massively. So you need a bit of everything.

SPEAKER_01 14:09

Yeah, I I and even referring back to the example I was I I think Tom would probably say once he got involved in it, started doing it, it was more complex than he thought, but he's continued to roll up his sleeves and solve it. So, Charlie, we should probably back up a little bit though and let everybody know what bounce actually does.

What Bounce Actually Builds And Sells

SPEAKER_00 14:27

Yeah, for sure. So what we try and do now is deliver insights that researchers can confidently share and defend as quickly as possible. And that is what we are trying to do. Back to like a very first principle idea. Most research is rooted in I have a decision I need to make or I have a question that I've received, and it is my goal to get a good enough answer as quickly as possible. So that is why we exist as a company. The product that we've built to solve that is two has two clear clear components. One is a retrieval system, and in the simplest form, the goal of that is to consolidate all past research in one place so that you can have a conversation with your data and extract insights that are fully sourced, sided, robust, backed up in evidence. It's the exact opposite of throwing something into Claude or Chow GBT. It's this idea of built by researchers, designed by researchers, rooted in research integrity. And the goal of that is to tell people what they already know, very simply. And that is the kind of evolution of knowledge management in the in the in the market research world, which people might be familiar with. And the second component is what happens when gaps exist. So let's say you're a tequila company, you say, Hey, I want to know how to price this new tequila product in the on-trade in Texas ahead of the World Cup. What should we do? Well, the first thing you should do is figure out all of the pricing research you've ran in this category or what might be publicly publicly available, consolidate all that information, but you might not have anything 18 to 24-year-old females, and they're a critical category for you. So when we identify that gap via our retrieval system, the second part of our offering is the ability to go out and run new primary research to fill that gap. So we have a system that takes that gap or takes any brief and will then recommend a quantitative research study to fill that gap. So very classic survey methodology, audience, and interlocking quotas to ensure it's robust. We run that fieldwork using humans' respondents, and then we will analyze that data with the original objective in mind. So you can picture it like a flywheel design product. They come in the top, they ask banks what do we know about this topic, we identify gaps, and then we fill those gaps. And it becomes like this repeatable decision-making system within an organization. And critical to that system is we have researchers pulling the strings the whole way through. So half our company are researchers, and half our company are software engineers building in the AI space. And what that allows us to do is effectively be kind of change management powerful people within these orgs. We work with Heads of Insight to reconstruct their organization around this ideal flywheel. And that's what we do. So we're about 50 people now, offices in New York, London, and Dublin. Uh we have about 200 clients globally, mostly Fortune 500s, but we work with startups and smaller brands as well. And yeah, I suppose the why we exist is just to allow more decisions to be insight-backed out of across the world. So that's really the the raise on debt.

Good Enough Decisions Beat Perfect Ones

SPEAKER_01 17:41

And Charlie, what did you mean by making decisions with information that's good enough? That wasn't exactly the phrase you used, but it was something along those lines. I mean, is that suggesting that sometimes the uh level of information can be over-engineered by the traditional industry?

SPEAKER_00 18:03

I think without your uh uh leading me to water, I think there's there's definitely a some decisions require 98-99% confidence. Decisions by the US government in relation to a war, they should probably have a very high confidence in what they're about to do. Yeah, trying to decide whether you should price something at 1 euro 12 cent or 1 euro 5 cent might require 75% confidence. And I think the reality on the ground is that humans make decisions with so many biases that all you're looking for is enough information to deliver enough confidence based on the speed required. Because my hypothesis is that a good enough decision made quickly wins over time versus a perfect decision made slower. And I think Barack Obama has spoken about this, the 51% decision. And if 51% confidence is good enough for the president of the United States, it's probably good enough for the category manager of a yogurt company in the UK. And that is not to say that's always the case. I'm not, you know, poo-pooing the idea of you know robustness, confidence, but it's rooted in what is the decision you're trying to make, what is the level of confidence you'd be satisfied with, and what speed do you need to make the decision by. Because slow research is really, really important. Slow research uncovers human insights and understanding that just requires time and that's necessary. But sometimes speed is the most important trigger, and it's how it's either I do nothing and make a decision, or I get as good as I can and then make a decision. Our role at Bounce is to accelerate people up that curve to get the highest level of confidence based on the speed. So I either need something in an hour or I need something in a week. That is going to increase the level of confidence or robustness we can deliver to that person, but it's all rooted in what decision you're trying to make, and it's all context-specific. Yeah, I think it's very well put. Like the biggest problem in research is like it's probably inefficiency and attribution. Inefficiency being how slow and expensive it is to actually deliver good enough insight. And attribution is when you make a decision back by insight, how did it go and how do we capture value of whether it was a good or a bad decision? I think those two things are the things that we're trying to solve. It's really hard to, but given the setup of structures and teams and businesses and how agencies and tools interact, it's quite messy and it's it's hard to then make change happen.

