Insights, Marketing & Data: Secrets of Success from Industry Leaders
Insights, Marketing & Data: Secrets of Success from Industry Leaders
JONATHAN WILLIAMS - ONE STRATEGY STUDIO (Founder) Part 1. Why the future of insight isn't asking consumers more questions, it's finding better answers. Doing more with existing data; driving more value out of new research.
Delighted to talk to Jonathan Williams, successful founder of multiple agencies who has consistently pushed the boundaries of strategic insight...and how technology can help. Among other areas we cover:
- Thinking about the creative process in music and writing
- Jonathan's journey and learning having successfully launched and sold Clear Ideas (to M&C Saatchi), MASH (to Kantar), and Discover AI (to Talkwalker)
- The creation of One Strategy Studio – an agency powered by AI personas working alongside human strategists to drive projects end to end.
- The paradox of time-based billing - the ore efficient you get, the less you can charge.
- Imagining a new direction for a major movie franchise.
And that's just part 1. In part 2 we move onto the impact of VC/ PE funding on the insights business, whether SaaS type models can really apply to the insights world and how to successfully interface AI with the insights world.
All episodes available at https://www.insightplatforms.com/podcasts/
Suggestions, thoughts etc to futureviewpod@gmail.com
My view is that in the past, over the last 20, 30 years, whatever, people have defaulted to doing primary research when they have questions to get answers to, or they want to be more consumer focused. They say, okay, in that case, we need to speak to people. And maybe before that was the case. The only way you could get to these answers was talking to people. But actually now there are other ways of getting to it, like what we're doing, you know, exploratory discovery work, leveraging secondary sources, internal sources. You can get much further, much faster and in better ways.
SPEAKER_00:Welcome to FutureView in a brand new season. And I think we've got a really cracking couple of episodes to kick off with. So Jonathan Williams has a brilliant agency side track record in the inside space. He was the co-founder of Clear Ideas, acquired by MNC Sarchi in 2007. He was the founder of the brand strategy firm MASH, acquired by Cantor in 2018. And he was the founder of Discover AI, acquired by Talkwater in 2021. He's now the founder of OneStrategy Studio, who are taking what I think is a really perceptive approach to AI, with what in effect is an agency, but driven by AI personas who are, in effect, assisting real, very knowledgeable, experienced people. I think it goes without saying that Jonathan knows quite a lot about successfully launching agencies and bootstrapping them as he explains, as well as integrating really smart innovations into the insights process. Anyway, I loved having this conversation. In fact, you might say that Jonathan is my new research crush. We talked for well over an hour, and I didn't want to miss any of it out. So the interview is split into two. In part one, here we explore a whole range of areas music, writing books, elevating research to the more strategic level, how agencies charge, and how one strategy studio might approach an example if they were guiding development for a new movie. Onto the interview. So, Jonathan, firstly, thanks so much for joining today. Really delighted to have you on the podcast.
SPEAKER_01:Absolute pleasure. Nice to be here. Good to chat.
SPEAKER_00:Fantastic. Now I wanted to get going with what has become the traditional icebreaker. So it's one of these kind of something people might not know about you type questions, or something they might find surprising that isn't commonly available on, I don't know, a LinkedIn or just through a simple Google search. So have you got anything along those lines you you'd like to share with the listeners?
SPEAKER_01:Yeah, no, I would. I mean, there are things that I have shared on LinkedIn. So people, some people know anyway, like I've written a couple of novels, although I haven't formally published them, but then I I I posted them on LinkedIn to people to let them know that I've been doing it. So I haven't been quiet about that. But one thing you haven't really talked about is that I play the bass guitar. So I've been playing the bass guitar since I was about 12, I think. So over 40 years, with a big gap in the middle, I think where I got very busy with work and kids and family. Um but yeah, and I I play in a rock band, I play jazz, and uh yeah, I like playing music. So so that's my thing.
