Marketing data is messy. But the right story makes it meaningful. In this session, Katy Howell and Brandwatch’s Barnaby Barron break down how to turn metrics into narratives that drive real business decisions. If you’ve ever struggled to make a dashboard matter – this one’s for you.

Katy Howell: Hello, hello, hello. So I’m back today with this week’s live, and we’re going to talk about diving into something that marketeers wrestle with every day. My name is Katy Howell. I’m CEO of immediate future, and I want to tackle this element of data that’s not the sexy kind, the messy, complicated, dashboard overloaded. What do I even do with this data – reporting, that kind of data. And one of the things I want to cover is data storytelling, because let’s face it, raw data in itself and pretty charts rarely convince your boss, and it definitely doesn’t inspire your your customers. So – I’m not doing this alone, I’m going to be joined by someone who knows this space inside out, and I’d like to welcome Barnaby. Let me just bring you on.

Barnaby Barron: Hello.

Katy Howell: Hi, Barnaby. Barnaby Barron is head of EMEA analysis at Cision Insights. He’s led global insights programmes for Microsoft, for Sony, and you’re a physicist by training. I love that. I did genetics. We’ve ended up in weird careers, haven’t we?

Barnaby Baron: Yes.

Katy Howell: And an analyst by trade, and a storyteller, I think, at the heart. And you know exactly how to turn numbers into action. So, we’re going to expect some clarity and honesty and some cute examples of how to turn these into stories. That’s what I’m hoping for. So let me, let me set some let me set some context first, so marketeers face a daily barrage of dashboards and metrics and expectations. And I feel like data storytelling is is the lifeline for taking that complex information into a much more compelling narrative that helps you create better decision makers, but also travels through the organisation better. And it’s really interesting because stories built around data are 12 times more memorable than facts alone, and visual content gets shared three times as much. So if we can bring our data to life, you know, another element to this that we want to think about is that 64% of marketers say storytelling improves stakeholder communication, which is why I said it helps internally, but most of us are sort of still fighting charts and wondering why nothing ever lands. So I’ve got some questions for you, Barnaby. Let’s kick them off. So, 87% of marketers say they struggle to make sense of the data they collect. What is stopping us from turning the data into something useful?

Barnaby Barron: Yeah, it’s a good question. Hi everyone. Nice to speak with you all today. I think the biggest challenge that marketers face in kind of making sense of that data is the amount of the data that exists. So, you know, there’s all kinds of data sources and streams that we can get. Even if I, you know, look at one of our platforms, Brandwatch, we get billions of pieces of content into that on a monthly basis. So even if you’re-medium sized brands, there’s probably hundreds of 1000s, 10s of 1000s of pieces of content that you can capture, and drawing sense and information of that is particularly difficult. And then you know, once you have that data and information kind of defining what it means for you and your brand, and ultimately turning that into action is really important and difficult to do. I think on that first point, the amount of data, often, you know, people are, you know, not, not confident dealing with data, because most people who are marketeers didn’t come into marketing or into social to spend their time going through spreadsheets or charts or data sets. So there’s an element there where you have to kind of build out your comfort in that data, understanding where it comes from, understanding what it potentially means. And then secondary is that pulling the insights out of the data, and one of the things you know I will always talk about with our teams as well as as customers, is that deriving insights is a non linear process. So you can spend three days staring at a data set being unsure what it means for you and unsure what it potentially means for the business, before eventually, maybe you look at it in a different way, and you can kind of derive an insight or an action from it. Sometimes you open up a chart and it’s immediately obvious what that means and the actions that you might need to take away from it. So, I think that’s the other side of it, where I would always say, making sure that you invest the time-to one of my previous customers I worked with, would always say, torture the data. And I think that’s absolutely true, like you need to make sure you have the time to really interrogate that data, to derive the insights and findings from it.

Katy Howell: That’s just, I love that. I’m going to use that now. But you’re right. In fact, the bit that really strikes me listening to you is that there’s an awful lot of creativity, isn’t there in analysing it, which you don’t think people always think numbers are facts, and that’s the way it is, but actually you need to think a little bit non linear in order to to do to do that. Before we plunge into this, because this is super interesting, and you’re absolutely right about torturing the data. But before we talk about story, what is data storytelling and why? Why do you think it’s so critical?