SPEAKER_01 20:29

Yeah, yeah, definitely. And do you have a particular area of specialization? I mean, and the reason why I ask that is for instance, like around sector, because I'd imagine different data sets, if we're looking at that kind of knowledge management piece of it, probably have different challenges. You know, even something that we've talked about, if it might be TV ratings as opposed to, I don't know, beverage retail data, probably have different considerations around them, I would imagine.

SPEAKER_00 20:55

Yeah, for sure. So yes and no. So on the we work with all sectors in that space because our our system, again, you have to remember that the data that goes into the retrieval system is all client-owned data. It's a closed system. So whether you're a tobacco company or a betting company or a yogurt company, the data, the differentiation is in the extraction of the inside and the citation and the identification of like data conflicts and that sort of thing. It's not in the um the data itself. Like our value is not in the data we provide. It's the same on when we run new research. We are we are integrated with panel partners to deliver the sample. So that's why that's what allows us to be industry agnostic. In house, our researchers are specialists in different areas, whether that be methodological or industry based. We're big enough now that we can kind of have enough people that are go to different areas. You know, if something comes in on, I don't know, creative testing, we'll know what researcher to put on it, or if a certain sector will say, Hey, oh, I know the person who's worked in that sector a lot. But the technology itself is built to be more industry and methodology agnostic. And what that means is like a lot of our clients, we will recommend they use certain specialist agencies or tools in certain scenarios. So let's use a scenario where we retrieve an answer for them and we say, hey, you probably need to go and run some qualitative research in the pharmaceutical space. We will say, hey, here's who we recommend you go to. It's almost the same with panels. There's a great transparency and trust that's built when if we are the orchestrator or the conductor of their kind of research execution, we can tell them who to use based on our vetting and recommendation.

SPEAKER_01 22:40

Got it. And obviously, I haven't seen the system actually in action, but in terms of the user experience, I mean, is it a sort of LLM type of approach where you type in and go, I don't know, what can you tell me about females 18 to 24 and their consumption?

SPEAKER_00 22:56

I have uh I have strong opinions what the future like UX is going to look like for a lot of these companies. So currently, I don't care whether any of our clients ever log into the Banks platform at any point. And the reason why I don't care about that is because I think one of the big things that have struggled, like have caused a lot of research tools to struggle getting in, struggle getting embedded in an organization is integration, workflow, alignment, like making it easy for you to be the thing they go to. So in a perfect world, the engagement with bands would look like one of two things. I'll get more specific in a second. Either they email us their problem, and our researcher takes that, uses the system, tells them what they already know, identifies the gaps, recommends the solution. And effectively, we are just a hyper-efficient full service agency as it feels to the customer. Now, our gross margin about 80%. So I don't care whether people have a SaaS platform or not. You know what I mean? So that is a perfect world as it is now. Researcher or a person comes to us with problem, we solve problem. We use that through a researcher trade on our system. There are self-service elements of our system if they want to engage in it. Like you said, they could type into the system themselves. What do we know about tequila drinkers in Germany? Or they could, when the results come back of a quantitative research study, they can pull out the insights, export it to an editable PowerPoint. But we don't charge for consultancy. So we allow the client to either use self-service or our people, completely dependent on their need, because we want to reduce friction. How I see that going is instead of emailing us, they we could just be integrated into their Microsoft Teams, their Slack, their Claude, whatever it is. So when they pose the question to Bounce, it could just be a webhook, which is Slack Bounce, and it sends the question or sends the brief. And that means that there is zero change in their day to have it in Imperial brands or in Coca-Cola to be able to extract insights, identify gaps, run the research. So right now it's researcher self-service or full service, depending on how you need. It's the exact same offering. It's dependent on the client experience. In future, I imagine it to be way more amalgamated within existing technologies, infrastructures, systems, and it's our job to it's right with them. So that's that's a massive change I see happening over the coming months and years.

What Goes Into The Knowledge Repository

SPEAKER_01 25:17

Yeah, that makes a lot of sense to me. And just so I've got a full understanding on the kind of what I think of as the kind of the repository bit of it, the knowledge management side. So what exactly do you integrate? I mean, do you take historic survey data?

