Storytelling of data to the rest of your team and senior management

Key Takeaways with Resources:

1. Understanding Data Collection:

• Focus on Relevance: Only collect data that will answer specific questions relevant to the business.
• Avoid Hoarding: Don’t collect data just for the sake of it. Understand why you are collecting each data point.

2. Initial Data Analysis:

• Thought Process: Analyze data with a clear objective in mind. Look for patterns, insights, and anomalies.
• Collaboration: Share data across teams to avoid silos and get a comprehensive view of the audience.

3. Crafting the Narrative:

• Setting the Stage: Provide context by explaining why the data matters.
• Present the Conflict: Highlight the problem or opportunity the data addresses.
• Key Findings: Focus on the most important insights.
• Conclusion with Action Points: Clearly outline the next steps based on the data.

4. Visual Demonstration:

• Effective Tools: Use tools like Google Data Studio or Tableau for visualizations.
• Psychological Cues: Use colours, shapes, and sizes that intuitively convey meaning (e.g., red for bad, green for good).
• Resource: Extreme Presentations for structuring data visualizations.

5. Presentation and Verbal Conveyance:

• Engage Stakeholders: Tailor your presentation to your audience, whether they are team members or senior management.
• Interactive Elements: Include clickable links or interactive elements to make the data more engaging and accessible.

6. Tailoring the Story for Different Audiences:

• Understand the Audience: Different stakeholders have different priorities. Tailor the data story to meet their needs.
• Common Language: Develop a common understanding of metrics across different departments.

7. Using AI in Data Management:

• Leveling the Playing Field: AI can democratize data analysis, making it accessible to non-experts.
• Caution with AI: Use AI for initial data processing, but always validate its outputs.
• Resource: There’s An AI For That – A directory of AI tools.

Practical Tips:

• Empathy and Collaboration: Foster a culture of empathy and collaboration between analysts and other departments.
• Prove Yourself Wrong: Approach data with the mindset of disproving your hypothesis to ensure objectivity.
• Third-Party Research: Leverage third-party research to supplement your data and provide broader context.
  • Resources:
    • WARC for marketing research.
    • Statista for statistical data.
    • Mintel for market research.

Final Thoughts:

• Continuous Learning: Engage with communities, seek mentorship, and continuously learn about new tools and techniques.
• Human Element: Focus on understanding human behavior and psychology to make data more relatable and impactful.

Additional Resource Mentioned:

• Dr. Grace Kite and Magic Numbers: For insights on long-term marketing effectiveness.
  • Magic Numbers

By incorporating these resources, your audience will have practical tools and references to enhance their data storytelling skills.

Transcript (May contain errors)

Speaker 1: Good morning, everyone. It’s so lovely to have you here today. Honestly, so excited. I am determined and I think we are going to absolutely have a brilliant hour today. Thierry, you should fill me with energy already in the 15 minutes that we’ve been chatting before we’ve gone live. Thank you. Thank you to everyone who’s already popped in the chat feature. A good morning. Just good vibes all round. It’s a very lovely thing. Thank you for taking the time. It’s a sincere joy. If you haven’t already, do drop in the chat feature where you’re watching from, like Sarah, Kate, Paul, Alex, Megan, Erica, Anya, Lee, Will, Karen, Owen, Patrick, Sarah, James, Nicola, Sarah, all have already so far. Let us know where you’re watching from. If you haven’t already, do also put in the chat feature where you’re watching from, because it’s always lovely to see. We’ve got Naz over there in South Africa, for example, which is wicked. My one ask for today is just keep that chat feature buzzing throughout the duration of today’s session, because it’s just lovely because we’ve got people in Portugal. Hi, Carlos. It’s lovely to see you. Thank you for taking the time. Let’s get going with an introduction of today’s speaker, who is the absolute legend that is Thierry Nguttiere, who’s the head of Data Insight at SALT Agency. I think you’re just going to love him because he’s a hero. He really is. He’s smart, he’s ambitious, and does something I really admire, which is he is good at something and he owns it, which I think is just a really positive trait in something that I don’t do enough of. I don’t give myself credit. Thierry does, and that inspires me, which I think is a lovely thing. He’s an inspirational man. Every time I speak with him, I’m filled with energy. I already am. I think we’re going to have a lot of fun today. Today will function as a Q&A. It’s just myself, Thierry, and all of you watching in today. If you’ve got any questions throughout the duration of today’s session, then do drop them in the Q&A feature, which is found down below. There’s a little taskbar with a Q&A feature. You can head into there and ask your questions. I think, I haven’t asked Thierry about this, but I reckon he’ll be up for taking some scenarios as well. If you’ve got any sort of challenges or scenarios that you’re facing presently with your data storytelling, drop them in that Q&A and we’ll try and take them throughout the duration of today’s session. Before we get going with today’s session, which is all about telling the story of your data, then I want to say a big thank you to this week’s featured sponsor who are Sticky Beak. Now, Sticky Beak are a brand new sponsor at the Marketing Meetup and they help you make your marketing more effective by getting genuine customer insights before you launch new brands, campaigns, products, or new markets into new markets. It can take as little as five minutes to get tested. The thing I love about them as well is the team are just lovely. They’re so nice. They’re based over in New Zealand and like they’re utterly delightful. Right now they’re giving the whole community £100 off the first test that you do with Sticky Beak. It’s worth just scanning the QR code right there, heading to the link. I’ll also pop it in the chat right now. What they’ll give you is £100 off. You might as well sign up in case you ever feel like you need to sign, get some testing done before you launch something else. It’ll make your campaigns more effective. Also a big thank you to our other sponsors, Frontify Exclaimer, Plannable, Cambridge Marketing College, and Redgate Software. They’re all here. They’re all lovely. They all mean that we can bring these sessions to you. Can I say good morning to Nicola on a train somewhere between Stockport and Sheffield, Marketing Meetup on the move. How lovely. With all that said, let’s get going with today’s session and ask Thierry the first question after we just sing his praises once more and say, Thierry, you’re an absolute legend. Thank you for being here today. We’re focusing today on telling the story of your data. That feels important because data is interesting, but until you’ve put some context and the story around it and people understand it, it’s useless. I want to sort of guide us through a journey today, starting right at the very beginning of data gathering. You’re tasked with a project. What data do you just typically start thinking about trying to gather and why?

