Decisions without Data: What stops insights being fully utilised?

Hello, everyone, and welcome to today’s webinar panel discussion. I’m really looking forward to this session. My name is Mike, and I’ll be your host. I’ll be moderating our panel of experts today. And this is really all about how insights, data, consumer knowledge gets used in organizations, specifically around marketing and brand teams and product and innovation teams. We’ve got a great lineup to, take part with us today. And I’ll hand over to our panelists to introduce themselves in a moment. Just to say a few housekeeping things, if you have any comments that you wanna make as we’re going through, there’s a little chat box down here on the right hand side. You can say hello. Feel free to tell us where you’re joining from today. If you’ve got any questions, you can put them into the chat. It’s actually easier for me if you can remember to put them into the q and a tab. We’re open to questions. Happy to have all of your comments and inputs as we go through. And I hope you’re gonna find this, an interesting, and rewarding experience because we’ve got some really good experts here. And I’m gonna go around the virtual room, just ask people to introduce themselves. Before we get stuck into the topic, I’m gonna give you a bit of background on where this came from and some of the data that’s really informing, you know, our thinking behind this. So, let’s start with, Jeremiah. Can you tell us a little bit about you, your role, and, who you work for? Hi, everybody. So I’m Jeremiah. I currently work for Diageo. I’m the head of, breakthrough innovation in the domain of luxury. It’s a global team that we set up to do innovation on capabilities and, new brought stuff beyond products. So we launch, kind of new interactive experiences. We launched experience in Apple Vision Pro and kind of paper bottles, things around sustainability, things around, kinda new uses of digital technology. My background’s always been in innovation and strategy. So I came from Unilever for about, like, twelve years before moving to the agile, like, within the last four years that I’ve been working here. And, it’s always been in this domain of working in strategy, working innovation back and forth. Great. Thanks, Jeremiah. I didn’t realize you were developing for the, for the Vision Pro. That’s, that’s a sort of small expert set of, of developers. Matthew, can you tell us a little bit about your role, where you’re from? I thought when you call me Matthew. You know what? You’re registered as Matthew. So Matthew. It’s lovely. I, I’m, I’m Matthew. I, I currently work at ASOS and lead the media and consumer marketing team. Been here just six months, having spent the last eight years, in Nike, in in Europe and in the US. I am historically, my roles, I’ve always found myself, working at the intersection between sort of insights, digital, and marketing. I’m really trying to understand how, these spaces can push forward and and I did and ultimately identify what are sort of further growth opportunities to make marketing actually a growth lever. Great. Thanks very much. Matt, sorry for making you uncomfortable. My, mother is the only person on the planet who calls me Michael, and it makes me deeply uncomfortable. So, there we go. Olaf, over to you. Yeah. Thanks. So, I’m one of the founders of, MarketLogic. So from the vendor side, what we are doing is we we’re providing a platform for our customers, which they can use to bring together their insights, research, and knowledge, around customers, consumers, markets, etcetera, and to exactly, of course, help better and fuse decisions with the insights. I’m responsible for innovation and product here, at our end. Yeah. And I’m very much looking forward to to the discussion and to join it from, let’s say, the other side, so to speak. Okay. Thanks, Olaf. And just, to reference absent friends, unfortunately, Stephanie Zammit is not able to join. She’s the head of insight and analytics for Bang and Olufsen, the premium hi fi business. She, unfortunately, isn’t able to be here and would have slightly balanced out the, the male skew of the, the panel that you’ve got today. So just for background, I wanna share a couple of slides just to, show you something that you can actually go and download. You can, you can access this report yourself. I’m just putting the link into the chat if you’re interested. This was a collaboration between MarketLogic and Insight Platforms. We conducted a survey of innovation product marketing and brand teams across North America or US, Canada, and, UK and Europe, and in large consumer facing companies. So we did two hundred certain interviews. We spoke to people about their, use of insights, about the types of data they use, about the types of decisions they make with data. And you can see here, you know, relatively, broad spectrum of consumer facing businesses, which, you know, we’ve got some good representation here from, you know, fashion apparel ecommerce and, you know, sort of beverages and, innovation there as well. So this is I’m gonna be sharing some of the data points from this study as we go through. But if you’re interested, do click the link to to download it because for me, there were some real eye opening statistics that we’ll we’ll get to at different points in our conversation. So hold up for about the data that we have to, to reinforce this. But I’m gonna ask for some input from our experts really to get us going. And the use of the term, you know, consumer insights, it’s banded about. It’s very broad. Can we just ground it in some reality? You know, what is it that we mean by that? You know, Jeremiah, when you’re working with consumer insights and data and Diageo, what are the tools, what are the sources that you tend to rely on mostly? Yeah. I mean, so because we work in the field of innovation, a lot of it is kind of insights and foresights. So we kind of use big panel data when we look at kind of how the the the industry is being shaped and kind of what categories are growing, kind of the edges, particularly because I work in breakthrough innovation, things that we don’t normally do. We look at the the fringe edges of of the category. And we