SPEAKER_00 25:32

Might you take desk research, focus groups, there are two forms of data in the market research worlds. There is unstructured data, which is PowerPoints, docs, Google Slides, industry reports, Mintel, all this sort of thing. Like basically reports with graphs and text and X and Y axes, and that is like output level data. Um, and we've built a model to ingest them, auto-tag them, understand them. So it requires no manual effort when we get that data to have a full extraction of that data and to understand it, and not conflicts, et cetera. We've had to build our own custom model then for ingesting structured data, which is things like Circana, Nielsen, spins, more complex Excel-level data, which might require additional context in order to be ingested and understood. We call that input data. And the reason why both of those are important is depending on the question you are posing, you might not want the summarized output answer. You might want the specific crosstop on the survey. So we've ingested survey data from all of the different DIY quant tools that are out there. We've ingested qualitative transcripts, we've ingested syndicated data sources, we've ingested market research report data, we've ingested what you name it, and it becomes, we can do that and host it in our system, or we can effectively through an MCP or an API integrate into where that data already lives. So if someone's already done the work to build up a repository in SharePoint, we should just be able to integrate through there and you don't need any effort. If you haven't built up a repository, we will build one with you and make it as easy as possible to do that. But the technical complexity to do that is exceptional. I see a lot of like people launching like repositories as part of their offering over the last few months. I'm excited to see when they realize the technical complexity of adjusting the volume of data sources and to be able to guarantee the citation to quality of the answers and the sourcing. It's taken us a long, long time to do it right. It's a very obvious use case of AI because it's like, oh well, throw in reports and summarize the answer. It's not that simple because if people are making decisions on the answers we are giving, we have to be 100% confident in them. And when we when gaps exist, we need to be able to fill those gaps in a very comfortable research first way. So that system has been years in the making, um, and we are right at the kind of frontier of how we're leveraging AI, how we're leveraging our own data, and how we're kind of building it with our clients to ensure that it is doing the thing it says it it does, because there's a lot of AI disillusionment

Citations, Conflicts And Researcher Control

SPEAKER_00 28:13

at the moment. And once the way we will pilot it is we'll say, hey, send us questions that you already know the answer to through manual effort, then run those same questions in cloud or co-pilot, and then run those same questions in bounds and just compare the quality of answer with the speed with the cost. It's a meritocratic system, and that's where I think that's what's driving a lot of our growth at the moment is you can actually prove you're better, which is something in the research phase that is very difficult to do.

SPEAKER_01 28:39

Yeah, very much so. And and and everything you were just talking about there, Charlie, I guess becomes part of my concern around the repositories. So I imagine myself, let's say I've sent it to you, or if I put it into the system. And and then is there's this question around what's the source of the data, and then related to what we talked about, what's the confidence level around this relative to the level of decision that I'm looking to make?

SPEAKER_00 29:06

There's a few ways that you try and solve it in the repository space. One is how do you help clients pose the right question with the right context? So we have like training on the structures upon which you pose a question in order to extract the right answer. Another thing is when we are pulling an answer, we are only pulling it from a smaller number of sources than it could pull an answer from. So let's say you've thousands of documents in there. Before we create an answer, we will show you the data or the sources we are going to pull an answer from. And you can manually select or deselect or add it anything else you manually want to do back to the researcher has their hands on the wheel. It's not just this free-flowing agent. Then when the answer is delivered, we will call out where there are conflicts in data. So let's say one of your reports says X and the other report says Y. We allow for manual overlay of which one are you going to take as truth, and then that trains it further. Then when the answer is given, every single statement has a citation, like a PhD style. So it's a natural language like you would see in a Claude or ChatGBT from a user experience point of view. But every statement you can click a citation and it's going to take you to the like Henry verified points that you would put in. So the quality of data that goes in is one of the really critical steps. And then there's all of these steps to ensure that it is effectively shareable and defensible. So the use case we say is that when you get that answer, you can go do a meeting with the stakeholder who was posed that question. And when they say, Well, why do you believe that? That every single statement is defensible, backed up in data, and makes the person look smart, feel smart, and be able to convince that person to act, which is another big challenge of the research space.

SPEAKER_01 30:43

Yeah, it is directly related to the project that we were touching on kind of before the call, where this question of conflicting data sets and going through the decision flow and going, what am I going to place the most credence on? Which is why this is why the human is so important, Henry.

