Hi, everyone. Welcome to More Numbers Live, a data show focused on turning data teams from support functions into their company's engine of growth. I'm Ollie Hughes. I'm one of the cofounders of count dot co, a Canvas based BI tool. We're all about taking data scenes from being dashboard factories into their organization's problem solvers. This week, I have Calum Balb with me. Calum is the analytics director at Omaze, and we're gonna talk about all about operational clarity. Now, Callum, that's me. Thank you so much for being here with us, and tell us a bit more about Omaze and what Omaze is all about. It's not a lottery company. It is, like, very special. Tell us what it is. Yeah. Nice to be here. Yeah. Omaze is a charity prize draw. So it started in America, but now based in in the UK. So you may have seen Omaze on your Instagram or on Facebook, or on TV. So, yeah, every month we have kind of a multimillion pound house, available as a prize. The idea is that people can enter, and, yeah, it's a surprise draw to to win win the house. The kind of twist is that, much of our revenue then goes to to charities. So, so far in the UK, we've raised over eighty million pounds, for UK charities. So recently worked with, the likes of Age UK, Outsiders Research, just did a campaign for Comic Relief as well. Yeah. We we would expect that number to be kind of over the the hundred million mark at some point this year. So, so, yeah, making making dreams come true, of course, with our with our housing our prizes, but also, hopefully doing some social good as well. That's the idea. It's a wonderful idea. I have also I have been very much tempted by the adverts I see in social media. It's a very hard thing to ignore as it scrolls past. I'm glad it's growing well and getting more to charity as well. So we're gonna talk today a lot about one of the tenants that, we think about a lot of more numbers is the idea of operational clarity. We're gonna deep dive into that. Your what you've done in Omaze and mapping your growth model, a sign which I think is, I'm so keen for audience to hear. But before we jump into that and go into the guts of operational clarity, maybe we can just sort of contextualize a bit of what it's like running data at Omaze. Maybe starting off actually just like where you where you've come from. Maybe tell us, as I love to ask, that that your first the first BI tool that or first data tool that you fell in love with other than Excel. Maybe we'll start there, and then we can go through your background a bit. Yeah. Of course. So I did maths and economics at university. So I was at London School of Economics graduating in in twenty thirteen. And like most good graduates at that time, kind of went into consulting. So I spent five years working, as, as a consultant at the analytical, subsidiary of McKinsey and Company. McKinsey have, for many reasons, a lot of internally developed tools. People in companies that I worked with kind of later down the line found it hilarious that, you know, we we'd have an office and we had our actual servers, like, in the office. Like, we weren't using AWS or anything, like, which people found hilarious. But, so the first tool that we used was an internally developed tool. And, you know, the other thing that McKinsey loves to do is is put Muck at the start of everything they developed. So we had a a benchmarking tool, which was also then called Muckbench, which I think is this weird, like Muckbench, yeah, which was like this weird, like, proto sequel kind of, user interface thing, which, we all thought was terribly clever at the time, but, probably in hindsight was, was an incredibly janky piece of subscription. They're obviously good at getting market advice. They know exactly what they're doing when it comes to naming things. That's it. Yeah. Yeah. Very, very imaginative. Yes. Okay. So, obviously, then you, so having gone from proprietary tooling, all over McKinsey, you then moved into the world of startups. You worked at Busuu. I think I pronounced that right. We had a great we had great fun there, been at Clio, and now at Omaze. So you've then got got into normal tooling with normal SQL and normal languages to write, which is really good. So tell us what it's like at Amaze now. Obviously, there's it's growing very quickly. So you've obviously worked very quickly onto a a growing data stack. So tell us a bit more about the setup. It looks like how you've thought about that a bit. And, yeah. So the data stack at Omaze, is, a Snowflake data warehouse, with Fivetran doing data ingestion from various places, DVT doing data transformation. So pretty pretty industry standard, I would say. It works very nicely, so I'm not gonna complain. In terms of the the front end for our analysts and our wider team, we're using count, as our as our place where the analysts kinda go and do their deep work. So I'm sure you'll you'll be pleased to hear. I am. But yeah. So yeah. So, that's kind of why we're doing our our kind of deep exploration, why we're doing our discovery analytics. So, yeah, that that's kinda like a specialist tool that I would say for for the more data literate, within the business. But then using ThoughtSpot, as our as our BI tool, so, you know, there's a couple of functionalities within ThoughtSpot that make it kind of a nice user interface for for the end business user. That's some nice stuff for the analyst as well, of course. But, yeah. So that that's kinda like our main stack at the moment. And so we're gonna dive a lot more into into the idea of operational clarity. And I I guess that one of the things that you we were always interested about is how count and thoughts posted together. Can you give any favor about that? Like, where you use for what? Because, obviously, we