Thierry Ngutegure: Yes. First, I just want to say thank you so much for having me, Joe. It’s always a pleasure to be partnered with you guys, especially the Marketing Meetup. It holds a special place in my heart. The community is insane. There are people listening from Australia. How wild is that? Talk about bringing us to the future. Yes, so with data, what I want to make sure everybody goes away with today is to feel energized and enthused about data. I think that we live in a world where we’re bombarded by the word data or the word AI and to the point where we ostrich moment and dig our head in the sand and we don’t want to really hear about it. Then it leads to us creating either misleading narratives or not democratizing data to wider teams. I read a really great quote by Tom Goodwin. If anyone doesn’t follow him on LinkedIn, do so. Really insightful post that he puts out. His view was around data seems so much more helpful than opinion or people or emotional belief, but 99% of what we see is useless. It’s a distraction and it measures precisely the wrong thing. I think that is the almost encapsulates the general feeling around data at the moment, where we’re all a little bit overwhelmed. We’re not quite sure where on earth to go with this and where to tackle it. I think that with that narrative, we almost have to unpick it right at the beginning before we even start talking about what we should be there for collecting. A gentleman called James Alderson, I used to work with him at a previous agency, is now the head of data arts, which is an incredible title, right? Super generous. He talks for the narrative around that. The reason people feel like that is because the people who are charged with growing brands today, they are not incentivized to grow it beyond tomorrow. The reason is because, CMOs’ tenures are getting shorter. Agencies are worried about the next pitch in the next year. We’re all worried about sales today. All of that creates a bit of an environment where when we’re talking about collecting data, we’re only looking for data that will prove our narrative today, prove our success today. We’re not really thinking about the longevity of the brands or businesses that we’re with. That makes absolute sense because it is human nature for us to have this self-protective mechanism of like, well, I need to prove my worth today because unfortunately we’re seeing layoffs and businesses going under and so on. I need to prove my worth right now. Then we end up collecting the wrong things, I think. I guess within that as well, there is almost like two problems. I think that within collecting that we’ve got, problem one is I think that we’re actually, we’ve got a bit of an addiction when it comes to collecting data. I did a talk a couple of weeks ago called like, help me, I’m a hoarder. It’s like, what do I do with all the data that I’ve got? It’s because we know it’s really powerful. We know that data, so PwC did a study where it said highly data-driven companies are three times more likely than their less data-driven counterparts to see significant improvements in decision-making. Decision-making, once again, which proves to that short tenure, et cetera, we want decisions today, sales today, we’re not really thinking about tomorrow. This only happens if we source, collect, transform and analyze and visualize data in a way that is uniformed. I talked about democratization of data across the business, like that can only happen once we understand that entire mechanism. When I say democratizing data, it’s not like here is a bunch of data, I am an analyst, why do you not understand this? Beat you over the head every time you don’t get it. It’s all about making sure everybody has access and teaching them how to get access and showing them that impact. I do think we’ve got into a bit of a hoarder mentality where we’re just collecting for collecting sake, and we’re not understanding why is that we’re collecting? What is the thing that you are looking to answer? I think instead of going straight to the metric level and saying, I would like to collect traffic, impressions, click-through rates, conversion rates, et cetera, I think we need to understand what is the question we’re actually trying to answer. At a C-suite level, for example, let’s take SEO, which is where I work in. At a C-suite level, ultimately, my CEO is asking me, what is the ROI of SEO? They’re asking me, how can this channel or activity give us a competitive advantage to our competitors? How does this fit into the company’s long-term strategy and help with future decisions? By asking that, it immediately tells me what metrics I should be showing that individual. I would be showing more revenue implications and opportunities within the market, as opposed to, by the way, this TikTok I posted has got six likes. Great. Equally metrics, for sure. They hold way different values depending on who is looking at that data. Then with that collection addiction, I think, creates also silos. Silos are ultimately me and you, Joe, we work in the same team. I work in the social team. You might work once again with an SEO. We collect data within our departments and for ourselves. We don’t share that data. That means as a social media individual, I understand how people engage with our content, the stuff that they like. I understand the comments. I might even get some customer feedback because sometimes when people are frustrated, they might comment on our post. From an SEO perspective, you understand the type of content they read, how they get to our website, what’s the referral originator from that. We don’t share that. We end up seeing two very disparate audiences. Then we come to a board meeting and I say, I’ve got this incredible strategy and we’re going to do it. It’s going to be phenomenal. You look at yours and go, well, actually, my audience likes this, et cetera. We’re talking about the same person, but we’ve come to the meeting room talking about two different individuals. I think that is one of the biggest problems in companies at the moment with regards to data silos and not sharing that data. Ultimately the irony is I probably might not need to collect as much data. If I just shared mine with you and you shared yours with me, we might actually be like, this thing does not make our audience different. This thing is actually the same across our audience and the greater UK. We already have an understanding of that. Let’s put that toy down. A couple of tips with regards to like data silos. Quite simple to be fair, identify what data you have and where. Where the hell does it actually live? Start developing a bit of a management strategy. This is like, how do we currently collect it? Can I export that? If I can export it, how often? If I’m talking about impressions exist probably across every channel, but they’re not the same. The way the data is actually collected is not the same. Therefore we need to say, well, impressions from an SEO is this, impressions from social media is this. Define those individuals. Then centralize it. This is allowing all of your data to be pooled in one source. When I do want to speak about my audience, I go, oh, brilliant, Joe. I’m glad you found that stat because we’re on the same pool and we’re drinking from the same well. Then we can start to automate the data then. This is where we can start saying, okay, brilliant. I’d like to see reports weekly, fortnightly, quarterly, monthly, whatever that might be. Even metrics within that might differ. What you want to talk about quarterly might differ from weekly. Therefore it reduces that bloat and that hoarding addiction where we’re like, we sit in our meetings and we’re like slide 842 about the same thing. That necessarily does not matter within this week. Then this is where we start to develop a bit more of a data-driven company culture where we actually empower individuals to go and use that data. A large portion of my job, I always say, you can throw a stick and hit a thousand better analysts than me. That is not my job. The sitting down and the mining and the storage and the transformation of data, et cetera, that is not my job. My job is once that is done, how do I empower individuals to take that data and run away with it and make decisions that impact us all when I talk about all the business, as well as our audience. I think that’s where we’re still a little bit weak in my opinion, because we’ll talk about AI later as well, but AI has come in and leveled the playing field for everybody. I still think we struggle on that storytelling part, that empowering individuals to go away and do with data.