Barnaby Barron: Yeah, good, good question. So the way that I kind of think about it is maybe, let’s split those two things out. So the data and the story. So if you think about the data, that is the kind of cold, the factual, the objective information and the story is that warm, emotional, subjective part, and we need to pull those two things together in data storytelling, particularly within businesses, and particularly within communications businesses, to engage our audiences in the right way. So if you’re thinking about reporting back on a campaign or an activity that you’ve done, you obviously need to do that to your senior stakeholders within the business. And if you just report that data, that cold, factual objective, what happens is that, and you alluded to this data in your context setting at the beginning, typically, only 5% of people will remember the data that you are telling them, you know, a month after you’ve told them that data. So that cold, factual pit gets forgotten very quickly, whereas that kind of story piece, the warm, subjective piece, you know, 12 times more more people will remember that a month later if they’re asked about it. So it’s 5% of people remembering versus 60% of people remembering. So having that story that you’re telling with the data is really, really important, and really what that is that there’s a few different ways you you will think about it, and it doesn’t always necessarily need to be a chart or a presentation. It can be even just a conversation that you’re having, but you’re using data to tell a story with your words in that conversation and communicating that kind of story. What I often will think about and encourage people to think about is understanding who or understanding how your audience would prefer to digest their story. So if we think about our stakeholders that we work with, I’m sure we all have those stakeholders who require a very detailed summary with all of the facts and information, and they’ll pick it apart data point by data point, we still need to convey a story to that person, but you absolutely should not have your less formal conversation with that person to convey that information. If they want that granular detail for them, you probably should be pulling together a detailed document with charts, with references, with information, but then still convey your story, looking at that information, whereas some stakeholders, yeah, maybe a conversation that you can have so understanding the best way in which to present that story, and thinking about if the audience is engaging that story are, and particularly if I’m thinking about senior stakeholders here, if they’re more visual or less visual, and if they’re more kind of spoken communications or written communications, and that can allow you to identify the right way of communicating with them. So yeah, if they are very visual and like written communications, probably a PowerPoint deck with the right information. If they’re less visual and more conversational, then perhaps that’s a presentation that you can have with them if they’re a senior stakeholder, or just a one on one conversation going through some of the data so understanding the right way to engage with that audience, to get them engaged with that story, I think is really important.

Katy Howell: And that’s really I was while you were saying, I was thinking, also, it’s that whole element of sometimes you also need to feed the one liner. And I always think LinkedIn did it rather well with their research study, which was the Ehrenberg-Bass data that only 5% of your buyers or a market at any one time, but the way that that it’s roughly where they were paraphrasing. But the point is that if you hear anybody say it, they almost say it the same way, in the same pattern. Otherwise it became a message that becomes easily, easily repeatable. And I think there’s a lot to be done with thinking about how you market your data. And, it’s really interesting here that Elena’s come up with a question. I’m just gonna sorry, put the glasses on and put it out. So Elena’s just asked us: What makes a data story stick with stakeholders? Is it about the structure of the narrative, the visuals you use, or knowing what to leave out?

