How Boots is operationalizing creative effectiveness

0:09Hello and welcome to the WALK podcast. My name is David Tiltman and today we are talking about how to bring creative and media strategies back together in advertising. And we're going to do so using a real life experiment. So what's the background here? Well, one of the things we often hear at WALK is that the separation of creativity and media into separate specialisms has made it harder for a CMO to have a joined up conversation about how to make their investments work. And in theory, it sounds so obvious. Strong creative needs high quality media to maximize its impact. And a media buy means nothing if the content going into it is wallpaper.

0:49But the reality of getting there is messy, to say the least. Now, over the past decade, the media part of the equation has become increasingly data-driven and optimized in real time. And until recently, it was difficult to do the same with creativity, where testing tended to be part of the development process or something that happened after the campaign had run. It was still quite disconnected from the media decision. But that's changing. One of the big themes this year is the emergence of large-scale creative data, often using AI, and that's evolving the ways creativity can be measured and optimized.

1:31Now that all sounds great in theory, but what does that look like in practice? Well, we recently caught wind of a real-life experiment in this space involving UK pharmacy chain Boots, testing firm Creative X and creative measurement company David. So we wanted to find out more. So that's what we're going to do in this podcast. We'll be joined by Peter Grants, the Interim Media and Marketing Effectiveness Director of Boots. We'll be joined by Anastasia Leng, founder and CEO of Creative X and by Ian Forrester, founder and CEO of David. They'll be telling us what they did, what they learned. But first, a word from our sponsor.

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2:52So Peter, Anastasia, Ian, welcome to The Water Podcast. Hi, David. Thanks for having us. Great to be here. Thanks, David. So I'm going to start by talking about the topic I sort of teed up in the intro, which is this gap between creative measurements and media measurement. There's been so much work, particularly on the media side over the past decade in terms of optimizing media audiences and distribution. And we're starting to see things change on the creative side as well. But these still feel like two different worlds. And one of the challenges is bringing them together. And Peter, I'm going to start with you because I've teed this up as sort of a real life experiment in doing this but I want to sort of dig into the background a little bit.

3:36You're working at Boots, big UK pharmacy chain, anyone who has lived in the UK will know who Boots is but what prompted you to challenge your agencies, your tech partners to prove they could do something different in this space? What was the sort of problem you were trying to solve? Well we've long had the discipline in place to measure commercial outcomes. We've used market mixed modeling for many many years then about three years ago we started pushing upstream and asking not just whether media works but why it worked and that was crucial because that meant building explanatory signals around exposure attention response and behavior and that's what's become our spine of our effectiveness approach and creative was the next logical step we're not fully deployed or operationalized yet but this pilot has given us enough confidence to see those creative signals becoming a strong guide to better decisions before media money gets spent so it was always going to be the next logical step it's moved into domains that are traditionally beyond media and that comes with its own complications and opportunities but yeah we started with whether media works then we pushed upstream into why it works and creative clearly is the next logical explanatory layer in that journey just to dive into that a little bit more So you have something like a market mix model.

5:00You use those sorts of techniques and you've been on a sort of quest to find what variables start to move that model. Is that a fair assessment? Yeah, it is. We've used market mix modeling like many have for a long time. It's become a popular flavor of measurement again, hasn't it, in recent times. I mean, we added to it geotesting about four years ago to strengthen the signals going into that model. But yeah, I mean, ultimately, it's about being able to explain the commercial outcomes that are being measured by market mix models and geotesting. Yeah. Okay, great. So you mentioned the pilot. So let's get into it with Anastasia and Ian and what actually this looked at.

5:39As I understand it, there are about three and a half thousand creative assets. So let's get into what you're actually measuring and what you were trying to do. So Anastasia, why don't you kick us off? Yeah, absolutely. So about two years ago, we started our journey under Peter's leadership with Boots and CreativeX. And step one of that journey was what we think of as just establishing creative observability. How do we take every single Boots creative that's being run on digital platforms, understand where it's running, how it's running, and what are the creative decisions that are being made within that creative, learn some elements about what's making that creative work harder, and then start to apply that through pre-flight testing.