Speaker 1: Yes, I love it. Mate, thank you. That was such a fabulous answer. It absolutely does. There’s so much that comes off it. I’m wary that we’ve only got an hour. I guess maybe if we can make this a short answer, because I want to then pick up on the last bit of your answer there, but it strikes me that there’s a really important piece which you spoke about there, which is that sort of collaboration moment where you’re speaking with other folks and you are asking about the goals that they want to achieve and how people are going to judge an impression in the same way on SEO versus social media, or at least have a common language for understanding that. In your experience, do you have ways that either killer questions that you use in those instances where people go, okay, I understand what we’re trying to get a common language here. I understand that Thierry is trying to understand me. He can subsequently report back to us and tell us the stories or indeed that conversation around bringing everyone together and sort of saying, okay, an SEO impression is the same as whatever. It feels like that conversation piece is really important. I’m not sure that’s naturally something that everyone will do. It’s quite easy to sit behind the desk and go, okay, here’s my Google analytics data. I’m just going to feed it back to you, whether or not. How do those conversations tend to go? Where have you seen a lot of success in bringing those to a really successful conclusion?

Thierry Ngutegure: Yes, absolutely. I used to think when it came to reporting, when I first started out within marketing, it was the more data I can give this individual, the better. At that point I was in analyst mode and I was like, well, obviously more is better in every form of life, right? It got to the point where like my reports were like, went from 20 something slices, 75 slides. Because I was like, buffet, take your pick. Then as anyone knows with a buffet as well, you walk up to it and the plate that you walk back with is higgledy-piggledy. You’ve got desserts, you’ve got chicken toast on there, spring rolls, like you’ve got all kinds, you’ve got French toast, it’s chaotic. That is a great example of like this whole choice paralysis, where then we all panic and pick up things that don’t really matter or have no cohesiveness to them. Then what I realized is I could step back and I’ve actually three broad categories I could ask questions around to understand what it is that individual wants to actually physically see. There is a market and audience and a business angle. I tackle it from with every single individual, I tackle it from those three attributes. Within the market, I’m asking you like, yes, who are our competitors? Within that, what do you think their competitive advantage is in comparison to us? Long-term, short-term strategies within that market. Has anyone else grown? Are there new entrants you’re worried about? What is it that they’re doing? Have you seen anyone do a campaign that is really cool and you’re jealous that you didn’t do it? What are all of those things? Then I talk about your audience. I ask them, what is it that about your audience? How do that? Is this an opinion that you’ve had? Because there is a lot of like bias that occurs when it comes to data, because we just pick the things that go with our narrative. Then we just loosely reference it. As long as it confirms our opinion, we’re not really too fussed. I’m like, well, how did you get that? Is that actually empirical evidence? Is that an opinion that you’ve heard? Did you watch on TikTok? I love TikTok, don’t get me wrong. Yes, it’s not something I’m putting in my thesis as a reference. Then, yes, try to understand what is that about your audience today, tomorrow, what makes them the same as each other, but different to other groups. Then I’ll ask you around the business. The business, what is our growth targets? What do they look like? What have we spent this year versus last year? Are there any pain points that we are that we’re not able to change within the business? The last thing is that you want to suggest that we, oh, we should go and acquire another business. Yes, well, we’re in debt, so we’re not buying anybody, right? What does that actual cash flow piece look like? Then it allows you to understand what that individual or the business actually physically cares about. When I was talking about the CEO who cares about their ROI and competitive advantage, et cetera, I can actually physically answer those questions, because those are the things that they actually bothered about. Then there’s a loose one as well that hangs around where I ask them from a personal level, what is it that you need to prove in your role, right? As that individual that sits in your role, what is it that you are asked of in order to prove success within your role as well? Because I have to appreciate that the three market, audience, and business aspects is almost like a broader long-term piece that we will look at constantly. At the end of the day, we do have to appreciate that, as we say, we’ve got to win a pitch next year again. We, marketers’ tenures are shorter than ever. We’re job switching, et cetera. It’s all about, revenue today as opposed to growth tomorrow. I do have to appreciate that those things occur. Then I ask them, yes, what does success look like within your role? What are you asked of? What is asked of you? Therefore, have that in the underlying piece. Love that, mate. Thank you so much.

Speaker 1: I know that you bring the practical stuff. Thank you for sharing those questions. I can imagine those just coming out as a list and immediately being so valuable. Thank you, mate. I want to now take us to what would be the next stage. I think you’ve provided such a wonderful framing in the sense that, you don’t just go and gather data. you speak to folks who understand why you’re asking the questions. Then, that data gathering would happen, and you’re bringing it together in the space. You’ve got a group understanding of it. Then, inevitably, you end up at a phase where it’s like, Okay, cool. We’ve gathered a bunch of things. I want to get an insight into your brain. when you sort of sit there and there’s a bunch of reports or a bunch of data that sit in front of you, how do you then begin to process these things to bring around that storytelling arc that we’re