also discussed, you're using count for some of the growth model mapping, which obviously is essential to this idea of clarity, which we'll dig into. But just maybe before we dig in, how does those two things work together? Yeah. As as far as counter is concerned, I think I I think about it in three different ways. So the first way, which is a bit more operational, is just, like, it's a great tool for for data modeling. So if we're doing, like, DBT work, like, it the the the canvas structure just allows the designs of bags to be done, like, really easily. So that's just, like, that's a that's a given that's in the bag. The other two are then, I would say, making reporting, I don't wanna use the word dashboards, but, like, making reporting tools that are difficult to visually create in a standard dashboarding tool. So, like, a metric map, and we're gonna talk about metric map later, I guess. But that's a classic example. Right? Like, if you think about a metric tree or a metric map, laddering down from a from a single metric, like total sales at the top and then branching off into this this kind of, like, you know, multilayered, multi metric thing. Like, just making that in a dashboard tool is just really hard. Like, I boosted it in Tableau, and it was a waste of time. So this is this is a tool in which you can do stuff like that. Like, there's a couple other examples where just like it's really helpful to have charts or, like, kind of visual artifacts in certain positions on a page. It just makes it easily digestible for the end user. And for a tool, like any dashboarding tool really that is just wanting you to have, like, great solve things, like, it's just not appropriate. So, like, count is great for that. So we definitely use count for that kind of thing. The other thing that you would be using count for at Omaze is, as I said, that more deep deck, data exploration. Right? So it's just very easy to interrogate hypotheses, interrogate kind of the hunches that you have in the data team or if you want to explore performances of, like, you know, for in Amaze's case, you know, if we have a house that performs particularly well and we want to understand why, well, that's kind of the place where we go. ThoughtSpot has its advantages. You know, I I wouldn't have I wouldn't have paid money for it if it didn't. So ThoughtSpot is is more of like the quick, okay. You've got, a metric defined here in a particular way. Like, ThoughtSpot makes it quite easy to cut that by different dimensions, maybe chop and change different things, and it's it's quick for that. So it allows me or indeed any business user that's familiar with it just, like, quite easy insight, that goes a little bit beyond the surface. But in terms of, like, then the deep understanding and then some of the more custom stuff that that a good data analyst needs to be doing, like, that's when we'd make the migrate account to, to carry on that work. Yeah. That makes sense. So really, ThoughtSpot is for that kind of operational quick, I need a metric quickly kind of a a decision which almost the individual needs to make needs to make very can make very quickly from what they see. Whereas, as you said, count is more for the holistic view of the business and then for the deep dive to actually make the business better. That's thirty one's for. So that's great to hear. Thank you for talking us through that. So, look, we wanna talk about operational clarity. It's one of the tenants that as a, you know, as a as a more members community we've been working at to defining the idea of, these are the tenants that drive value in a into a business from data team's perspective that these are the north stars of a data team. So operational clarity is the one of them, and we'll discuss it in more depth being the kind of a pin that a lot of things can a lot of benefits flow from, problem solving, minimizing time to decision, and then measuring yourself. We wanna spend today just thinking a lot about the operational clarity, really defining that really clearly with the benefits of that. And it's something which you as you mentioned before, you've been doing throughout your whole career at Busuu trying to build metric maps. You've been doing that at Clio and now also to May. I think you set that up from the start to have sort of metric trees and and the growth model clearly visualized for the business when you join. So before we dive into how you execute on those things, maybe we should just spend a bit of time defining the definition of operational crash and one point maybe more importantly, the pain of not having it a bit. So I guess, to kick off then, I I would love to just talk to you about this idea of signal to noise and just, like, where when you've not had operational, where you've come in maybe to amaze all the businesses, when you when there has been a lack of operational clarity, what do you think is what's going on? What does it look like? What does it feel like? I think it feels like a lot of different metrics that people don't necessarily understand how they interrelate. So I think, you know, most businesses, certainly businesses that have grown to a certain scale, you know, they will have knowledge of, like, what their sales are, what their acquisition cost is, what their, spend per customer is. You know? They they will know this stuff, and I don't think as an analyst, you're likely to go into, you know, a fairly well established company and kind of tell them this. They're gonna know this. Maybe what the next level looks like is okay. Well, this is actually how they tie together. Right? So if you've got your total sales number at the top, actually, like, what are the things that mathematically drive that number? How do those different jigsaw puzzle pieces