Vazen’s Charlotte Kiddle & Yasmin Ayoub Sanusi on Smarter Retail Data

What if one of retail’s most valuable datasets has been hiding in plain sight? In this episode of the Greenbook Podcast, Karen Lynch sits down with Charlotte Kiddle and Yasmin Ayoub Sanusi of Vazen, formerly Virtual Store Trials, following their double win at IIEX Europe’s Insight Innovation Competition for both the Audience Favorite Award and Industry Impact Award.

Charlotte and Yasmin explain how Vazen transforms planogram, transactional, store, and shopper-related data into practical intelligence for brands and category teams. They explore how AI can make years of shelf data easier to query, reduce range-review analysis from days to hours, and reveal opportunities for assortment, innovation, and shelf optimization. The conversation also examines changing retail dynamics, cross-functional decision-making, and why the next phase of AI in insights will be defined less by hype and more by practical impact.

Key Discussion Points

• Unlocking planogram data for better decisions: How Vazen makes complex shelf and retail data accessible to brands, category managers, and insights teams.
• Turning AI into a practical retail tool: Why reliable, standardized data matters more than AI hype—and how Ask Vazen enables teams to query retail data in plain language.
• Reducing range-review analysis from days to hours: How AI-assisted analysis can quickly identify assortment changes, delistings, share-of-shelf shifts, and potential sales impact.
• Responding to a changing retail environment: How range reductions, premiumization, retail media, and a shift from category-based to occasion-based merchandising are changing shelf strategy.
• Connecting insights, category, and brand teams: Why better access to shared data can help organizations make stronger cross-functional recommendations—and ultimately influence what happens in-store.

Resources & Links

• Vazen
• Greenbook’s Insight Innovation Competition

You can reach out to **Charlotte Kiddle** and Yasmine Ayoub Sanusi on LinkedIn.

Many thanks to Charlotte Kiddle and Yasmine Ayoub Sanusi for being our guests. Thanks also to our production team and our editor at Big Bad Audio.

Transcript

[Karen] Hello, everybody. Welcome to another episode of the Green Book Podcast. I'm Karen Lynch. I'm happy to be hosting today. On this episode, we're going to be talking about two individuals who are part of an organization, well, it's now called Vazen. It was VST (Virtual Store Trials) that you might know them as, but they were critical to securing the win at IIEX Europe, a double win, actually, in the Insight Innovation Competition. So we're joined by Charlotte Kiddle and Yasmin Sanusi. And they, again, took to the stage, did their startup pitch and walked away with two different awards, both the Audience Favorite Award and the Industry Impact Award. I'll explain a little bit about what those are just momentarily. But first, I want to just take a minute and welcome them both to the show and allow them both to introduce themselves to you, kind of what their role is. I'm going to stop there for a minute and say, welcome to the Greenbook Podcast, ladies.

[Charlotte] Thank you, Karen. I'm Charlotte, obviously. And I head up the Insight part of Vazen, as it is now. It's difficult to remember the new name. So, yeah, my background actually is all in market research, well, like the Insight world. I did a big stint in communications, innovation, and then retail and moving to Vazen was very much about me moving into the tech world, but still retaining quite a lot of insight and kind of retail part of my job, which I loved. I'll hand over to Yasmin.

[Yasmine] And I work with Charlotte, so my name is Yasmin, and I work with her on the Insights team. It's been amazing just to learn from Charlotte with all the experience that she's just mentioned. I also work kind of on the client side with another tool that we have as well. So it's been great to kind of get both sides of Insights, but also the client kind of success of it as well.

[Karen] Yeah, I love that it's Vazen and I went to Vazen. So thank you for correcting me right from the beginning without really correcting me. You're both so subtle about that. Normally, it's so funny, normally, I say in the very beginning, let's make sure I've got the pronunciation right. And yet I totally took for granted that I knew how to pronounce the new name. So anyway, I'm so glad you're both here. I don't know who wants to kind of take this one, but for listeners who aren't very familiar with the company, let's just talk about, you know, what the company does, kind of what problem it solves, because that will set the stage to actually what you pitched on stage and why kind of the new, you know, how you leveled up and why it did, you know, secure such a win.

