In this episode of The Breakout Growth Podcast, Sean Ellis and Ethan Garr sit down with Tom Smith, CEO and Founder of GWI, a London-based consumer research software company.
Tom shares the 15-year journey of GWI, from its bootstrapped beginnings to its current position as a global leader in consumer insights. The conversation explores how GWI has scaled by leveraging technology, particularly the recent integration of AI, to make consumer data more accessible and actionable for companies worldwide.
What You’ll Learn:
From Bootstrapping to Scale: Discover how GWI transitioned from a scrappy startup to a 600-person global company by evolving its product and staying ahead of market trends.
The Power of AI: Learn why Tom believes AI is the most exciting development in market research in the last decade and how GWI is integrating AI to democratize access to consumer insights.
Customer Insights at Scale: How GWI helps organizations, from TikTok to Chelsea FC, make data-driven decisions using a vast dataset of over 20 million consumers across 55 countries.
Navigating the SaaS Transition: Hear about the pivotal moment when GWI transitioned from selling research services to building a SaaS platform and the impact it had on the company’s growth.
Building for the Future: The challenges and opportunities of integrating AI into GWI’s product and how it aligns with their long-term mission to make consumer insights accessible to all professionals, not just researchers.
Key Takeaways:
“AI is the most exciting thing that’s happened to market research. It’s going to transform how people access and use data.” — Tom Smith
“Our success came when we focused not just on selling data, but on making that data useful and easy to access for everyone, not just researchers.” — Tom Smith
Timestamps:
[00:00] – Introduction: Sean and Ethan kick off the conversation with Tom Smith.
[04:00] – Tom shares GWI’s backstory and early struggles bootstrapping during the 2009 financial crisis.
[09:00] – The turning point: GWI’s transition to a SaaS model and how it changed their business trajectory.
[15:00] – Real-world examples: How companies like TikTok and Chelsea FC use GWI data to drive decision-making.
[20:00] – AI integration: How GWI is using AI to simplify consumer research and enhance user experience.
[29:00] – The challenges of scaling a 600-person company and staying agile in the face of rapid technological change.
[40:00] – Reflections on growth: Tom discusses the power of subscription-based growth and how his understanding of growth has evolved over time.
Why Listen:
If you’re curious about how AI is reshaping industries, particularly market research, or want to hear the story of a company that bootstrapped its way to international success, this episode is for you. Tom Smith’s journey with GWI offers valuable lessons for both startup founders and established businesses looking to scale.
Links & Resources:
GWI Website: Visit here
Follow Tom Smith on LinkedIn: Connect here
Subscribe to The Breakout Growth Podcast: Subscribe here
Join the Conversation:
If you enjoyed this episode, share your thoughts on social media and tag us! Don’t forget to subscribe and leave a review—it helps more people find the show.
Transcript:
Sean (00:01)
Hey, Tom, welcome to the Breakout Growth Podcast. Yeah, very good. Thank you. Great to meet you too. I’m joined actually in person by Ethan Garr here. I think this is first time we’ve ever recorded in the same room. Normally he’s East Coast of the US and I’m West Coast, but he’s out here on vacation. So we decided to, in the spirit of this program, run an experiment. Hopefully it will not be one that we suffered too much.
Hey, it’s Aaron. Welcome to the Breakout the Podcast. Yeah, very good. Thank you for everything you do. And I’m joined actually in person by Ethan Gohar here. It’s first time we’ve ever recorded in the same room. He’s East Coast of the US and I’m West Coast. But he’s not here on vacation, so we decided to, in the spirit of this program, him experiment. Hopefully, it will not be one day.
Hi, how are you? Great to be
That’s great. So far so good. It’s working really
Yeah, exactly.
Yeah, it’s good to see you. Actually, Sean, we actually did record a podcast together. It was the very first podcast recording you did when you interviewed me before I was a co -host. Yeah, that was actually a one. I was a UFC guest. Right. UFC’s got a good business and news program. Exactly. So it’s a big question of does it extend our reach of development or?
This is the very first podcast about recording you
Yeah, but that’s where I think when we did that it was you as a guest as opposed and also both of us off the same computer. being on two separate computers, this is a new experiment. Always the big question of is an experiment a repeat of an old one or a new one? This is brand new one. Awesome. So you’re the founder of GWI, Tom, and for anyone who’s not familiar, it’s a London based consumer research company.
Awesome, so you’re the founder of GWI, Tom, and for anyone who’s not familiar, a London -based consumer research company. But it’d great if you could give us some more context about GWI, what you guys can do to focus on.
But it’d be great if maybe you could give us some more context about GWI, what you guys specifically focus on, how big the company is, et
Yeah, of course. Absolutely. So GWI is a we’re applying technology to market research, right? So we are collecting massive scale survey data. We do about a million and half surveys a year across 55 countries now. And we deliver that data to our customer base through a simple to use online platform that enables anyone to source instantaneous consumer insights
Mm -hmm.
their audiences and consumer segments. I’ve been running the business now for 15 years. Founded it in 2009. We’re UK -based business, but we have customers, about 1 ,200 customers from all over the world. We’re nearly 600 staff. We’re mixed between UK, US. We have big team in Athens to do lot of the engineering and data science work. We’ve got people in Singapore.
