Want to truly know your customer? Check out Bounce Insights. The Irish startup claims its software can determine exactly where your customers are, what they’re looking to buy, and even what packaging draws their attention. How? AI-powered software, of course. Founded by Charlie Butler, Rónán Dowling-Cullen, Joshua Stafford, and Brandon Dooley, Bounce Insights is a market research firm that gathers consumer habits to help brands like Coca-Cola, Unilever, and Tesco better develop and promote products. This type of customer research is far from new. What is new: The speed at which Bounce can gather and analyze data. The team says their software can deliver insights seven times faster than traditional surveys. “That's the way research has to go if it's going to have any chance of competing at the highest decision levels,” said Butler. Bounce has generated $6.5 million in funding from investors, including ACT Venture Capital. Next stop: the U.S. market.
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LifestyleTranscript
00:00If you do research on what the problems of the consumer are, it's down to you, the innovators and the brands, to understand those problems and then execute.
00:06So I think good research leads to unique insight, leads to better decisions, which leads to a better brand.
00:14We are here with Charlie and Ronan, the co-founders of Bounce Insights. Thank you so much for joining me today.
00:20Thanks for having us.
00:21So I want to know about all you guys are building in the media and marketing space, how you guys are innovating on a very legacy industry.
00:28Before we get into that, I want to know a little bit about you.
00:30Can you let me know where you're from and kind of what you're doing today?
00:34Sure. I'm from Dublin. I grew up there, live there now. Most of our team is there. That's our headquarters.
00:40And then I'm the co-CEO alongside Charlie, so responsible for finance and technology mainly, alongside the strategy.
00:46And I'm Charlie. He builds it. I try and sell it.
00:49So kind of classic founder duo. We met in university in Dublin, so I'm from Ireland as well.
00:54Currently living in New York and spent 16 months before that in London, so I kind of move around with where our business is.
01:01And yeah, I handle all things commercial and trying to convince people to use our product, which I think is pretty good.
01:06Very cool. Can you guys walk me through exactly what Bounce offers for your clients today?
01:11Yeah, so we're a consumer research platform.
01:13We help brands understand their consumers so they can just make better decisions faster.
01:18Market research at its core is just a greater understanding of the consumer.
01:22The classic example is Jeff Bezos says that they keep a chair empty in every room to represent the consumer.
01:27I suppose Bounce tries to fill that chair in every marketing category or commercial team and brands.
01:32And we do that through surveys online, as well as knowledge management,
01:37which is effectively just telling them what they already know about their data.
01:40So the combination of AI in those two areas means that you can get it faster, better and cheaper and makes research a bit more accessible
01:47instead of just being a thing that big companies do when they have a lot of money.
01:51Yeah. How exactly are you guys conducting this research?
01:53Who are these surveys going out to? How are you gathering the data?
01:57Yeah, so we go out to any consumer that brands might want to speak to.
02:00Usually that's in the Western world for the brands that we work with, so North America, Europe.
02:05And then how we conduct it is we use AI to design the research.
02:09We gather the data using our proprietary technology, and then we use AI to analyze that data as well.
02:14And then we started layering on top knowledge management or retrieval, as we call it,
02:18where we analyze secondary data as well for the brands.
02:20So they can give us some of the data they already have.
02:22We gather some new data and analyze it all using AI.
02:25And in terms of accessing the consumers, it's very simple like invitation, good experience and incentive system.
02:30So if you want to speak to 500 tequila drinkers in Germany or 100 software engineers in Mexico,
02:36we go out sourcing those people, inviting them into the experience, asking them their opinion,
02:41tracking their time in the survey to make sure that they're paying attention,
02:44verify they are who they say they are, and then we capture all of that data and collate it.
02:48So the same way that you try and predict an election, you need a statistically significant sample size,
02:53we bring that kind of research lens in quite a simple way to a lot of the brands
02:57so that if they're making a decision on, should we go with packaging A or packaging B,
03:03you should know what do the consumers think.
03:06And there's a common misconception by a lot of kind of tech founders that,
03:10oh, well, market research is silly because consumers don't know what they want.
03:13But I actually think that it's this idea that you're not doing good research
03:16because if you do research on what the problems of the consumer are,
03:18it's down to you, the innovators and the brands to understand those problems,
03:21understand those jobs to be done and then execute.