[Charlotte] Yeah, so Vazen as it is now, and I found it really difficult when I was learning the pitch this phrase, like Yas will attest to this. I spent a lot of time saying it, that we are a retail data AI platform. And what that means, in essence, is that we care a lot about planograms, which is about shelf data. So where products sit on shelf, how much space they take up, how that changes week on week, how we can influence those types of decisions with retailers and shoppers. So I guess our reason for being, and we spoke a bit about this in the pitch, is that there's this huge data set, a planogram data set, essentially, that all retailers have and brands want access to it, essentially. And we have kind of recognized that. And so what we do is work with retailers to take their big data sets and we've created a kind of data infrastructure out of which we have developed a number of tools which allows brands to access the planogram data. So you can either access it from understanding what's on shelves right now. So you can say, I'm interested in what's on Kroger's shelf in Ohio. I don't know. And you can access that piece of data. So you can say the here and now versus the past. We can visualize things with people. We spend a lot of time testing new ideas from brands, which is super interesting and like very relevant to the insight world. And then we'll probably talk about this in a bit more detail. But we've added this Ask Vazen layer where we have all of that data, which you can then use your own AI. But essentially, the brands can access it using their own AI tools to ask it questions like, where's the space on shelf for me to launch my new product? What should I delist? What's changed in different places? So I think you summed it up really well in Amsterdam when it was about kind of, we've taken something that hides in plain sight and made it accessible to brands.

[Karen] And I think that's exactly it. For those of you listening, the Industry Impact Award is given out by Greenbook. So at each event this year, we started to award three different awards. There's the Experts Choice Award, which is given by the panel of judges that review all of the applicants to the Insight Innovation Competition. And then they choose the top five and the top five pitch for the Insight Innovation Competition live at the event. And then the judges then choose from those top five, which ones they deem as sort of like this, what they deem as maybe an investable, scalable idea from that mindset. They're kind of thinking about who might they back financially. Then there's the Audience Favorite Award. That's kind of crowdsourcing. And we can't get into their minds necessarily. We imagine it has to do with what's resonating with the audience, like what they either are like, that would work for me, or I can see the use cases for that. Or gosh, I like their stage presence. They told a good story. Or they really convicted me. We're not really sure what the crowd is looking for. But that was one of the awards that you won was Audience Favorite. But the Industry Impact Award, Greenbook is looking for something that we believe really will impact the future of Insights or is addressing an immediate need that we are tapped into by being tapped into the industry the way we are. And yeah, hiding in plain sight. You know, in my experience, I'll talk from my own experience first. I have a background as a qualitative researcher. Many of my clients, if not, I would say 90% of them were CPG brands who were sold in retail establishments, whether it was Kroger or anybody in any family of retail shops. Fred Meyer, all of the large chains, I'll just stay there. And we looked at planograms a lot, right? A lot of decisions were made, whether it was in the supplement business or the food and beverage business or the, you know, snack business. You know, a lot of decisions were made about packages, what size package, where can we fit? How can we get how can we get attention? Where are we going to be on end caps? Where are we going to have this type of promotion? And planograms were a part of their daily lives when you were on a brand team. It was it was one of the, again, daily facets of a brand manager's life was looking at planogram data. So it was when I saw you take the stage, I was like, oh, now that's an immediate use case. And so when we talked about it at Greenbook, the day, you know, the night of the pitches… the event ends, we go into a room with closed doors  and we talk about all the pitches. That was really what it was. It's like this is something that it's like this data exists. This need is strong, present. Today, they solved an immediate need and it was visible and all that information's right there. And you made it really usable for immediate decision making. So that is what we saw. And anyway, that is why we deemed it sort of worthy. So congratulations again. And we are really glad that the audience saw it as well. And we can just imagine that they were kind of with us. At least that's what we that's what we thought. So take me to that moment. That's what we thought. What were you thinking when when you kind of won the double honor in that moment when we announced it the next day?