Mm -hmm.
And yeah, said customers everywhere. So we have very global footprint from day one.
And are you mostly collecting through kind of a traditional survey? how do you actually get the data into your system?
through kind of traditional survey like how do you actually get the…
Yeah. So we work with, we’re collecting basically all through online surveys, right? So if you’ve ever completed an online survey, know, multiple ways you can do it, you can do it for a desktop, you can do it for mobile. We work with a number of partners and we built a technology that enables us to identify people across all of these research panels worldwide, about 30 suppliers. We’ve got this virtual panel of real people, which is around 20 million people worldwide.
Mm -hmm.
And we will survey selectively from that group of people. And in each country, we’re looking for specific bucket segments of people. So we’re looking for the right age, age profile, right education profile, right income profile, right ethnicity profile. And we use all of that. We then weight that to the population. So the million and half people we’ll survey every year will give us a viewpoint into the thoughts, feelings, attitudes of 2 .8 billion people worldwide.
and all the internet markets that we cover. And what makes us different to say traditional market research is global reach, scale. We’re running the data on lines. It’s highly frequent. And it provides an incredibly rich kind of perspective, like 200 ,000 data points on every single individual that you would never get from traditional surveying methods. And the result is
Dataset we’ve built, which is now 15 years old, is the most detailed viewpoint of consumers worldwide that’s ever been built. And it provides a very unique perspective on, and I guess what’s really unique is all the data is comparable. So you can say, OK, what does a consumer look like in my target audience in Spain? How does it look like versus the one in Italy versus the one in Bulgaria versus South America?
And that global perspective is increasingly important and increasingly what companies need.
Pretty cool. So can you tell us a little bit about the backstory? How did this all come about? I think you founded the company in 2009. Can you tell us a little bit about how you got from there to
Thank
Yeah, it’s been a long how long how long do you have? mean, it’s been a long journey So yeah, I was never its first time founder founded I’m on you know sole founder not with anything work with anyone else. I previously worked in advertising I was a market researcher by trade And my job was to work with all of the industry research products that existed
Hahaha
And they were used by marketers and advertisers to help understand who their target audience was, to build a better understanding of those audiences and to use the data to build media and marketing plans. And I worked for a very large agency. It was very sophisticated and had every single data product you could possibly buy and spent millions of dollars a year on buying data. The problem was none of them answered the questions the clients had. So at the time, I was lucky enough to be working
through the 2000s and social media was transforming advertising and marketing generally. And all the clients wanted to know about Facebook, Twitter, YouTube. How do I employ these news ways of connecting with consumers? going to be, you can see how important they are to consumer behavior and how to find out about information. But it’s very hard to put marketing resource there if there’s no industry data to support that.
You know, all the stuff that I was using was still being collected in paper based surveys offline. It was all about TV and radio and print and, and they were very slow moving products. asked for my space to be added. And they said, it would take two years basically to get the data back. But at that point, my space is blown up and didn’t exist. And there was something else new, right? So it’s kind of crazy that what he’s billions of dollars of advertising spend or being allocated with no.
You’re right.
Hahaha
up -to -date data. So I okay, how do we solve that problem? I started running research in that role to really understand consumer usage of social media. It’s fairly rudimentary, but it kind of fill a bit of a knowledge gap. Ran that for a couple of years. I realized it could be a product. So I left to build this market research product and
Well, the long story is that I launched 2009 when the world collapsed and the financial crisis came and everyone decided that this is not a great time to buy new products. So it took a very long time to get first customer, but it took me a year of knocking on doors and pitching my PowerPoint presentation. first customers, Microsoft. that was like, okay, nice.
great first customer.
Maybe there’s an opportunity here that somebody needs and they needed global data to understand their MSN, which dates it a little bit, their MSN customer and what they look like because they need to explain that better to advertisers. So that was like step one. And we went from there. Now it took, I ran it in that format, the business for about five years. It was entirely bootstrapped, built a couple of million revenue and we were selling this research product.
to the kinds of companies that had always bought research products. In the world of marketing, would be big media and creative agencies, big anyone selling advertising, but very big international companies like we were working with BBC and Guardian, and we started working with Twitter at the time. But it was a fairly small business. Now, my vision at the time was
The demand for consumer insights was huge, right? And actually the current research industry in a way that data has been collected, sold and delivered did not service that demand. And everyone that was coming and asking for the insights, you know, we would do like, you know, insight downloads and infographics and podcasts and webinars and all the people that sign up would never market research as they were.
business owners, salespeople, marketers, people who work in an HR product. mean, any kind of professional you can imagine, not market researchers very rarely. It’s like, well, you know, the demand for the insights, like the actual end user of that information to make a decision is not the market researcher. They’re the kind of conduit between the data and the end user, but nobody’s delivering that data directly to the end user.
So how do you do that? need software. So we need to build a simple software solution that anyone can use in a consumerized format. So that’s what we did. We built software around the data. It was about five years in. And it transformed the business from a small, addressable market, kind of interesting, but not really growing that fast. It’s something that really, really scaled. And it shows the difference that
know, important it is to distribute your data to the right people in the right format. And the importance of software to distribute. And it completely transformed the customer base from being predominantly researchers to being, well, now it’s like 80, 85 % of our user base are not in a market research role. They’re in a, it could be in any other role. Sales, marketing, business owner, C -suite, really broad.