SPEAKER_00:Fantastic. And obviously, we're gonna get into some of this as well, but are you kind of quite technical? Are you a coder as well as uh as a general sort of strategist and an insight person?
SPEAKER_01:Absolutely not. Um so I don't have any technical training or technical expertise when it comes to computers development, don't do any of that. But I have always been, I guess, on the edge of strategy and technical things. So I was in the world of quant for a long time. Um, and you know, being that go-between between the analytical experts and the clients, it's being able to bridge that divide that I think is probably what's been a consistency across my career.
SPEAKER_00:Got it. Yeah, so you're sort of the the translator or the bridge. Part of the reason why I was asking is I think I think it's quite a widely commented upon fact, actually, that people who are quite musical also tend to be quite mathematical.
SPEAKER_01:Yeah, I think I think it's interesting. And there's there's a I mean I'm getting into it at the moment, there's a lot of heavy technical theory that you can get into with music, but at the same time, there are people who just do it all by year, it's quite natural. I don't know. When when the two things come together, I think real sort of deep knowledge and expertise and technical capability combine with that thing that you can't quite capture in terms of empathy, understanding, human connection. I think that's where it happens with music. I think it's probably better to be creative and have something to say than it is to be technical and not have anything to say.
SPEAKER_00:Yeah, that that's a good way to summarize it. And now, of course, I have to ask, what have the two books uh been on?
SPEAKER_01:Right, yes. So I write books about vaguely geeky characters in some sort of slightly not quite sort of a little bit different world, slight, slightly science fiction world. So the first one was a world in which there was uh time traveled, so it's like a it's a it's a time travel novel. And the second one is about a guy who uh creates a nuclear bomb in his shed, there you go, which is not technically possible these days. I said it in very ordinary settings uh and then just push the limits of what's actually real, just a little bit beyond what's real or normal or ordinary. Just beyond ordinary is the sort of the strap line for the things I like writing about.
SPEAKER_00:Um that's actually the uh George Martin thing, isn't it? The author of Game of Thrones, in that he said he actually didn't really make up any of sort of the plot lines. I mean, famously it's out of Wars of the Roses, but I think it's history from all over the world. But he said you just take history and you turn the volume up to 11.
SPEAKER_01:Yeah, I mean that's that's the same as Julian Fellows, I think. And with um Downton Abbey, I think Downton Abbey is the same. They took he took historical events um and then turned them into stories. Um, you know, changed them a little bit, but actually kept the heart of them. I've spoken to authors who who work in crime and and they research real crime to use in books, and they say they can't use a large proportion of it because it's so extraordinary and so unbelievable that in the work of fiction people wouldn't believe it.
SPEAKER_00:Well, hence all these true crime podcasts that just seem to keep on going on and on. They they they never seem to run out of storylines.
SPEAKER_01:Exactly. And I I think it's just the tip of the iceberg, is the impression I get. But uh when you go to fiction, obviously people people might not believe that it's actually possible that people would behave like that. These things will happen. And and it and and and it does, and they do.
SPEAKER_00:Well, I should probably drag us back onto the actual subject matter rather than talking uh for ages about this type of area, as interesting as it is. So would you mind just starting off, just running you through your progression in the industry before you set up one strategy studio? And and maybe any lessons you've learned along the way.
SPEAKER_01:Yeah, so I mean, I've always been agency side. Um, so I've I've not worked client side, and this is like 30, 30 plus years now. Um, so I started off in research engine. Well, I actually started off in AC Nielsen, um doing, you know, tracking and analytics around retail, uh, then at Research International, very sort of um where I kind of got my research grounding and then added value, where I got a lot of my kind of strategy and innovation and more kind of, I know, elevating the research to the strategic level, I guess, is what that sort of stage of my journey was all about. But like about 20 years ago, I started doing startups, you know, and was joint founder or founder of agencies. So that's kind of been my my life for 20 years. Um, so started off with Clear, um, co-founder of Clear, which is now part of MC Saatchi, um, and then MASH, which is now part of Cantar Consulting, and then previous to one Strategy Studio, I was a founder of Discover AI. So I started my AI and insights journey about sort of six or seven years ago. Funnily enough, the the one consistency through all of that is probably you know Unilevo and some other clients who I've worked with consistently across every agency. So it's it's quite interesting. You get a perspective on some clients you work with a lot through the ages and over time, even though you've not worked there, you feel like they've been part of your working life. And I think that's that's an interesting part of the agency world, as I think actually, is that you you see client side from a different angle, perhaps.