6:19And we did that by starting to measure the creative for something known as the creative quality score, which gave us some really good indication around how do we make more fit for platform creative for Boots, which is what we think of as that last leg of creative execution, the last mile of the creative effectiveness race. And we started to see some really good results there. But PETA continued to push us to do more and go deeper, move beyond creative quality. And so we started to think about what are the other enrichment signals that we could get to build an even more mature understanding of the creative.

6:51And this is where David and Ian entered the journey. And again, Ian will tell you more about David's specialty, but they're really experts in attention and emotion measurement and really understanding things about the creative that were previously thought to be qualitative. And so we started to embed that data in and build, again, a much more sophisticated understanding of what was really making that creative work harder. I don't want to steal Ian's thunder here, so I'll let him talk about some of the David magic and how we brought these two things together. Anastasia, just before we hand over to Ian, I just want to land the creative quality score that you were using, because that's a very specific definition of creative quality.

7:33And it's why you could then pair with the work that David was doing, as I understand it. So just explain what that creative quality score was that you were adding in to Peter's model. Yeah, absolutely. So when we see most brands start to get into the territory of creative effectiveness and creative measurement at scale, the challenge is really, how do you get all the creatives in one place and start to measure them for something in a way that brings the whole organization behind? And there are lots and lots of things that you can measure in the creative. But the challenge is, you know, how do you measure something that's not going to alienate part of your team?

8:10Because creative measurement, whether we like to admit it or not, is a very controversial topic organizationally. Oftentimes, you know, I think about the hard part about this is actually not the tech. It's the change management of getting your organization to think this way. So what we've seen in over 50 global deployments is the first place to measure is something that we call the creative quality score. And the creative quality score is a very basic, I think, relatively uncontroversial way of saying, hey, we're not seeing if this is great creative or not. We're not measuring the creative idea.

8:40We're measuring the way that creative idea is being executed for the different platforms that it's going on, i.e., is it fit for the different requirements of Facebook versus TikTok versus YouTube, which are all a little bit different, have different user engagements, have different ways that the creative is received. and optimized for those platforms. So what we're effectively doing with the creative quality score is we're starting very downstream and saying, hey, let's make sure this ad is set to perform and fit to perform on the platforms where it's being exposed to consumers, used as a way to get all the creatives actually in, and then start to move upstream into more sophisticated creative measurement as we go.

9:23Fantastic. Thank you. Because I think it's worth understanding that before we then bring Ian in, whose company David looks at something quite different. Ian, you tell us what happened next. Yeah, thanks, David. So this is why we're so excited to work with Anastasia and CreativeX, because what we do is very complementary to CreativeX. So if CreativeX are measuring the foundational fit for platform piece, we can be layered on top of that to see not just is the creative fit for platform, but is it going to be effective? And specifically, we're measuring the core drivers of effectiveness. So is the creative capturing attention?

9:58Which emotions is it evoking? So we go very deep on emotions, 39 different emotions that we're measuring. Every single creative that goes through David gets measured on these 39 emotions. We've also got brand recall and next step intents. So intent to recommend, search, purchase and share the creative. And that's the kind of pre built on some kind of pre-testing model. Is it real consumers or synthetic consumers? Where are you sort of getting that data from? Yeah, great question. So we've been super intentional about building the training data set upon which to build the AI. And at our core, we have a human ground truth, human training data set.

10:35So we're not using synthetic audiences and then factoring them up. We're literally showing creative to real humans. Those humans have their webcams turned on when they're watching the creative. So we're gathering a mix of facial coding and eye tracking data and survey data. From real humans, we've got over 5.5 million people in that training data set as of now. And that number grows almost on a daily basis. That's really important because that's the data that we have to model against and to validate against to make sure that when we make predictions, they're accurate and they're actually being validated by that human ground truth versus asking an LLM 150 times to mimic being an 18 to 24 year old female.

11:15And it will produce a result, obviously, whether that result is accurate or not, you can't test it because you haven't got that ground truth data. So this is why we've been very intentional about building this training data set from the ground up to be able to, A, produce the metrics which we know to be important with regard to creative effectiveness, but also so that we can validate and make sure that when we produce a metric, when we predict one of these metrics, then that's actually accurate based on our ground truth data. And that point about predictive, that's an important piece, isn't it?