also work on kind of aggregated foresight reports. So we have a proprietary one insights in the agile, which we call distilled, which we actually share to everybody. It’s based on, social tracking, on kind of conversations that happen around the world, and we kind of try to see and extrapolate where things go. And that’s kind of almost like the landscaping piece about where do we innovate, where do we start the briefs. And then, obviously, as the briefs progress through an innovation process, that’s complemented by a mix of qual quant panel data that then informs that mix development. And, you know, it’s very the tools become very bespoke to the nature of what gets developed, but that that allows us to refine, concepts and and kind of test in reality. Does this thing does the thing actually deliver against the brief that you have, to innovate in? Yeah. And do you have do you have access to, you know, your own channels? Have you got direct to consumer data for some of your markets, or is it principally through sort of retail? That’s also funny one. When I was in consumer goods, there was some of that. You can buy some retailer data. I think increasingly, there’s desire to kind of start aggregating some of that, data internally. So we just relaunched our d two c platform, malts dot com, which is our way to kinda engage with, single malt whiskey drinkers. It’s a bit of a mix, particularly for a an a larger, more established company like us in in this world. Yeah. Matt, you had slightly different sort of set of data sources, I guess, haven’t you, for ASOS direct to to consumer? Yeah. I mean, naturally, it’s similar with Nike. I we think about data across especially from media side, it was paid, owned, and earned. Right? So we think about our paid channels that that typically come through a lot of the core platforms that you would imagine, owned channels, whether that’s our CRM side, whether that’s more of our sort of, ongoing organic social space, and then earned is through our partners as well. So we access these in different places. We have different tools in different places and third party aggregators as you can imagine. You also have a myriad of, like, consumer insights, typical survey tools that you can imagine, right, through for brand health and and brand lift type work that we would often do for campaign specific. Yeah. Okay. And, Olaf, what what are you typically seeing in terms of the mix of data sources, you know, that are that are flowing through the MarketLogic platform? Is it is it kind of primary research as principal, or is it secondary sources trends? Yeah. Great question. Of course, that’s that’s quite a quite a mix of things. Of course, there’s always there’s always a lot of primary and secondary research in there. There is always also touch points to, very much DX data more and more, that we see and also very much to to all sorts of voice of consumer information, and and more first and data that that our customers collect. But also it depends and and it varies quite substantially across the the nature of the industry that our customers operate in. Of course, in the consumer goods space, this is, usually very much, deeply understood, and there’s rich and deep and and also own data, and some other industries where we work like financial services. There there’s a lot more reliance on on external data that’s, like, syndicated and and Yeah. Do you think you know, Mark, you talked about owned, earned, and paid. Do you think that the the volume of data is is kind of proliferating? Do you think there’s, like, there’s there’s more sources of consumer insights data that you can put your hands on, or or do you tend to kind of think the ones that are meaningful is actually still the same small basket of sources. We have a lot of data. Everyone has a lot of data, I think. Right? And I think the what’s really important is just trying to think about how you shift from a bunch of data points to extract the insight. Now some platforms and some spaces enable that more easily. Some partners are a little bit more got their stuff together. Right? So if you were with a Google, you can have clearer insight at a very granular level of what’s going on than other places. But I think often and this is where the skill set of a of a typical insights person comes in, is really trying to help to connect those dots. Right? So, yeah, there’s a lot, but there’s definitely opportunity to sort of synthesize throughout. Yeah. Yeah. And what about, when it comes to decision making? You know, what, Jeremiah, you know, you talked about trends and foresight a lot, but what other types of decisions are you using consumer data and insights to to drive? Yeah. I mean, I mean, when when we look at innovation as a whole, obviously, there’s there’s kind of where you play in strategic intent. But once you kind of nail that bit, then you we we we use what we call a a DVFS lens. And if you’re familiar with the strategizer kind of lean startup work, it’s pretty much, you know, the desirability, viability, feasibility, and then some companies add sustainability at the end. A lot of the consumer data supports your desirability judgment, a little bit of your viability, particularly when it’s linked to price. And I think there’s lots of ways that you can slice that or inform, how you make a judgment call on consumer desirability and and how you think people will kind of pick something up. And, you know, it’s that tends to be the decisions that that, you know, consumer insights and data of this sort kinda really influences. Yeah. Okay. Interesting. It’s a framework. So it’s it’s more the, you know, the sort of the consumer pull end of things that you’re talking about. You know? Yeah. And, I mean, you know, it’s it’s it’s judging and sizing that particularly when you’re confronted with trade offs on your, feasibility and viability, kind of decisions. It’s never just quick quite clear cut. Right? There are certain things that you can do to invest more in the product or shift the pricing around, kind of what’s the margin expectation given that. And get getting to what the trade offs look like and making a call as a business on where you kinda fall on one side or the other, is what data driven