SPEAKER_00:Yeah, it's it's interesting, isn't it? Once you, if you have an embedded relationship like that, you start to really understand their issues. I like to Owen Miles at Universal Pictures, who I did an interview with, I think he turned it around. So I asked him what makes a good agency and a bad agency. He should probably work for the UN or something. Because he said, actually, there's no such thing as a bad agency we work with. If there is, it's our fault. He said, Because we haven't embedded them in what we do, they don't fully understand what we're looking for. But I think there's some truth in it as well. It's not just a political answer. And so if we just backed up a little bit in terms of Clear and MASH, so what were they doing?
SPEAKER_01:Clear is still going. Uh, so I don't want to say what they are doing now, because I don't want to get things wrong. Uh, MASH is now an integral part of Cantar Consulting, actually. So, so, so both companies still there in in one form or another. Um, so I can I can speak to what they were all about when we founded them and built them from the ground up. It's really all about um taking that kind of um strategic agency approach, right? Which is is is to say, you know, how how do we take really strong market research, insight, methodology, capabilities, understanding, but then apply it in a way that's very commercially impactful, in a way that's flexible and creative. Um because you know, there's all sorts of agencies and always have been, but you tended to have the sort of larger, more technical research businesses or the more kind of planning type strategy businesses. And we were really looking at how you merge smaller, creative, insight-driven strategy businesses to really help clients. And that that's what what those those businesses were were all about. MASH, when it started, was all about actually leveraging existing sources of insight in order to do that, saying don't don't leap immediately to primary research, um, but but work out work work out the sources that are there that you have access to and leverage the true value of those before you move on to primary research. And in many ways, a lot of what I've done since then continues on that journey.
SPEAKER_00:It it seems like you were fairly early and actually managed to meld this kind of strategy and traditional insight world, which many agencies have really struggled with. Um, so how did you go about doing that? I mean, how how did you sort of have the credence and the impact to be able to break out of kind of the research box that I think many agencies get put into where they're told, tell us what the data says and then leave it to the consultants to interpret it?
SPEAKER_01:It helps to be starting from scratch, right, doesn't it? So if you're a legacy research business with certain people, methodologies, expertise, and relationships, then you know there'll be a tendency to place you within a certain box in which you're kind of understood and where you've got a vested interest in terms of your existing business. And I think this is a common theme, isn't it? You've you've got the opportunity to do something new and present yourself in a new way when you're a new company, but you're starting from scratch, you've got to build the relationships, you've got to build up the business. And I think that's a good parallel with what's happening in terms of trying to do things in a new way with AI at the moment versus build it into existing models. This, this, you know, do you do you reinvent what's already there or do you do you create something new? But I certainly think in terms of being a strategic agency, if that's what you present yourself as and you're starting from scratch, you you you can talk about what it is that you're gonna do and how you're gonna help people in terms of getting to outcomes rather than just help them with research processes. And I think one of the core things we were able to say in Clear and MASH is that we were methodology agnostic, right? So if you've built up a business where you have 20, 30 qualities uh whose time you need to feel to do qualitative research, then you know, if somebody comes to you with a problem, then qualitative research is probably going to be a major part of your answer. It's very hard to avoid. One of the advantages of starting up fresh is that you can start with a neutral model and then say, well, we're gonna apply the methodologies that best answer this question. And we have no vested interest in a particular model because of how our business model has been set up, and so it can evolve in a in a direction that's more flexible. I think there's something in that.