11:45So you've got two things going on here. We've got the creative quality score, which is, as we say, fit for platform. And then we've got the David data, which is attention, emotion, and some kind of prediction of outcome. We'll get on to the results in a minute. But in theory, what are you trying to get to here? A system that predicts as best as you can whether something is going to have an actual commercial outcome. Precisely, yes. And that's why we have built a system in the way that we have. And Peter, as I say, we're going to come on to the pilot results. But I guess from your point of view, being able to sort of match what this data was predicting with your own data gave you that route to sort of really validating whether this was a worthwhile approach.

12:31Yeah, that's exactly on the nose. There are no shortage of ideas. There are no shortage of propositions. So being able to prove something or at least start to get some robust evidence that the predictive power of these models actually means something in the real world was absolutely crucial for me. It was a stage gate that we had to go through to progress the relationship. I think one important thing to note here that often gets lost is, you know, we talk about deeper attention and emotion data. Sometimes to brands getting into this area for the first time, it can feel a little bit like this dark, invisible box, right?

13:06And everyone is saying, look, our algorithm for attention to motion is best, etc. And so how do you actually figure out which of these proprietary systems is going to be most effective at driving results for you? And this way, again, we thought the partnership is especially complementary because on the CreativeX side, we're tracking every single live ad that's running. And every few hours, we update all the media performance of that data. So we were essentially able to see in near real time how, as David's signals were coming through and those attention and emotion scoring components were coming through, how they were actually correlating to media outcomes then and there, which gave us a view to say that, yes, not only is the human ground truth that Ian and his team have been building, not only is that effective overall, but tied to boots on media data.

13:56here's the effectiveness we see again right here right now with their consumers in a real environment triangulated against their own media data and the real prize here of course is that we spend like many large retailers and cpg manufacturers will do millions of pounds a year on media across a hugely disparate range of platforms and in many ways the the work that anna and the team have done to bring that together it can't be overestimated how important that is that that foundational step of actually getting everything in one place and being able to see it through the same lens. That's super important and often overlooked, and I did touch on it earlier, but it doesn't cost much to have an idea.

14:35It starts to cost millions of pounds once you put media into the platform. So it's absolutely crucial that you have a sense of what's going to work and what isn't going to work before you start doing that. So I'm going to get into the results now. And I think what's interesting is the way that the two datasets combine in a really sort of interesting way. So I'll read out the results. We're going across two platforms. We've got Meta and YouTube. So the Meta results were that using just the CreativeX data, the sort of fit for platform data, generated a 24 % uplift in view-through rates. But when combined with the David data, that turned into a 47 % uplift.

15:17On YouTube, we've got similar results. We've got creative X data driving a 17 % reduction in cost per completed view. But when combined with David data, we've got a 27 % reduction. So headline, this looks quite promising. And the two data sources together working on both these platforms. And Anastasia, Ian, I think I understand there's a very strong correlation between the predictions piece we've talked about that Ian was talking about a moment ago and the actual performance. So were you finding that this sort of combination of data was accurately providing a prediction of outcomes? Yes. So can I just be very, very pedantic here for a second?

16:01Of course, please do. Sorry, it's a very annoying personality trait of mine that you're all going to have to just bear with. But I think what's really exciting here, and which is why my pedantry is around talking about this as creative X data versus David data, is what we're really talking about are two initial signals, right? the first signal was the creative quality score that fit for platform. Then Peter's view was, okay, how do I boost that signal by adding other elements on top, which is where we got another signal from David, which was around some of their brand recall signals and creative effectiveness scoring signals.

16:39But that is not to say that that is the final formula for creative effectiveness. And there aren't more signals that can be extracted both from CreativeX and from David to continue building and sort of peeling back the layers of the onion of what is Booth's unique creative effectiveness formula, because that's really what we're doing. I think this is an incredibly promising start that's being borne out in the data, but I think there's also a lot more we can continue to unravel around how do we push these numbers even further and get even more clarity around what's going to work and potentially even cut that data at a campaign objective level, a category level, an audience level.