03:23So I think good research leads to unique insight, leads to better decisions,
03:27which leads to a better brand.
03:28It's kind of a simple train of thought.
03:30And is it that like this package versus this package that you are gathering data on
03:34or what are the different metrics that you guys are finding out about?
03:38Yeah, we do a bunch of different stuff.
03:39So we do pack testing, we do NPD, we do usage and attitudes,
03:42we do innovation, brand tracking, all of the above.
03:45Are there any major differences between generations, locations?
03:50Like how are you guys thinking about demographic as you're gathering this data?
03:54So hugely so.
03:56So the classic in market research is like your age, gender, regions, social grades,
04:00way of predicting how people will act.
04:02But a big change that's happening is kind of the individualization of every consumer.
04:06So let's say, for example, myself and Ronan could fall into the exact same category for a survey.
04:11Similar age, similar background, similar earnings, whatever it might be.
04:15But myself and Ronan, if you did know us, we make such drastically different decisions around brands
04:20and what we care about.
04:22And I think when it comes to surveying and identifying an audience, it's the personalization.
04:27So yes, you can start with age, gender, region, social grade as like a starting point.
04:30But understanding what brands I buy, why I buy them, what I care about,
04:34you know, my brand affinity or my brand equity.
04:36I think understanding that and from the brand point of view,
04:39when you're then surveying me or asking my opinion,
04:41it's connected more to proper customer segmentation.
04:44So we're trying to get out of this very old school way of thinking within market research,
04:48both in the technology, with how we treat the respondents,
04:50but also in terms of how we understand and collate the wider data set.
04:54It's just bringing kind of a modern approach to it and making a bit more general.
04:57So Ronan mentioned, we do quite a lot of types of research.
05:00But if you think about it, the area we're trying to fill is the information gap
05:05or question that arises in a given day-to-day environment in a brand
05:08where they don't understand something fully.
05:10And then we want to have the idea of, well, let's bounce, like let's get an idea.
05:13Let's just get a piece of insight that helps us make that decision.
05:16And that's all we're really doing.
05:17And we just use research to do that.
05:19How did you guys realize that this was something that needed innovation?
05:23What about the industry?
05:24What about your experiences inspired this?
05:27Yeah, well, we didn't have a huge amount of industry experience at the time.
05:30I mean, I was studying computer science.
05:31Charity was in global business.
05:33We were both in university knocking heads.
05:35We started as a consumer panel company.
05:37So we were trying to solve the problem of engagement in like survey responses.
05:41We did a really good job of that in Ireland.
05:43And then we realized that globally it was difficult to scale, but also not as big a problem.
05:46There was great panels all over the world.
05:49So we started working with Tesco and Diageo closely in understanding what problems they
05:55specifically had in those markets.
05:56And really it was focused around speed and then the quality of the insight that they got
06:00at the end.
06:00So we started layering in our AI technology to design the research, gather the data and
06:05analyze that insight.
06:06I think sometimes we're better off being lucky than good.
06:08And we found ourselves in an industry which was dominated by repeatable human tasks.
06:14There's this feeling of no one ever got fired for using IBM.
06:17No one ever got fired for using Ipsos or the market research agency in that local market.
06:21So there's that feeling because when people are buying data, they're usually buying trust
06:24and comfort in a decision.
06:26They're not actually truly buying the insight.
06:27So we found ourselves in an industry that was dominated by very talented, expensive and
06:33experienced humans doing every task, which meant it was kind of ripe for disruption.
06:37But that wasn't any sort of business strategy plan.
06:39We kind of fell into an industry.
06:41And the more we kind of went into it, the more we realized it lacked a lot of technology,
06:46first principles thinking.
06:47It was filled with exceptional people that we felt weren't giving themselves enough credit.
06:52Market researchers would scream and not be listened to.
06:54And I think we are trying to elevate them to the C-suite in terms of the types of decisions
06:58like every senior person in a brand or every junior person in a brand should be able to
07:03get research.
07:04But before we entered the space, it felt quite inaccessible.
07:07And truly, it was just expensive.
07:09So only the big brands did it.
07:11But if you were a challenger brand trying to come into the market, you would spend all your
07:14money on advertising and no money on understanding why are people buying or how are people buying.