Barnaby Barron: Yeah, it’s a great question, all all of those things are and can be important. What I try and do is, if I’m thinking about the kind of data story that I want to communicate to stakeholders, and there’s a great book by Nancy Duarte which covers some of this, there’s a framework within that, around kind of creating a story arc which you would use to communicate to to those stakeholders. And that’s the same story arc that you’ll kind of storing structure that you’ll be used to in any kind of story from when you were very young, engaging with stories. And you would start there, if we’re used to maybe a story having a beginning, a middle and an end, you can relate that in kind of maybe business terminology to make it slightly more applicable to our day to day work, that beginning is the kind of situation in which you’re facing. So in there you may be talking about, you know, a problem that the business is facing, or,
you know, an objective that the business faces. Say, we want to grow our number of members X percent year on year. That can be your kind of beginning situation that you’re starting with. You will, then, inevitably, as you go through that have particular complications, which is kind of your mess, your middle, or is what sometimes maybe called the messy middle, in a story where things get more complicated, and in that you may be able to articulate. Well, in previous years, we’ve had good growth, but we haven’t hit the growth we wanted, because maybe we didn’t have the budget investment that we needed, or maybe the campaigns that we had didn’t land with our audiences in the right way. And you can use data to articulate those challenges that you face, and then ultimately, you want to take that to get to your end, or your resolution, where you’re saying, you know, well, if we invest more, then we feel like we would be able to accelerate our growth as we haven’t previously. Or perhaps, if we reorient our campaigns to reach these different types of audiences, we give ourselves better opportunity to hit our goals and objectives. So you can kind of think of that very typical framework that you would have for a story of a beginning, middle and end, but think of that more in your kind of business circumstances around okay, what’s the situation, what’s maybe the complication, the challenge that we might face, and then what’s the ultimate resolution, which should hopefully be the action or insight that you want to take to help you hit your objectives that you articulated at the beginning, and I would say as well, the particularly on the point of visuals, I’m a huge advocate of keeping simple as keeping visuals as simple as possible. I think often, we sometimes get this feedback in our product. I was actually speaking to a customer earlier this week who gave us this feedback that they would like some of the charts to be, a bit like, sexier. They’d like some more interesting charts there and things to look at. And, you know, the reality is, like, charts and visuals have been around for hundreds of years. Like, if there were things that we should be using, rather than, like basic line charts or pie charts, which were better, we probably would have discovered them at some point in the last 200, 300-1,000 years. So like, sometimes they’re maybe not the sexiest things in the world, but like a pie chart with the call out of the key data point that you need to be concerned about is often maybe the best way to visualise things. So keeping those things really simple means that when you’re telling the story, and you maybe have a slide or a visual showing that the person will very quickly be able to understand that visual and then carry on listening to you, versus if you have a, you know, a chart which has 50 bubbles on, and the accesses mean different things. And the bubble size means something else, and the colour of the bubbles means something else. They’re not going to listen to you. They’re going to spend the next five minutes trying to figure out what the heck the chart says. So keeping things really simple, and as part of that, like knowing what to leave out, is really important. So there’s actually two kind of, maybe fathers is a strong word, but there’s two kind of pioneers of data visualisation. There’s Tufte and McCandless. And the idea with those two different views is that – I need to get these around the right way. Edward Tufte, these like dicta. His idea was that you hold the data above all else. You present the data as it exists, very factually, and you let the reader read that data and interpret what it means for them. Whereas McCandless his view was much more that you can adjust the chart to help tell you a story. And I’m very much that school of thought. A really good example of that is like, if you have, maybe you have a year on year chart where you’re showing growth, and you’ve gone from, I don’t know, let’s say 80% to 84% and that 4% was really, really hard, and you did loads of work to make sure that you were hitting target there, or, you know, achieving growth year on year. If you have an axis that goes, I’m getting very in the weeds here, but I think it’s useful if you have an axis going between zero and 100 then that growth is going to look really small. It will look like that 4% and it won’t look very impressive. But if you have an axis that starts at 80% and goes to 90% then that 4% growth is going to look like 40% growth, and it will help you convey that story, of the hard work, the actions that you took to drive that improvement year on year. And I think, you know, as long as you’re not, you know, misrepresenting the story or the the results of the data, I think it’s fine to take some of those, you know, hacks to help emphasise the point and the growth that you’ve achieved, or the story that you want to tell.

Katy Howell: And I think it’s, it’s a practice skill, because you need confidence to do this. You need to keep practising it. Because you mentioned something there that I thought is really, really important, which is that we become so we run down such a rabbit hole with data that often what happens is, you put all the information out there because you don’t know which ones to pick. And I think, I think that sometimes can throw you and I am really for visual storytelling, because most of my colleagues are visual thinkers, because we’re agency side, and because I think it can, you know, alongside a table or a chart, which does appeal to you know, some parts of the board will prefer to see the hard numbers. In the main there’s a great, great deal more you could do. I mean, there’s a comment here from Tom that I just want to put up, because I think I’ve only just read this through my fuzzy eyes. But anyway, using visual representation can help assure, eg, 1000s of engagement find the equivalent to that figure, like a football stadium with all the seats visible. That kind of context can really help. And we do that a lot. I kind of talk about jelly beans, because very recently, the guys have put the number of impressions as the number of jelly beans, and what, you know, how many stadiums, or how many, I think it was a swimming pool, how many Olympic swimming pools you can fill up? And I think this is a really good element to it. But I’d like to now bring up CJ question, because this, I think, plays onto what you’ve just said, which is, how important is emotional connection to data storytelling? I think that’s really, it’s a great question.