SPEAKER_00 31:00

Like the the conductor of this orchestra is the most important person, choosing the pace of the music flow, choosing who goes when, choosing who you go to next. That is why we're building a read a system for researchers to elevate the quality of researchers in organizations. We're not one of these companies that is like, there will be no research function in the future, research doesn't matter. Now we finally democratize insights. We're like the reason why we're building it the way we are with research and insights professionals is because we know how to do it, we know how to do it well, and we know the importance of the conductor through this whole process. And when things are wrong or where things are challenged, the ability to then dive in and play one of the instruments, or you know, to use that analogy is going to be so, so integral to make sure that the insights functions are the ones that are building these intelligence networks in the companies and not people who don't understand research integrity and guidance and consumer psychology and the emotion behind what decisions actually drive us decisions. It's

Synthetic Data Skepticism And VC Incentives

SPEAKER_00 32:07

really important. Yeah.

SPEAKER_01 32:08

Uh Charlie, a bit left field, but where are you on the whole synthetic data digital twins side of things and all the rest of it? Is that part of the offering that Banks?

SPEAKER_00 32:18

No, it's not. I'm very glad I'm not in that world because it's uh it's a very fun debate to sit from the sidelines and eat popcorn and watch. Um my opinion on it is I've a few thoughts on it, but uh like I don't take advice from people who aren't experts in a space. I don't think anyone should take my advice on this. But here's my from people who know comp uh I know founders who are building in that space, and I obviously know look, we've had to speak to them because in the future, instead of providing human respondents, some of our clients might want a synthetic response. Some of my thoughts are on it are this. One, we are already doing forms of synthetic modeling. I sin I think it's one of the worst marketing positionings I've ever seen. Totally agree. Yeah, calling it synthetic is just a terrible idea. It makes it feel fake when it's not fake in a lot of these cases. So I think they have a marketing problem. We are already doing forms of synthetic modeling. If you've ever done MacStiff or any of those methodologies, you were doing forms of mathematical modeling to give confidence. So this is not new in that case. Third, and this is more on the concern side, we haven't seen it, but we aren't as close to the frontier of this, be good enough to really roll out to clients yet. But as soon as it is, we will. We are very open-minded and a clock of a client are very proactive, they want it to be true. But my concern is this, and it's the same in the AI moderated qual space, and again, some amazing businesses in that space. When I see a lot of venture capital money flooding a category or a space, because the incentives are great if they're right. And what I mean by that is because the the amount of money that goes into human sample and incentivizing and the amount of money that gets spent on qualitative research right now is so high and it's so slow, those two processes, the incentives for synthetic to or AI moderated call to work are so high that I think it skews the human dimension. So I think it skews humans' belief that it will be true. When you want something to be true, you are more likely to believe it will be true. So I have skepticism purely because the amount of money flooding into the space, and because the incentives for wanting it to be true are high. And naturally, as a founder, my spidey senses tingle a bit. And I say, I don't think that there is a high risk for that not being as far along as it could be, because people are flooding capital and want it to be true. And if it does work, it will be transformative to the org. If qual does replace quant, huge ramifications for the industry. If synthetic does replace sampling, huge ramifications for the industry. But back to the Morgan Housel thing, things that don't change. I am still a bit skeptical. I wish all the founders in that space the best. And if they solve it, it will make my business better. So I actually don't even force in the race. It it will make me it will help Bounce deliver its value proposition faster, better, cheaper, which is always good. So they're my thoughts.

Growth Plans And Making Researchers Influential

SPEAKER_01 35:07

So what comes next for the business? Where where do you hope to be in I don't know, three to five years?

SPEAKER_00 35:14

Honestly, if I have a year like the last year for the next three to five years, I will be very happy. Back to the point that we spoke about at the start around you know, the juice is the squeeze, or you know, Adam Brand's phrase, you know, instead of the pursuit of happiness, it's the happiness of pursuit. They sound cliche and cheesy, but it's how I live my life. Right now, we have doubled the business in revenue every year for the last three years. We will double again next year. We are growing at a really crazy but very manageable speed because the productivity of the business with less people is amazing. I love what I do. I get to hire incredible people who I love working with, and we are in control of our destiny. So we raised a small amount of capital, like $7 million over two rounds. We are now a cash flow break even again. So we are using our revenue to continue doubling year on year. So everything feels within our destiny, which is really important when the macro environment is the way it is. Like, you know, with what the in advancements in AI, with wars going on across the world, with all of the stuff going on, there are so many uncontrollables that could kill bounce that if I thought about those, I would be sleep at night. So all I think about is very controllable items, which is what we call a bounce, like a great company. And a great company is where people are like growing in their careers, well parried, enjoying their work and feeling challenged. Like it's quite a simple set of ingredients that we try home. And if we can keep going the way we are with our vision from a product point of view, there are so much technical challenges that are really rewarding to solve. There are so much business challenges and clients to work with and try and win that are really exciting. And I think the industry is in such a fascinating place that I'm honestly just like I'm 28 years old, so I almost forget. I've obviously been doing this since university, and like I have no ambition to retire. We are do building an exceptionally interesting business and an exceptionally interesting time with exceptionally interesting people, and long may that continue. That's kind of my uh my current feeling on the whole matter.