decision making can can influence. Yeah. Yeah. Matt, how about for you? Because I I mean, this may be a very naive perspective from the outside, but I imagine for, you know, for retail online fashion, it’s an incredibly sort of, like, you know, fast turnover. You’ve got an awful lot of different things to trade off between promotions and, you know, inventory management and all of that. I mean, what what are the main decisions that that you use Datron insights for in your role? Yeah. I think, especially, as well as, right, as a digitally native company, insights and data extraction, let’s say, is embedded at all levels of the organization. And and and and what I mean is, you know, you can have the execs are obviously on a day to day trying to understand or a week to week trying to understand the business performance. But on a day to day, you know, other parts of the organization may be looking at specific channels to understand where can we optimize and where can we sort of plan differently. So, yeah, I think we’re lucky here that it’s naturally part of the DNA, for those that work here. And and and actually to a point, especially in some of my past roles, that’s the case as well. It really becomes, how business is run, sort of all levels of the organization. It’s kind of a nice segue, actually. I just wanna share some of the data from the report that we had. And this is, I think, backing up from what you described in ASOS, now, generally, across the, you know, the types of consumer facing businesses we we talk to, either completely dependent or very heavily dependent on, you know, consumer input, consumer data to make decisions. Do you think does this does this tally with, you know, with with what you see in either in ASOS or maybe from, you know, from Nike where you were, in terms of this level of enthusiasm, do you think? Yeah. And I’ll give the example of Nike. I think there’s definitely opportunities to for for data to not become a crux. Right? That there’s a desire for, insights to sort of be part of the decision making process, which is great. But, ultimately, sometimes that can cause data paralysis. Right? That’s kind of where, you know, informer companies, let’s say, you’ve seen scenarios whereby, like we discussed earlier, there’s lots of information. And, actually, decisions aren’t being able to be made because they’re trying to look for the silver bullet insight to guide that next decision versus, actually, I truly believe insights should be the thing that tries to make, decisions smarter. Meaning, there are people who have worked in businesses and categories and organizations for years and have an amazing amount of experience and knowledge to fall back on, often, we sometimes put insights ahead of it and say that’s what we have to follow versus actually something saying, you know, that’s probably the wrong decision. Yeah. Actually, I can see that data says that, but actually it’s the wrong decision. Following your gut and insights sometimes, but guided by the insight is where I think, some companies in I’ve seen in the past sort of fall down on. Yeah. Okay. Interesting. Knowing how to balance those things. Ravi, yeah, absolutely. We’ll have the the all of the data that we’re showing is in the ebook. You can download it from the link, the white paper there, and the recording for this will be available right after. In fact, you can watch it using the link you joined, today if you need to go. So, yeah, don’t worry if, if you can’t be with us for the full session. So, Jeremiah, do you think is that does that reflect Diageo? I mean, Diageo always seems from the outside to be quite, an insights led organization. Do you think this largely tallies with, with your experience? Again, like, so I think there’s there’s role for insights, and there’s role roles for it to inform business judgment and decision making. But it’s it’s it’s an input to business judgment and decision making. And, again, like, particularly in the world of innovation, it’s it’s such a multifaceted decision that you have to make. Consumer data is you know, I talk talk to my teams about, again, this DBFS framework. It’s kinda one input and one lever, that matters. But, you know, being able to manufacture something or actually commercially scale something and being able to do that profitably within the financial framework of your business are kind of other levers that you have to square. So it it doesn’t it almost doesn’t work in isolation. And then kind of having the the business sense and the depthness to kind of balance these things and and actually run the business is, is is something that that’s quite important because I think to what Matt said earlier, sometimes you could be awash with a lot of data, but actually making sense of that and figuring out what are the most important things to pay attention to are ultimately what would make the difference. Yeah. Yeah. Okay. Insights. So broadly, you know, we’re feeling organizations are, you know, insights led, if not kind of enslaved. You know, we talked a little bit about your different sources. Mark, you talked about kind of owned and and paid data sources. You know, we’ve got the kind of the the DBFS framework, which I think is a is a great one to structure things. We asked people about which types of data sources they used and how often. And there’s some interesting differences, I thought, here between marketing and innovation team. So, you know, perhaps we’ll start with marketing. You know, much bigger skew towards performance data, sort of CRM, where that’s in place, or any kind of, you know, direct to consumer, data analytics. Relatively less weight compared to innovation on some of the things that you started with, Jeremiah. So, you know, consumer trends and, you know, research reports and that type of thing. Matt, what do you think? Do you think is this a a reflection of of your experience in, you know, either in ASOS or or in previous roles? Yeah. It feels pretty fair. I think there’s also probably a slice of these data that you would have within a marketing department. Right? That would have more of a skew one way or the other. Yeah. But I think this naturally makes sense. I mean, you will lean on certain, certain