SPEAKER_00:Yeah, it it's um related to a subject I think we're probably gonna get on to in terms of trying to squeeze research businesses into being product-based businesses, which, you know, or SARS-type businesses, which may or may not be applicable. And we'll get onto the venture capital and the private equity component of it. Um but but before we move on a little bit, how did you charge, if you don't mind me asking, when you were uh at Clear and Mash? So was that billed on a per hour basis, uh, you know, in the way in which consultants might do?
SPEAKER_01:Yeah, and I think this this is a really interesting topic, is about how agencies charge and what what clients value and how they value it. Um I think the model the model of the industry and and most agency industries has been built up on day rates. And there's a fundamental challenge with day rates, which is that the smarter you are and the better you use technology, the less you get paid. So it's it's it doesn't suit advances in sort of expertise and um expertise and technological unlocks. What it does suit is is if you know in order to get something done, you've got to go through a lot of process and management and project execution to get there, and you've got to build a pyramid of of people in order to create that execution, then then it can become an efficient and sort of profitable model. You know, if you're selling 100 people's time and you're selling it on day rates and you know you've got these large projects to push through that that structure, then that day rate thing kind of makes sense. But as soon as things get more consultative based on creating value, based on unlocking technology to be more agile, then actually the day rate model becomes a problem, I think, uh for everybody. You know, back then you had procurement people saying we only we only pay£10,000 for a workshop. It's like, well, how many days is the workshop? Who's there? What steps are you going through? What value, what value is created? None of those questions mattered in that point because they only paid 10,000 pounds for a workshop. So it's it's like trying to turn consultancy and value creation into widgets and measure it that way. It's very hard. But so then what do you do? Do you sort of, if there's downward pressure on the day rates from procurement or or just generally from competitive in the marketplace, do you price things honestly? Do you press a button on the meter when people's times overrun? Do you do you sort of overestimate the number of days or or can you count all the management time? It then becomes a bit of a a dance to get to a proposal that makes sense for day rates. It's sort of presented as day rates, but if you overrun, you can't charge more. And I don't know, it's it makes the world harder. Um, but but in in in a world, in a world where people's time executing projects is how things sort of work at the heart of it, it's sort of okay, or everybody can get by on that model. The more we move away from that, the more, the more outdated and and hard to work, I think it becomes.
SPEAKER_00:I hadn't thought of the irony about the fact that if you push for greater transparency, which on the surface we all think is a good thing, then you actually discourage the agencies from innovating in some ways because they want to charge the days where they're doing things. I'm saying kind of in advertising couples like, you know, it costs us this long, sorry, it takes us this long to script to write the questionnaire, it takes this long for programming, so on and so on. And so I guess that's something that probably have to wrestle through collectively with clients as well to understand where there's a more fair means of accounting value.
SPEAKER_01:Yes. I mean, it's a shift I think the industry has to go through as less and less of what we do becomes about large groups of people executing process and managing project flows, um, and it shifts more to value creation and technology-driven solutions. It it I think it needs to change.
SPEAKER_00:There's also the question of the whole cost per interview model, which lots of agencies are still using as a sort of a benchmark. If we're in a world whereby, let's say you're using, let's just say for the sake of argument, you're using some hybrid of relatively small amounts of human data that's just that's keeping an AI model uh constantly fresh, then actually you may not have that many more interviews for a given kind of project, but you're still delivering a lot of value. And so I suspect this is something that's going to rear its head and cause a problem for quite a few agencies who are costing on that basis.
SPEAKER_01:Yeah, as I I think, you know, in a world where the number of people you interview and the amount of time you spend interviewing them is at the heart of how much things cost and how much value is delivered, it sort of makes sense or it's a a reasonable benchmark to use. Um, the more you move away from that, the more irrelevant those things become, and the harder it becomes to create a sensible model off the back of them, I guess.
