#18 What is analytics maturity today? (with Steen Rasmussen)

The Measure Pod
The Measure Pod
#18 What is analytics maturity today? (with Steen Rasmussen)

This week Dan and Dara are joined by Steen Rasmussen from IIH Nordic to discuss how to approach and access analytics maturity in the modern era since COVID-19.

Check out IIH Nordic’s 4-day work week over at https://bit.ly/3HTtjyr.

In other news, Dan measures himself, Dara stays in and Steen gets social!

Please leave a rating and review in the places one leaves ratings and reviews. If you want to join Dan and Dara on the podcast and talk about something in the analytics industry you have an opinion about (or just want to suggest a topic for them to chit-chat about), email podcast@measurelab.co.uk or find them on LinkedIn and drop them a message.


[00:00:17] Dara: Hello and thanks for joining us in The Measure Pod, a podcast for analytics enthusiasts where each week we dive into an analytics topic or a challenge, or we discuss an opinion. And we always try to have a little bit of fun along the way. I’m Dara, I’m MD a Measurelab. I’m joined as always by Dan, who’s a Lead Analytics Consultants here at Measurelab. So this week we are privileged to be joined by our first external guest who is Steen Rasmussen, who’s a co-founder from IIH Nordic. So Steen, firstly welcome to The Measure Pod, would you like to just say a little bit more about yourself and your background?

[00:00:57] Steen: First of all, pleasure to be here. I’ve been looking forward to this, I’ve been hounding you to be like first star guest appearing, right. But from my side, it’s really because I feel I have an agenda in the analytics space. I’ve studied public relations, a combination of business economics and language. And that was kind of my entry moving into digital. So I started as a copywriter, moved into usability. And I was so ridiculously frustrated with usability because there was somebody being the ambassador for the user, but nobody was being the ambassador for the business. So I started having a commercial focus on websites and analytics was core in that. I cannot program if my life depended on it. Well, maybe I could, but what I can do is, is look at the numbers and potentially turn it into business and bottom line value. And that is kind of where I see analytics needs to go.

[00:01:52] Dara: Completely agree. And I’m saying that as a fellow non programmer working in this industry. We seem to becoming a rarer and rarer breed I think as time goes on,

[00:02:01] Steen: I think actually a part of it is, well, I’m not as young and fresh as you guys right. And back in the days when I started the digital, we were just a bunch of guys who happen to be at a place in the time. A ragtag bunch of random people just watching up in analytics and digital. And that’s kind of fun because it makes the subject really open, but it also challenged the standards. So I think that there’s a huge work in standardizing our approach, making analytics boring.

[00:02:37] Dara: You were quite a little modest, there Steen. I totally appreciate the background and kind of almost. I was describing myself this way, as well as almost kind of falling into analytics. But since then you have gone on to build a very successful company. Do you want to just give us a little bit of a summary of IIH Nordic?

[00:02:53] Steen: Uh, well, 20 years ago I started a company called Deducta with two other guys, and the first person we hired was Henrik the current CEO of IIH Nordic. And Henrik and I found out that we thought that the key success criteria of success for our company was customer satisfaction, and not necessarily shelter and profits. Because the customer satisfaction would determine the long-term profits. So Henrik and I left to found, uh, IIH Nordic, where we now working, covering Scandinavia, have some amazing international customers, really with this data-driven approach saying, how can we do more with the data we have. Because that was really one of the key foundations when we started the company saying, well, there’s so much data being gathered. And a lot of it was just saying, okay, look at this five is bigger than nine. Okay cool, move on. Like really you’re stating something stupid or really obvious. And then before analyzing, actually going onto another graph, right. So going in and actually saying, how can we leverage this to actually create some business value, that was kind of the cool thing. We also had another ambition was we wanted to create the best place in the world to work. We kind of disrupted ourselves along the way and said, hey we have all this data. And we’ve, if we turn the focus on us and analyze ourselves, what can we get out of it. And that began a journey toward a four day, 30 hour work weeks that we’ve been running since. So the idea that by using data internally and not just for marketing purposes, there are immense resources that can be unlocked. And that’s kind of a huge ambition. And today we we’ve worked with enterprise customers across basically the world. That’s how sexy it got along the way.

[00:04:45] Dara: So what’s your topic or what’s your opinion Steen, that you wanted to bring on the show today and discuss, or maybe debate with us depending on whether we, we agree or not.