that you've got in your spreadsheet in a big long list or, like, in a big grid formatted dashboard, like, how do they actually play together and help you to understand what's gonna drive that number at the top. Because typically, there will be, like, specific KPIs that a business cares about, normally sales. It can be other things. But, you know, understanding a okay. What two things multiply together to make that sales number? Okay. Take that first one. What two things multiply together to make that? Getting that structure, step one, I think, and maybe that's the thing that can be missing sometimes. Obviously, that allows you to have a view of, a, what's driving current performance, which we do, and, b, it helps you to understand actually what are your biggest levers to grow that top number. You might have a fascination with a particular low level metric, be it CAC, be it, spend to customer, whatever, might not be the most important thing once you put it in the context of everything else. So I think getting a sense of, you know, a, how things are interconnected and b, actually what are the bits and pieces that are the most impactful thing for you as a business to be working on to move that top line number. I think that's the introduction of operational hierarchy, I think, is is really key in achieving both of those things. I love the way you describe it as sort of con contextualization, like, context that a metric by itself is meaningless unless you can contextualize it into the rest of the business, the other metrics that define the business. I think the, what what I often find is, as you said before, is without without that, people have a number, but they have no idea really. The cognitive load of work and all the number means and how it impacts business is very high. And, really, what you're what you're describing there is the ability to to let the business see itself and see how everything fits together and then see what really matters consistently without getting distracted by lots of noise in a particular area. And do you in general, do you when you've been into an organization where you've brought this methodology in, because I think it's like you tell that you have brought in a few times, how do you get the business to recognize they need it? Because as you say, they they would quote back to the numbers on sales. They quote the number of customers back to you pretty happily. Where how do you convince that this needs to be built? Like, that this is there's actually another level of a little level of clarity, another level of understanding that you could reach. Convince is probably a strong word. I think if you, if you do this exercise in a, as an analyst and you do kind of provide that structure, provide that visualization, I think, you know, if they are in the in the business of improving that applied numbers, which they tend to be, I think they kind of see the value. It's like seeing seeing kind of what they know, but presented in a new and and maybe interesting way. Yeah. In terms of, like, proving its value, I think it kinda goes back to the two points that I made before. Right? Like, if maybe, you know, there was a month of sales that wasn't so hot or maybe, you know, they felt the business underperformed, And they just had that niggle in their mind of just, like, never quite got to the bottom of why. I think having that structure of, well, actually, you know, if you follow the thread, then it's kind of, you know, these customers didn't perform so well, and they didn't perform so well because, you know, their average sales were a bit low, and their average sales were a bit low because they didn't buy these products. And it's just like giving that example of, you know, you have that hunch, and, actually, we can demonstrate it with this piece of work. Or we can actually say, you know, you have that hunch. Actually, I don't think that was right because if you look on this part of the metric map, that was actually kind of fine. It's over here where your problem was. So I think, you know, presenting those KPIs back in a structured way that maybe they're not familiar with but kind of intuitively makes sense is kind of nice for them. But then also starting to demonstrate value. And, you know, once you do have those metrics set out in that particular way, like, it's it's quite easy to start doing that, like, if you spot these areas of opportunity. So I think, you know, in terms of winning people over, there's a lot to be said for just, like, you know, just having a first version of it. Maybe it's not the most complex thing in the world, but just like, you know, the first three or four layers and then demonstrating its value, pinpointing areas of improvement, areas that maybe weren't performing so well before they weren't aware of. I think that's that's where you start getting buy in. I I you unpack so much you don't unpack what you just said that, but I think that's exactly right. And just to unpack it more to sort of talk about it a little depth, you mentioned a few things. One is the idea that without, without kind of a this operational crash, this kind of prioritization, natural understanding of what the business is, you do have that kind of risk of people, like, having their myths. Like, you mentioned CAC. Like, we're just fixated. I've been in many business before where they're sort of fixated on CAC as the big problem. But, actually, they're sort of overoptimizing focus on a thing which is actually, say, three or four levels down. They don't have the ability to see the impact at a big level. It sounds ridiculous when you say it, but it is very easy to get distracted by the small KPIs that have got the kind of most buzz and that buzz to be just prioritized. And then the other thing you're