SPEAKER_00:So moving on to the AI piece of it. So as you mentioned, you were fairly early. So could you update briefly on what Discover AI was doing at that point in 2017 and for how long the business kind of lasted for, and then how things have changed.
SPEAKER_01:Yeah, I mean, I think I'll I'll probably talk in generalities rather than going into the detail about Discover, because again, it's a separate business and they're doing their own thing now. But I think across all of this you can look at what was happening, what's possible pre-large language models, LLMs and ChatGPT and OpenAI and Claude and all these things, and and and what what's possible and happening now after. So Discover AI and and and what we kicked off then back in 2017 or whenever it is, um, was was pre-lu these large language models, and therefore it was really all about leveraging algorithms to enhance the kind of the human analytics process. That's what we were really trying to do. Uh create a piece of software that uses the technology to make it easier for human analysis processes to happen to get to better, more breakthrough insights. And I I think that's kind of what was at the the heart of it there.
SPEAKER_00:So that's in effect, sort of using uh even as one example, just sort of machine learning for uh projections, that type of thing. Was it or was it more sort of back-end um enhancements and efficiencies?
SPEAKER_01:So I I mean when in Discover AI, it was an exploratory tool. So it was all about going to online sources and unearthing insights within those sort of those kind of sources. So there's all sorts of applications of AI and other technologies that are about enhancing the traditional research process that actually I I don't do. And I probably should make that clear. So there's a world of primary research, and there's a world of layering AI onto that primary search to enhance, to evolve, to change, to make it more efficient, make it more cost-effective, make it fast, whatever it is. Right? So the the area that I've been in, whether it's Discover or OneStrategy Studio, is to say there are other ways of getting to research or insight-based outcomes. You know, ultimately what we're trying to do is to be consumer-centric and to make better decisions and drive brand growth. Um, but the ways there are data sources and approaches you can use to do that that aren't traditional primary research. And those are the ones that I've been focusing on really all the time.
SPEAKER_00:Would you be able to give give an example, Jonathan, in terms of how that works now? So, for instance, within OneStrategy Studio.
SPEAKER_01:Okay, so I mean, let's go back to the point we're saying about AI pre and post, I was saying large language monitors, but really generative AI. So pre and post there. So pre those models, you were creating tools that just accelerated the human analysis process, you know. If if I wanted to go online and read a thousand websites to see if I could get some insights from those in order to answer a question, to get some consumer cultural expert brand perspectives on things, I could go in and manually do that, couldn't I? Right. So what we're saying is like if you get the models going out for you and starting to pull that information, combine it in interesting ways, and presenting it to you as an as an analyst, then you can get to those insights quicker in a more cost cost-effective way, and you might get to places that you wouldn't normally get to. But really, the the the strategist or whoever it is is at the heart of that analysis. You're just giving them a tool to enhance their analysis process. And that's really what I think things were pre generative AI. Post-generative AI, and this is what we've been doing with OneStrategy Studio, is to say that the models, the large language models are capable of more than that. And when we set up OneStrategy Studio, we kind of set ourselves a challenge, which is to say, how good can they be? Right. And I'm not talking about if you jump on ChatGPT and write a prompt, even if it's a good prompt, how good do the results look? Because you see a lot of those on LinkedIn, right? People who just say, Here are my 10 prompts for being a you know a BCG consultant or whatever it is, or a McKinsey consultant. And normally, you know, the prompts are pretty basic and the results you get back are pretty basic as well. And if that's all that consultancy was, then yes, it would go out of business on the basis of AI by. I don't think it is. Um, but what we did is we looked at that whole process that an agency would go through and say, if we take an API directly into those models and teach them how to act like an agency, how to source the right online sources and combine it with uh client-owned internal sources, teach it to look at consumer, cultural experts, and brand sources and make connections and links between them. Teach it then to pull those together into observations, into themes, into insights, then teach it to look to see, well, what does that mean in terms of opportunity platforms, innovation spring bubbles, positioning start points and strategic direction? Then teach it to say what happens when you ideate on top of those opportunities and come up with ideas, what happens when you write concepts off the back of it, what happens when you run through sizing analytics, all these different things, teaching the models to go through all of those processes. And what we found is that they're very, very, very good. Not if you just jump on and give them a simple uh prompt, but more if you get them to go through a journey of discovery, right? So if you're working with a strategist, right, and you just said, here's a here's a brand challenge, you know, how do we grow this brand? What do you think the answer is? Just like that. In a room with no stimulus, with no inspiration, with no research time, they