Barnaby Barron: Yeah, great, great question. And it’s, it’s, it’s so important because the we talked about the fact that through stories, people are more likely to remember what you’re telling them more likely to engage with what you’re telling them the like. The big reason that is, and there’s, there’s research that backs this up, is because telling stories brings us much closer together. So when you’re telling a story, it engages all of like, all the different parts of your brain, and it engages the part of the brain of the listener, so it engages like language processing, language comprehension engages like the auditory cortex, the olfactory cortex, like shared memories, the amygdala, like it engages every part of your brain, whereas, like just the data. Does not engage all of those parts of the brain. And what that does is that it means that when you’re telling a story, it creates this like shared connection between the storyteller and the listener, which is why your listener who’s hearing that story is so much more likely to remember that information, and also they’re much more like, I think you may refer to this as well, in your in your context setting, like they are much more likely to take an action off the back of that. So, like, these stories and the data kind of only really matters if someone’s taking an action, like if you’re doing insights reporting for your social work or your communications activity, yeah, kind of doesn’t matter. It’s only you know reporting for the sake of reporting, unless people take action off the back of it. So telling that story which creates that connection, which is then more likely to drive action is really, really important. So I’ll say, like, it’s very important. I think the other thing that I always talk about with various teams when it comes to kind of storytelling, I think particularly true, if you’re, I mean, it’s true in house, but maybe even more true if you’re like agency side, like I am, and you’re working with customers who are in house, is really what you want to be doing, is like, you want to become the mentor to your customer in the story. So if you think about, you know, there’s lots of, lots of classic examples. One of the ones I will talk about is like, we want to be the Mufasa to our customer’s Simba. So in the moment when our maybe our client, gets lost in the data and doesn’t know what to do, we can be the ones who kind of come in help communicate that story to them, so they know what the data’s saying, and then they can kind of help take that action away. So particularly, yeah, maybe agency. So when our clients or customers are stuck, we can be that mentor who gets them over that hurdle, and yeah, helps them kind of understand and again, it kind of helps to build that emotional connection, which we know is really important to to data storytelling and driving action ultimately.

Katy Howell: So, you know, we the building of confidence. There’s an interesting element to that is, do you think that AI can help? I’m sorry, sorry, I’m mentioning AI again. But go to might be, you know, plugging your data into AI and querying it that way. How does AI support the storytelling side of it?

Barnaby Barron: Yeah, it’s a good question. I quite heavy user of AI because I think you’ve got to get on board if it’s going to take lots of aspects of our jobs over the next 50 years. You’ve got to understand what it’s good at, what it isn’t good at. I think I definitely use AI a lot in terms of interpreting data. So, and we will do this as well as when we’re starting reports for customers. We will, you know, use the AI tech in our platforms to, say, summarise this content for me, maybe highlight some of the key things that have changed. I even do that with like our some of our internal business data on our customers and engagement with the platform. Put that into AI and say, tell me what this means. And it’s really good at, I would say, taking the first step of the process. So if we think about, yeah, if we think about data generally and analysis, there’s a few steps you want to take. You want to kind of analyse the data and understand what it’s saying. Then start to interpret that, analyse data, and start to build some hypothesis around what it means, what you might want to do, off it, and then kind of validate or invalidate those hypothesis by, you know, cutting the data in different ways, looking at if that’s true for competitors, etc. Is it an industry trend, or is it just for your business? And AI is really good at that first bit. AI is very bad at the moment, at some of those later steps and interpreting maybe what it means for you. So when I think about AI and kind of how we’re using AI, it’s really to accelerate some of that, like initial data analysis and information to say, Okay, we’ve maybe got, if you think about a maybe typical client set up, you may have 10 or so competitors that you’re tracking across social, traditional audiences. You maybe have 10 topics, themes that you’re looking at. You look at sentiment, bunch of different things. So maybe up to 50 different tags or categories. It’s incredibly difficult for a human to look through all of those 50 things in short order and see what’s moving what maybe matters. The AI can very quickly. You ask it, say, call out to me the biggest changes month on month or quarter on quarter, and give me a bit of a view of the key stories or what’s potentially driven that it can do that piece really well, and we leverage it to do those initial steps, but then you kind of have the human come in, do the understanding, the intervention, to really understand what what’s going on. So like a good example of this, where AI can really fall down, is we, one of the, one of the things we do is we track timesheets, like, I think every agency tracks time sheets. I put all of our timesheet data into, actually, kind of co pilot, because we use Microsoft and said, Tell me. Tell me some stuff about our timesheet data. What’s going on. And the first thing that came out, it said there is a very high correlation between number of timesheets and hours worked. And I was like, Okay, thanks. I think kind of figured that out myself.