17:20There's so many opportunities for taking this further. Yeah, I mean, I'd add to that as well. These numbers are really nice headlines. And even if we were only to reduce Boots CPCV by 27%, you know, that'd be a massive when you're looking at millions of pounds worth of saving, right? Which in itself is obviously significant. But I think what Anna's just touched on there is that this is really scratching the surface of what this partnership can deliver like you we look at the work we're now doing with boots around optimizing their creative not just choosing the creative to put money behind which has the best creative effectiveness score and the best creative quality score but really going several layers deeper and pulling apart like 5 000 assets you're producing this year how many are over indexing for boredom or for for confusion and why is that like why have you created content which is confusing how can we then brief our in-house creator teams creative teams and how can we brief our creators to not do that going forward so there's a whole strategic and ideation piece which is now kind of unfurling within boots which these numbers are just kind of the tip of the iceberg of and this is what the really exciting thing is is where we take this partnership next Peter when you saw these results what what was your reaction I mean pleased I imagine but did it surprise you the sort of depth of the result um i mean there was some relief to be honest i would want and expect this sort of thing to be able to be surfaced for us so i was pleased to see that but as these guys have just alluded to i mean that they know my nature as well as i do this is just the next stage that we're at in trying to understand the ultimate objective of all of this and that's to be able to predict commercial outcomes downstream because a 24 % uplift in view through rates does not in and of itself mean that we're going to make more money out of the advertising downstream so that's the next big fundamental step that we need to take I mean there's always going to be fine tuning around any of these bits of the measurement spine and these guys have heard me say this before Michelangelo was asked when he was creating the statue of david how he believed as a 22 year old he could achieve something that many of his predecessors hadn't and he said well the statue is already in the block of stern all i need to do is to get rid of those pieces that are not needed and as you get closer towards the finished article you need increasingly fine tooling in order to achieve what you're setting out to to achieve and that's in many ways what these guys have just spent the last few minutes describing you need to deploy increasingly fine tooling in order to create something that you're that you're trying to create further downstream and peter one thing that strikes me from these results particularly the youtube ones where we're talking about a reduction in cost per completed view is that in some ways this is about the efficiency of media and i i guess from your point of view as a as a client are you trying to make media more efficient or are you trying to drive greater commercial outcomes at the other end of both is obviously the obvious answer but what's your take on the balance of those two ways of thinking about optimization well i mean you're right driving efficiency is in service of creating better outcomes downstream but if if the question is are you looking to save money by increasing efficiency then then no we're not we we very much see media investment as a as exactly that as a way to drive commercial outcomes as a way to contribute towards business growth so it's a bit like the jack welsh school of thought at ge you look at the worst 10 you take that away and you redeploy it you know if you don't if you don't do anything other than that if you can identify the poorest performing 10 of any portfolio and redeploy it somewhere else you you're going to get better outcomes from it and that's that's really the philosophy i think that's that's in some ways much easier to do than it is to create an ideal and then struggle to hit that

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22:01So I'd like to spend a bit of time really through the sort of rest of the podcast talking about where this sort of takes us. If this kind of analysis is becoming more and more possible well what does this mean for the way we think about creative measurement and the way it combines with media measurement so anastasia ian i mean maybe just talk through the strategic shift we're starting to see with creative measurement which is as we've discussed moving away from creative measurement being something that either happens as part of the development process or way way after the campaign into something that's that's more part of of campaign optimization as you go to market?

22:44Yeah, I think that the keyword here actually is, in my opinion, the word analysis, because I think we are now at a point where this stops being analysis. And I look at analysis as, you know, when you traditionally think of analysis work, it was something that was done one off, the results were documented in a PowerPoint, in a spreadsheet, you had a presentation to socialize it, everyone gave each other a pat on the back, but the organization didn't systematically get any better at doing the thing that it was actually trying to improve on. And I think the difference here is we are staring down, you know, the gauntlet of all these AI tools.