[Yasmine] It was such an honor. I don't think, I’m sure, I mean, Charlotte, tell me what you think. But I don't think we expected to win both. It definitely was amazing to be up there after so many amazing teams or before or after them. And the two awards meant two very different things. Like you kind of spoke about, I think it was really nice to get the Audience Favorite just because maybe they saw the value in that and hopefully also liked our pitch, too, and how he presented it. But they could see, like, you know, people who, like you said, your clients who work in the industry, it saves them time. And I think a lot of these teams are really looking to save time at the end of the day because there's a lot of data, Charlotte mentioned, to work with. And then Industry Impact. We do feel, obviously, you saw the pain that these people are probably feeling on a day to day basis. And to be able to help them was amazing. But also, I guess, for the team, our wider team at Vazen, I think everyone's working really hard to help people in the industry use this data and get the most out of it. So I think it was a nice win for all of us.

[Karen] Yeah, wonderful. Well, yes. Congratulations again. So let's talk about the Vazen, the elephant in the room for us is Vazen. So here we are. For those of you listening, again, this recording is happening in September. The event was June and all of a sudden we went from VST to Vazen. So the rebrand happened in a few short months, even though it was probably in the works all along because these things don't happen quickly. But let's talk about the name change. What prompted it? Give me the backstory and, you know, and how it better reflects the work that you're doing.

[Charlotte] Yeah, it's interesting, it was very much in the works and it was a conversation we had was, do we go as VST or do we go as Vazen? And we decided that VST was the right thing to do because that's how we had applied to it. And that's what we were known as, you know, within that kind of award. And VST stands for Virtual Store Trials, right? And that's exactly what we were when we were set up eight years ago. We were set up much more to kind of test ideas on shelf and to help brands influence retailers. And that is still a very core part of what we do. That's what Yasmin and I do every day. But the data part of the business has become much bigger and is growing really, really fast. And Virtual Store Trials was really, as a name, much smaller than the product and the proposition was really becoming. So we wanted to have a name that could encapsulate everything that we did and allow us to grow out of VST. I think it was probably like we wanted a V still. And it just gave us the opportunity as well to relook at our look and feel and our brand colors, what we stood for, all of those different types of things. And the name probably is a more kind of technology name as well. It moves us from “just,” I say with inverted commas because it's not “just.” It takes us from insight to more than living within the insights world.

[Karen] Was there any kind of, you know, question from current clients? Did you have any explaining to do? Were they fully on board? Did they get it?

[Charlotte] Yeah, like interestingly, a lot of clients have just gone, all right, that's fine. Because there's a couple of things in there like nothing in reality has changed for any of our clients, like it is the same account team, it's the same product, really, it's the same terms and conditions, our address hasn't changed. So, the experience of who they interact with and the data they interact with hasn't changed. Really, the name’s caught up with the experience that they're having, if nothing else. So we've had a couple where we've needed to send a letter to explain what we're doing from a legal perspective, but that's as far as it's gone. And I think we should, it was the right time, because if we were bigger than this, then it becomes a, oh, right, I need to remember who it is. But actually now we can kind of like really just pour ourselves into the amazing name and create awareness around it.

[Karen] Yeah, that's great. That's great. So I want to dig into something that you said, which again, leads into the next part of this conversation and why this product kind of jumped out at us. And I'm imagining the audience and there are again, there were many brands in the audience in Europe this year in particular. So I imagine it spoke to them, but you were talking about part of what the brand needs to do and why they look at planogram data so specifically is they need to convince retailers to give them shelf space and to give them not just shelf space, but the right shelf space or the most appealing shelf space, eye level shelf space, optimal shelf space, if you will. And a lot of package design decisions are made based on where they're going to sit on the shelf. And one of the last projects that I worked on before I joined Greenbook, it was, you know, there are tradeoffs of the cost of certain packages and the design there based on, well, if we do this size package, then we can't be in this area of the shelf. But if we do this size package, we can get to this area of the shelf, but it's not as optimal as this area of the shelf. And they won't move these packages unless we have this size package. I mean, it is so complex and and I think that a lot of people don't really know this, but I'm wondering if in this world right now and this level of intelligence that you're offering, is there something going on right now that makes it even more critical to have this kind of data and this intelligence at their fingertips? Like what is happening in the ecosystem, the retail ecosystem that is kind of driving this need at this moment?