Thierry Ngutegure: speaking about today? Yes. This is where it’s quite interesting in the sense of our lived experiences and what we know of each other as humans. It’s a really bizarre thing to almost go like, well, how am I going to present data? I’m going to go understand human first. That really the person who’s going to be absorbing that. I like to take a bit of a leaf out of novelists books. Because I think they just have a really compelling way of telling a story in quite a nice way that we all understand. Isn’t it interesting that books and authors have an ability to tell a story that we can all read, understand, but also take away the message we want to take away ourselves. I just find it really compelling that they do that. I steal a little bit of structure from their perspective. Within storytelling, there is a four big pieces that sit together. There is like setting the stage. This is like introducing the context and importance. If you were to take that from like a data perspective, it’s like contextualizing where we are today. What’s brought us here? Why are we here today? Why are we talking about this? What has happened within the market that we’re all sat within this room speaking about this thing? Then I present the conflict. This is the problem or challenge that we’re all then therefore facing. We’re here today to address this problem or challenge. It might not even be a problem or challenge, like it might even be an opportunity. We’ve set the stage and then now we’re all presenting the opportunity, the conflict, the challenge. Then we’ll progress to some sort of climax. This is like key findings. At this point, I will have done a bit of research and therefore I’ll be presenting these key findings. Now, emphasis on key, because I think sometimes, like some of my presentations, I will have done a piece of research that, as I say, is 74 slides long. Then one. Emphasis on key, because there’s a lot of assumptions that we make when we’re presenting data like this. Data literacy is a great example of this, right? As analysts or even individuals who work with data on a daily basis, we assume that people understand and contextualize data in the same way as we do. A great example of this is there was a test done where they showed individuals the weather chart, right? They said, there’s a 40% chance it’s going to be raining on Saturday. Then they had a list of different understandings of that 40%. There were people who were like, well, is it 40% of all rain that could fall on Saturday will fall? Is it 40% of the day? Is it 40% of the area will receive the rain that is available? Is the entirety of the day, will it spend 40% of that time raining in 40% of the location, right? There’s loads of ways that you can chop that up. Obviously for us, we’re like, well, duh, it’s just a 40% chance of it actually raining on that day. It’s not well, duh, for everybody, right? The quicker we understand that, the better. Therefore we can start saying, well, these key findings, this is actually what physically matters. Sometimes the irony is I might do a lot of research and get loads of stats. I don’t even present it in a statistical format because I think there is also a lost art in qualitative data where we have, we’re too busy sticking numbers on things because we know, oh, it’s objective. If I put a number there, no one can question it, blah, blah. There is also a bit of a lost art and a respect that needs to get put back into qualitative data. I might find a statistic actually and go, oh, let me validate that with some qual data from our audience to get an understanding and show that in a different way that actually is better digested by my audience, right? Yes, progressing to said climax and showing your key findings. Then I’ll conclude with a resolution. Action points and decisions. One of the worst things I used to do was allow the individual to take away their own conclusion with data because I actually realized that we, the five of us would scatter in five different directions, but I gave them the same stat. Then here I try to empower them to say, actually, of those five roads that we could take, I do believe that the most powerful to the business of these two. If you’d like to go away and look into that, it is these two narratives or these two roads I would like you to walk with this data. This is the parameters I would like you to operate within. Yes. Setting the stage, presenting the conflict, progressing to climax, and then concluding with a resolution. That will go from a deck to a data visualization, Google Data Studio, Tabulo, whatever you want, everything. It follows that narrative. Then if anyone’s like a keen to get into the data, everything has a bit of a button that’s like push to Google Sheets and you can download a view of that and you can go away and look at it yourself.

Speaker 1: That’s incredible, mate. Thank you. Again, endlessly useful. to have four steps like that, super simple, super easy. I want to pick up on something that Sarah has mentioned in the chat, but also you’ve alluded to it with your answer saying that everyone will interpret everything based on their own bias, their own context, even their own privilege, as you and I were speaking about before we went live today. There’s a way of asking this question, which I’m not sure whether it’s the right words, but hopefully you’ll get the spirit, which is like, how do you take out your own, how much of a responsibility do you have to own the narrative and sort of say, this is what you should take from the data versus here’s the data, ta-da, take your own narrative from it because this is a group perspective and we’re going to figure this out together as a company. Do your role when presenting these things as saying, this is what you need to think about these things? Do your role as someone who’s like, okay, these are the facts, now can we have a chat about it and figure out

Thierry Ngutegure: what to do together? Yes. I’m very much more around, it completely depends actually, because you almost have to read the room. because sometimes I will come in and say, these are the facts. Can we chat about it? If I believe that the individuals within the room are open for a conversation about it, are open to challenging their own views, then there are some people where I’m like, they’re, the data I found is so far from their view that they won’t, they won’t hear it. They’ll be like, well, no, that’s not what I know about our audience. I’ve been in this job for 25 years and I know, what works and et cetera. I’m sure a couple of people at Nokia said that one time. yes, I, it completely depends as to, the people within the room and whether or not they’re willing to be open to that conversation. Now, if the, if they are open to that conversation, ultimately the greatest way of removing yourself and your bias or attempted to, there is no a hundred percent way. Right. I think there’s 150 unconscious biases, right? I think even AI at one point has probably got, it’s probably created a new one knowing that. Right. it’s, you’re trying to mitigate damage. The point of entry I go in with is when I’m looking at a piece of research or a piece of data, I will inevitably come in there with my own hypothesis. Do I think this is correct? Does this thing happen in life? Is it repeatable? Who has done it? Et cetera. I go in not to prove my hypothesis, right? The scientific empirical way is to prove yourself wrong. In what scenarios is my opinion wrong? Because then that allows us to actually go, well, there’s quite a few scenarios where this is wrong. Therefore actually we shouldn’t apply your opinion in these scenarios. Whereas I think that if you go in and you say, in what scenarios is my opinion correct? You will bend those scenarios to your opinion. I think, well, actually, yes, it’s correct most of the time. Yes, that’s absolutely fine. I think building a culture of trying to prove yourself wrong is actually really powerful and allows more of a scientific approach, to data storytelling and actually using data in a compelling way to make decisions. as opposed to, well, I’ve seen this statistic that says I’m right. Yes, I could, I could, I could give you one of those right now. That’s absolutely not a problem. There’s more research proving you’re right than there is, the way, try and build a bit of a culture of attempting to prove yourselves wrong. I think that’s really powerful. It’ll also build a really honest, candor within your business too, because people will be more than happy to go, this is actually wrong. Then I, oh, fantastic. In what scenario, what did you test it in? When we actually put this type of creative out, we initially thought that our audience really loved, was to put like trip, TripAdvisor or TrustPilot logos on there. actually the conversion rate for that really just didn’t work. They actually preferred, the content that converted better was actually the one with people, within the center of that creative. Oh, amazing. Okay. We know that during sale periods, the TrustPilot stuff might work, but during BAU, actually we should probably focus on the, on the human first, human centric creative. Let’s test that. Let’s test both of those going forward. Out of that, you’ve got two, you’ve got two opportunities for success there. that worked really well instead of going TrustPilot worked. All creative becomes TrustPilot logos on there. Let’s go. Then you’re like, why is it not working? No, it’ll work because it worked in