Katy Howell: I love it. I love it. I have to say, I use it a lot. And that is, you know, back in the day when I use Brandwatch when it was very early days. And you download these big spreadsheets before the AI was integrated, you download the spreadsheets. And I used to use, you know, you’d set up your conditional formatting, you’d pull out all the data. I’d have a big bit of paper next to me. I’d be scribbling away, going, Oh, that’s interesting. I need to look at that. I need to what I think is, I’m with you. The one thing that’s useful is, if you know, is interrogating the data, yeah, is there anything that proves this? What about this? What sort of data is there around this? Because it will go off on its own little tangent, because it doesn’t really understand why you’re asking. So can’t in context, unless you’re prepared to write, you know, 20 pages on context. But it is amazingly faster than the days when I would have 30 spreadsheets open with all the Conditional Formatting put in them, and then, oh, God, it used to take forever. I mean, it was three days work just to clean the data into a place where I could then start thinking about what it meant. And I think, you know, that has been the joy, but you’re absolutely right. I don’t think it can story tell very well. I don’t think it understands how to turn the data into an emotional thing, because we’re emotional human beings, and AI is not emotional. So it’s not able to do that. And I think, I think that is a really so I think, if you’re what’s the beauty of it is that if you’re not comfortable with numbers, AI can help you get more comfortable with numbers, so long as you make sure you tell it that if it does any jiggery pokery, it needs to show you the the equation is used, because sometimes it just makes stuff up, so you need to make sure it doesn’t smell properly.

Barnaby Barron: Yeah.

Katy Howell: So just take just a warning on GPT that one, it does love a made up formula. But I want to turn this a little bit around, because one of the things that I did this week was put out a poll about how marketers can speak to CFOs, or, to be honest with you, anybody else on the board that isn’t marketing, and you’ve worked with some some pretty large brands, so you’re very aware that, you know, quite often our board or our leadership team, or the people that sign off our budgets basically don’t really care about things like engagement metrics. How should we be talking to CFOs? How would you change the story tell, to appeal to the board?