So that transformed the business. And then also the nature of the types of companies buying the product transformed from being just big, large enterprises with the resource and the head count and the people to work with data to basically any kind of company. And now we work with about 1 ,200 customers from the biggest companies in the world down to like very small SMEs
people with three or four people in the business. And we built a much more global footprint in customers. We now have customers over 50 countries. So by putting the data into software and making the simple experience simple, you’re able to open up this big addressable market. And that’s really what we were working on now for the last sort of nine, 10 years is scaling through that software.
Such a great insight. I’m a big survey guy personally and probably most important thing I do in every role that I’m in. For years, I have always preferred one platform over other platforms because it’s so easy for me to analyze the data in that platform. It’s easy to miss how important that is.
Such a great insight. I’m a big survey guy personally and kind of my most important thing I do in every role that I’m in and for years I’ve always preferred one platform over other platforms because it’s so easy for me to analyze the data in that platform and I think it’s easy to guess how important that is.
Yep.
It’s so important. mean, if you’re the products we use in day -to -day life, you tend to steer to the one that’s easiest to use, right? And that whole market research industry neglected UX and user experience. So that was important. And the other thing is by putting it in software, you could make it accessible and cost effective and easy to buy, which I think most industry research products are very expensive, very high.
Yeah
entry points. So did a lot in terms of transforming the buying experience as well. So yeah, it’s really important.
Mm -hmm.
And is it, I mean, from what you’ve said, especially with the panels, it feels like it’s not necessarily researching against your direct customers. It’s more looking at the broader market or do you also help with research with your customers directly?
And is it, I mean, from what you said, especially with the panels, it feels like it’s not necessarily researching and getting the correct customers. It’s more looking at the broader market, or did you also help with research with your customers directly?
- So when people come to the platform, the data is representative of all consumers in a market. So they’ll be using it to understand. I mean, there’s a huge number of different ways you could interpret the data, but generally you’d be looking at like, who’s, who’s your target market customer? what do they look like and trying to build a greater understanding of those. because we collect lots of data about
Okay.
product usage and brands, you know, from everything for the cars you drive to the supermarkets you shop in, where you bank. I mean, it’s really like very comprehensive.
Mm -hmm.
Can you describe maybe an example of how a customer uses this research in the real world today? What is a really good example of how people are getting value from the platform?
Yep.
Yeah, sure. So I can give you, I’ll pick three examples because they’re different segments. So we work with, the whole advertising industry. So any publisher who’s looking to sell ads on their platform, they need to use independent third party data to validate their audience. Right. So we work with TikTok, snap two examples. If TikTok are pitching and advertising a brand,
We can give the example of, they’re pitching Samsung, for example, they want to be able to say, I know using GWI data, the Samsung, the owner of Samsung TVs or the people who are thinking about buying a Samsung TV are far more likely to be active on tick tock. They’re more like spending money on technology. They’re more influential. They want to paint that story of the data to prove the value of the audience and to make the sales argument
Samsung should think about putting their money into TikTok and not another platform. so that’s one example, work extensively with sports and gaming. so we’ve worked with, Chelsea football clubs, an example where they would use GWI data to understand the fan base. They say, this is what, this is how many fans at Chelsea have worldwide. This is what they look like in different markets. and they’ve used it to win sponsorship deals. So some of the shirt sponsors.
Mm
would approve that there’s a strong relationship between the Chelsea fan and that brand and it makes sense for them to put the investment into Chelsea.
So just on that one real fast, in that case, they be bringing you a list of people they want to survey or would you be generally finding the Chelsea fans from among the general populations in those countries? Okay, yeah.
Okay.
Yeah, definitely the latter, right? So the kind of compelling thing about GWIs, you come to the platform, we’ve done the data collection already because we’re running it continually at massive scale. And that’s good for a couple of reasons because A, you know how pressured it is in the workplace now. You need answers straight away immediately. I don’t have time to go and run a new market research study to go and find the answers.
Got it, yeah, yeah.
I rarely have time to do the desktop research to go and find it, but I like if you can come into GWI, I’d say for 95 % of the use cases, the data is already there. We’ve collected enough data that you can answer that question and build that insight on demand. And that’s really important. all of the customers and clients at GWI are all using the same data.
It’s how they manipulate and use the data and how they apply it to their use cases. really
So, and then obviously like we’re going through a pretty massive transformation right now with AI. I’m curious how that starts to factor in or if that starts to factor in. Does it start to affect kind of how market research is done or is this going to kind of run parallel to the AI movement?
And then obviously my clothes are a pretty massive transformation right now with AI. I’m curious how that starts to factor in or how that starts to factor in. Does it start to affect kind of how, especially how our research is done or is this kind of parallel to that AI?
I think AI is the most exciting thing that’s ever happened to market research. I couldn’t be more bullish about the potential, right? Especially if you think about where we see the value for AI is helping people answer questions from the data, basically. we have already integrated AI into our platform for customers.
Okay, cool.
So you can do natural language search to get answers. And we’re about to release a whole new version of the product that’s built entirely around natural language. So at the moment, if we onboard somebody, the product’s simple to use, but you still need to help somebody understand where to look for the right data to answer the question, what types of data make sense to use, how to interpret the data. So you’re kind of teaching people a bit how to do
Perfect. Yeah, that makes sense.