areas of information when building out foresight innovation plans than you would on a day to day insights side. What what’s really interesting is how do you make that be how do you make sure there’s an actual feedback loop? And I don’t wanna keep using a freight feedback loop, but, like, how does it come back on itself so that it won’t beat the other and and there’s a consistent, thread throughout? That’s, I think, maybe that’s a good question for for Ola. You know, how do we, how do we ensure that these things are not, you know, in isolation? And, you know, how do they how do they kind of feedback and and reinforce each other? I guess, is that that’s part of the use case for a lot of your customers. Is that is that fair? Yeah. I mean, first of, of course, the the the core use case is to make sure everybody has access to what they need, but also, as was mentioned earlier, I meant that often then leads or it can lead to the to the situation that you really have so much data and so much pieces of information, which can feel a little overwhelming. So that that also is an important aspect to put the right lens and context on on what you’re trying to learn there. And to a certain extent, of course, technology can help there, but to a certain extent also that means a conscious way of approaching, the the data pieces you wanna work with to make sure it really distills down to what makes sense for your concrete, problem set at that moment. So, that is something that we feel is going to be more and more relevant going forward, not only to have all the data there and then and all you can eat buffet of information, but rather also find ways of then condensing that from the perspective of what the use case, what the situation, and what the role and and mission of the moment is really. Yeah. And, Jeremiah, do you think does this reflect, you know, your experience? Like, this this sort of weighting of, of different sources of data, you know, relatively more on the the qualitative insights and the trends in innovation teams? Yeah. And I think more than the relative weighting, I think if I think about, like, innovation and the way that you’d you’d tend to be forced to look at a spread of many different things, because it’s a multifaceted business decision, because you’re, effectively, you’re creating the next kind of the future of the business by creating new products to put out in market. And I think, by nature of that not being, you you kind of you you push against the edge of of what is business as usual for the business. You tend to kind of lean on a more varied toolkit. Yeah. Yeah. Okay. Interesting. We’ll come back to the data, for a few other points just to, just to see. If anyone in the audience has any comments, any, any narrative or any questions, then feel free to to post them as we go through because we can deal with them as we go. We don’t need to wait until the end. Are there are there decisions that I mean, you talked a little bit about this. There’s there’s decisions that are clearly directed by consumer insights and data. Mark, you talked about decisions that you might look at it and go, actually, we’re gonna make a different decision. Are there decisions where you, you know, you just actively don’t need or don’t use don’t refer to consumer insights and data at all. Is that I mean, does that does that come up? Consumer. No. I I don’t know. Not really. I mean, it’s hard. Right? I think anyone who is, I mean, I imagine the audience and anyone in this room, you naturally you naturally go there first anyway because it’s just part of your DNA. I mean, lunch, picking my lunch, though. Without insights, actually, that’s a lie. That’s a lie. Right? You you use reviews sometimes. Evidence driven. Yeah. So I don’t know. No. Not really. It’s just part of it’s part of our human nature. Okay. Alright. I’m I’m well, obviously, leading you towards some more data that we’ve got out of this. So, you know, we’ll, we’ll come back and review it. Yeah. Okay. So lunch, that’s one thing. Jeremiah, I mean, you know, you It’s an innovation. Innovation. It’s often the case of, you know, you want Yeah. No. I think more than when we make decisions not with data, I think the more interesting question is when we say we don’t want we we don’t want to gather data around a specific decision, particularly if you know that it doesn’t change the decision. And then actually gathering that data is, an inefficient use of time. So, you know, one of the briefs when we have an insight brief going out is that given the nature of what you get out of this piece of research or this piece of data that you’re trying to gather, how does it change decision making? And if it’s clear that your decision making can’t change because of trade structure or kind of the way that the product works, you know, then then it doesn’t make the the the data doesn’t actually serve to further, the the process, and it’s it it becomes a bit of a useless exercise in gathering the data, if that makes sense. Yeah. Yeah. Okay. Great. Mark, good, good input in the in the comments there. I’m just gonna share some data, you know, in the spirit of, of being data informed. So this is what we uncovered in our sort of survey of big consumer companies. Overall, it’s about people say about sixty percent. You know, it’s obviously, this is, an inexact science with trying to estimate the share of decisions that you take using insights and data. But overall, it’s about sixty percent, but big differences between marketing teams and innovation teams. So about two thirds of decisions in marketing, you know, informed by data. A much lower share in innovation. What do you think? Does that does that chime with you guys? Jeremiah, you know, you talked about in some cases, it’s just not efficient to gather the data. It’s not the right thing because of where you are at the stage in the process. Do you think that’s all the story here? And and I think for me and, again, like, when I said that when I gave the DBFS framework at the start. Right? So the consumer insight and consumer data is one of levers. You have to make a judgment call against kind of your kind of technical feasibility capability and and and your viability kind of overall business