Barnaby Barron: Yeah, yeah. It’s a good, good question. And I think it’s it is really difficult. If it wasn’t really difficult, then we wouldn’t, you know, consistently have the surveys that say one of the things that marketers or communicators struggle with most is communicating their value to the board or to the CFO or whoever it might be. I think what’s really important is to, first of all, make sure you really understand what the business objectives are and what those you know executives or what the CFO will really be looking for in terms of demonstrating value to the business. And then you know, as a communicator or a marketeer, make sure that you also have objectives which clearly ladder up to those organisational objectives. If there’s stuff you’re doing that doesn’t ladder up to those organisational objectives, you probably shouldn’t be doing it. And I say that like you maybe be surprised how often things like that happen, to making sure that you have that clear link between the organisational objective and your objective, which can be articulated very quickly. If you can’t articulate it in 60 seconds, then it’s probably not, not actually a connection. You’re jumping through too many hoops. And then once you have that kind of key information is articulating it in as simple and kind of comprehendible way as possible. So when we’re doing reports, before we get to that, your final kind of presentation point with customers, where I would say, as well, a report is very different to a presentation. I think people often do a PowerPoint slide and present that PowerPoint slide, and it almost if you’re doing that, that slide almost definitely has too much information on. If it doesn’t have too much information on, then you probably didn’t do enough analysis. So making sure you’re kind of streamlining what you’re presenting. And then we will, on that slide, have one chart, and in you know, one maybe a complex sentence, so it’s a little bit longer, but ideally one sentence articulate what that chart means and what action you should take from it, or what finding you should derive from it, so that it can be very quickly digested. One of the things with particularly like CEOs. CEOs actually, if you look at the decisions that they make versus maybe middle management and see, do CEOs make better decisions than middle management, they actually don’t. They broadly, if you look at the research broadly, make the right decisions as often as middle managers do and make the wrong decisions as often. The big difference is that a CEO will make that decision extremely quickly, so you need to have as little information as possible to get them as quickly up to speed so they can make that decision. The other thing that I would say, which I think particularly is difficult for marketers and communicators, I think particularly for some of the more earned media communicators or social communicators, is your CFO will typically be looking at things at the end of the funnel. So they’ll be looking at like, how many sales did we make? How many, you know, new business prospects, engagements did we have? Like, they will typically be looking very end of funnel, and for earned media or social media, quite often we exist and the work we do exists at the top of the funnel is building awareness, building, you know, protecting the brand’s reputation, building advocacy, which you may make an advocate if you’re like, a great example is automotive brands and automotive companies where people will typically, the average person is only going to get a new car every few years. You need to be building that advocacy and awareness for your brand years before they’re making that purchasing decision. And you’re not going to be able to correlate and say, like John Smith read this news article and he engaged with this social post in 2022 and then in 2025 he bought a new Audi. It’s not going to happen. So you need to be able to really tell that story and paint that story to your executive board of that full, full funnel, and where you exist in that funnel and demonstrate that that awareness advocacy that maybe exists earlier than intent to purchase is equally as important. Otherwise, one of the common, you know, issues I’ve seen exist within brands is because that end of funnel you can track a click on an advert which drives a sale, the CFO will say, well, if I spend 100k on ads, I will make 300k in sales. So just put all the money into ads at the end of the funnel, and year on year, you’ll see that that return will decline because you’re not doing the top of funnel stuff. So that’s probably the hardest thing to articulate.

Katy Howell: Yeah, but you’re back again. Sorry.

Barnaby Barron: But yeah, that like being able to demonstrate to a CFO or the executive board the importance of that top of funnel activity, where it’s very difficult to draw a direct correlation to outcomes, is really important and probably the most difficult thing to do, but hopefully doing that with like supporting information, being able to track what’s the conversation for our brand, what’s the sentiment of that conversation, the messaging and that conversation builds a picture of supporting evidence that hopefully demonstrates the importance of that top of funnel activity.

Katy Howell: Exactly. You’re not going to find the A to B to C, but I we use a lot of things like correlation, as you rightly said, which is things like, you know, have has social activity increased search demand. That’s quite easy. Social media increased website visits. So it means you need to be very connected to your counterparts, quite often in large organisations. Which brings me to this next question, which is about planning. Because I always say, I’ll say to our clients and people marketers, is, you know, if you don’t plan for your metrics at the beginning, retrofitting – when you and I were talking before you mentioned about award entries. And I thought that was, that was, that was quite funny. Was, yes, retrofitting results. So how do you plan for this? How do you plan up front for for this data collection to prove?