[00:04:55] Steen: So over the last two years during COVID, there’s been this talk that things have changed and move forward in e-commerce and everything, and seven years of growth in two years and all this. But one of the things I do not think have followed this is the concept of mature use of analytics. If you do a Google search for digital analytics maturity models, or web analytics maturity models. Well, from my perspective, I don’t really feel these models are mature anymore. That there’s a level of complexity that is missing from our understanding of what a mature set up is. And that’s kind of the conversation I wanted to have you guys, because I think with the experience we have together across multiple customers and our understanding there. How do we define what is a mature customer, and what is kind of the key aspects of that? So, yeah, bring it on.

[00:05:54] Dara: And I guess I’m even thinking of a question before that, which is, can you even define what analytics maturity is?

[00:06:02] Steen: Now you’re teasing, but from my perspective, so one of the things we like to say is we want to work with ambitious customers, right? And for us, how do we define that? We define it in a sense it’s the customers that are not just interested in a fix, but actually momentum. And I think it’s one of the things that I see in the models right now that are missing, because they’re statics, all of them. So there’s no speed of change, or there’s no momentum. So yes, I would say there is a way to understand it, but it is much more complicated. There was a couple of models out there that has aspects I love. Symphonic did one of my original ones I loved because all the other models start at zero. So whatever you do, you become more mature. The symphonic model starts at -1. So, if you don’t do anything, you’re actually not mature at all, you suck. And I like that because otherwise people get this Dunning-Kruger like perception that they’re kind of cool. But they’re not, because they haven’t really done any of the analytics activations. Like, look at me mommy, I have Google analytics. Like okay, congratulations, but it’s set up wrong and it doesn’t work. You don’t get a gold star for that, right. So, so having this understanding that you actually start below zero, and have to get the basics in order, and then you can start talking about maturity. I like that.

[00:07:32] Dara: Yeah, you’ve got to put a bit of work in first, before you can start to actually say how far along you are. The other thing I liked about what you said, or at least what resonated with me was about how often it’s a static approach. And I think we collectively, as an industry, can be guilty of falling into the same trap, maybe the companies do where you think of it as a fix, you think of it as like somebody will, will phone you up and say, oh, you’re an analytics consultant, can you fix my analytics for me? Or can you make me mature? And it doesn’t obviously doesn’t really work that way because it’s a fluid thing, and by the time you’re finished, or you think you’ve finished, you actually need to go back to the start again. So all of these models that assume you go through these five steps, or whatever number of steps, and then you’re finished well they’re doomed to fail really aren’t they.

[00:08:18] Steen: Especially because one of the things we talked about a bit, when we talk about people coming from ragtag backgrounds. So I go to you guys, I’m a customer. I tell you to put an implementation of analytics on my site. You do it, and I don’t hire you to maintain it. And in awhile I leave the company and somebody else comes in, and look at it and say, what the hell is this? And then they cancel everything and throw it out. Even though you scored seven on the maturity model. Because they don’t understand that they come from another background. So it’s the big reset. Instead of actually building on what is there, then we keep rebuilding or reinventing the analytic setup because we have no coherent religion in this.

[00:09:05] Dan: I think that that could be this kind of gamification of this score. And it’s like, I need to take my client from a three to a five, whether they want it or not, or whether they’re ready for it or not as well. So quite often as analytics people, we’re trying to push this improvement on our customers, even if they’re not fully aware that this is happening. And then we have a sense of pride. I got this client from a three to a seven and we parted ways on good terms, and it was all lovely. And like you said, Steen, which we see time and time again is you work with someone new and they’ve had a full analytics implementation set up two years ago, but it’s just aged and kind of crumbled where it hasn’t been maintained. But they thought they did a really good job because they got to a level seven. And I really liked the way you phrase it earlier about the momentum of change, that mindset of change. It’s around the desire to improve and change rather than just jump a step and continue as you are.

[00:09:57] Steen: And for me, in that exactly line of thought, one of the things I like about it is also saying it’s the sense of urgency if something is broken. That’s actually a sign of maturity in my part as well. Do we respond if somebody say, oh my God e-comm is not tracking. It’s like, yeah, it’s fine. We’ll get somebody to fix it. We’ll put in the budget for next year. That is not mature. Oh, then they go out and say, yeah, this is broken. It’s really business critical, but we have to ask seven agencies because we want the cheapest. That’s not mature, right. We had a customer that actually went in, when we were working with them. There was specific assignments in there, but what they also told us was add 20% extra for eventualities that hasn’t been invented yet. Because otherwise we cannot maintain our momentum digitally if we can’t adapt to the changing world. If there’s a new flip-flop channel coming up in social that we need to adapt to, right. And it’s not like, yeah, well we have to go put that into next year’s budget. That’s another factor that I think could be interesting looking at. Now I’m throwing too many factors in here, but it’s suck it up, boys. It’s the business savvy of it, right? So you can either be budget driven or business driven.