pointing out there is the idea that when you have a really contextualized visualization of your business, you're actually problem solving without realizing it. You actually have the root cause what's going on, in a methodical way, and it's the it's the start of many. It it just allows your problem solving to be much more rooted in fact and an and and and it impacts you going sort of top down on a problem rather than bottom up with this is interesting. What's going on here kind of mentality. Yeah. I think that's right. And I I think just to just to add to that as well. So, like, to take that example of, you know, a a month that's not gone so well or, like, a period of time where, like, sales is disappointed. Like, if you set up the tree in a in a mathematical way. Right? So, like, I talked about the idea of, you know, a metric being broken down mathematically into two things, and then you keep on a mathematical kind of breakdown of these these metrics one by one. You get to a place where you can size that opportunity, like, in terms that the business understands. Right? So, like, if everything ultimately ladders back up to sales and it does so mathematically, you can start talking about, well, this gap in sales per customer is worth this many pounds in total sales or, like, this gap in acquisition cost or this gap in retention or whatever. And once you start being able to put it in those sales terms, then suddenly you're talking like a language that is common. People are gonna understand it. People need exactly gonna understand, things when you start talking about sales. And I think by doing that mathematical breakdown, it it gives you that ability to take metrics and gaps in performance that are maybe a few levels abstracted from that, but then to put it into terms that everyone in the business can kind of understand. Yeah. I love that. It's a unification. So if we were to improve CAC, what does it actually mean to the top level? Because we we've got all the other factors coming to play as we go up the tree. And it just, yeah, it allows you to prioritize prioritization just naturally falls that over too, as you say, in a way which we everyone can buy into. That's that's really interesting. It it's it doesn't have a lot of analytical maturity. Right? Rather than here is sales, which is sort of the base level requirement, it's now saying, here's how sales can extend everything else. And now we can start to do things like, if we were to improve x, here's the outcome of that. We actually understand the weightings of these different relate these different metrics to each other. We're now taking numbers and actually using them to explain how the business works in a way everyone can buy into, understand how they're driving towards it, and helps us to avoid distraction because we actually have something rooting us back to where the impact is gonna be as you go. That's that's that's it's really cool. Thanks, Eric. Thanks for taking us through that. And the other thing which it touches on, I guess, is the idea that you can do sort of company wide level. I think you have done that at Omaze. You built a kind of a a metric map, which is link literally the top end sales, I guess, is what you're saying at the top level. But you can apply this logic to any any part of the business at at a lower level as well. Like, maybe you take because what can be a sort of the top of a tree and say product might ultimately be a sublevel of the bigger company wide tree. So you don't have to start those listening at a company wide level. You can start lower down, I guess, and just apply the methodology of breaking metrics apart and showing how they fit together sort of anywhere. Is that fair? Yeah. I think I I think that's right. So, you know, there you can if you start at total sales, which is the example I keep going back to because that's what I personally use. But you can kind of just take that indefinitely. I mean, it's gonna get really silly by the time you get onto, like, the seventh to raise layer, and it's probably stopping being useful kind of beyond that point. But, like, stake an example. So that that tree that involves sales, I think about four nodes down gets to, like, new customers. And I kind of, you know, I kind of maybe break that down into, like, marketing spend and cap. Yeah. And that's kind of where I'd stop, at least in the context of battery. But, clearly, like, there's a whole kind of world of insight that lives kind of beyond that, you know, once you start getting to things like, you know, channel breakdowns, maybe funnels on the website. Yeah. You know, there's there's any number of additional things you could have there. So, yeah, a new tree maybe becomes, okay, these were our new customers this month. Let's start again. And, you know, by the fourth or fifth layer down, you're you're starting to get into some of the the more, the more relevant kind of drivers of that that you wouldn't necessarily consider in that kind of grand company wide tree that looks at total sales, but would be important for, like, an acquisition team or, like, a great team. That's cool. And so and once that what before we can move on, I wanna just make sure we capture this. For someone who's listening to this show, he's going, this sounds interesting. This sounds like a different kind of role for identity than I'm used to. I'm used to just mapping metrics on a dashboard, board, building to the requirements of the business. But now you're telling me that I should go away and think about mapping metrics, building a metric a map of the business with metrics in this tree format. Metrics is only one of many things you can do by the way on the on the concept operational clarity. But, like, what what would you say to them who someone who's