07:18So it's smarter use of money.
07:20So that's where we're trying to really change the thinking around the industry.
07:23Yeah, and the people working in Insights are amazing strategists.
07:27They understand their brands really well.
07:28They understand the problems their brands have really well.
07:30But they're having to spend hours every week trying to pick the perfect question to reach
07:35the right consumer, which really isn't a problem that they need to be solving.
07:38They want to be looking at their brand and then we take it from there.
07:41Yeah.
07:41And you say that sometimes it's better to be lucky than good.
07:45You fell into this, but you can only fall so far.
07:48You then have to start building.
07:49So what were some of the steps that you guys took to then realize this is an issue that
07:53I'm seeing here, an untapped part of the market?
07:56How can we build a full-fledged business around this?
07:59Yeah, I mean, we kind of have a philosophy that we speak about a lot now around listen,
08:02sell, build.
08:03It's this idea that, and the reason why we now know this is because we failed excessively
08:08and continuously for many years at not doing this.
08:11This is me trying to convince myself to take the own advice.
08:13But this concept of listen, sell, build is truly listening to your customers' problems.
08:18So we mentioned Diageo and Tesco and Coca-Cola, some of these early brands.
08:21We just sat and listened to them and spoke to them and tried to get to the crux of what
08:25the actual problem is.
08:26Then we actually tried to sell them the solution before we built it.
08:29So the idea of people will say they like something, you know, the book, the mom test,
08:33this idea of like people will really want you to succeed.
08:35So they'll tell you stuff that isn't true.
08:37But if they will part with money or they'll invest their time and money into something,
08:41you'll know it's actually solving a problem and then it's built.
08:44So this idea of myself and Ronan, Ronan's on the technical side, I was on the customer
08:48side, and we would just spend so much time understanding why market research agencies
08:53weren't solving their problem.
08:54And when it came to building, it was this idea of co-creation.
08:57So you kind of build it piece by piece.
09:00You figure out the pricing model piece by piece.
09:02And I think one of the things that startups have that big companies don't is agility
09:06and proximity to customer.
09:08Like if you can truly sit there and listen to them and they tell you what they want.
09:11And in our industry, they were begging for something better.
09:14We actually, it was all about execution from there.
09:17And the last few years have just been about kind of keeping that DNA while scaling and scaling
09:21and trying to get it to as big a scale as we can with the idea that every decision
09:26in a brand is backed by insight.
09:28And we just want to increase the percentage of decisions that are made with bounce data.
09:31That's kind of the ultimate vision of the company.
09:33So he's promising things and then handing it off to you to figure out, right?
09:36Yeah, exactly.
09:36And COVID made that much harder because to start with, we'd be at a table and there'd be a client
09:40and be kicking at me like, that's not possible.
09:42Like, don't do that.
09:43And then over Zoom, yeah.
09:45So yeah, we have to just deliver.
09:48Luckily, he's exceptionally technically.
09:50So I would, if I did the problem, like here is the problem and I think this is the solution
09:55and we have someone who will buy it, that's a good motivator.
09:57We have two other co-founders, Josh and Brandon, who aren't with us today.
10:00But Josh, Brandon and Ronan are just some of the most intelligent, like commercially orientated
10:06product people that I've ever met that it just makes my life so easy that I'm sitting
10:11there with a client and I just know I'm offloading a unique problem that is technically complex,
10:16but they are like commercially so empathetic that we will win, be just purely based on
10:21that.
10:21So I think that whole part of the early years was just so fun because we just backed ourselves
10:26so much to do what no one else was doing.
10:28And that was like our only thing going for us.
10:29Everything else is so hard in a startup.
10:31Yeah, definitely.
10:32When you were having those early conversations and deciding like, okay, what are the actual
10:36pain points here?
10:37What were some of those that you guys came upon that you started to innovate for?
10:40So the really big thing in the early days was speed, right?
10:43So right now the research process, if you use humans for the whole process,
10:46which most brands do, it takes about four to eight weeks to get from, I have a problem,
10:50I have an information gap, and then I now have the insight to go and act on that.
10:54For us, that takes about 24 to 48 hours.
10:56And that was a step two process.