Speaker 1: that one scenario where I was right. Let’s go. Yes. Love it. there’s been a bunch of comments in the chat here, folks really, appreciating, your perspective on things. we’ve got Rachel sort of saying, try to build a culture and attempting to prove yourself wrong. It’s going to be my key takeaway. I absolutely love that. Thank you, mate. It feels really healthy. I think it just feels like a healthy, open-minded approach to things, which is fabulous. I want to take a question from Katie before returning to the narrative for today’s session. because, it loops into this idea of, being happy to be proven wrong. Katie asks, how did you gain the confidence to represent yourself and your evident passion for data in the way that you do? I think this is, you speak so wonderfully Thierry, how do you show up as you do and feel confident as you do? Because, that feels like actually quite an interesting sort of

Thierry Ngutegure: thing. Yes. I think that, especially within data, it’s really interesting because it’s a really broad umbrella. there are some people who are data scientists and, large language model engineers. Then there are some people within the business who just are the individual who can use Excel. Therefore they get the data title and they’re like, Oh, well, I’m trying my best equally on both sides. I think the way I’ve built confidence within data is admitting that what I’m not good at. It’s a really empowering thing because I, I’m not the best analyst in any way, sense of form. I could get ripped apart around, my thoughts around a cookie-less future and how should we be doing this thing, this analytic thing, this, Oh, this really intricate thing within this tool. How do you work that? I have absolutely no idea, but I know what should come out of that magic box and how to communicate that to audiences. That’s where I’ve always sat. I’ve always been really honest in that I sit between the people who can do and the people who are trying to understand what those people can do. I think that’s where the chasm has grown a little bit wider and wider over time. We’re struggling. we’re taking a lot of the humanity out of the things that we do, even with the presence of AI, et cetera, the quicker we get, we seem to actually be going away, from humanity, more. I do think we will come back soon. because I think we’re all struggling. We’re all feeling the pain, I ultimately just admitted that that’s not my bag. That is just not for me. That’s why I’ve built an incredible team who I tap into and say, Hey, this is the opinion of individuals around this tool around this way of working. What do you think? Then I’ll question them and go, right. Brilliant. Thank you so much for your opinion. I understand that now, instead of being this all encompassing, data individual who understands yes. Everything from large language models to, this law firms, Excel spreadsheet export, it’s impossible to cover all areas. Yes. Yes. I love it. No, thank you, mate.

Speaker 1: I love that. We’ve got, Celia in the chat saying love this positioning of sitting between those who do and those who try to understand. You’ve nailed that. Actually as a marketing point, that’s super easy to understand as well. Kudos to you as well. I think that’s the thing that I do well, if I can credit myself for anything in marketing is that I do framing well, and that framing is beautiful. Thank you. I want to take us back to, the process that we we’ve been discussing today and you you’ve alluded to it, but I’ll ask you an over question around, visual demonstration of data. you’ve spoken about the four different stages that you speak about, but how do you visually demonstrate this to stakeholders, picking up that you’ve already mentioned, the clickable links within, slides and stuff like that. Do you have any other tips for presenting back to folks, from a visual perspective, on new insights

Thierry Ngutegure: data? Yes, absolutely. There’s this, structure that I use from, extreme presentations. If you go to extreme presentation.com, they have this, like diagram, which basically breaks up, data visualizations in four components. It’s like comparison. When you’re comparing certain metrics or datasets, distribution. Showing how things just should be over time or whatever that might be. then there is like composition and relationships. The way things tie together or the composition of certain things or, competitors, et cetera. It’s really cool because then it shows you, right? If you’re trying to look at comparison, here are the charts that allow a really great visual representation. yes. Thank you very much, Josh. Yes. That’s that allow you to be able to understand the, how you should be visualizing that. That’s really powerful. I use that it’s literally the background of my laptop, right? In a way like helping me, can always be thinking about that. Then there’s the other thing around like human psychology, because at the end of the day, we are presenting this to humans and humans are really funny things because, yes, humans are really funny things because, we are all uniquely different, but we are also collectively the same, right? the things that make us different, obviously I lived experiences, et cetera, but she, at the end of the day, even from a genomic perspective, we’re not that different from each other. Then things that help, well, things that make us the same can also help us tell this compelling story to many people and make it apply. For example, if I, if you saw the color red Joe on, a presentation of a chart, what does red say to you? Bad things. There we go. Right. It’s almost like a collective behavior that we all therefore understand. Even those little things, visual cues can really, really help. They’re called preattentive attributes that allow us to tell a story before anyone has read the story. Read immediately. You didn’t have to see the story. You’re like bad. What is going on? Right. Then there are things like, shapes or circles, squares, triangles that can be used to, identify different groups. If you saw those on a different chart, you’d be like, well, circles are all circles at the same, right? Squares are all the same triangle. Immediately. There was a narrative there that you’re like, Oh, these are different groups. Right. there is also sizes. If something is larger than something, it means that thing has a larger value, no matter what it is you’re trying to present. The circle is bigger. That is bigger than that. Great. That makes a lot of sense to me. there is also like placing, so relationship hierarchies. If I put something on top of another thing, that’s more important than the thing that was below it. Right. That actually helps. Then there is motion as well. If we’re talking about something over time, if something moves from left to right, I’m like, well, that is time has occurred or distance has been traveled. Loads of those little like pre-attentive attributes can occur before you’ve ever read any of my data before you’ve read anything from the report, you’ve already taken away something. Right. We always say that like sometimes, body language is X percent of actual communication. Similarly. Is that as well. then take advantage of the fact that we have a collective understanding of things, like that and, and use it within your presentations. Mate, that’s,

Speaker 1: that’s just fabulous. It’s funny because when you describe it like that, you go, yes, of course, get it. Have I ever put a presentation together like that with those things in mind? Have I bugger? Thank you. That’s actually really useful. Something I’m going to sort of think about an awful lot, going forward. it strikes me that you reel those things off so easily because you’re coming from experience and, you’ve done these things and you understand them. If folks want to find out more about stuff like that, where would, do you have any resources or is this just stuff that you picked up over the course of time? how do you start learning about that sort of stuff? Because that’s fabulous and useful, but I want