But yeah, I think so.
Mm -hmm.
the analysis and the research. And if you apply AI on top, all of that goes away. Right. So you can now become coming to GWI and ask a question as you’d ask, do a Google search and you say, so that example I gave you earlier, you could say, if you’re working in a, as a sales executive at TikTok and you need the data to prove to Samsung, you’d say, TikTok users more likely to own a Samsung TV?
Mm -hmm.
You can ask that type of question. It will mine the entire data set and bring the answer back.
So you could technically ask that question with like chat GPT today, but it’s not going to be on your proprietary data set. it’s going to be kind of probably not that useful of an answer. Yeah.
So you can technically ask that question with like chat, but it’s not going to be on your proprietary data set. So it’s going to be probably not that useful. Yeah.
It would give you a very generic answer. But I actually think the power of AI combined with proprietary data that’s very well structured and kind of walled garden is super powerful. Because the answers are really specific as well. So you don’t get the same kind of generalizations, hallucinations. You can believe in the answers more if you believe in the underlying data.
with proprietary data, yeah.
Yeah.
that’s awesome. Yeah.
Yeah. And it starts to really democratize access to that data. So I think both qualitative and quantitative data, when you can start to combine those, I think you’re seeing the same thing on the quantitative side, but I haven’t really heard many people talk about the qualitative side. So that’s super cool.
Yeah. And it’s certainly really democratized access to that data. So I think both qualitative and quantitative data, when you start combining those, think you’re seeing the same thing on the quantitative side, but I haven’t really heard many people talk about the qualitative side, so that’s super cool.
Yeah. I mean, actually, because all our data is underneath one to zero, it’s all quantitative data. But the API and…
I mean, I mean more quantitative, like actual behavior data inside your product.
I mean, I mean, what was it like actually being created in such a product?
Yeah. Yeah. Yeah. Yeah. No, I understand. And, it’s very, it’s the kind of core part to democratizing access, like you said. and you know, if we’re going to deliver the product to the people that really want to use it, it’s ultimately it’s making, it’s about making it simpler to use. And AI has a huge, huge role in that. and it’s, yeah, it’s, really exciting. I think there’s some other.
Mm -hmm.
things further down. I mean, other things that we’re bringing in as well as being able to build visualizations through prompts.
I was going to ask, can you do that visualization through the AI, like create a slide that looks like, know, that highlights this or something?
was going to ask, can you do that visualization through the AI? Like, create a slide that looks like, you know, that highlights this.
Yeah, so that’s stuff that we I mean won’t come this year, but next year but like the Yeah, so the moment if you’re building a dashboard or an analysis you’ve got a kind of picture type Choose what dates to put in there do your filters and everything. Yeah, you just built a build from a prompt. So Build me a dashboard. does this and yeah amazing
Super cool.
So I was just curious, you told us a little bit about the journey and I know originally you’re bootstrapped, now you’re much larger scale. Does that change your approach to how you think about AI and how you, you have different resources to seize on opportunities. Can you tell us a little bit about how you think about AI differently now than you might’ve thought about it eight, nine years ago when you maybe were in a different situation?
that’s a question. Yeah. I mean, in some respects it’s, might almost be easy if you start with a blank page, cause you haven’t got existing product and user base to kind of, you know, to service and deliver what they already need. So, and you think what you can build now with AI is quite remarkable with relatively little resource. So, you probably look at it slightly differently. However, on the other side of things,
The volume of data that we’ve now managed to accumulate and the level of technology and process we’ve built around the collection of management of data at scale makes this far more interesting because I think, as I said before, the combination of what you do with AI along with the data asset is really exciting and you can do a lot. I mean, I think in truth, we’re able to build a much more
probably aggressive roadmap around this now, because we have got the teams, the resource know how and the data’s there as well. So we’re moving very quickly and we’re asking the teams to do a lot. And that wouldn’t have been possible in the early days to another resource. However, you do have to do things very differently. Like we’ve had to be very clear what the vision is around that. And there’s…
you’ll have concerns from internal teams about, you know, we’re to be selling and delivering the products in a different way. that compromise? It’s a cannibalized existing business. it, is it really what our customers want? You know, since there’s a lot more time about bringing people along the journey than just building something and see what
Mm -hmm.
Yeah. I want to go back to something you said earlier, because I think it maps to this well, you know, that it feels like there was kind of a big, almost, I wouldn’t say turning point, but like, figuring out of the business when you realize like, making the data super useful instead of just having a sales process focused on getting people to buy the service. once you focus on making the data useful,
Yeah, I want to go back to something you said earlier, because I think it maps to this as well. It feels like it was kind of a big, almost, I wouldn’t say turning point, but like, a figure in the business when you realize like, make the data super useful instead of just having the sales process focused on getting people to buy the service. But once you focus on making the data useful.
That was a big turning point in the business. That seems to map really well to this AI piece. But I’m curious, is that the point where you felt like that you had this, at least figured out to where you could build a really interesting business in this space? Or was there a different point? And then maybe what were some of the challenges that stood in the way of
that was a big turning point in the business. That seemed to map really well to this AI piece. But I’m curious, is that the point where you felt like that you had this, at least figured out where you build really interesting business in this space or was there a different point and then maybe what were some of the challenges that stood in the way?