might come up with a good idea, they might not, they might not give you good strategy, they might not. Same same with a model. If you just say to a model, what's the answer? It'll get you a certain way. If you say, model, go off and do the equivalent of four weeks of research, etc., and process and act like an agency, then come back to me, then you'll find that what it can come back to you is very strong. Um, so we did that for a while, and um at first we thought maybe 100% automation. It was always like, how much can the models do? Let's let's not assume our humans have to do this, humans have to do that. Let's push it as far as it can go. Um to the point where we got to automate the entire project journey. So we could go through a what would be a four, six, eight-week project journey, you know, foundational insights, opportunities, ideas, concepts, etc. Um, and that can be automated. Um but what we found was that that what you need is the power then comes not from necessarily the models coming to the absolute answer, but the ability to then run projects, look at the results, iterate, rebrief, refocus, and run. And it's a there's a new human process that comes out of the back of it. So that's why we ended up taking that technology and turning it into an agency. What you need is the 100% automation in the back office really sort of driving new ways of, you know, that that that speed, that leveraging of the AI expertise, combined with human expertise, experienced strategists, learn, you know, working with the clients, work at how do we brief this? How do we take learnings from the first draft? How do we feed them into the revised brief? How do we refocus the platforms? How, you know, and what you find is lots of little interventions, not prompting because everything's going on within the models, but little interventions on brief, on focus, on how to take it forward. And that seemed to be a really powerful model of the best of project automation with the best of AI expertise, uh, sorry, of human expertise coming together working in collaboration with clients. And so that's where we felt things needed to go was an an agency model supported by AI automation technology. And that's how we ended up where we where we have. So I feel like I've gone on for quite a while there, but it's it's a long answer to a short question. But hopefully it summarizes the sort of journey that we've been through.
SPEAKER_00:Yeah, it it does. And and I think we'll I'll ask for some examples to the extent which you give them like you know, sort of a hypothetical example in a moment. But if I'm understanding it correctly, it's almost like each model is a variation on a human researcher. And one model might be and call it a fairly simple model, which is a research manager who's trained to think in a certain way. Whereas you might have another model that's a director that's a specialist in auto and has done a lot of automotive work and is thinking about the market like in a certain way, but then is working with their supervisor, who's a human, who's keeps on training it in relation to the client brief. Is that a reasonable way to think about it?
SPEAKER_01:Or yeah, I mean, we anybody who's familiar with our branding will know that we have these five characters. And my my my uh youngest daughter's really into anime and manga, and she got me into anime and manga. So when we were developing these models, we we were thinking about well, how do you personify them? And something about turning them into actual people just didn't feel right. So we talked to turned them into sort of cartoon or manga cartoon characters. So we have Sammy, we have five. So one of them is Sammy, and he's all about sourcing, about curating data sources that will answer a given question. And our data sources are bespoke to every question, every project, uh, chosen to best optimize the potential answers based on how the brief is written. Um, then you've got Hayden, who is the like the insight specialist. He's the one looking across all of that data. He's making the equivalence of hundreds of thousands of observations, he's combining them, uh, you know, a bit like data merger in a workshop where you've got post-it notes all over the place and you're trying to find the themes and you're trying to pull them together in terms of insights, looking at not just what people are saying, but how it's being said, image, you know, cultural context and all these things. As I said, consumer cultural brand expert perspectives, bringing it all together. You then got Skylar, who is the strategist. She's all about making connections and connecting the threads across all of these different uh insights that Hayden comes up with to find the opportunity areas, to find the sort of the nuggets of something tangible to take forward. You then got Ollie, who's all about narrative and storytelling and and creating language around it and bringing it all to life. And then you've got Finn, who's all about ideation and creativity and and generating ideas so that you have a flow from sources to insights to opportunities to ideas and and and the insights driving the ideas and ideation and concept writing. So um, yeah, so we we think of it in terms of those models, but they actually sort of that they're kind of they're all interlinked in the workflow. Um and and you don't really need we we don't have expert models in like one for for for health and one for for for you know personal care and one for food and things like that. The way we've done it is that the the the frameworks and journey and processes and models are all in place to to unpick any challenge, and then it's how you frame the brief that makes it specific to a category or a type of challenge, whether it's innovation, positioning, segmentation, you know, all kinds of different types of challenges. So that's that's how we think about it anyway.