profitability view of, what you’re trying to innovate in. So, you know, it that for me is kind of how I read that that, you know, it it brings in a lens on, potential consumer traction for something that you’re gonna put out in the market. But as an innovator, you’re gonna have to square that versus the business reality of what you’re trying to build and, both from is it possible and can we make money off it. Yeah. Yeah. Okay. That’s nice. And then, Matt, what do you think? Do you think, marketing teams are just inherently more insights driven or, you know, you you have more of a need to, to close yourself in reassurance? I think just because of the the prevalence and the access to information, these naturally go that way. Yeah. You know, you can always find a, you know, you can always find an insight or or a data point to start a conversation. So I think you naturally go there. That’s why it’s probably higher. Yeah. So fast. And I know we don’t wanna jump to questions, Mike, but I’m still No. It’s Mark’s question. Yeah. So, Mark, let’s, let’s read this out for those of you who are watching this on the recording. So Mark’s saying that, you know, when it comes to what stopped insights being fully utilized, it’s often there’s a conflict between two sources. The two things are saying different things. You know, it’s often about experience or gut feel to be able to navigate through that. But does the panel have any examples to bring that to life where you’ve had, you know, conflicting data, conflicting direction between two different sources? Matt, stop. Yeah. I mean, it’s funny. I I mean, I don’t necessarily have the the answer. I think it I think it it happens a lot, scarily. Right? Survey in particular, surveys where you try and understand brand sentiment or or sort of brand perception shifts, You find these, I think I think you find at least on the at least on the brand side, when you have these differences exist, it starts to cause and and and provide questions as to why why which one is right at all. I’ve you know, without there’s not necessarily an answer, but I think there is an opportunity, to sort of inwardly look at some of the more consumer insights space and understand how robust some of those insights are that we are providing versus, you know, there are scenarios where I’ve seen in the past that sort of are relatively tenuous and and I sort of have a lot of variance and error around them. That’s often where, things fall over. K. Is it is it a function of scale? So, you know, do you tend to find that bigger numbers are trumping smaller numbers? You’ve got a survey of a few hundred people, but we’ve got data from, you know, two hundred thousand, then it must be more insights, or are there other factors there? It could be first in it could be first into the conversation versus not. Right? Let’s take I I had, a scenario in the past where we were looking at different marketing effectiveness tools. We were looking at some solutions around experiments that we had done. So maybe experiments, and we’re looking at some of the lifts that we were seeing in in maybe or MTA. And and trying to triangulate those things became, at a at a base level really super hard, and they are working on significant data. Right? So it’s not like it’s, a sample of a hundred. It’s just it’s just different methodologies are looking at different things to extract different, outputs and really understanding on the hood of that is what’s needs to happen. That’s where the expertise is so important versus it just being anyone doing this, more poorly. Yeah. Okay. Jeremiah, what do you mean? Sorry. Go ahead, Oth. Right. Let me chime in here. I I also think on the other hand, of course, it can be, yeah, somewhat problematic to have conflicting information in order to make how to make sense of it. But on the other hand, that is also source of additional information and reflection in the first place. Either you may need to challenge your sources, your data, the methods, or maybe you need to challenge your model of things. But then also on another level, in our experience, is is an interesting aspect, especially with with customers who have larger, organizations, many categories, many markets. Typically, they have a lot of information that actually goes unused, which would still give relevant color to to question a decision where you already have a lot of insight maybe from an adjacent category, from a different market that transfers. And that’s also, at least in from our point of view, something that that has opportunity to better utilize these, like, learnings across these adjacent fields and and bring them to the surface even if they surface conflicts, but that helps also to to elevate the discussions. Yeah. Okay. So, actually, you know, letting things rot on the shelf where you could actually be using it to inform the decision. Yeah. Jeremiah, what do you think? Have you got any, any example where you’ve had conflict in there? Yeah. It’s an interesting question. It always happens, particularly because when we when we work on stuff, a lot of it is kinda discreet qual or quant, and the methodology is always, you know, they’re they’re it it the work gets written for the the way that it gets done gets written for the work that’s on on in question. So this happens all the time. And I think and to to what Olaf and and Matt were saying, it I think it the interesting thing is going deeper into how was the question asked, how was the data gathered. But almost, I have been in situations before where people kinda just argue with, like, an arms race of data, which isn’t helpful. But, actually, the the I find a better way to think about is that, actually, it’s it is interesting when you have two pieces of data that don’t actually kind of align. And, actually, when you go deeper into why they don’t align, sometimes the insight is actually more compelling when you figure out why that difference is. Is it in a way that, one piece of data was gathered at at at the start of a journey, at the concept level, and the other one is kind of later on, were people primed by certain things? Did they you know, was was the segment was it on a particular segmentation? Was it over a particular time