Yeah, I think the point where I realized this could be a really large business was after we launched the data and software. And we basically started operating as a SaaS business. We were selling subscriptions to software, not selling the research. And you started to see a couple of things happen, which made me realize if you panned it out, it could be something really big, is that you were signing customers that you’d never think of signing. That was the first one.
You improved your retention rates. You were seeing growth rates on existing contracts grow and all those and a number of users that customers are putting onto the platform are increasing substantially from what we were able to sell before. So all those kind of indicators were like, okay, this could be this could be something that’s quite big and you know, if you model it out and you look at the power of compound growth for your SaaS platform, it can go a long way.
But yeah, I think the most compelling bit is when you start. It’s the diversified customers in lots of different markets. And you think, well, we’ve got a couple of sports teams and they have finally value and renewing and growing. So you know that this must have value for, you know, 50, 60, a hundred sports teams. It’s the same when you start selling, we started selling more it’s corporate brands. and you think how large addressable market is.
and you suddenly start to sell into different types of companies, different markets, and they’re renewing and growing, logically that means you have a really large market opportunity. But there’s no way without the software, without building SAS product metadata, none of that would have happened. So I think you could see that quite early as soon as we built that. It’s interesting.
Yeah. And where do you kind of sit inside the stack of customer research? Because obviously, kind of external market research is part of the equation. then part of it is also, how do I find my most passionate existing customers? Sometimes you can find that through the external, but a lot of times you already have your own lists. that different products? Is that a risk to the business that some of those other products are also…
say it’s like a stack of customer research because obviously, kind of, external market research is part of the equation and then part of it is also how do find the most passionate existing customers? Sometimes you find them through the external, but a lot of times you already have your own lists. Is that different products that you, is that a risk to the business that you, that some of those other products are also entering into the…
Yep. Yep.
entering into the more panel driven side of things.
the more analog driven side of things.
Yeah. it’s a great question. So there’s, tends a bit on the buyer and the company type, but generally most of our big companies would be buying multiple data products and running custom research elsewhere. quite a lot of them run custom research with us and we can like fuse the data together. So it depends. It’s situation a bit specific. now there are lots of companies who will buy, you know, have lots
huge numbers piece of software and go to market stack of software, you know, you think of order and we kind of we’re not really integrated in that at this point or working towards that we set a bit separately and generally sort of more in marketing and planning and strategy and less downstream. Now, in the in a lots of companies are buying the product have never bought
Mm -hmm.
research product before. they, so you’re like, if you look at when we, we lose deals, it’s rarely to competition because they’re not really competitive tenders. Typically the company said, okay, we need to find out this information. Like we need to figure out how to do, I don’t know, launch a product in this market, or we need to figure out what our personas look like in different places. And they don’t know how to do that. And they come across GWI as a solution. So maybe we’re the first time ever bought a research
insights product, or they are running market research in a traditional way, working with a research agency. And they realize how much time it’s going to take, how expensive it’s going to be. Like if you think, if you’re a company that had, was operating in 10 countries, for example, and you want to collect data about your market and customers across those countries, if you went to a research agency, it’d take about six to nine months.
you run that study end to end, it would be extremely expensive. You’re talking hundreds of thousands of dollars. And you might not collect the data you realize you actually need because it’s going to be quite limited in scope. So by taking that approach, you’re saying, actually, we’ll buy a product like GWI when they’re already collecting the data. You get the same thing for a fraction of the cost. It’s instantly available.
And you can constantly go in and ask me questions and query things and play around with the data. So it’s kind of it’s a more modern approach to consumer insight. So often people have not bought this data product before. We might be the first one.
Yeah, I was curious around that with, you started, were selling to companies like Microsoft. Now you’re talking about selling to companies, to, you know, sports organizations. How much, how much is the job of your sales team just to simply educate the populations, you know, potential customers that this can be powerful and this exists versus are they, you know, are people starting to realize, see that like there’s value out there. We’ve got to go find
Or do you really have to go do the work to source out these companies that you think might get value from this and explain how to use this?
Yes, a massive education layer. we So people tend to discover GWI they might we produce a lot of reports, you know might do stuff like everything you need to know about Gen Z or You know social media trends stuff like that people read it. They find it interesting But they don’t realize there’s a product behind it and the job of the salesperson is to educate them about how they could get access to data and how they apply it’s what their questions and challenges
There’s a huge education piece there. And the other side of it is market research is not traditionally, generally speaking, has not been moved into software. It’s not self -service business. There’s a few, there’s some products, don’t get me wrong, but as a whole, we’re talking about a business that’s a hundred billion dollars annually, and most of it’s still delivered by people in service. So that, you know, if you think of nearly everything else we do at work, it’s through a piece of software like.
CRM, I manage all of our HR, self -service and manage through everything to do with contracts and holiday. Nearly everything you do at work is a piece of software. And market research hasn’t really gone down that path yet. So there’s a lot of education about the idea that you could buy a piece of software that can solve these research questions. And
It’s a challenge for our sales team. You need quite a lot of knowledge of products. You need to have the out to use the data if you’ve got to prove the use cases. And yeah, but it’s also a fun challenge for them because it’s kind of as an intellect, you’ve to learn a lot of new things. It’s got an intellectual challenge to it. The stories and the data are interesting. And then the kind of clients that are interested in it are kind of exciting clients to work with.