Thierry Ngutegure: to know more. Yes. I’m just, I was going to say I had the book here, but it’s actually And so weirdly enough, I don’t read any books about data. I read books on how to tell stories. I, you read books on, human psychology. go and read up things on things like color theory or read upon things like, how to use customer, how you can use psychology to understand consumer behavior, books that circle around, understanding humans as a whole. Then you’ll have more takeaway from a data perspective, because my fear is that the understanding of data and the way we’re moving within the, within the world is so quick that take GA4 for example. Somebody put that in the comments earlier on GA4, we knew it was coming. It was something that was a little bit different. We panicked and then we’re like, Oh, I’m going to have to do it. Then every time we open GA4, it looks different from what it looked like last week. You’re like, this thing is a nightmare. Right. It, by the fact that it’s continuously shape-shifting, I cannot keep up with that. What I can keep up with is human understanding of what to get out of that. I understand that great. It uses machine learning. It can plug the gaps when, data is not there. Fantastic. Now, not only, now I can actually set up my own custom events. Therefore I can see exactly what people are clicking on the website, how long they’re watching the video, et cetera. Right. I can see more, right. What questions do I need to answer? Okay, cool. Now I can do that. I can do that much more, much faster in understanding the human behavior side than I can ever keep an up, with the, with the progression of a specific tool or a way of working or engineering. yes, my brain can’t comprehend people who can do that.

Speaker 1: I honestly hats off to them. don’t take this for it all the time, but there is like the Bezos quote isn’t there, which is, don’t focus on the things that are changing today, focus on the things that will stay the same in 10 years. That’s how you build a strategy, very paraphrased, very misquoted and don’t take it as a hundred percent of the situation, but, humans are humans, as you rightly point out, I’m now going to ask you a question, which goes to the exact opposite of this because, we mentioned that we might speak about AI in today’s session. by the way, folks, I just want to point out that we’ve got about 18 minutes left. The intention is that we’ve got nine open questions in the Q and a, as well as at least one good one in the chat that I’ve seen already. If you pop any questions in the Q and a that you’d really like answering, we’re going to do our absolute best to get through as many as possible towards the end of the session. Please do use the Q and a feature to do that. I want to ask you about AI probably just to finish off my bit on here and sort of ask you about how that’s impacted your role, the tools that you’re using, that is completely opposite to the point that you just made about humanity and keeping up and focusing on the things that stay the same.

Thierry Ngutegure: Yes. What I love about AI firstly is that it’s actually completely, leveled the playing field. Right. By that is there was once upon a time where if you wanted to even manipulate data in any way, sense or form, no matter what platform, that you, that you have, you have to go to an individual to do that for you because it took a level of, let’s say technical understanding of this thing can do this. I can move this here in order to get your answer. I need to click these buttons. what AI I think has done is leveled the playing field from a perspective of that can be done quite competently by anybody now. Right. Anybody who feels empowered can go and have a look at that. There’s a website called there’s an AI for that, which does this quite cleverly. You can go on that website, and you can type in data analysis or, I think I checked by this morning, it was 182 AIs that will help you do that. Then there is a data visualization perspective. There’s about 34 of those, that are in there. Yes. Thank you very much, Josh. I’m just the unsung hero of today. Let’s connect after this. I love this energy. You can go on there and quite literally just type in and then, sort it by the most liked, which is typically like the most saved, tool. You can see what other people are doing. That allows you to level that playing field from the perspective of manipulating sent data. I can put data into that thing and it will manipulate it for me. There is still human intervention because at the end of the day, this thing has no contextual understanding as to why you’re putting this data together and sometimes can make statistical errors. It just can. You can double check his homework, but it’s really good at that. You can say like a teacher, show me you’re working out. If the broad aspect of how it should have got there, you’re like, right, cool. That makes sense. It means I don’t have to physically do the insane amount of data I would have had to do manually. It’s done that for me. It’s really leveled that playing field. What we then do is if you’ve ever seen the analogy of the Lego bricks into a house is it’s allowed us to understand what color those Lego bricks are and what is completely possible. We then become overwhelmed and just produce the data visualization that we all have. We’re like, that’s safe. Thank God. Yes. I’m glad I did that. Whereas I think the storytelling bit is a thing that AI still really doesn’t do too well, because if I tell it to like pull 10, 15 insights from that data that you’ve been given, it has hallucinations. It’ll just brought like bare face lie and be like, this thing is this. I’m like, well, it isn’t because I didn’t give you that data set. They’ll go, oh, sorry, Thierry. It’s a weird dystopian thing where I’m like, are you in the room? Why are you apologizing? Yes, it still does really struggle with the actual understanding of the story that I’m trying to get at. I still will, I would say to individuals, please do use it with caution, but use it for more of that manipulation of data, that first bit of getting it into a place where it is useful to you at speed on a broader scale. It’s really powerful for that.

Speaker 1: That’s fabulous. Thank you. That’s really great advice. Thank you very much. Let’s head into the community questions. There’s been some chat in the Q&A. There’s been some chat in the chat about folks not being able to see other people’s questions. I can show you there’s 11 open questions right now. If you would like to add yours, then by all means do. Thank you to everyone who’s added in your questions so far as well. Zoom changes things. It’s weird, eh? Anyway, so let’s get going with the first one. This is going to take us all over the place, Thierry, but you can handle this, you being you. First one from Sarah. How do you manage the demand for vanity metrics? Do you push back or try to redirect it to different

Thierry Ngutegure: metrics? Yes. I try to redirect it by understanding why is it they’re using a vanity metric. Vanity metrics are just, they’re really powerful because they allow a holistic understanding from everybody as to what I’m trying to say. Then we all understand it’s a vanity metric, but yet we still constantly use it. We’ll try to redirect them. It might even be really powerful for you to build a list of what you perceive to be vanity metrics and then what those vanity metrics show. Then you can build almost like a matrix or a table to show, well, if you are using this vanity metric and you’re trying to understand this thing, here are three other metrics that are actually way more powerful. We maybe can get it on a, so they might be using that vanity metric that only moves like a month. You might have found a metric that does the same thing that does it weekly or et cetera. Then they can be like, oh, actually I can keep a tag on this and actually view it on a more granular level. It allows them to make that connection that actually, the thing that I’m looking at is comprised of three more important