SPEAKER_00:Got it. And so if I was to take a sort of a hypothetical example, kind of vaguely related to some of the other stuff we've been talking about, okay, let me think of an example. I am a film studio, but it's Fast and Furious 15. I'm gonna do another one. And and and then within the brief, it's gonna go, okay, we know from The success or otherwise of the previous movies, these are our stronger markets, these are our stronger demographics, so on and so on. Would that be the type of thing that you could prospectively work with, but providing some guidance on where the studio should focus next?
SPEAKER_01:Yes, it could be. I mean, again, I've I've not watched the Fast and Furious movies. But I am right, they're car movies, aren't they? Right?
SPEAKER_00:They are car movies, or actually, if you talk to Universal, they're about family.
SPEAKER_01:So what you would try to do is translate that into some sort of brief that is like, you know, where are cars in counterculture at the moment? And how's that overlap with family? That's the sort of question when you start to get to a thing that could form a really strong inside brief. You you could start exploring the world of cars and masculinity, right? And say actually what we wanted to look at it from a different angle and say, well, where do cars play masculinity and what might that mean for family? You know, some sort of framing of the brief makes it interesting. And that's actually just an interesting part of the whole process is when when clients come to the brief and you talk about how you might brief this in, it triggers some really interesting conversations about how specific they're being, how broad they want to be, what's in and off the table. And it's a it's an important part of the process. The really valuable thing about the way that we do this is that you can brief in a certain way, you can get to the end of the project. If you're doing it a traditional way, that's it. You've briefed it one way, you've done the field work, you've done it. It's very hard to then say what happens if we look at it in a totally different direction. Whereas when if we do it, we say we we've done it about exploring the worlds of masculinities and cars and the overlap with family, uh, and we came back with some results and say, well, actually, it's it's not taking us where we want to be. Let's try a different brief where it isn't that. But it's all about, you know, what could speed mean in the future? You know, but that these could be for innovation. So it could be like where you're just trying to come up with ideas for a new film. It could be positioning where we've got a film, but we want to understand how to position it for different audiences and how to talk about the proposition. It could be for segmentation, it could be saying, well, who are the different audiences behind uh the fast and the furious and understanding them and therefore understanding you know who we could target next and how and sizing the different opportunities, cultural analysis, semiotic analysis, what's the visual world of the fast and the furious, and how could that evolve to be more progressive, for instance? These are the classic kinds of questions that you can can answer. Any question that you could put through a research or strategy or innovation business can be approached by these models because that's what they're designed to do. They're designed to act and behave like an agency. And I think that's kind of at the heart of what we're trying to achieve.
SPEAKER_00:Okay, let's stop there for the meantime. In part two, which I'll put up next week, we move on to Jonathan's thoughts on business models for insight agencies, hint no more day rates, how AI successfully interfaces with traditional consumer research, thoughts on raising money or not for research businesses, and what the impact of V C and PE funding has been for insight businesses, including whether SARS can really work. Thanks for listening to part one, and hopefully see you next week.