Yeah. I think when we spoke a few months ago, you had actually mentioned that you had traditionally been sales led and that makes sense based on what you just described, but that you’ve more recently been moving to a more product led approach or at least trying to add a product led layer to that. Can you tell us a little bit about that with those challenges? What does that look
Yeah.
Yeah. Yeah. The moment the kind of what we just talked about is like the, I think from the technology wasn’t really there to be able to make this type of product immediately usable without someone on boarding you explaining the data. if you had like market experience from elsewhere and you’re an expert using market research, but I think, you know, AI genuinely changes all of
so the, the self signup and self use product that we’re building is built all around AI. And, so it’s a natural, like you start with natural language, you ask questions and that doesn’t need any training. Right. Everybody knows that to ask a question. It’s very easy to show people to kind of prompts to use and get good results. It’s the format that’s familiar. The user experience is familiar. We’ve done it elsewhere. It’s kind of how chat GPT works.
It’s not the similar to how Google works or certainly how Google is moving to. So we are now able to put our data into a format where somebody can get value without someone, human being involved. So that gives us the possibility to build a genuinely self sign up PLG model around the data. And, know, we want to do what you can do for Mark. What companies have done to other fields like Canva has done for design and made it accessible to everyone.
we want to do for market research. And now that potential is here. You can do it because of the tools of AI. So the experience from later this year will be very much you’ll able to discover the products and the trends and the insights that we’re publishing. You then be able to start using a free version of products that’s search -based. And then that ladders in. There’s different layers of complexity and detail with the results, depending on your level of need.
It’d either be free or paid for or company buy. that’s very exciting, but we couldn’t do that before. So
Mm -hmm. You may have mentioned already, but how many people work in the company
To show I have Okay, so is that.
Wow. So I imagine it was a little easier to turn on the dime in the early days. I’m curious, with the emergence of all this new opportunities, how do you kind of keep everyone in sync and like how much does the sort of vision or mission of the business evolve as these new opportunities are coming
I’m curious, like, have the ability to just all this new opportunity. How do you kind keep everyone in sync? like, how much is the sort of…
Get.
Yes, it’s definitely different. And in some ways you have a lot more resource to do things, but in the other ways, you just need to bring everyone on a journey. You need to talk about clearly, this is the vision and the mission of what we’re trying to do. And this is why AI is important, for example. And I think where it made sense for us particularly is that our mission and vision was always about taking the product to the biggest possible audience.
And the professionals that need it, not just experts. And that’s been the operating model for some time. So it’s like, look, we’re not just interested in AI for the sake of being AI. It’s genuinely, it generally helps us achieve that mission because it just, it makes it easier for the user. not just, I mean, you see a lot of things where AI just plugged in for the sake of it. It’s not actually adding any values to the end user. So it’s here, like it’s genuinely.
Mm -hmm
it transforms how you access and interpret information. So I think that was pretty well understood and we could quickly convey that. But it does require, you spend a lot of time, you know, telling the story to the company, town halls, internal, you know, presentations, team presentations, Q &A sessions. You have to do a lot of work to, know, messaging takes a long time to land.
Mm -hmm.
Keep everyone in sync. Yeah, it’s my, I think it’s, you know, the focus on having it be useful because I, for me, the years I’ve spent in Silicon Valley, like it’s so, I think my ability to help grow Silicon Valley companies is based on my own skepticism for things that sound cool, but don’t seem particularly useful. so,
to get everyone in sync.
I’m, I’m, feel like I’m always kind of doing that translation layer there. But when I, when I look at the impact of chat GPT, for example, cross generationally went like the way my kids are using it, the way I’m using it, I’m finding I spend more time in chat GPT now than, definitely any other app or website. But knowing that, you know, my, my daughter, who I spent a lot of time with over the last month, she is spending equally as much time.
And obviously using it in probably different ways, but it just shows like it’s a massive impact generationally where it means that there’s a behavior change and expectation changes that are happening across the general population that now it’s not just, and yeah, and then expectations are going to be for something like this.
Yeah, absolutely. I mean, that’s also part of the the story that you should have to make the argument. But if you’re talking to people internally, it’s like, why does the product have to evolve? Because consuming expectations will shift completely. And actually, that’s why one reason we had to put day 20 software before is because that was the expectation. Is that’s how people want products to be delivered. The next phase will
the expectation be aligned with chat GPT and AI and how it works.
Yeah, I watched the movie Blackberry last night. I don’t know if you’ve seen that movie, yeah. I mean, I think it’s such a good demonstration of this is that just like, know, essentially Blackberry was on fire, but as soon as that iPhone came out, people’s expectations changed. Their needs didn’t change, but their realization of the possibilities changed. yeah, that’s why you can’t.
I’ve seen it, it’s great.
That’s why you have to keep evolving.