SPEAKER_01 37:20

One of the intriguing things for me around it, Charlie. I mean, I should be careful about this because I don't want to slag off the whole industry, is that the consumer insight sector's been so stodgy and it's been really difficult to change, and that researchers, by their nature, always see the problems rather than the opportunities. But it seems like you found a client base that doesn't see it like that. Yeah.

SPEAKER_00 37:46

Yeah, but you also have to you have to help them gain their voice. Like, I've a huge part of our job is giving people confidence to elevate themselves within the organization and not be defensive but be offensive and show them how a really clear, easy use case of AI that they can bring to their boss and look really good. So I think we're trying to take an opposite tack to a lot of the software and research companies that have entered this face over the last 10 or 15 years. And a lot of that is research first, it's quite empathetic to the problems. And I do listen to, you know, insights wanting a seat at the table. Instead of moaning as to why they don't have a seat at the table or why, you know, you know, teams are getting cut. Our job is to try and help elevate them and help them become the most important person in the team, to help them transform the way insights are delivered. Because back to why research exists as a function, it's ultimately just to help companies make better decisions that are rooted in human understanding. Like that is it. So it's a really important role these people play. And if they lack confidence or if they lack articulation or they lack entrepreneurial skill sets, it's our job to give it to them and help work with them. And yeah, we've got exceptional clients. Like we've clients who gave us a chance when I was a student with no understanding of research, barely a platform, and they're still working with us today, like Prinoricar, Diagio, Coca-Cola, Tesco, some of these really early customers. And now we're working with you know 10, 15 markets and teams within a lot of those organizations because we've co-created, we've listened, we've built with them, and we have a very compassionate understanding of what the future looks like. And I think that's something that a lot of tech companies have lacked and still lack when you hear them speaking on stage around replacing research orgs and we're gonna make your job faster. It's like people don't want to work faster, they want their life to be easier. So I just think we try and think about things a bit differently and work with them. And maybe that's what has allowed us the success we've had to date and hopefully allows us to continue on this very fun, hard journey.

Fundraising Advice Treat Equity Like Debt

SPEAKER_01 39:49

Have you got any kind of key elements of advice you would give to other founders if they're looking to raise money, particularly in this type of environment?

SPEAKER_00 39:57

I could do hour rays on this topic alone, but I'll try and keep it brief. Um raising money is raising debt, is the first thing. Only raise money if you absolutely have to, if you are a venture business. And what I mean by venture business is you are have the ability to actually deliver venture returns. A massive mistake raising money is when you don't have venture returns, you give away a portion of your business, and then you are effectively destined for failure in both stakeholders' sense. So, a lot of the reasons why a lot of software companies in our space are failing is because they raise too much money at too high a valuation that they were never going to achieve, and they've had to do mass sets of layoffs, restructures, re-orgs, and now they're trying to turn their software business into an AI business. And that is challenging and not really the fault of a lot of companies, but raising equity is a serious, serious thing, and you should treat it like debt, is kind of the first thing on that. The second thing when going into fundraise is you need to treat it like a sales cycle, like a funnel. If you can assume a win rate of 0.5% on getting a term sheet, you need to make sure you're speaking to enough people. You need to make sure you are going to the right venture capital firms. You need to do your homework, like you're trying to do pitch to a customer. Too many founders, I see they're like, Oh, there's no way we can raise funding. The environment's so hard, there's not enough money, it's so unfair. How many companies, how many bench firms do you speak to? And they go, Oh, like 20? And you're like, What? Speak to 200 and then maybe come back to me and like learn from every single meeting you've had. So I think there's a naivety around the process. I think there's a naivety around when you should and why you should raise, and what are you actually trying to achieve as a company? So I think a lot of that zero-to-one homework is my are the two most critical things you can do. And then the third is be aware of their incentives. Like a venture capital firm exists. And yes, some of them might be lovely and some of them might be mean, and some of them might be able to introduce you to the on-person or help you in a specific challenge. Most of them is capital to deliver a very important function to scale and then exit. So again, being aware of their incentives, be aware of what you are trying to achieve. And then if you are going to raise, make sure you treat it like a numbers game. These it's a very ruthless, hard slog, and you need to make sure you're putting enough volume at the top of the funnel if you expect something at the bottom. So that are they're kind of the top of mind things that I would say. Well, I'd love to disagree.