Barnaby Barron: Yeah, good, good question, and generally it will be part of we would always encourage the customers that it’s a core part of their kind of, normally annual planning that goes on. So I spoke about the importance of those objectives. Really, each of those objectives should have a metric associated to it. So maybe let’s use an example of, say, one of your key objectives is to or some of your key objectives within comms are to build awareness of the brand. You know, maintain positive opinion of the brand, and, you know, make sure there’s awareness of some new product offering that we have or some new feature that is being launched. So in that, you can be looking at things like share of voice for your you versus your competitors, to make sure that you’re maintaining the same level of awareness in the conversation. You can look at sentiment when it comes to that maintaining positive opinion of the brand in these different product areas, you could be looking at the percentage of content that mentions those new products, whatever it is. So making sure that you have those metrics tied to those objectives right at the start of the year is really important and then making sure that you can track them over a relatively consistent period of time. So most of the you know, most of the big programmes that I’ve been involved with in have kind of run in the past, are broad objectives. We would not change them every year. Our media list would stay the same for at least 12 months. I say stay the same. Like, there may be instances where, you know, a new website like GB News gets created and we’re like, well, we should add in GB News. Like you can make those small changes. But if you’re halfway through the year and you know, you’re thinking, Oh, I don’t have enough news coverage in business titles, or enough kind of that conversation, then the answer shouldn’t be, oh, we’re just going to add twice as many business titles to our media list. So you need to have that level of consistency against those objectives, so you can kind of track them ongoing, and understand what progress is being made to them, I think, as well keeping those as kind of streamlined as possible and as easy to work with as possible. We did this was a while ago. Now, a few, few years ago. I think it was just before the pandemic, actually, but I think it definitely rings true still. Our research found that the more messages a brand has, the lower their overall message delivery is. So like making sure you know, if you have 20-30 messages that you’re focused on delivering, you’re just diluting the amount of times that you’re going to deliver each one, and then overall, you end up with a kind of muddled, confused message. Whereas if you have kind of three to five clear messages that ladder to the overall aims of the business, you’re going to be much more successful driving them consistently. And like I said earlier on, like, if you’re doing activity that doesn’t ladder to one of those, like, probably shouldn’t be doing the activity. You should be spending those tight resources somewhere else. So yeah, kind of making sure you start at the beginning. And the last thing that I would say on that is kind of research and analysis often is an afterthought, like people will do post post game analysis to understand what’s happened. But there’s nowhere near enough, like pre game analysis that happens to understand, like, Okay, if we want to reach, if we want to reach Gen Z, more like, let’s go out. Let’s do some research. And it does. It can be kind of cheap and cheerful. There’s a lot of data and information out there, but let’s go and do some research to understand that audience, like, what type of what platforms are they on, what type of content do they care about and typically engage with, and how can we, in a, you know, in a way that is suitable for our brand, meet them where they are on those different platforms? So as well having that kind of pre game analysis to help you plan how you’re going to undertake and do that activity is like equally important. But I know from our customer base at the moment that 95% of our customers get that post game analysis piece, and it’s about 20% who get the pre game analysis piece. So I know that there’s a disconnect there in terms of what most companies are doing.

Katy Howell: It seems it’s always been extraordinarily hard, even though it’s the most wanted element of a campaign to get those metrics funded. So what happens when you have zero budget for for measurement, so to speak?

Barnaby Barron: I mean, there’s a lot that can be done on zero budget. I used to long time ago. Now, about 10 years ago, I used to work for a theatre company who had zero budget, and I did their monitoring analysis for them. And obviously, just by being on the platforms like there’s a huge amount that you can get from Facebook, from Instagram, from x, whatever your you know, social channel of choice is through natively through those platforms, in terms of engagement with your posts, engagements with the content that you have, you can then also do a little bit in terms of like listening on those things there are, there are free editions of some social listening platforms, which can give you a little bit, but if you’re a really small brand, it’s probably going to be enough for you, which definitely you can use. And then, kind of on top of that, there’s loads of other data sources which are free and really good to get. Katy, you mentioned one in terms of search trends. Like Google search trends are free, like you can use it. You can get some very interesting things out of there and use that to, yeah, identify trends that are growing. The other thing that’s free, which is like, also really useful, is you can get the visitor data from Wikipedia. So that’s free, anyone can go and get that. That’s super useful as well for tracking your brand, as well as tracking, like, general conversation about particular topics and areas, or not conversation, but like interest in particular topics and areas. And the benefit of that over maybe a Google search is you can get a little bit more specific into particular pages or topics or discussion areas, so slightly different purposes, but can both be used there. So there’s an awful lot you can do free. And then even just beyond that, like if you don’t have monitoring, or the ability to pay for monitoring, if you’re a pretty, if you’re a smaller brand, like things like Google Alerts are going to be good enough for you. Obviously, if you’re like Coca-Cola, that’s not going to fly, it won’t work for you. But there’s a lot that you can do out there for free. And then, you know, even beyond that, if you’re a medium sized business, there are platforms starting at a few 1,000 pounds a year, which can then give you a big step up on what the free stuff can get you and you know, if you can invest the time to learn that platform, understand how the monitoring and analysis stuff works on it, it can then get you a kind of step up if you can’t afford maybe one of those more enterprise like solutions. So there’s certainly a lot that you can do for free, and there’s a lot of support groups and kind of help around things like that on LinkedIn as well.