period? And, actually, when you’re able to figure out what’s driving at least some of the biggest differences, they will you’ll never be able to kinda square all the data in the world. But sometimes that does become more insightful when you’re able to figure out what’s driving the differences because, actually, then that that gives you a better almost like a deeper level of sophistication in in kind of understanding your modeling of the world and kind of what decision to take. Yeah. I like that. The, the actual the process of reconciling the conflict actually uncovers fresh insights know, in the in the process of trying to work out what’s going on. Yeah. Interesting. What what do you think of the the sort of barriers? I mean, we talked a bit about, you know, conflict between sources. You mentioned about the potential to be overwhelmed by data. There’s, you know, there’s kinda so much stuff now. What else do you think is inhibiting more effective use of insights and data? You know, what what stands in the way of it being used better? Matt, let’s start with you. I think too much data we discussed. I think also being really clear on how things should be used, like an education piece, at different levels of the organization. I found actually where where you have opportunities to have better stakeholder mapping and conversations, to identify what are some of the concerns that different parts of the organization and different levels of the organization have when it comes to a key area, is really key because it allows you to then build out a clear roadmap for education, so that things don’t just land on someone’s lap and they’re like, what the hell is this? So, yeah, I think education is a massive piece. And when I say education, it’s not just a one time deal. Right? Yeah. Some people will say we’ve done it. We’re good. I think it’s not about that. It’s about it’s about constantly, communicating and educating how things can and should be used and where we will try and get better from that. Yeah. Interesting. Well, how how do you see it in, in the audio, John? And you see that that barrier is being more about sort of educating people, more about the kinda being overwhelmed by data. Any other big barriers that that that I totally agree, and I have a hundred percent on on that. And, you know, almost I I when I was thinking about this question, I was thinking about, like, people appreciating and seeing the value of the insight and the data and and both kind of appreciating what what it can deliver for the business, but also appreciating the right way to use it or at least even in the mountain and see if the data kind of knowing the right things to call out to make certain decisions. Because in some ways, it is a little bit of an art that there’s kind of lots to pick from. Mhmm. But if you can find the right ones or if if if there’s a process or a structure that guides people towards, the right kind of, data to use. So we have a consumer choice framework within the alcohol industry, within Diageo. So it allows us to almost segment for this particular locations in these particular markets. Here are the here’s the sizing and here are the behaviors within that that location space. Mhmm. And, you know, knowing that that’s the first piece of data you go into when you’re kind of constructing your, your product mix, is hugely helpful. Yeah. I wanna share some data from the, from the survey. And, I’ll have I deliberately haven’t asked you for input yet because I think this is a a good point to get to get your perspective. So you talked a little bit about, you know, having too much data or, you know, the the education side of things. Now there are many different aspects to this. But interestingly, when we’re asking marketing and product teams, innovation teams, they’re coming back and saying these are less of an issue than some other sort of structural things. So, obviously, budget, you know, is a challenge, but it’s more about you know? And I think maybe this maybe this reflects back on, Mark, your comment about conflicts between data. You know? There’s too many sources, too many formats. It’s not integrated. You know, it’s in too many systems. What do you do you does this also do you think the the sort of the infrastructure around insights is a factor there as well, or do you think it’s more about the culture or the human side of things? You know, we relatively few people describing their insights team as a bottleneck, comparatively comparatively. But, Matt, how do you see this? Do you think the for you, do you think the the education and the kind of the data overload is a bigger factor? It just depends on who this is really who we who we’re really asking this. Right? So I think the the users of insights and data within an organization are often in in in particular areas of the business and at particular levels. And so, yes, there’s an opportunity to better sync, the different systems together and better understand how we’re we’re understanding the paid owner and, more holistically. But, actually, though, whether that data or whether that insight on a page is actually used, that’s the difference. Right? It’s fine. I honestly believe the biggest headache is then trying to make sure we translate insights into into true actionable decisions that can be made, versus it just being a, the amount of the amount of two hundred slide decks or hundred slide decks I’ve got scattered around from partners with no clear use on the back end is is crazy. Yeah. Olaf, you see you work with a lot of different organizations. You see the challenges that that people have. Does this reflect, you know, the frustrations or the, you know, the way in which you hear about insights being limited? Yeah. Well, I I think it does to a large extent, but also, as Matt said, I don’t think it’s an either or discussion. It’s a matter of, on the one hand, having the infrastructure and ability to to get the data, to to bring it together, to make sense of it, but also to remove the, let’s say, friction and the barrier to to work with the data, that does help. So make it more accessible, make it more easy to work with it. But, ultimately, it needs to be elevated into into relevant outcomes in the business decision, and