[Charlotte] It's a really good question. And it's a super interesting time in retail right now, there's lots of, sort of, these big trends around you hear that retailers don't actually make money from the food, they make money from the retail media they put in there. So the actual like making sure that they're making the profit from the products they have on the fixture becomes more and more important. So retailers are looking at things like big range reviews all the time. But I think one of the big themes that we see is efficiency, which is coming through everywhere. So there's a lot of cuts coming through across categories. And so it's incredibly important at the moment for brands to know how much space is there. And we get a lot of questions around that macro space. How much space does dairy take and how much space do I take within my subset of dairy? So the questions aren't really just about this small bit of shelf. It's actually about the larger aisle or even the larger kind of area in the supermarket. So I'd say, yeah, it's really important because of the range cuts, I think, which are coming through a lot. There's also a lot of reimagining how the shopper shops. So we recently, I'd say within the last year, I wouldn't say before now, there's a reimagining away from categories and towards occasions. So if you imagine you go to a supermarket now, you're like, OK, cool, I have my chilled, I have my fresh, I have my ambient food, my frozen food and my ambient food might be tins and then pouch, whatever it is. But actually, the reimagination is dinnertime, lunchtime, at home with my children. So all of those things, as you as a researcher must have spoken about an awful lot like these quite difficult ideas, I think, for consumers and shoppers to think about. But actually, that's how they do think, you know, I want something that I can bring my family together with. And I think advertising has done that so well for so long. And now I think retail is trying to catch up. And that is super hard. You think about the supply chains, the space, the this, like does a can of beans go next to a can of soup? Like, what is it? What sits with what? So I think there's actually quite a lot of reimagining going on, which is difficult, and difficult for brands to influence as well. And I think also there's like some quite big trends around premiumization, which you might have also seen in the US is like people are eating out less. And so they're looking for the eat-in option within the supermarket. So again, you have to make space. And how do you make space to take out of your core and add premium products? Who's doing that? Is it the big brands doing it? They're challenger brands. So that’s, sorry, this is probably absolutely loads of other things happening. But I think those are the things that we hear most about, that brands are really being asked, tasked by the retailers to really think about. And so they're not just influencing, should I have a multi-pack or a single pack now? It is, do we have products that really fit what shoppers really want? And I think that's where supermarkets or retailers are really then looking to make the range right and then ultimately make a bit more money.

[Karen] Yeah. So much of my work was limited to, again, package design. It's qualitative in nature and it was largely on package design. And I did some great eye tracking work with package design. And so my eyes were open to much of this stuff. And so I was privy to backroom conversations and all of that. But my knowledge was limited. It didn't quite go into the data of it all, which is what I want to start to talk about next, because you're talking about data, right? So you're unlocking some data and you're also bringing different data together. So talk about the data, all of the data points that you are bringing together, because there's so much more in what you are offering than just what we're talking about. Share all the data points you're bringing together.

[Charlotte] So we can talk about it from the retail data that we receive, because that's the biggest source of data. And what the retailers share with us is for their whole estate. So every single shop that they have with any given geography, every week they send us where every product sits. So imagine those are like one line of data each. That's like billions of lines of data we receive every week. That sounds very basic, like where something sits. But alongside that, you get how many of them there are. So how many facings wide, facings high. So that gives you like an understanding of how much space they take up. You also get that. We get all their dimensions, which is a very underestimated piece of data. Like who tells you about dimensions of products? This data does. You try and go find that on the Internet. It's not easy. We also get within that understanding of like casing and trays, like merchandising approaches by different categories. And then alongside that, we also get transactional data so we can understand from ultimately what's planogram data, your distribution, like your true distribution as well. Not just something is sold is something is on a shelf. And you can look at how much has been sold and it's not a total estate. It's by plan as well. So you can say like that's very interesting. Now, alongside that, the retailer will tell us the type of store it is. So is it a convenience store due to a really big store? So we can start to cut your data that way. You can cut your data by affluence. So is it a store in an affluent area versus a less affluent area? And then you can add to that as a brand, all of your attributes. So if we take dairy again as an example, right. OK, I know it's all yogurt. Well, is it protein? Is it Greek? Is it shots? Is it whatever? So, you know, you get all of that data and then you as a brand can add all of your attributes. So even all that great data you get from the retailer can bring everybody to the same level. But where you as a brand can make it your own is that you overlay all of your attribute data, which we can do within our system as well. And then also coming into that system is like open source data. So things like weather. Which, again, really obvious, but you know, in the UK we've had a massive heatwave. We've had five heatwaves this summer.