coming to this completely from a kind of I build reports to the business mentality, how would you help them think about it differently and help them see the need to take on this sort of bigger role? I think the first step is that, you know, we probably assume that a lot of the metrics are already defined. Right? As we spoke about earlier, you know, most businesses, even, you know, ones at relatively early stage to their growth, will have a handle on, most of the metrics that, you know, I I would have in in the metric tree that we use at Omaze. Right? So, really, it's just an exercise in taking those existing puzzle pieces and putting them together in the right way. And, you know, doing that is not a trivial exercise. Like, it can easily take a couple of different goes because, you know, a a a metric can be broken down in many different ways. And and sometimes, you know, if you break it down in one way, that doesn't lead to fruitful layers below, so you might wanna go back and try again. But, you know, it's it's a few hours of sitting down. I I would even do it with, like, a pen and paper of just like, okay. Total sales, I can break that down into the sales of two different customer segments that add together. Like, that's a mathematical breakdown of total sales. Or maybe it's total sales is, you know, total customers multiplied by the average spend per customer, etcetera. Right? So all of these metrics are just set. Like, the business probably already knows them. They probably exist on a dashboard somewhere. It's just a case of taking a couple of hours maybe just to, like, sit down with a whiteboard, with a pen and paper, and just, like, take those post it notes of metrics and stick them together in a way that kind of makes logical sense. So I think, yeah, the upfront work of having those metrics already there is is probably already done, and it's just the organization thereof that is is maybe the lift that you need to do. I guess if they if they don't already exist, then there's sort of also the other a bigger problem, which is that the business doesn't really understand how it's growing. It doesn't really get what's driving performance, and it's measuring the wrong things. I'm probably relatively confused about how it all fits together and almost certainly unoptimized. So there's kind of a two either it should be there and you can just paint it clearer, which all benefits we've described, or actually is a bigger fundamental issue here, which means any work the data team does is gonna be massively utilized. And I guess the only big reason we I think we we've seen people, take this all as a data team is because you have all the business data. You have the holistic view of the business. You just you're the one who can weave it together and support the business and understanding itself as well. So that's that's really helpful. Like, the it you're right. It's not like you're starting from completely scratch. You can pick up from where you can pick up from what's already there. Hopefully, there's something you can use to start mapping things better. I think I think our point around missing metrics is a really good one, actually. So, like, you can get into certainly, if if there's a business where, like, it's been the same leadership team or it's been the same kind of group of people that have been basically working on the thing for a long time, you know, you you can get a bit set in your ways. You can kind of have, like, well, these are the metrics that we've just always used. And, you know, it can sometimes happen that the metrics that are appropriate for a business three years ago are less appropriate nowadays for whatever reason, like maturity stage, whatever. So, yeah, going through that exercise and almost forcing yourself being mathematical in the breakdown of metrics, as you say, can sometimes surface KPIs where it's just like, ah, we just we literally never thought of that. We've never thought to look at it like that. And that can often well, often is a bit strong. That can sometimes lead to, like, some quite interesting trails of thought. It's like, oh, well, maybe if we try to optimize this by doing this and, you know, you start you you kind of unlock a new kind of way of thinking that maybe maybe wasn't quite there before. I think this is I I completely agree. Like, it's that you you I think the real challenge to imagine, I think, that they often struggle about is because we look at them as all day long, every hour we're looking at numbers and metrics. We think these things are obvious. But, actually, if you're an executive in a business which is being bombarded with information in Slack channels, emails, external clients, internal requests. The the the the confusion, the the soup they're waiting through to get to understand what's really happening, what really matters, is ten times harder than it is for even more than than it is for an analyst who's looking at the match catchboards every day and is tracking these metrics, make sure they're right all the time. And it's that void of clarity, which I think is what we're really trying to close here. It's like, let the data team take the cognitive load off the business to understand itself. And I think this is one thing I would love people to walk away from what I've seen you do so well on Amaze is just level up this simplification of the business to make it really blatant what's going on. The fact you can always have one report, one metric to what business is completely seen in is amazing. Like, that level of clarity, just one one metric tree that see you can that the whole business can run by, basically, is amazing outcome of and such a valuable asset that the data team can deliver to the business. And you're never gonna have someone say no thank