that’s where the technology is an enabler, but there’s more around it, of course. But also, I see a little bit of, I would say, beforecation around there is, of course, number of high stakes, highly involved topics where there’s a lot of focus. There’s a lot of work with the insights. There’s a lot of energy, and and everybody’s really on board and does what needs to be done, to really reflect all consequences correct correctly. But then there’s also a long tail of smaller, more business as usual things, topics, smaller micro decisions, if you will, where these barriers of this friction adds up, and then it becomes a bit too tedious to go on maybe and try and pull all the data pieces. And, that is something where where we also believe a lot can be done to, lower barriers. Yeah. So there are differences here between the responses from the sort of marketing and the, innovation teams. This is the same question just split by those two groups. What stands out to me here is that for the innovation teams in red Mhmm. More likely to find that the insights colleagues are a bit more of a bottleneck and that they’re less confident in the the skills to be able to work with with insights and data. Jeremiah, do you think that that’s a a Yeah. A sort of widespread conclusion in your openness? And I think if I think back to kind of what Matt was saying earlier is we I I see that as well, this this problem of, like, a lot of data and then not much of a so what. And if I go back to the other thread I mentioned earlier about when when you’re working with innovation teams because you’re building kind of a a picture of or building the future for the business and you’re using multiple sources of data, a lot of it then boils down into more of an art than a science of what data pieces do you bring together to inform certain pieces certain decisions. And that’s why I I wonder if, you know, this makes us more much more reliant on internal insight teams Mhmm. To be able to assemble all of that information. And you can commission a basis report, and you can get, like, a single color. But you you need to understand the nuance that sits underneath, and lots of other things will feed into that. And because of the the way that you’d have to synthesize things from several places to kind of shape the mix and inform the trade offs that you have to make, And, you know, those trade offs will vary wildly from project to project. That’s probably why I I I would imagine a picture like this would appear. Yeah. Interesting. I’ll read this a little bit. I wonder if it’s a function of insight and research teams often being part of a marketing organization, you know, historically. You know, do do you think insight teams are less geared up or give slightly less attention to, to other parts of the organization? I’ve always been very fortunate. And, like, everywhere I’ve worked, we we’ve always had a a highly specialized, innovation insights team. Now, obviously, not every company would be wired that way. And I know that, you know, a lot of companies, they kind of have a bread and butter business, and and it’s it’s not quite at the same pace or level of urgency to be able to kind of innovate as as rapidly as, for example, like a CPG player or, like, like what we do in Diageo. Yeah. Okay. Fair enough. Nicely, a delicate response, so thank you. And, so just to to wrap up, if anyone has any questions for our panel before we we sort of bring this to a close, please put them in there. Mark, good question from you. Anyone else, please ask now before we, before we sort of move to the end. I’m interested to know what you think would help drive better adoption or more, you know, more effective adoption of of insights in organizations. You know, we we do have some data. We did ask people. There’s a there’s a huge variety of, of responses there. But, you know, Matt, you talked about, you know, things that don’t have tangible action, you know, as as an outcome. What else do you think could drive better use of insights for decision making? I mean, actual, I mean, education is massive. Actual educate actual education and value of mark of, market insights teams are also key. I think insights organizations aren’t very good ironically at marketing themselves and showing the value as to why they should have a seat at the table. Yeah. So that’s definitely a, a barrier in my mind because it unless there’s a clear reason why they should be there, there’s often there’s often times I’ve seen in other organizations whereby it it then just becomes a crux and also becomes a an area where it can just be sort of eliminated from conversation. So it’s a huge opportunity, I think, to sort of better market themselves. Yeah. Okay. Be more, you know, be more proactive, be more, assertive in the way they communicate. Yeah. Jeremiah, what do you think? What what else could help drive more adoption? You know, it’s it’s it’s it’s a it’s a tough one because I I I really agree with Matt. Like, I think it’s it’s really about showing the value of how insights can create business opportunities, and and kind of, you know, specific decisions where, having the data and having the right kind of people in the room, clarity on the so what from the data and the insights I I share the experience of kind of having to wade through kind of a lot of regurgitated facts and data Yeah. Without a clear point of view on on what what the business should do differently. Yeah. And I think, you know, by being sharp about that and being clear about the value that that brings, I think that will do a world of good to kind of insights teams. Yeah. Great. Olaf, what do you think based on your experience working with lots of companies? Yeah. I I I would agree and maybe twist a little bit and say also, yes, better at marketing themselves, but also, of course, using more and more intelligently, maybe also using tools and technology to make more time and space to invest the human expertise of the insights team to come out with the elevated so what and to focus more on understanding the stakeholder, bringing across the story, and less of spinning the wheels maybe in the more mundane tasks that take a lot of time but don’t really elevate the outcome that much. I think