[Karen] Yeah, yeah.

[Charlotte] Yeah, of course. You'd expect water to go up as sales or whatever. But has space gone up or did it, you know, what has been linked to weather patterns? What has been linked to location patterns? All that kind of stuff. So it's a lot of data coming in. And what our engineering team have done a brilliant job at is consolidating that into a very specific way that they look at it. We don't look at it like that. We look at it through our app. We look at it in a much more, show me the most common, like the most common planogram for dairy in the UK. And it pops up. We can look at it. We can analyze it. We can download it. It’s an amazing source of data, really.

[Karen] So for those of you who are audio only at the moment, the same thing is happening to me right now as happened to me when I was in Europe. I'm hanging on every word and I'm fascinated. So once again, it's happening again, because to me, my mind is mulling over the possibilities. I'm thinking, oh, imagine I am a dairy company and I'm trying to figure out if I, you know, I'm making yogurt, small company making yogurt. And I'm trying to figure out if I want to start making Greek yogurt because I don't make Greek yogurt right now. I can find out, you know, what kind of shelf space is allotted to the Greek yogurt in such and such an area. And I can also find out how much of it is moving, right, in a specific area. And I can find out seasonal trends in a specific area, in certain stores. And I can get all of this data that is very specific and very usable if I even want to broach a new category. So it's driving category exploration, not just current brand exploration. Am I right there?

[Charlotte] You are absolutely correct. And most of our clients are category people is and that was a real shift for me. Moving out of brand and into category was quite a shift. And I think like if what I just sort of explained, I realized is sort of without the AI overlay, right, that's what you can do with just sort of looking at it yourself. But if you were like you say, I'm interested in this dairy category and I want to launch a new product, you can say, you could ask, knowing the current trends, the current sales, where's the white space within this, within what’s there? Now, what AI is finds difficult to do is to go, all right, well, you come up with the protein yogurt before it existed. Probably couldn’t have done that. But it could say, right, well, it's really interesting. There's no peach flavored one. And it could start to like really give you ideas of where your current category could move. And the great thing about it with the AI overlay is that you can do that per planogram. So you could say in a one bay planogram, there's a real space for X, Y, Z. And in a six bay planogram, there's actually space for that times 10. You know, like so your recommendations to the retailer are not general. They're so super specific because it allows you to get to that level of detail.

[Karen] Yeah, it's really, you know, and again, I'm having the same kind of reaction that I was having in Europe, and I'm sure it's the same reaction that the audience had when they were listening to this. And I'm not just saying this. I don't know of anybody else doing any of this kind of work. It's a really unique business model and hats off to you and what your company is doing, to both of you, really. You know, it is very, obviously, AI is enabling so much of the work that you do and so much of this analysis. And yet, you know, we're not really, we haven't mentioned that word yet. Right? Here we are. We haven't even talked about it yet. But but how are you approaching AI in such a way that it is just kind of, you know, it's just sort of practical at this point. It's not even part of a hype and part of the hype, it’s not even what makes this great, and yet it is power behind it. Right. So how are you approaching it right now?

[Yasmine] I think everyone's been asked to use AI at the moment. But I think this is something that Charlotte mentioned in our pitch. You know, at IIEX was the AI is only as good as the data that you give it. So it's really important that we're giving it, you know, the data and the work underneath it to actually get this data to be used well, it's cleaning it, they need to be standardized. We have to make sense of the data before it can be used. So I think that is the easiest way for then, clients and for category managers, even us to then use the data well. And I think the fatigue is not necessarily with AI. At least it's just how people are using it and people aren't necessarily describing and showing how to use it properly. So I think probably that's the way we're using it. So it's kind of an extension. It's helping you do the things much quicker. It would otherwise take people hours or days even to find the same data. So it's kind of speeding up the process for a lot of people that can use it in our context.

[Karen] One of the things I know you all, you all have seen it. We interviewed Nick for an article recently. And one of the things he shared about Vazen’s strength is that it comes from years, nine years, actually, of continuous shelf and planogram data. And, you know, one of the things this is linked to kind of the AI question is that, you know, we know that a foundation of solid data is critical when we are training AI and when AI has something to pull from. Tell me how that's beneficial in your organization specifically.