Katy Howell: Brilliant bit of advice. And you know, as much as we want to do everything, sometimes you don’t need to do everything in order to to prove the case and to create great choice. Scott has asked a lovely question, which is maybe a great way to round up, actually, which is, he says: great conversation. I agree. Who do you think is using data to tell stories? And what’s your favourite story told with data? I’m putting you on the spot.

Barnaby Barron: What a big that’s a big question. Do you know, I am, I’m a big sports fan. I’m a big football fan. I really like a lot of the work that The Athletic do. Who are sports publication. They do content on YouTube. They do, like paid for premium sports content. I think they do a lot with data storytelling. And what I like about what they do is they’ll use a lot of, like novel metrics, and I think they do a really good job of explaining what some of those maybe novel metrics mean to people, so that they can engage with it and kind of understand what it means. And do that in, like, a very visual way and kind of a fun way, which I think is really good. I think in some of the if I try and think from more of like a business perspective, there’s in terms of maybe the my favourite story told with data. I won’t tell you who it was, but one of the customers that I worked with, we did a big piece of work where we were looking at how people engage with specifically news content based on their location and the language that they consume. So typically, organisations will be structured by by geographies. So you’ll have a UK team, a France team, etc. But actually, particularly in the world we live in now, that’s maybe a really stupid way to organise an organisation, because – particularly a comms function. I’ll say that because I’m obviously in the UK, I don’t look at an article from the New York Times and think I’m not going to read that article because I’m in the UK. It’s just for US people. And similarly, you know, you have a huge amount of, like, Spanish speakers in the US, you have a huge amount of you’ve obviously got Portuguese speakers in Brazil and Portugal, and again, they’ll be reading content across all those different regions. And what we did there was we really looked at the language of the content and then where the readers of that content were based. We had some very cool, cool visualisations around locations and actually where they were versus the languages of the content that they were reading, and that actually led to, like, huge organisational changes within that business, because it doesn’t make sense, like, if you have half of the readership for The Guardian living in the US, and some of those journalists being in the US doesn’t make sense for The Guardian to be a UK publication. And you know, similar examples around the world of where you know, a lot of the actually, the majority of readers of like tech publications in Asia are actually reading US tech websites. So if you have hyper local news that you want to get through those publications, so you’re doing it likely is maybe not always the best way. So that was really interesting, and like led to some very big changes, which were always kind of exciting within organisations, but very difficult to do, because obviously, kind of goes against how a lot of organisations tend to have been structured, and kind of needs often need to remain structured because of, you know, legal entities and selling into regions, things like that. So, yeah, that was very interesting. But yeah, there’s a couple of examples.

Katy Howell: Brilliant. Listen, you’ve been fantastic. Do you know I could talk to you for hours, because I think we could really dig into the whole creating stories and also building confidence. And the different ways that you can you can maximise data, I think you might have to come back and do a whole session.
I think you can see that data storytelling isn’t some sort of weird gimmick, and it’s it’s the only real way to make your data mean something to people who sit outside of the marketing function. You know, we all might go so many impressions, it’s so exciting. What the hell are they talking about? So I think whether you’re building a creative campaign or or reporting to the board, you need to understand how you connect those dots for the people that you need to speak to, because data is not the enemy, but bad storytelling is, I think. Thank you very much, Barnaby, and it’s been really, really thoroughly enjoyed myself. Is, oh, I could talk to you for hours. I really could. Thank you for everybody who listened. Any questions, keep keep them coming. I’ll keep an eye on our chat on LinkedIn. So if you’ve got anything you want to add to this, please do and we’ll respond where we can. Thank you very much. See you soon.

Barnaby Barron: Thanks, Katy, thanks everyone. Bye.