today, a lot can be done by tech on that end, which then frees up really, the expert brain for for the elevated tasks. Interesting. So I think, you know, to wrap this up, we’ve got a great, sort of summary. Now bear around two things. One is this is distilled from open ended feedback that we asked to the marketing and product teams. And the second is that the question we asked was not how can your organization make better use of insights, but what would help you to use data and insights more in your work. And the answers do skew towards more technical, you know, integrated data solutions. So being able to centralize and integrate data, being able to visualize it better. You know, lots of comments about, you know, better scope, better quality of of insights. The education piece, interestingly, people don’t identify themselves as being in need of, of more education. That’s not the same as saying, you know, that there’s not a need for education. But the hierarchy of, you know, priorities for people does skew towards being able to get better access, more immediate access, more integrated access to data. Cara, you’re saying there hasn’t been much talk about creativity. Great. Both, you know, innovation marketing teams talk about it as a primary need in their day to day. But, you know, what what sort of, what do you think about, you know, creativity? How do you drive creativity in in insights? Mark, you talked about the teams insights teams not always historically being that, you know, front foot proactive about communicating. What do you think about how do you drive more creativity in the insights and data you feed into either campaign planning or, you know, innovation pipeline? This is a loaded question. I mean, again, past experience, not now, but in other organizations who are very creative led, let’s say. It’s about finding the right balance of insight that doesn’t, squash creativity. So I found often again, it is part of the education. So to me, it’s all linked to education. It’s all part of identifying when we think about creativity from a creative content point of view, what are the information sources that are available? And, again, educating those creative teams on how these things can help versus dictate. How can it make the creative five percent smarter, or how can we optimize things on the back end, more consistently as well? So, again, it’s more it really I I don’t know. Maybe it’s just my experience, but often, especially in that space, there’s just there is a desire from creative teams to sort of utilize insights to guide their work, but often there’s just a lack of consistent education around what can be available for them, where, and when. And so there’s a lot of work that can be done there, I think. K. Great. Jeremiah, what’s your do do you see there’s a bit of a tension there between, you know, sort of creative innovation and, grounding it in data? Yeah. I mean, not not really. I I I think maybe, at least in my head, the way it works is you have your landscaping pieces, and you gotta know where you want to do, you know, innovation or invention, from a strategic intent, perspective. And once you’re there, then it is a creative endeavor to actually, you know, invent and create something new. And in that world, there’s no substitute to, high level of kind of empathy and consumer, intimacy. You know, are you in the midst of the people you’re innovating for? Do you talk to not only people in the middle, but people at the edges, that particularly maybe solve for some of the problems that you’re trying to innovate against? And that’s kind of where, I guess, the flare or the creative spark within an invention mindset for an innovator is is is fully realized. So I think that that’s where the creativity comes from. Now as you shape that mix, then you bring in, you know, testing and data just to see, what you’ve invented. Does it does it fit and perform in the market as, against against the the the brief that you have or or the the space in which you need to innovate in? And then on top of that, does it fit within, you know are you able to make the business trade offs on on feasibility and viability to actually physically make what you’re trying to do and and, actually make money off it? And, you know, that balancing act, is is the day job. The creativity is the the fuel that comes up front and throughout the process of problem solving. Yeah. Okay. Great point, I think, at which to, to kinda draw things to a close because, I love your point there about needing to stay closed to get that empathy and understanding of people. And, you know, we saw right at the start the tendency for innovation teams to be much more, much higher users relatively of qualitative research and ethnography and those types of, you know, getting close to consumers, as inputs. Well, this is the, just before we wrap up, this is the, the white paper again. So I can the link is just if you scroll up in the chat, you can download that for all the rest of the data. So, do check that out because there’s lots of great insights in there, valuable stuff. Matt, I know you have to run to another event where you’re, you’re taking the stage, so, we won’t hold you up any further unless anyone has any other quick questions before we, before we finish this off. I just I do wanna say thanks very much to our expert panelists. Thanks, Olaf. Thanks, Mark, Jeremiah. Thanks to, to your teams for letting you carve out an hour to to speak with us in our audience. This has been really great. And I think it’s a, you know, it’s a really complex area. How do you get the balance right between proactive decision making and, you know, informed by data? How do you get creativity in there? Great input at the end, Cara. How do you get access to insights for the right people at the right time? So, you know, lots of, lots of things to balance for people who are trying to plan this out and, you know, run insights organizations. But thanks again to, Jeremy Tai Gemma Tai Diageo, to Matt Hamlin of ASOS, formerly at Nike, Olaf Lensman, one of the cofounders of MarketLogic Software. Thanks to all of you for showing up, and I hope you’ll be back for some of our events in the future. Thanks, everyone. Thank you. Thank you.