100%. Yeah, no, that was really fascinating. And then they tried to catch up when they realized that they did need a screen on the the device. And it was too late, right? So you’ve got to stay ahead all the time. You’ve got to have the foresight where things are going. I think that’s the hard thing about being a CEO of a scale up company is the things that you want, you want, and the things you know that you need to deliver always, you’re always operating kind
Right. Yeah.
a couple of years in front of where, yeah, it just takes time to build and deliver. yeah. So then you’ve got, and then when they actually build the stuff that you’re talking about, then you will, again, you’ll like start thinking about what’s next. So you never actually get to what you want. So you have to mediate those and kind of stay grounded to where you are
Right. It takes people out of their comfort zone and they don’t like that, but it’s needed.
Sorry, I was muted there. Do you feel like things are moving at such a fast pace right now that you have to approach just the management of this very differently in that AI is changing things so quickly that it’s very hard to, like, I’m guessing there was a time, in the early days when you’re bootstrapped where your, how far ahead you’re looking was very short. And as you started to go to scale, you start looking farther out. But now, because things are moving very,
so quickly, do you have to like sort of shrink the outlook because you know that change is going to happen so quickly in that in the shorter
yeah. The interesting thing about your bootstraps is you don’t even have, you’re only worried about where the next sale comes from or where the next invoice is getting paid. So you can’t really lift up your, you know, if you go to build some really interesting technology, think it’s very hard to do bootstraps for that reason. So you can’t really look long -term. You can only look like you don’t know if you could genuinely fund it in three to six months time. So it’s very difficult to build those big, big pieces.
yeah, I mean, we’ve had to re tour, re engineer the business and how it’s operating to move much quicker. we’ve like reorganized product teams around new types of products. we introduced new ways of working that bring in, I’ll go to market teams and legal and finance working in, in pods with the product team, which I know many companies do, but we hadn’t been doing. that really helps drive alignment.
seems to be the most important thing to move quickly. Is that everyone on the same page? So you’ve had to work really hard on that and ways of working. So it’s kind of all those operational elements around like everyone has the capability to build what you need to build. But if you don’t have everyone operating in the right way, the right ways of working on the right alignment, it’s just going to take too long. And we set some pretty aggressive timelines about new products, monetization of new products.
we’ve got a very aggressive, fast paced roadmap at this point, which is exciting. And, yeah, but yeah, it’s quite difficult to get that kind of momentum going when you’ve got a lot of people because you need to get everyone on the same
Mm
Yeah, and I can imagine too that you can’t continue to grow at any kind of meaningful rate when it’s just chasing that next sale like it was in those early days. So I’m sure your perception of growth has changed a lot during that time. one of the questions we love to wrap up with is just like what you feel like you understand about growth today that you might not have understood a couple of years ago. Any thoughts on
That’s a good question. I mean, I just remember in the very early days, there wasn’t really any clear understanding of, my side, I’d been a founder before. was like the level of the power of kind of compound growth on top of subscription products is really, really substantial. And you feel like you’re with quite small numbers. But if you look at how that can grow over time,
Mm -hmm.
and you’re willing to give it a time to stick around. It could be substantial, meaningful. Yeah, so you’ve got to take a pretty long term perspective and turn loop become a lot more sophisticated about thinking about where growth is going to come from, which segments to focus on, what kind of product mix is going to drive the next scale of growth. So we put a lot more time into that kind of modeling
And without that understanding, it’s difficult to justify some of the changes in work you’re doing now. But again, we’re now we’re moving to more PLG model, we have to refactor how we think about growth modeling from think about we’re selling to X number of companies and this many sales reps, you can sell this much quotes in as many companies and how many people are signing up this month and that’s going to translate into this many buyers, this many people translate to company buy.
Mm -hmm.
It’s a whole different way of running an engine and operating a business, which is a whole other learning curve that never been through before.
Where do you get that learning from? Have you found good inspiration and any sources for
Actually, the board have been fantastic with great board and they work with a lot of PLG led companies and they put you in touch with people who’ve been through this transition before. That’s great. We can find amazing information podcasts and online and in terms of companies being successful. I I look at a lot. I like to look at peer companies, particularly this sounds a bit nerdy, but reading, looking at like public companies.
Mm -hmm.
Because the amount of information around public companies is huge. If you look at their… Yeah, I do. There’s so many learnings in those things and most people won’t go through them. Yeah. 100%. Yeah, I know. So there’s a lot in there. If you look at peer group companies, probably like five, seven, eight years down the line, they’re all the things that you want to do. Like it’s a playbook that somebody else has done.
Yeah. And I love studying the earnings reports on public companies. Yeah. Because they’re all showing the roadmap of how they’re going to hit their numbers the next quarter.
Yeah, I love standing there making reports on public employees. They’re all showing the program of how they’re going to hit the numbers the next quarter.
Mm -hmm.
So the learnings in there are really substantial. Yeah, and obviously we’ve hired people from companies that have done, made these kind of shifts before.
Yeah, the beauty of having 600 people on the team is that, yeah, any knowledge gap, just, you could, bring that, that skill in hopefully.
Yeah, someone’s done it before somewhere. You’d hope so. Yeah. But it is an eye opener that I mean, I think you never stop finding something new that you don’t know about. You’ve got to figure out and learn. But that’s what keeps it interesting.
Mm -hmm.
Well, Ethan, I have some key takeaways, but I’ll let you go first if you’ve got any takeaways that you want to share.