[00:11:20] Dan: So these are the prongs within this maturity model, right? But what are the component parts of this? When you’re looking at data maturity or analytics maturity, what are those component parts that you would likely assess to get an understanding yourself? Even if you’re not scoring a numerically, where would you look at, or where would you go, what questions would you ask?

[00:11:40] Steen: So, so I would look at several things, right. But the first thing would be like integration of external data sources. Are we adding more than our own bucket? That is for me something too, to see if we’re thinking bigger than our own tracking. That would normally be a sign of maturity, saying yes, we have added Google Trends to our data set. Or we added some other external data set, market data from our vertical. That is hyper mature because we have a tendency in analytics to just focus on our own bucket on the website. So that will be one thing saying how much do we enrich the data that we have. Do we just focus on doing a nice clean analytics set up, or do we understand that business is in context. So we need some external information as well. So that will be one thing, but this is just still data gathering, right? The next level is visualization. When we do visualizations, do we do them, and reporting on that level as well, but visualization with dashboards and reports. Do we do them with in mind? When I do presentation I have this saying, there’s two types of dashboards we can make. One is the machine next to the patient’s bed in the hospital telling us that the patient is alive. That’s one type of dashboard they’re really common, there’s not a lot we can do with them. And then there’s the other type, that’s the GPS where a defined a destination where we’re navigating towards that and really using that to understand momentum again. How fast are we going towards our thing. These would be two things, and then finally for me, this is the new kid on the block. And especially with getting obvious with GA4, is the data activation. How much are we integrating things toward being able to use analytics as an asset directly? So we don’t need to analyze and give somebody a report, and then they will take it to their team and they will implement it. And we’ve just lost the time momentum that analytics can actually go in and redefined segments and change the site and do all this magic stuff that is actually available now, pretty much out of the box with GA4.

[00:13:53] Dara: That’s true maturity isn’t it? You were talking about almost getting analytics to work for you, rather than the other way around.

[00:13:59] Steen: And I really see this as a maturity sign. This is where analytics needs to go, because that will be our justification. The web data, it’s just one data set. We have this image of walking down the analytic stream towards the data lake. And we’ve been feeling really cool because we’ve been walking in front. It’s like, yeah, look at us, we’re really cool at analytics. And then we come through the data lake, and then it’s just BI has already built hotels and everybody is lying in swimming in the pool and with motorboats and we standing there on the edge looking, oh, okay. Apparently we’re not first like we thought. The danger is getting sucked into one of these other frameworks that has a stronger foundation.

[00:14:36] Dara: But is that actually hinting at a problem as well, where this is still maybe viewed in silos. So you’ve got different stakeholders, both internally and maybe different external agencies or partners, approaching this from different angles. Do you think there’s a general lack of a kind of consistent approach to this?

[00:14:55] Steen: I think everybody would like to take ownership of that approach. So we have like a Game of Thrones like situation with the fractions fighting from the lake, because that’s kinda our future. Are we going to be obliterated, or are we actually going to survive this and have a chance? So not just being the nerds in the distant corner of the fancy BI office, that provides this dirty data that nobody really likes. So what is it that we bring to the table that they can’t? And I actually think that that is exactly one of the things, that we can work with dirty data. We can work with imperfect data. You give most BI people the data set that you work with on a daily basis, they will break down crying because It’s ugly and full of errors and bad. And it was like, get it clean, take it away.

[00:15:42] Dara: It’s so true. It’s back again to the route into this space that we’ve taken. It’s been scrappy. And when you’re, when you’re used to work in that way, you kind of become almost comfortable or at least familiar with dealing with problems, because no implementation is ever right. You’re always dealing with data discrepancies, dirty data. But we’ve had to just get on with that haven’t we. We haven’t had the luxury of having clean data.

[00:16:06] Steen: No absolutely. And I think It’s the kind of trap we fall into sometimes, that we get distracted by perfect instead of being focused on outcomes. And then for me, again, could be a maturity consideration. Are you mature when you accept that we won’t have perfect data, this is good enough, so now let’s do shit.