10:58Like we started out, like I was saying, with Tesco and Diageo, and we were able to turn
11:03around data in kind of seven to 10 days just because we had three engineers who were nerds
11:07and wanted to automate things and didn't want to take lots of time.
11:09And they were like, don't worry about that panel thing.
11:12You guys can deliver insight to seven to 10 days.
11:14That's amazing.
11:15Double down on that.
11:16So yeah, really stepping up the automation and then stepping up the AI was like the early
11:21innovation to that speed.
11:22And I think the speed is almost like the Trojan horse.
11:25Like speed gets us in the door with brands, but actually coming back to that idea that
11:29people are buying comfort.
11:30Now there's a real drive to the quality of insight.
11:33And what that isn't, that's not just like the quality of the consumers who are giving their
11:36opinion.
11:37And it's actually like the first principles understanding of what is the original problem
11:41and then tying that to the inside at the end.
11:43Because I'm using a lot of analogies now, but the people want a quarter inch hole, not
11:47a quarter inch drill.
11:48So when someone comes to you with a problem, they don't care how you really get them there.
11:52So we like 24 to 48 hours is exceptional.
11:54It's taken us years to get to that level of speed.
11:57But actually it's 24 to 48 hours too slow.
12:00People say like, if you asked our clients now, they would say, I want to be in a meeting,
12:03get asked a question as to what should we do and be able to pull the insight from what
12:09we already know.
12:10Like there's so much research out there already.
12:12There's so much data out there already.
12:15The speed at which we can access and understand that data and then only run new research on
12:19where there is gaps.
12:20That's the way research has to go.
12:22If it's going to have any chance of competing at the highest kind of decisions in these
12:26brands.
12:27Yeah.
12:27And that's where there's lots of excitement around our retrieval engine.
12:30So that kind of secondary data analysis, knowledge management piece, because you don't need
12:33to waste to go and gather data and wait for the turnaround for those responses.
12:37You can just mine your existing data and get an answer to a question in that meeting with
12:41that stakeholder.
12:42Right.
12:42And how are your then users receiving this data?
12:46How, like from you guys aggregating it to being in front of them, what does that process
12:50look like?
12:50Like, so from the client side or on the consumer side, on the client side, it's in a perfect
12:55process.
12:56And this is the thing about how software is changing.
12:59They would not even interact with the software.
13:01There is a world in which they could email us a problem.
13:03That problem gets like automatically ingested into our system.
13:06We tell them what they already know.
13:08It does a gap analysis.
13:09They email us saying, yes, I want to trigger that new research to run that new survey to
13:13capture the data.
13:14And then we use AI to basically perform all the survey analysis and like summarize what it
13:19means, the so what, and that so what could get sent straight back to them without logging
13:22into a platform.
13:23This idea of in the early days, we were too obsessed with the platform.
13:26We were so obsessed with how amazing a lot of all it could do when you realize that the
13:31way most people want to interact is without change.
13:34They want to send a problem and then get a solution.
13:36They want to ask a question and then get an answer and then show your homework.
13:40So if you get questioned on why are you making that decision, you can go, oh, well, here's
13:44the analysis that, you know, I didn't do, but someone else has done for me.
13:48So I think if funnily enough, it sounds counterintuitive, but I would be really happy if sometimes people
13:53didn't even use our product.
13:54They just came to us with a problem.
13:55Now, right now, how it actually works is, you know, they do look at the survey in the
13:59system and they make sure it's all good.
14:00And they look at the insights in the dashboard and the best users will go in and like interrogate
14:05it further and create their own custom charts and, you know, personalize it to that brand.
14:10But I mean, that's for the power users.
14:12And I think we actually need to lift the floor so that only, so like non-research savvy or
14:17non-data experts can engage and have insight at their fingertips.
14:21Like that's what winning feels like to me, not just making the existing research that
14:25bit more productive.
14:26So it's kind of stepping into changing the industry in my mind.
14:29The way that you get there is through that double clickable piece that Charlie was talking
14:32about.
14:32Like you need every piece of insight to be verifiable and like to have the backing behind
14:36it, right?
14:37Like you can't just go to your boss and go, we should go with color X instead of color
14:41Y.
14:41Well, that's what I tried.
14:42Yeah, yeah, yeah, exactly.
14:44Exactly.
14:44Like you need to have the backing behind it.