[Charlotte] I think having that history of data rather than building a company on an idea of AI is quite a different thing. And I hadn’t quite clocked that, I think, until we started Ask Vazen and that part of the business is that we were, I think people talk a lot about moats, don't they? Right. They talk about like, how can we create an idea that we can build and we can build continually? And I think that is that data that like historical data gives us a bit of a like AI moat. Right. So we want to use AI to understand it better, to inform better decisions from our clients. And yeah, the data gives us a reason for being, over and above like we do AI because we don't do AI. We're using it to make the decisions that are already being made, better decisions, faster decisions, more in-depth decisions. Right. I think that's quite a clear distinction in how AI is used. Like Yasmin and I, and the rest of the team, we use AI all the time. We use it in, you know, in our everyday working life. But I wouldn't say that's how Vazen uses it. We're using it very specifically to help people ask questions. And I think the big one for us as well is that we want to be quite AI agnostic. So we want to say, here's the data that we have created and it is infrastructure like structurally the same. So you can look at Tesco, you can look at Kroger, you can look at Carrefour and all the data coming in will be a bit different, but we will structure it the same to allow you as a brand to go, OK, well, in the UK, this is what we look like in France. This is what we look like in the US. This is what we look like. Do we look the same?

[Karen] One of the questions I have is like for an example, like when you talk about Ask Vazen, you know, you can ask these questions of the data in plain language. Right. You can just ask. Give me some examples. Like how are you able to ask it in plain language, because that's an AI assist. Right. So what are some of the types of questions where you can get these data-backed answers, but you can ask in plain language. What are some examples or some some kind of use cases that you are able to share without sharing anything that's too proprietary?

[Charlotte] Yeah, it's nothing too proprietary in it really. But yeah, like one of the big things that we support on our range reviews, which is such a category specific phrase. So a range review for people that maybe haven't heard of it, which I haven't before I worked at this company, is where a retailer will say we're reviewing the range and it's going to be new in September. You have this amount of time to influence it and then it will change. So Ask Vazen can do two things there. We can look at a pre or post range of view. And I guess the post range of view is where we can save a lot of time, which is it's a very simple job that takes about three days for a category manager to look at. And what they're doing is saying what has changed? In a very simple language. And so you can, essentially it's briefing AI, the AI that they Ask Vazen to do that. So you would ask it, look at this time frame, look at this time frame, what has changed? Tell me what's the share of shelf by brands that's changed by subcategory, by different attributes, what new products have come on, what has been delisted and what impact. So you can get that as a picture of what that looks like. And then you could say, how does that change across the estate? So has that range of view really cut hard for my brands in the north versus the south? You can ask all those types of questions. And then you can even say, like, how does this compare to the range review before? Or what was the impact of that on sales? What do we think the likely impact might be on sales now? And yes, and those range reviews happen every six months. So imagine every six months you have to sit down for half a week to a week and ask those questions of the data, loads of different Excel sheets. You ask it to Ask Vazen. I know because we've just done it for a client that that could reduce from three days to three hours. So what else can they do with their time? Loads more that they can do with their time.

[Karen] Yeah, I just had supply chain management use cases in my brain, too. I was thinking about whenever there's a storm here in the U.S., whenever there are hurricanes, the things that, you know, you mentioned water before during the heatwaves. And I started to think whenever there's like the threat of a storm, the things that typically sell out, you know, how interesting to track that trend, you know, how early, how many days before a storm things are selling out of the space that they have allotted for them. I just imagine it would be an endless curiosity I would have working in an ecosystem like yours that there would be just question upon question that I would want to query. And it feels like it could be almost endless. I would want to talk to it every day.

[Charlotte] Yeah, and I think that is exactly what you can do. Yeah, we did have one client who said, I've just got co-planned for Tesco and I'm going on holiday tomorrow. And I'm thinking about cancelling my holiday, honestly, because I'm so excited. And I was like, please go on holiday.

[Yasmine] It kind of changes what people are asking because they have that ability to ask so much now over, like Charlotte said, the whole estate. They can ask more endless questions, endless bits of detail that they otherwise probably would have never been able to get to. They can now go into, I don't know, a specific product within a specific store and find out all that information where they otherwise couldn't have. It would have taken days. So they don't have the resources to do that. So it opens up for a lot more to learn themselves.