[00:16:29] Dan: It’s just making do right. We just really good at making do with what we’ve got in front of us. And you’ve mentioned GA4 Steen, but as tools change, we’ll pick it up and we’ll use it and we’ll figure out different ways of applying it. And maybe it will be better, maybe it’ll be worse, maybe it’ll just be different. And I think this pursuit of golden data is a fruitless task, it never works. And trying to have this endless cycle of cleaning and getting perfectly validating data of like, you know, we’ve got 99.9% of our transactions in GA, but it’s not good enough, I can’t use it. Half the time, we’re just like, yeah, it’s about 10, 15% out, but it gives us this and we have to take this with a pinch of salt, we have to understand this caveat, but we can do this with the data. Compared to other people we’re kind of scrappy, we fell into this. We’re just using the tools that are provided to us. And, and often these are marketing tools that we’re using in sometimes not marketing context. I know a lot of people that are using Google Analytics to track just content consumption on websites and apps. And, it’s built by a marketing company for marketing purposes, but we’re taking it and we’re making it do what we want it to do. And I like that attitude and, and making it work for us, as you said, Dara.

[00:17:34] Steen: I think, again, this is one of the big things saying this also goes beyond the marketing side. So, so what can we actually do with analytics? And I think this is one of the mixed frontiers for us saying yes, it’s been living in the marketing department. The CMO has been paying our bills and stuff, but can we say something about market demand? Can we say something about like or dislike of our product and our brand? Very often, if I gave you the question saying can you tell me this, then if like yes, I probably can. Or you would say no, but I could find out. I could make a setup that could catch this. It’s sometimes it’s about asking better questions, or expanding our horizon. And going back to the maturity thing, I see that as a maturity sign, as well, in relations to being open to adapting the data to the business. Because I’ve been in a lot of meetings in a lot of organizations where people have been saying okay, cool. Can you give me a customer journey? And then it’s like no, I don’t have the data. But that’s the wrong answer. It’s well, I can’t right now, but if it’s important to the business and you provide me the budget, I can. Because that is actually what we can, we had we’re scrap collectors. We can build anything out of data if we just get the time.

[00:18:55] Dara: Is there a risk there as well? Something that I’ve certainly seen, I’m sure you have too, where we can overcomplicate as a result. So I’m totally with you on the being scrappy and work with bad data, and being able to figure something out. But one thing that I’ve always thought was interesting is you sometimes see people in our game try and over-complicate almost to show what they can do. And go back to something you said earlier, even on the client side where they might have lots of additional data sources and they might have lots of reports and dashboards and everything else, but often they’re maybe surplus to requirements and it’s almost kind of, over-engineering rather than asking the right questions. So almost, I guess what I’m getting at is, can a sign of maturity sometimes be understanding that less is more? And it’s not about having more tech, more solutions, more dashboards, but actually having fewer questions, but the right questions and having the right data, even if it’s not perfect data, but having the right data to answer those questions. And make decisions, take actions.

[00:19:51] Steen: Yeah, absolutely. I was thinking in relations to kind of saying, how do you use the data you have? For me, an immature version is this is about the website. Then the more mature version saying this is about digital. And then where the actual version comes in and saying, how can this be integrated in the overall strategy? So where we remove all the website and the digital stuff and saying well, analytics is actually something that is supposed to support and provide momentum in relations to our overall business strategy. So there’s no such thing as a digital strategy detached from the business strategy. If you have business goals, then a digital strategy should be developed around the business goals of the overall business strategy. That is probably also one of the classic immaturities, is that you have a digital strategy that is not aligned with your business strategy.

[00:20:46] Dara: So here’s a question for you. What do we do about that if that’s the case? Because we don’t necessarily have much sway, if we’re working with a business where their digital strategy is disconnected from their business strategy, or it’s in conflict with it, or whatever, and we’re going back to something you said where it’s might be the CMO paying our bills. How much say do we have in that, or do we have to just wait for those businesses to realize they’ve got to look at the whole, and then we can play a bigger part. Or do you think as part of our role as analytics professionals, do we have some say in that, can we influence that?

[00:21:22] Steen: If you see this relations as a trusted advisor, where we’re saying why should this CMO hire you again. It’s because you cover his back and saying, listen, I know you want this and we can deliver this, but I can also see that as the next step, we need to do this to adjust this in relation to the overall strategy. What do you think about. So going in actually making them aware without saying, no, I won’t do the first thing or you should absolutely do the second thing. Just being open and honest saying you have a bigger problem than what I’m fixing right now. And I think you should be aware of it. You don’t have to use us to fix that, I just need you to be aware that you need to fix it. I’m trying to upsell you something, not necessarily for us, but for your sake.