23:19There is increasing shareholder pressure or private equity pressure in Booth's case, probably, to demonstrate how you're using these tools. But they come with a sense of a fear of a loss of control. And so what I think is interesting here is you can go from analysis to actually fully operationalizing how different systems talk to each other in order to drive this change. So you can imagine where if you have your own formula for creative effectiveness that you have demonstrated and proven, how do you then directly feed that formula into every Gen.AI tool, every creator execution, every decision that you make forward in order to make sure that the organization's boats all rise together and this intelligence doesn't live and die by the PowerPoint that it's in.

24:09I think this is where we're seeing a lot of brands start to evolve as we enter this era of what I like to think of as self-driving advertising, where the machines are going to be increasingly talking to machines to get things done. But you as the brand still need to make sure you're in the driver's seat and you can always take the wheel. So this is about having this data in that model that can then be read and applied, whether that's by a human or a machine or whatever it is. I think it needs to be done by a machine. I think this data needs to be machine readable and machine executable. Look, I don't want to pretend that the cost of socializing these learnings to humans goes away.

24:45It doesn't. I mean, most organizations live and die by the ability of their teams to actually execute the strategy that has been put in front of them. So that doesn't go away. And look, this is the big trend we're seeing right now with a lot of CPG and beauty brands is there is an increasing outsourcing of creative to mechanisms you don't control. That's either the creators or that is, you know, gen AI content. So, but how do you make sure that all of those are not going to erode your brand and are going to leverage all the learnings you have from branded content to make more effective advertising?

25:22This is where the system connectivity bit and the full end-to-end observability, I think, becomes critical as we start to evolve into this world yeah i mean i totally agree with what anna is saying there that connectivity is really really important because you need that to move away from like the one-off insight piece where you analyze a piece of creative or a campaign and then you make some learnings and that doesn't then plug into the next piece of activity that you do and of course the world doesn't really work that way anyway anymore because creative is being made continuously so you need to have that continuous loop that feedback loop where you're learning continuously and you're feeding that back continuously and the missing piece that has prevented that well there are two one is the connectivity just being able to access the creative at sufficient scale to be able to analyze it and that's part of what creative x have done so well it's like being able just to access that creative at huge scale which is why the integration with creative x from our point of view is really powerful because anything that goes through creative x can come to us automatically at the push of the button that just makes it really frictionless for the client but the other missing piece is then the data the quality of the data which you then need to feed back into the system to learn from to then improve your decisions and now we have the ability to generate the right data at scale so that's the combination of creative x and david you're generating that at scale so you're then learning at scale and then when you have that level of data and feeding that back into gen ai for example which can take all of that data and learning synthesize it hold it in its head at once and then come up with some really interesting so what's and like how are we actually going to change the creative as a result of all of these learnings that just becomes super powerful you still need the human in the loop to make sure that it's not going rogue and you know you're still on brand and and all the rest of it but the the tools that like those humans have at their disposal are just so much stronger now with this wealth of data and the connectivity and the continuous flow and that learning loop which we're now creating.

27:26David, you know, in Cannes this year, you presented something that I think caught a lot of people's attention in talking about what you called us praying to the gods of virality. Essentially spending a ton of media on content that each did not have more than, you know, 10k of media dollars put behind it, hoping... Yeah, it's a sheer volume, sheer volume of content being created, most of which has very small amounts of budget behind it. Most of which is very small, does not hit any meaningful levels of scale, but still disproportionately costs the brand a lot. So the question is, is some of this work here that Peter has spearheaded at Boots, essentially a way to protect against that, right?

28:11and to start to make media decisions much more intentionally based on a deeper and better and continuously evolving understanding of creative effectiveness. Peter, just bring you back in because this was a pilot. So how are you sort of embedding this into Boots marketing workflow or what comes next? Well, it is still early days. We are taking the first steps in this journey. So we haven't actually presented the work that these guys have done widely internally yet it's exciting but that's all it is right now but in terms of where we are right now we have the we have the creative x platform uh up and running we've had that for a while we've got ian's david lens on top of it and we've got stage gates now where all assets have to be pre-flight tested before they get any money behind them so it's it's operationalized within boots retail that's operationalized to a lesser extent in number seven and we're rolling out across the other business units, hearing care and opticians.