Speaker 1: things that I’ve just not paid attention to. Love that. That’s wonderful. I think the important thing about that as well is I love how you demonstrate that you listen to them. You didn’t say, no, you’re wrong. I’m going to do this. You’re like, well, thank you for sharing that. With my experience and knowledge, then we, I think there might be something which can help you better. I just love that as a, that’s a helping thing rather than sort of a wielding power over someone else, in a way, which I think is really

Thierry Ngutegure: fabulous. Thank you. I definitely agree. On that point as well, with regards to like power, I think that this is, this is, it’s almost been a power dynamic actually. From an analyst perspective, you’re producing data, like it almost is the power, right? You hold the power. I think individuals feel a little bit overwhelmed because I have to go to this analyst for a thing that I can’t understand. This person is like, why are you not understanding this, you idiot? I’ve given you a 75 slides, what the hell’s going on? It becomes a bit daunting. Therefore I won’t go to you to understand this thing because I already didn’t understand it. Now I’m quite fearful of the way we approach this thing. Then it creates like these groups that constantly are moving further and further away from each other. I think you are, you’ve hit the nail on that head with regards to that empathy piece of actually, like I’ve been to businesses where their analysts sit in hermetically sealed rooms on their own. I’m like, you failed immediately at that point. They physically have to travel a distance in order for someone to even talk about it. Right. The first thing I ever do as an analyst, I literally sit within PR team, social teams, like HR teams, ops team. I just sit down because as soon as that human to human connection comes, I get the tap on the shoulder and go, dear, I was trying to pull this report. I’m not quite sure if I’m doing this right. I’m like, we’re doing it right. Here is three ways you can do it faster.

Speaker 1: even just being with people. Yes. Yes. I love that, mate. We got Sarah in the chat

Thierry Ngutegure: saying free the analysts. I’ll get t-shirts made, Sarah, right? The problem with you is you

Speaker 1: bloody well will. Don’t challenge me. Yes, no, we don’t need to challenge you. Cool. We started off today’s session and this is a version of the last question, but we started off today’s session with this. You spoke about the problem that Christine is also speaking about in her question here, which is how do you get marketing to start thinking beyond the short term goals when there’s so much pressure to generate those MQLs sort of pipe, that is probably the million dollar question in a lot of ways. Could you share any experiences that you

Thierry Ngutegure: have in that? Yes, definitely. I’ve got great resource for this. Dr. Grace Kite and her team at Magic Numbers are frigging phenomenal at this, right? What they do really powerful and succinctly is that they look at historical data to show the long-term benefits of specific channel activity in a really nice and succinct way. How do I stop you thinking about today? I proved to you that had you actually thought about this with a long-term view five or so years ago, this is where you would be right now. The investing market is a great way of doing that. I follow a lot of investors because they really communicate that in a great way and go, right, well, if you’d put a hundred quid in Berkshire Hathaway 15 years ago, you’d actually be a millionaire today. You’re like, damn it, if I just not bought that latte. It’s a great way of showing like success would have occurred had you took this long-term view a while ago, but it’s not too late. You can still do that today. the team at Magic Numbers and Dr. Grace Kite do that in a really powerful and compelling way. Absolutely. For those of you

Speaker 1: who are interested in going back. We’ve had Grace speak twice at TMM and Joy from her team spoke last week on- Yes, they are frigging awesome. They are, they’re absolutely incredible. Thank you, Josh. Josh is a popular boy in the chat today for linking up folks to Dr. Grace Kite and the team from Magic Numbers. They are great. In fact, not being paid for this, but they’re doing a course later in the year for those people who would like to understand data works as well worth checking out. They’re good people over there. Let’s take the question from James, because I think this is a pertinent one to folks. Oh, Josh, he linked last week’s webinar as well. He’s doing great. James says, what’s your advice for a marketing manager who needs to gather and analyze data to drive decisions for the wider business, but isn’t well-versed in data science analysis, et cetera?

Thierry Ngutegure: Yes. This is where I would lean on third-party research sometimes. Walk do a great example of this, for example. They are, them and like the likes of Satista, et cetera. Let’s assume you’ve already got your questions that you want to answer. I would, I want to understand whether this channel works for my audience. I want to understand whether my creative is performing, whatever, right? The channel aspect, you can lean on quite a lot of third-party research, whether that be on something like Satista or Walk or Mintel, but Mintel is a little bit expensive, but there is a lot of third-party research out there. You go, for example, do a lot of free statistics on understanding the general population and what’s going on. There is things like Hype Auditor and free tools that help analyze your audience to get a bit of a broad understanding. A lot of third-party tools you can lean on from that perspective. Then there is a perspective then of like your own internal data, keep it really, really simple. Within those questions that you’re trying to answer of like, is my creative performing, for example, well, what data do you already collect? What is it that you can physically see? What can you afford to pay for? That might be nothing. I can’t afford to pay for any other tools or any data. That’s absolutely fine. What is our first-party data understanding? What are people already giving you and what do I understand about them? Then don’t get lost in that data once again, because you can just look at all that data and feel overwhelmed and be like, where the flipping, how do I even stitch this together? This makes no sense. Go back to those questions that you were trying to answer and then nitpick to like the nth degree, the three things that truly matter and that you need to get buy-in from your heads of service in order for either business growth or business decisions. For example, you’ve said that business decisions, like what decision is it that we’re making there? Is it how much we’re going to be spending next year? Great. How much have we spent already on TikTok? What was the conversion of that? Because you already have that data, right? Yes, be really succinct in what it is that you’re looking at, especially if you’re a one-person team and you’re maybe not as data literate. Another powerful thing as well is like, if somebody within your sector who is doing this really well, DM them and ask for an opinion. I have no fear of, no one’s DMs are safe when it comes to me asking for help. Make sure that you DM absolutely any business opportunity I’ve ever had, any job I’ve ever had, et cetera, has come directly from me reaching out to other humans and saying, I have this problem. How would you attack it? I’ve seen you do it really well. Talk to them. I’ll even talk to my competitors and just say, what you did here is sick. I love it. How can I replicate that myself? You’d be shocked to see the empathy that people have when you reach out to them in a really meaningful way and ask for some support. That works really, really well. I can see there’s some, there’s other people as well who have put in some tools. Yes. Mintos, Autista, yes. Otter, et cetera. Yes. There’s loads of tools out there that will help you. Once again, I posted it earlier, but I’m sorry, I post, I’m taking Josh’s credit now. You can’t take the credit for Josh. Yes. There’s an AI for that. That’s really powerful as well. Put that in there and have a little play around with some AIs who can, analyze and succinctly give you that data. Once again, be careful what data you put into these AI models. There’s been loads of examples where, big business data has been put in there and it has the ability to be accessed by God knows who and what and where. Just be super careful about what you’re uploading into these systems. If it’s just a broad, general help, they are usually quite good