14:46And because we have that, we're starting to build that trust with some of our customers
14:49where they just give us a problem.
14:50We give them the insights.
14:51Totally.
14:52In terms of the marketing industry in general, obviously consumer insights are critical in
14:58successful marketing campaigns.
14:59In practice, what has this, you know, what has the success looked like?
15:02For your clients and the ways that they're able to reach their target audiences?
15:06Well, I think the frustrating thing for a company like ours is that we're like not connected
15:12to the outcome of the decisions right now.
15:14Like if I'm being honest, the dream for any business is how many decisions were made with
15:18bounce data?
15:19And then how did they perform versus if they didn't or a competitor who doesn't?
15:22We can't get that data right now.
15:24It's like a real like frustration of ours because that's what you would ultimately want.
15:27Right now, I think there's a real push with AI around like it's just going to be faster,
15:32better and cheaper.
15:32You used to be able to choose two.
15:34Now you can have three.
15:35But actually, it's way more interesting instead of being a cosplay that we can move into,
15:40oh, you made this decision with bounce and that led to more revenue, more category, a
15:43better campaign.
15:44And it's actually quite tricky for us at the moment to say, oh, Coca-Cola succeeded or
15:49Mondelez succeeded or eBay succeeded because of using bounce.
15:53Like right now, we're more of an efficiency play just because of our proximity there.
15:56But we have kind of big plans on figuring out how can we connect the actual outcomes from
16:01those decisions because our hypothesis is that people who are closer to their consumers
16:06and make decisions with insight become better brands.
16:10And I suppose it's difficult for us to actually go out and prove that now.
16:13Yeah, exactly.
16:13We're stepping to it, right?
16:14Like we started out as just providing panel and then we stepped up a level in the value
16:18chain to actually running the surveys for them.
16:20We've stepped up again now to doing secondary and primary data.
16:23And then what you're talking about, what Charity's talking about, is when you layer in predictions.
16:27So you're able to predict different outcomes and go, okay, you will generate this much
16:30more revenue or this much more sales or this much more units purchased, depending on which
16:35tactic you take.
16:36So that's really exciting for us.
16:38It's coming down the line over the next couple of years.
16:40And the other thing as well is like all of our research is so confidential.
16:43Like if you think about it, we're doing like innovation and new product research for
16:45these exceptional brands that like, it's so funny, I'd be in meetings and like, oh, can you talk
16:50me through your use cases?
16:51And I'm like, I can't because I'm under NDA and we're working with so many competitive
16:56brands because they go because we understand the category and how it works.
17:00So it's really funny.
17:00We both can't connect to the decisions sometimes and are under such confidentiality that we're
17:06like, you just got to trust us.
17:07We're really good, which is difficult from a commercial perspective.
17:09But luckily, some of our clients allow us to share some stuff after it launches.
17:13What have been technically some of the challenges, but also the opportunities that you're seeing,
17:17especially how much is changing with AI so quickly.
17:20What are you seeing from a technical side of things?
17:22Yeah.
17:22So definitely the advent of large language models that really accelerated our progress.
17:28Like we've been AI first since the start.
17:30We built our own AI models and our own AI algorithms to run and automate our technology.
17:34But all of a sudden, you now have LLMs behind the scenes that are kind of like the engine or
17:39the platform that you're building on top of.
17:41And the rate of innovation of those is incredible.
17:43So we're layering that into our survey design, into our analysis.
17:47But we get that automatic improvement because we build what we call LLM ops, where basically
17:52we are able to, whenever a new model comes out, we test it against all the other models
17:56based on the data we're using.
17:58And whichever one performs best, we hotspot in.
18:00So we get this constant automatic innovation without having to invest in it ourselves.
18:03So that's been incredible and really accelerated us kind of maybe two years ago.
18:07I think that's like the way I think about it is that the problem hasn't changed in what
18:11we're trying to solve.
18:12Just the tools we are using to deliver the solution has changed quite a lot.
18:15So we were trying to do a lot of this ourselves.
18:17It was really difficult because we just, we were trying to like, okay, how do we do survey
18:21design using technology instead of people?
18:23Like that was a highly complex problem to run and figure out.
18:26And then suddenly you could like fine tune a model to try and replicate how do you design
18:29a survey?