29:10So there'll be a single way in which to evaluate and measure creating effectiveness across the whole business. If I could just come back to Anna's point now, we did some work with these guys maybe a year ago, and we found a very long tail of assets that attract very little media spend once in flight. And it is a problem and it is something that we are getting to grips with. But it's also worth saying that a large number of assets that do make it out there are performance media assets and they're templatized and they don't cost an awful lot of money to make anyway. So it's not just a simple case of chopping the long tail.

29:46Some of these things might be geo-activated or heat-activated. You get into the nitty-gritty of performance media. So it's not obvious that you just want to chop the long tail off. It's where you're spending a lot of money on assets and they're not attracting media attention that is the place to look. Yeah, yeah. And do you anticipate that this could change the sort of conversation you have between things like creative teams and media teams? Is that the goal here, that people actually just have a sort of much more of a common language to talk from? Yeah, it is. I mean, and when you do things like that, you do take away a lot of the subjectivity.

30:23And the subjectivity, as valuable as it can be at times, it also does create a lot of friction. And we've all felt this in our work lives. So the point is about having a better shared conversation, not one discipline taking over the other and increasingly becoming evidence based in the pursuit of that. You know, the creative team should not become slaves to media metrics and the media team shouldn't be pretending that it can judge creativity from a dashboard. But what all of these signals are helping us to do is to create a better conversation. We can talk around things that we use in the same common language.

30:56That's what we're trying to do. what is the evidence telling us and to what extent do we want to follow that evidence? And I guess just for the last few minutes, talk about the integration issues here, because we often find when we look at effectiveness, that one of the biggest challenges is not necessarily the theory or even the work itself. It's the actual ability to integrate what different teams are doing into a holistic whole. So I guess, Peter, just to stick with you for now, when you look at this data and the way your teams are structured yes it's there's a common language piece but are there issues just in terms of joining the dots everyone having access to the same data or everybody having a single frame of reference if you like yeah there are and integration i think has always been a challenge and no more so now with increasingly large data sets and signals so yeah that that remains a challenge it remains a source of work so the goal always for me is to try to integrate the data as far as you can and then to make it as accessible as simply as it can so to cut through these platforms are available to all of our marketing comms teams they're available to our in-house design studio at the beehive the beehive create assets they put put it through this they pass go if they're successful if not they go back onto the drawing board to some extent so all of that is happening the challenge has been integrating or incorporating those additional steps into workflows because we're always up against very tight deadlines.

32:25And so that's remains in our business an ongoing challenge to try to create space for this new and valuable way of seeing the world. One thing that I think you've done really well, Peter, which might be practical for other folks who may be listening to this, and I know it sounds really obvious, but actually a lot of folks don't do this, is Peter brings all of his partners and gets them in the same room, I think once every three to six months. But again, I know it sounds like one of those basic things like, oh, yeah, of course, it's so obvious. But many brands who do this and one of the big call to actions he gave us about 12 months ago was basically go and figure out how to work with all of our partners and get all the data in one place so they can travel seamlessly from platform to platform.

33:06Obviously, we're here talking about the work that we've done with David. But as part of this, you know, Peter's vision, correct me if I'm wrong, Peter was like, look, that's not enough. we want creative data feeding into our MMM model systematically. So we don't have to do it via spreadsheets every time. We want media attention data in this as well. So go and talk to some of our other partners around this so that every system and every plumbing is connected. And I think this is a really important call to the industry, especially at this point in time, because with AI, every piece of the puzzle has sort of been floated up and the pieces haven't landed yet.

33:43And I think there's a, everyone's trying to figure out who they're going to compete with, who they're going to collaborate with in this new world. And what's been helpful from Peter is like, look, we work with all of you. Please find a way to work together to make my life easier. I wish more people would do this. And Anastasia and Ian, what more from a sort of vendor point of view or a data point of view could be done to make this kind of solution more common? It feels like we need more things that look like this. So how can we make that happen? Yeah, I mean, look, my background's in psychology, so I'm going to give you not a data answer or a tech answer.