[00:22:08] Dara: Yeah.

[00:22:09] Dan: This is actually a factor of being an external party to this as well. And so being an agency or a consultant, this is almost like a little superpower you have compared to an internal, even if there’s an analytics team internally, they might not have the ability to question those sorts of things. And I think it’s almost our job as a third party, to be able to question those. And kind of one of the reasons why we’re brought in too, to be able to ask the hard questions that people internally might not want to ask. Whether that’s from an analytics perspective or not, like you said Steen, pointing them in direction of like, these questions need to be addressed. Whether we do it with you or not, but we’re highlighting, or we are flagging something to you for you to address with our support or not. You might not get that from an employee that might be three or four levels beneath you. For example, for the CMO.

[00:22:57] Steen: Yeah man. And sometimes it’s difficult getting it up to the CMO. Because if we bring it up with the employee three or four levels down, then like, okay, I can’t do anything about that. Can you take me to your leader? Yeah right, that’s not going to happen all right. But just opening the conversation and bringing it in, that’s a strong start. And I think it actually loops back to one of the things that you talked about before Dara. Can we be caught up in something too fancy smancy? Yeah, we can absolutely. But we can also go in the other direction and actually be seduced by data. And for me, of all projects, Cambridge Analytica is something. If I had called you guys before this turned into a shit storm, and before the project was turned into what it was turned into. Saying hey, I have unique access to a data set from Facebook so we can target users to help sway their opinions and relations for customers. That sounds like a fun data project. Nobody was saying hey, hang on, that sounds like a loaded gun that can be used for wrong, right. Ooh, it sounds like a fun project, yeah. And I have a lot of money, ooh double fun. And then I’m like, oh my God, I build a super weapon and killed a country.

[00:24:08] Dan: Is there a blueprint for an organization to become better or more mature with data? Have you noticed any trends around quote unquote mature organizations from your context, and the structures that they’ve formed around data?

[00:24:23] Steen: I think I would flip it back to what I talked about before, it’s the sense of urgency, how important is data in the organization. So it doesn’t matter where people are placed. It’s if they flag it, do people respond? It’s just what I’ve seen in the sense of different places and different organizations, and having people being called a chief analytics officer sitting on the board, but nobody listens. So you’re apparently in a place of high power, but you have no influence. And just in another meeting the other day, there was a guy we’re sitting talking to the CMO, and she kept referring to the other person, and he was just a web analyst. So she kept directing the focus to him because we couldn’t sell her anything without his acceptance or his blessing. In that sense, then she had a stakeholder for her that was on the paper powerless. But in reality, like in good Machiavellian style, sitting behind the throne and pulling all the threads.

[00:25:23] Dara: There’s is a Dominic Cummings reference there somewhere. I haven’t thought means anything to you, speaking to Machiavellian. I really liked this idea of kind of urgency being such a key criteria and how quickly a business is willing to respond to a data issue. It makes me think, does this actually run deeper than just data itself? Is there something about these kinds of businesses that makes them more likely to respond to that? So almost is it not about the data itself, is that almost like a byproduct of them thinking or running their businesses in a different way to begin with?

[00:25:56] Steen: I think that’s a good point. I think it plays a lot on the different thinking. Because two types of companies I’ve seen that has been doing really badly. And this has been companies focusing on having a zero error culture.

[00:26:09] Dara: Hmm.

[00:26:09] Steen: They’re very non-responsive to errors because errors means somebody has to step up and take a kick in the nuts. Because it kills the momentum and the urgency because people are really afraid. And the other culture is where there’s no activation, there’s not a culture of activation. They might have a lot of urgency running about doing a lot of stuff, but they’re not seeing data as a door opener, is the data change agent? There’s been all this flag on data-driven, they say yeah, data doesn’t drive anything. It’s like, well, of course it drives you to action. And that’s kind of in my definition of data-driven, it’s not that data drives the action it’s that it pushes us to decide something or act. So, so it’s not data on its own, but it’s, it drives us to do something.

[00:26:59] Dara: Yeah, it does. So being willing to respond quickly isn’t enough, you need to actually be responding by taking the right action. It’s not just fix the thing that’s broken. It’s actually what can we activate, what can we improve, what can we optimize based on this data.