[Karen] Yeah. Yeah. You know, and I go back again, I go back to putting the piece together about some of the package design work that I did, for example, where we were doing shelf testing with logos, which might seem very straightforward. But when you compare, you know, looking at logos on a loaf of bread, for example, we're looking at the logos on a loaf of bread. And when you look at how bread is laid out on a shelf, you know, you don't see very much of that loaf of bread. You see the, you know, the square on the back of the loaf of bread. Sometimes you might see the top of it because bread is on shelves very interestingly. But maybe you get two squares on, you know, a very large bread aisle. And it's not a lot. And so you think about and yet it has to move, right, because bread is perishable. I just think about it. It's just so interesting to think about. It's just so interesting to think about it in certain categories, how important it is to have this data about and tied to how much of it's purchased. And so how much space is allotted and how quickly it is purchased and what day of the week it's purchased on. And the puzzle between does it change based on package redesigns all at once, combining, you know, marketing questions with supply chain questions. I just think, again, I could probably talk to you guys for hours about this because you have just piqued my interest. So how do all of these relationships between these different roles in an organization, shopper insights, category management, you know, the retail execution teams, how is a product like yours influencing how these teams interact internally within an organization? Because your buyers or your clients are not just one.

[Charlotte] Yeah, it's a good point. And I think that they probably need to be joined up a bit more. I think that's our feeling generally is that, you know, the decisions the category manager is making about these things should be inherently linked to what the brand manager is making about these things. And those clients where we work most effectively are aware all of those different parts of the business kind of work cross functionally and know that the data exists. Like I think sometimes this can sit within a category team and then you talk to the brand team. They don't even know that the data exists. Right. So where cross functionally that works and much better decisions can be made. And, you know, things like we touched on a little bit earlier, but you know, retail media is hugely based on the brand team getting it right. When the category team don't talk to the brand team and they're like, we've made some signage. And you think you need to talk to the brand team because you need them to work together to really elevate what's going on on-fixture. So I guess, yeah, where they work together really well, I think better decisions are made and where they're too separated, you know, they live within different functions, then it's much harder to enact good change. I think what you end up with is like principles of good, right, rather than actions of good.

[Karen] Well, I'm looking up at the clock, you know, how I always say to people, oh, we'll definitely be off at a certain time. So I look at the clock and I get the 10 minute warning. I think to myself, all right, it's time for me to start to wrap in and ask the questions that I really need to ask before we wrap. So what are you all, you know, most excited about for Vazen to build next?

[Charlotte] I'm really excited about more retailers. Ultimately, that's where, like from a growth of company, like commercially, that's a really exciting space for us. We've got our two co-founders, Nick and Josh, been out in Bentonville for the last two weeks. So we know who lives in Bentonville. So that's, you can kind of take what you want from that. But yeah, like the view there is obviously we want to expand and grow. And really, that means just more of that data coming in. I'm super excited about the growth of our visualization tool as well, because off the back of more data, I think there will come more need for clients to take what they learn and then build out future fixtures. And like the things that we can do within that and the analysis that we can do for the future is very exciting. And to get to a point where we can use looking backwards to look forwards. So at the moment, we do a lot of kind of shelf testing where we do a lot of A-B testing and we say, what do we think the impact of these changes will be? And we, me, Yaz, Olly and Jenny, as our team, can say, yeah, this is a great idea. Like run with this and influence the researchers to make change. But actually, I think what we want to get to a point of is like, actually, let's ask AI to build out five different things from based on the data that we have and actually visualize it. Like, let's see what that would look like. Can we come to something unexpected with the data that we weren't expecting to see on shelves? So that really only comes from more data.

[Yasmine] I was going to say that change following what you said, change that fixture as well. So to see how over time we've helped to make these changes based on what clients are able to build and analyze on their own fixtures, to be able to use the tools we provide to actually see that happen in store, I think that would be nice. Because obviously we've done a few and seen a few through working with leading cereal brands, for example. They're able to argue after range review for a specific bay, like a protein bay, for example. So I think seeing more of that change and building on the tool to make people able to share that data, I think, is really exciting as well.