34:17I think, again, a lot of the effectiveness of these types of transformations, because this is what it is, obviously, it's a long term transformation, which is probably never going to be done in some way, is about how do you clearly communicate a vision to your organization of what you are trying to achieve and then correctly articulate each thing that you're doing as a way of getting closer to that vision. So yes, you need more data. Yes, you need more tech. But again, I've seen some of the most souped up data and tech organizations that aren't actually able to land these kinds of changes because they don't put in the time to help all the folks who are making these decisions every day, whether it's creative decisions or media optimization decisions, understand exactly what the vision is, how this thing that they're being asked to do today, which is new, and let's be honest, most people hate change, how this change they're driving feeds into that vision, and what is the commitment they need folks to make in order to help realize this vision.

35:19So I think spending more time on, again, the soft stuff and bringing the organization along is actually what typically makes a difference whether these things land or they fall by the wayside. Yeah, I would 100 % echo that. And just to build on it a little bit, I think the validation piece, which Peter performed here with their outcome data, that's absolutely critical in terms of getting that message to land within the organization. Because you've shown that this creative data is very strongly correlated to the outcomes that you care about. And then suddenly the CFO is like, okay, well, that's interesting.

35:52Let's push this down throughout the organization. But not only that once you start creating this data at scale and you're showing that it's linking to outcomes and you're showing that not only is it linking to outcomes but you can understand why it's linking to outcomes but then you're not hitting people over the head with a and therefore you must do this and therefore you've got to put a cat in it every single time or you know like some of these early ai predictions the recommendations that came from them i think annoys creative people and marketers because they're just not practical or realistic and that's what we we try and stay away from we're not saying you have to put a cat in everything we're saying the cat evokes warmth and it's the warmth is a method of action here and actually creatively there are a million ways that you can evoke warmth and so when we speak to creative teams and marketers that's like a much easier message to land because their creativity is still the purview of the creative person you're just pointing them in the right direction.

36:49So yeah, how you land the message and how you communicate the data in a way which is non-threatening and which brings people along for the journey in terms of how you're going to get there and why you're taking this journey in the first place, i.e. we're going to get to the outcomes that we want is critical. One of the soapboxes I've been on, and it hasn't stuck, but I would be remiss if I didn't give it another shot here is I think the way we talk about creative effectiveness and all of that, you know, I really believe the words we use matters. Sometimes I think we'd be better off we talked about this from point of view of content effectiveness, because when we see creative effectiveness, people naturally start to think of influencing the idea.

37:31We're not doing that yet, right? A lot of what we're doing about is taking the idea and think about how do we actually translate that idea into advertising into pixels that are going to have the greatest effectiveness. And I've been trying to get the industry to want to separate how we talk about creative effectiveness versus content effectiveness, but it hasn't caught on. But, you know, I'll leave that out there in case anyone wants to join my crusade. That sounds like a good one to come back to. Peter, I'm just going to close with you. What for you would success look like in 12 months, 18 months from now?

38:03What would you want to do next with this? I think there are a couple of things. And I just want to say, first of all, in response partly to your last question, I think my message to the industry is be clear about your aim you're working for a commercial enterprise so be really clear about that and i think once you're clear about that if you're competitive or if you want to win or you want your business to do then that should guide you and orientate you really quite easily i think many many people in many businesses lose sight the fact that you're there to make money for the business in the short medium and long term and everything all of this is in pursuit of that to match your question about what does success look like in in 12 to 18 months i'm look, I would love to have this absolutely operationalized across the whole of our business, all the business units using it, to know what good looks like in this sense, to have templates in place, blueprints in place, and for it all to be nicely flowing in well-organized and well-governed process flows.

38:53I've said to these guys before, my ideal is to deliver the perfect campaign. I remember a racing driver many years ago was asked after several years of racing towards the end of his career, this guy called Alain Prost, he was asked, why do you still race and risk your life when you've won everything? And this is a guy who'd done millions of laps. And he said, because I've never driven the perfect lap. That's what keeps me going. And I think having an ideal, having something to aim at is good for all of us. You might never get there, but it's a worthy pursuit. That's a great place to finish. Peter, Anastasia, Ian, thank you so much for your time.

39:26Really enjoyed that. Thanks, David. Thank you. That's all for this edition of the Walk Podcast. if you like what you've heard please do follow us on your podcasting platform of choice and if you really liked what you heard then do leave us a review until next time thanks for listening