SPEAKER_01 42:20

I don't.

Optimism, Self-Awareness And Founder Flaws

SPEAKER_01 42:21

Now, on a slightly lighter note, what would your partner say are your best and your worst trout?

SPEAKER_00 42:27

Oh gosh. I don't know if you've ever met an Irish person with self-deprecation or compliments, it's not something we naturally come to. I think the UK are the same. I'm quite optimistic, blissfully optimistic, and I I live on energy and enthusiasm for anything. You know, going for uh we're going for a nice dinner tomorrow, and I can't wait for it. Like the energy I bring to small things in life and big things in life, I think hopefully is reverberates to other people around me, like the people I hang out with. I like to be hopefully very enjoyable and optimistic to be around and a good problem solver because of that. Um and I like to think I'm considerate and doing things for other people and putting people first, whether it's in work or Ellen, my partner, or anything like that. I think there are two things that I at least aspire to be like very real. We would call it in our in Ireland being sound, which has two meanings being kind and being reliable. And I think I would love to be described as sound and optimistic if I was to be described as anything. There are things that matter a lot to me. And then, yeah, flaws. Um, I do think there's a selfishness that comes with building a company that is I need to check myself with, like, particularly if I'm lucky enough to have kids in the coming years, there's a selfishness that comes with that. And I luckily I have exceptional friends that uh would call me out if it ever became too much. But I imagine that's a trait you need to watch when you're building a company and you're so obsessed about trying to make that company a success. Yeah, I think that's probably something to be to be very aware of.

Fiction, Podcasts And Switching Off

SPEAKER_01 44:03

Final question: what are some of your favorite sort of recent pieces of media? By which I mean it could be books, music, whatever, TV, film, all that type of Yeah.

SPEAKER_00 44:12

So I actually, for the for about I haven't read a non-fiction book in maybe four or five years now. I stopped. I've I love fiction books. My favorite books are kind of general historical stories, like fictional stories. So the classic, like Kite Runner style books, pachinko, these types of amazing books. I just love those styles of content. It takes me out of the world of bounce and it takes me into the real world in a way that's really refreshing. So you'll never catch me reading nonfiction books. In college, school, I would have tried to read them and I never became a reader. I never got into it. And it felt like work, it felt like I was switching off bounce and then switch on like learning. And most non-fiction books can be an essay, or they could be a long read, or they could be a New York article. So I'm generally pro-fiction books for a lot of reasons. Big podcast person, but again, because I think so much about work all the time. Most of my podcasts are non-startup related. Although, I mean, the best podcast probably like the founders podcast is unbelievable. It's an amazing the deep dives they do on companies and founders or acquired. Sorry, founders and acquired are the two, the company and the founders ones that I love. And Tim Ferris is someone who I listen to since I was like 14, who had a huge influence on my life. Yeah, they're probably the ones from the top top of mind. As he said, I think you'd be you'd laugh. My Spotify is probably sports podcasts, Olivia Dean, Jamie XX. It's like it's nothing that you would put as the like you know founder that is all he thinks about his work because I put so much of my time into Bance that I think when I get to turning on my brain in another way, I think I get a lot of clarity of thought and outside thinking from fictional books, music, interesting podcasts. I don't know. Not everything has to have utility towards growing Bance, I suppose, maybe is the answer. And a lot of that is switching off well, enjoying other forms of content, being connected to the real world, and not just being like an AI obsessed founder who doesn't think about anyone else because I don't think that's very healthy either.

SPEAKER_01 46:17

Charlie, thank you so much. It's been great, really, really fascinating, and really enjoyed talking to you. Thanks so much, Henry. Well, I hope you enjoyed that episode as much as I did. I can genuinely say that Charlie is one of the more impressive founders I've had on the podcast. Obviously, what he's built with bounce is impressive in itself. But what really struck me is how thoughtful he is about the role of researchers within all this change. There are plenty of people talking about how AI is going to replace insight teams. Charlie's perspective is almost the opposite, that the best researchers have become even more valuable when they're equipped with the right technology. Thanks as always to Insight Platforms for their support, and of course, to you for listening. See you next time.