18:30What does a good survey look like?
18:31And how can you retrieve that?
18:32Similar on the analysis.
18:33So it's accelerated kind of our journey towards our desired outcome.
18:38And that's been just, as I said, sometimes you're better off being lucky than good.
18:42Timing matters a lot with companies.
18:44And when the AI kind of wave and then when things started to improve, we found ourselves
18:48with a foundational set of customers, you know, we were profitable, making quite a lot
18:53of money for our size.
18:54And we're able to like invest quickly, be agile with our clients so that they could test
18:59and co-create stuff with us and try and solve actual problems with AI.
19:02Whereas there's a lot of kind of, as you say, hype and what does it actually do for
19:06me?
19:06And yeah, it's kind of a better search.
19:08You know, that's kind of what people materialize it as.
19:11Whereas for us, it was, we were able to go very practically with solutions to everyday
19:14problems and they just want us to use it.
19:17Like they still want the same problem solved.
19:19However, whatever technology we use to solve it, they don't really mind.
19:22And I think that's kind of an important thing for me on the commercial side, because
19:25people are a bit scared of it going, oh, well, if, you know, I open the door, are you going
19:29to run through and then suddenly I don't have a job?
19:30Whereas it's actually just a means to solving the problem.
19:34Right.
19:34With regard to the future of the marketing industry and how you guys are kind of coming
19:39into it from the tech side of things, what are any predictions that you have about how
19:43brands are going to innovate on the ways that they're touching their customers, the way
19:47that they are reaching their customers and how they're marketing within the next, like,
19:49let's say five years?
19:50Yeah.
19:51Well, I suppose our vision is that every decision in a brand is made based on insight and that's
19:57not possible currently because just the resources needed to be able to make every decision based
20:01on insight is almost impossible.
20:03But the speed at which we're able to turn natural language into research is accelerating
20:08as well.
20:09So we see that change happening where instead of it being just like, okay, what are our six
20:12big things that we care about this year as a brand?
20:15And it's every meeting, every decision, every conversation, every debate.
20:18Me and Charlie were debating about some tactics earlier.
20:21I would love to be able to go out to our customers, which is much harder because they're B2B, and
20:25just ask them and figure it out in that meeting or mine data we already have so we can have
20:29an answer without having to, you know, use gut feel or guesswork.
20:32Yeah.
20:32And I think the removal of gut feel and decision making, it's not like you have to completely
20:37rely on data, but it should always be multiple data sources.
20:39And I think there was a pursuit of the democratization of research and insight over the last like
20:4510 or 15 years with software, but it hasn't really worked.
20:48You know, a lot of research and insights teams within these brands are kind of like the gatekeepers
20:52of knowledge and information.
20:54So you're reliant on people really effectively coming to them with problems, then them having
20:58the time and energy to be able to solve them, and then that information flowing through.
21:02Whereas if you can get the accessibility, as you said, you can get the speed up, you can get
21:05the cost down, and you can protect that research, the fundamentals of good market research,
21:09you should be able to, from the most junior to the most senior person, have everything
21:14we know to be true, that one source of truth for decision making, or if there are unique
21:19custom problems you're facing, the ability to get that quickly without the need of being
21:22a research expert, without the big budgets that, you know, a big brand needs, I think
21:27that will transform how marketing decisions are made.
21:30And I think that the ultimate goal here is that you have better products for people that
21:34is more personalized.
21:34So in the advertising world, you know, there's obviously a lot of advertising that's become
21:38hyper-personalized, and you're kind of going, this phone must be listening to me, because
21:41how could I possibly have this ad that personalized?
21:43I think it has to follow on the brand creations side, and how we connect with people should
21:47be rooted in the problems people have, how do they feel?
21:51And I just don't think there's enough.
21:52And as I said, our goal, if we're successful, is to elevate research to being the thing that
21:56everyone does as a default, rather than kind of something that's going, we don't have
22:0020 grand to spend on that project.
22:03You know, we're the experts, we can just make this decision.
22:05And that happens far too well from in our minds.
22:07Yeah.
22:07Well, thank you both so much for joining me today.
22:08I'm so excited to see all that you innovate on in the marketing space and how many more
22:12insights we can get.
22:13So thank you for joining me.
22:14Thank you so much.