[Karen] Yeah, super cool. So as we wrap, what's the question that you wish I'd asked that I didn't get to in our time together? Kind of your final word here that I can let you chime in on.

[Yasmine] I think probably what it actually takes to make these recommendations to the retailer that are actually executed in store. Charlotte and I and our team, Olly and Jenny, we do work a lot on obviously the data behind it. And that's obviously what the brands can take straight to the retailer as an arguments case. But I think it's then going the last mile. That's the hardest part to then actually now you've got the data, you then have to make that recommendation. And it's quite difficult for a retailer to actually implement that change. So you just need to prove enough that the recommendation will be good and it will make a difference and help the category grow. So I think that's probably a really hard bit we wish you asked about just to see how people get that over the line at the end to actually make the recommendation real.

[Karen] It's one of the hardest parts about being a researcher, right? It's not just deciding what recommendation to make, but also then being persuasive enough about the recommendation. And then also kind of letting go of it because I was just having a conversation with another podcast guest recently about, you know, what do you do? It was actually somebody who spoke. Do you remember the woman from WEX who spoke at IIEX Europe and she was talking about like sometimes not every recommendation can be acted upon. And sometimes you make the recommendations and like even if it feels very much like that's the right recommendation, sometimes you have to say, well, we cannot act on every single recommendation. So that's the tricky part of research, right, is, you know, you sometimes put forth the best recommendation out there and then you have to let it go because it's not always in your hands, right, what your clients do.

[Charlotte] Exactly that. Yeah. And also, I think like acknowledging that sometimes our clients have to do that, too. Yeah, the thing that they really want to do might be right for that, and it's that kind of tension as well between brand and category because category have to recommend what's right for category and brand what's right for brand. So that tension internally, and then if they're taking a joint recommendation to the retailer, like, how have they answered the tension of what's right for category and what's right for their brand. And yeah, obviously the best is both.

[Karen] Yeah. Yeah, but can the company move forward? So, interesting, such interesting stuff. All right, final question for you both then. And I'll let you both chime in on this because it's a big one, you know, Greenbook's tagline, the future of insights, and especially now that you've both been in this competition you both participated in a pitch. Imagine you are now listening to future pitches, it's next year you're at IIEX Europe again, you're listening to other pitches, what types of things do you expect to see, what do you think the future has in store for insights professionals?

[Charlotte] Well, I imagine that next year it will continue to be how AI is being used, and I think it will be increasingly practical in its nature I think we've, as this has been said many times but we've moved away from theory and into practice right, that's where we are. So I'm excited to see how people are putting it into reality, practice, and finding, I think, you know, looking for something like we've done like find something that hasn't been done yet. Right, what I, you know, you hear a lot of similar ideas like I want a standout new way of thinking about something. I want to see more women on that stage, just putting it out there, like, there was us, there was one other lady, everyone else was a man. So let's get more of us up there.

[Karen] Yeah.

[Charlotte] So yeah, that's what I'd be looking for.

[Karen] Yas, anything to build on there?

[Yasmine] I guess again to follow Charlotte, I think just a point she mentioned on all the different teams that are kind of maybe working separately is to bring them all together. If people can work more cross functionally across insights I think it would really help. And obviously we're providing some answers and reports which is obviously very helpful but the evidence that kind of helps with the decision making is really quite important so it'd be nice to see how that kind of grows, as Charlotte said with the age of AI, how that can help us grow and make it easier to make these decisions.

[Karen] Yeah, yeah. Well, congratulations again to both of you but really great work that your company is doing. I am just, I'm thrilled to know that you're doing it, I'm thrilled to know your company exists, and I'm really grateful that I was there to see what you put together because it's just something that fascinates me and that I know fascinated everybody in the audience, really good work. Really good work. So, thank you for joining me today.

[Charlotte] Thank you. Thank you for your time and thanks for like all the support in IIEX. It felt like a really lovely space to be able to make that pitch in.

[Karen] I'm so glad. I'm so glad. So that's our show for today friends. Emma, thank you for being our producer extraordinaire, really appreciate you. Big Bad Audio, thank you for doing what you do for us, time and time again. And to everybody listening, thank you for showing up, week after week we really appreciate you for tuning in, and we'll see you next time on the Greenbook podcast. Bye everyone. Take care.