Yeah, I just, I mean, I don’t think anybody will be surprised that, you know, a big part of this conversation was AI and what the impact is. But it was really interesting to hear you say that, you know, you think AI is the most exciting thing that’s happened in market research. I think I can’t remember, in at least the last 10, 15 years, you said. And I think what’s interesting about that is the way you’ve described
how from your early days of bootstrapping to now at scale, how you have to think about this differently, but you also have resources and opportunities to think about it differently and approach it differently. And I think that’s super exciting. And I think, you know, one of the key takeaways for me is like, as you scale, you have the opportunity to be more aggressive, but you also have to be more thoughtful and intentional about how you lead your teams through those transitions. So thought that was super interesting,
Sean, how about you? You always have a bunch of good takeaways to pull from these.
Well, you got most of the good ones, but I have one additional one that I just kind of heard as a theme that carried through, is Tom’s focus on value. heard it a couple of times, like making the data super accessible to the team was how they got a lot more value to the team of the client is how they got a lot more value from the product. But even as you started that discussion about AI, you were like, but we need to make sure we’re not
going after AI for the sake of what’s new and cool, but how can we bring more value to the customers as we add in that AI? think having that theme throughout those that ultimately you can’t build a big sustainable business long -term without pretty intense focus on how do customers get value from our products and services. So it’s good that you haven’t lost sight of that as you make that turn toward AI.
No. Yeah, it’s easy to get, it’s very shiny and, know, but I think, yeah, if it doesn’t solve a problem, don’t use it. But I genuinely think it’s, I think a lot of AI is looking for use cases, but I think in our world, it’s just, it will transform how people are going to operate and work. So in a good way, yeah.
Yeah, yeah.
For sure, for sure. just to kind of reiterate on something I said before is that the hardest thing to change with people is behavior. And the behavior is actually changing before you’ve added it in there. People are used to those natural language queries. Like for me, it’s probably one of biggest shocks
And just to reiterate on something I said before is that the hardest thing to change with people is behavior. And the behavior is actually changing before you add in the new thing. People are used to those natural language queries. For me, it’s probably one of biggest shocks is…
70 % of my use case with WhatsApp has shifted from I used to Google it. not WhatsApp, sorry, chat GPT. I used to Google it. Now I chat GPT it and get usually much better answers. And of course, Google is now building in the AI layer themselves. the funny thing is I can’t imagine now paying for Google, but I pay the $20 a month for chat GPT
Yeah. Yeah. Now you watch chat chat. Yeah.
Yeah, so do I. That’s a good point.
Yeah. so, so like, I’m not going to pay a second one. And as long as I’m paying it, I’m going to keep using that one unless Google is way better. And, and I, and I think that’s unlikely in the short term
And I think that’s something we can a short run into.
Yeah, yeah, it’s interesting. But just a little point before was very interesting about how it doesn’t matter what product you’re selling, expectations of the user are going to change, right? They’re going to want the same thing. So you need to evolve for sure.
Yeah. Well, congratulations on all the success. To be able to bring a company from bootstrapping all the way to a 600 person team evolving into AI now, which seems just like such a natural next step for consumer research, is definitely exciting times. Basically, if you think about AI in almost every business and you spend some time
Yeah, well congratulations on all the success. Like to be able to bring a company from bootstrapping all the way to a 600 person team evolving into AI now, is just like such a natural next step for consumer research. It’s definitely exciting times and I…
And I think there’s like that natural tendency like how is this how is AI going to transform that
deeply thinking about it, you start to get ideas of how that business will be impacted by it. But I hadn’t really done that deep thinking around consumer research. now that we’ve gone through this, just seems so obvious and makes a ton of sense.
away, like it’s going to go away. Whereas here, it’s like, the natural one is how is it going to just make it better and better and better and more useful and valuable? And I think that’s what you’ve really pointed out is that it just becomes the accessibility throughout organizations for people to get the information they need in real time and have it so nuanced. think that’s where there’s incredible value that we found.
No. yeah, it’s absolutely true. And actually one of the things I didn’t mention before is one of the fears when you start talking about AI and the product is people think we’re doing it because we don’t need the people in the business. Like it’s a replacement for people. I’m like, that’s absolutely not the case. And actually if you look at across the border industry, there’s a huge skill shortage with data, data analysis. And most companies don’t have enough people or can’t afford the budget for all the people they need. So AI enables.
you to get value from something and give it to all the people that need it without having to build these vast teams and they’re never going to be able to afford to do so. It’s not taking people out of the process. It’s just making everything more accessible. Yeah.
Right.
And most of the data scientists and experts don’t like to just be running a bunch of queries for people. They prefer to be doing the deep work of really trying to understand something on a deep level.
of trying to understand
Yeah.
Exactly. You could do the interesting part, the interesting layer. Yeah,
Yeah, but enabling people to democratize that access to information becomes really important. Well, we could keep going on with this conversation for a long time, but I’m excited that we had you for the amount of time that we did. So Tom, thank you so much for bringing us up to speed on GWI and to everybody listening, thanks for tuning
Yeah, enabling people to get access to information. We could keep going on with this conversation for a long time, I’m excited that we had the amount of time we did. So Tom, thank you so much for bringing us up to speed on GWUI and to everybody listening. Thanks for tuning in. Thanks, everyone.
Yeah.
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