Speaker 1: at that. That’s fabulous. I don’t know whether I’m doing you a favor here or not, but it struck me when you were saying reach out to folks, then you should connect with Thierry on LinkedIn. Actually, what, you are a generous man and I know that you give

Thierry Ngutegure: what you can. Yes, it benefits me as well. I learned, like the amount of things I learned from trying to help individuals. I’ll promise you, if I don’t know, I’ll just say, I don’t know. I’m, that is, that is my superpower of going No. That’s not for me.

Speaker 1: That’s fabulous. I think your answer, hopefully I can see a couple of questions here, so I’m going to read them out, but I think your answers hopefully reassured some of the folks with a couple of more of the questions. The questions that I hope that’s also answered is as someone that worries about not giving enough data to make a point, how would you pair it back so you’re not data hoarding? Then there was another one, which was as a certified sparkle hearts, marketing girly, don’t quote that up and clip that up for, how can I get to grips with handling the data and challenging myself, to be the one who looks at the data and tells the story accurately whilst feeling hugely out of my depth. I feel like your answer there for me, when I was hearing you speak, spoke to focusing on the data, that’s going to, help you, probably picking two or three data points and just focusing on those things rather than feeling like you have to look at the, the totality of everything, because it can be overwhelming and it can be scary. I’m not a data person. I wouldn’t characterize myself as that, but, that feels achievable to look at two or three things, which is really useful. Thank you. we’re coming to the end right towards the end, actually. probably a quick question, but, as Josh, has been the MVP of today’s session, with the chat, then, we should take his question in the Q and a, which is Josh asks, you obviously started Mother Nutter from scratch. this was your peanut butter brand. where did you start from a data perspective for a new business, as opposed to working with established brands or

Thierry Ngutegure: companies? Yes. For a new business, this is my favorite, favorite thing to do. I always go towards, an audience problem, right? Take, Mother Nutter. The problem within the market was that, when you, so I did a lot of market research, quant and qual to understand, do people even buy peanut butter online? Do people even like peanut butter? What I actually found by specifically asking my audience was that typically peanut butter, isn’t a thing that people, when you first start eating it, you basically it’s in your cupboard. you inherit it from your parents. You inherit it from a partner. You develop a taste for either crunch or smooth, and you stick to that throughout life. There isn’t really any fluctuation within it. There’s been no real great culinary experience with peanut butter over time. It’s practically, remained the same. The only fuss we ever kicked up on was, I tested whether, having palm oil or non-palm oil, stickers on the, on the product actually meant anything. Ironically, not that many good peanut butter brands actually use palm oil anymore. If you do not signify that for your audience, they won’t purchase it. It’s the thing they’re looking for, even though it was never there. It’s not a practice that we use anymore. Right. I immediately looked to my audience, even though I was the customer I needed, you need to validate that to a wider audience and understand who is it that you’re creating this for? What matters to them and what levers can you therefore pull? What I therefore found with regards to peanut butter was that there wasn’t really an engaging brand, right? Liquid Death have done this. Liquid Death’s problem was like, how do rock and roll stars drink water on stage while still looking cool? That’s ultimately what they’re doing, right? Liquid Death was born. They rebranded literally fizzy water. There isn’t anything fancy around it. They really understood what is it that their audience wants to signify when they are drinking that water. I applied the same thing to Mother Nutter, which was like a, quite a cool edgy brand, that stood for something. Peanut butter was just the way we spoke to you. it was an excellent product from the ground up. it allowed you to pull out a jar and individuals immediately know who you are. That is, that’s the why we’re buying Nespresso machines and, the full on barista thing, are you a capsule guy or are you a full on bean gentleman? It signifies things about people. Yes, really understanding your audience right at the beginning

Speaker 1: is super, super powerful. That’s fabulous. Thank you, mate. just a wonderful way to end today’s session. As you’ve been given it, as you’ve been given your answer there, there’s been a lot of love thrown your way as well, with the usefulness of today’s answers. Thank you. you’ve been so generous with your knowledge and your time today. Thank you. Thank you to everyone as well for watching in today and just been so blooming wonderful. it, these sessions matter because you show up as you do and the fact that you’ve shown up so wonderfully, is just fabulous. It’s such an enriching, elevating experience. Really appreciate all of you. before we head out today’s session, I just want to again, say thank you to Sticky Beak who are this week’s featured sponsor. absolutely go and claim that a hundred percent off a hundred percent off a hundred pounds off. Don’t quote me on a hundred percent off. That was not what I said. They’re very lovely people. when you get the follow up email, if you could say thank you to Anna, who’s a CEO as well for sponsoring us, then we can continue to bring these sessions, to you. also we’re back next week with a previously unannounced session, on, with a very lovely man called Nick, who will be speaking to the four pillars to boosting your marketing effectiveness culture. A little bit around the themes of what we’ve spoken about today, where we’ve been focusing on, trying to get other folks understanding how marketing works and building that sort of long-term marketing thing rather than a short term culture. I say all this as, the chat is filled with thank yous for you Thierry. Thank you. Thank you everyone for joining today. we’ll hopefully see you next week. take care a brilliant hour and, we’ll see you very soon. Bye. Take care.