[00:27:12] Steen: Yeah, I often go out and I talk now on return on analytics. If you look at some of the big clients, how much they spend on licenses and people and meetings and training and stuff and say, does this work actually lift your business that much? Very often the people they’ve gathered are immensely talented and powerful and clever, but they don’t have a business focus. So they’re not lifting in a business direction. They’re just lifting in a data direction. And then we get a tendency to kind of let me focus on the, and on the ass of the elephant. Because that’s kind of really interesting from a nerdy perspective, but it doesn’t really move anything or change anything right. But look at the data quality of that ant, it’s amazing.

[00:27:57] Dara: It’s back to that point from earlier isn’t it, about it kind of almost over-complicating to show how clever you are rather than actually doing just what’s needed within the whole context, what’s going to drive the business forward. Not what’s going to make me look clever as an analyst, it’s what’s going to unlock something that we can actually change and do something about to make an improvement. Is there a maturity model in this, where it’s basically boils down to momentum of speed of response to data issues and a willingness and ability to activate that data.

[00:28:29] Steen: I might add another dimension, but it actually ties into the momentum, but it’s actually friction. It kind of loops into a lot of this with the organization, how much bureaucracy, and how much stuff do you have to do? How much are the momentum being slowed by internal forces?

[00:28:45] Dara: And, and you’re right about the friction. Cause I’m sure you’ve had a lot of the same experiences where the individual you might be working with is very willing to make changes, to act quickly. But they can’t within the limitations of the organization they’re working with.

[00:28:58] Steen: Yeah. And what we see very often is these people that are highly driven in a friction organization, they will leave.

[00:29:04] Dara: Yeah.

[00:29:05] Steen: They will end up getting so frustrated, so all their momentum will tear them out of the organization. And then

[00:29:11] Dara: the cycle repeats.

[00:29:12] Dan: It keeps us busy though, right?

[00:29:14] Dara: It does. All right, that’s been really interesting. Been really great having your own Steen, and I think we could probably continue talking for hours, if not days. But this is the usual bit in this show where we switch from analytics and talk about what we’ve been doing outside of work to wind down. So we’re going to put the spotlight on you first, as our guests. What have you been doing to chill out outside of work lately?

[00:29:36] Steen: Well, I’ve been having to recalibrate my life. Over the last year I’m Corona was too generous to me. So I actually started refocusing on a lot of other activities. So I basically dropped a lot of the stuff that you should supposed to drop, a lot of food and a lot of a passive entertainment. So my Netflix consumption have gone down radically. So instead I’m actually spending time with my kids and with friends and stuff. Loneliness from COVID has pushed me to become much more social and much more actively social in the sense of just being able to hang out with people and enjoy the quality of them.

[00:30:16] Dara: That’s such a wholesome answer. Mine is going to follow really nicely, but in the opposite way when you mentioned about Netflix. So what I’ve been doing for the last week, or maybe 10 days is watching the latest series of Narcos. So I’ve been, I’ve been, I’ve been guilty of binge-watching Netflix for the last week, but aside from that, I completely agree with you. I think coming out of lockdown has been a really good reminder of how nice it is to get out and see people in the real world. But yeah, shamefully, I have been, I have been watching a bit too much, a bit too much. Netflix. What about you Dan?

[00:30:48] Dan: Well, mine ties in quite nicely to yours Dara. So, two episodes ago when we had Steve on the podcast talking about WHOOP and the self quantification. I succumbed and I got myself on a two month trial of the WHOOP band. And I think it’s fascinating having all of this data about your own body is actually fascinating. I’ve just been for the last couple of days, looking at my data almost obsessively . I tell you what it’s made me do, is get out of the house more, go skating more, go walking more. I’m really liking it so far.

[00:31:15] Dara: Well, thank you both for putting me to shame this week. I definitely need to get out and drop Netflix for the next couple of days. All right, well that’s a wrap for us for this week. You can as ever find out more about us on measurelab.co.uk. Or you can get in touch via email at podcast@measurelab.co.uk, or just look us up on LinkedIn. And if you have a topic or an opinion that you’d like to come on and discuss with us, please let us know, please do reach out. Otherwise, join us next time for more analytics chit-chat. I’ve been Dara, joined as always by Dan and also this time by Steen. So it’s a bye from me.

[00:31:53] Dan: And bye from me.

[00:31:54] Steen: And bye from me.

[00:31:56] Dara: See you next time.

Written by

Daniel is the innovation and training lead at Measurelab - he is an analytics trainer, co-host of The Measure Pod analytics podcast, and overall fanatic. He loves getting stuck into all things GA4, and most recently with exploring app analytics via Firebase by building his own Android apps.

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