you to more clarity or more simplification. Everyone's desperately trying to make things easy to understand and and help understand what really matters. And it's a data team one of the way the data team can do that really well. Yeah. I agree. I agree fully. Cool. Well, I mean, so I wanna dive got we got a few more questions here. I want one that comes from the community about about how to get going on metric trees. But before we do that, is there any, advice you wanna give to people just getting to data, whoever earlier on their career that you think was really helpful for you as you went through the various jobs that you've had? I don't know if this was a piece of advice that was given to me, as opposed to kind of something that I wish had been given to me earlier. I think, getting getting to grips with statistics is a really, really boring answer. I think is really important. Yeah. I I don't know if it was just how it was taught or just my natural inclination at the time, but, like, I just so someone who does have a math and economics degree, like, I really didn't get on with statistics as a discipline, in academia. And it's only when I kinda came back to it, you know, in my, like, late twenties, early thirties, kind of through I I mean, I did a a big science course between consulting and, and working at BC. But, yeah, it it kind of clicked a lot more for me kind of later in life. It's like, I wish I had understood this kind of, you know, six or seven years ago. And I think it's it's been a big unlock in terms of just, like, you know, understanding, understanding, like, causation instead of correlation, being able to implement, like, a Bayesian testing framework, Omaze. Just like those little those little unlocks that just, like, I think can just elevate you as an analyst. And I do think they are unlocked by just, like, having a fundamentally good understanding of, like, of statistics. So, yeah, I think that would be I love that. I I people talk about, like, the idea of, like, sort of hard and soft skills. Yeah. The technical skills are the thing to learn. I I actually think this is if you if you have that desire to learn technical skills, you don't wanna, like, lose that. I would say learn statistics more than languages because there's actually a business facing valuable capability to learn. If if you have that, you can help a business be better, and it's still technical. It's still hard logic in that way, but it's not tied to a language. And if it put it another way. Put it another way. If a data team isn't the people who know statistics best, then there's the then who what are you doing? Like, you've if you you're part of the base level of statistical rigor in the business. So if you're good at statistics, that doesn't mean everyone else is gonna level up to your level? So it's a it's a great hack. I love that advice. I think in I think just increasingly as well, you know, businesses are are starting to think about AI. And, you know, some businesses are are thinking about it because that's their business model, like like it was at Clio. Some businesses are maybe just, like, a bit scared that they're missing out on on the train, that AI kind of represents. And, you know, AI seems incredibly fancy and, you know, it it is. It is fundamentally just, like, really, really fancy statistics. So I think having that grounding in statistics can maybe like immunity is not quite the right word, but it can kind of give you a a sense of grounding and a sense of proportion when kind of, you know, a lot of AI hype is being thrown around a lot of the time. I think it can it can help keep your feet on the ground a bit. Love it. Right. I want I've got one last question because I want to look this is back to metric trees, from the community. I asked asked people that we're talking about operational clarity, asked some questions. This one came back. We've got about a minute to answer it. But, the question here is, how I I I really believe metric trees. I believe in the power of them. How do I get my wider business to get on board with the project is basically the question they're asking. How do I get started? If I could take it first, Kyle, actually, one thing I would say is that metric trees are only one way of delivering operational clarity. You can turn a metric tree on its side because of process flow map, and that might be easier to get people to to buy into that rather than the kind of a a a tree which is slightly more visual paradigm shift than people often see process flow maps. So you could think about, the key is you're just mapping metrics as a map and drawing relationships between them all. The orientation is up is up to you whoever's gonna get buy ins. That's one very quick tip I would recommend to anyone I I meet. Anything else for you, Calum? I mean, to that point, I would say the same thing for, like, funnels. I think, you know, I've often done trees where, like, one branch just starts becoming a funnel where it's just, like, x people at given stage of funnel multiplied by conversion rate at that stage of funnel. So, like, yeah, it it to to add to your point, I think the same is is true of of funnels. In terms of, like, actually getting it kick started and and getting people on board, I think the most powerful thing to do is is just to, like, have an example and demonstrate its capability. There's a couple of ways to do that. I think the first one is just to, like, have a relatively small kind of proof of concept where you are starting with, like, that higher level metric and maybe having, like, three layers, demonstrating its value through one of the methods that we spoke about earlier, be it like diagnosing root cause. One other thing that, we've used a metric tree for in even the last few weeks is, is ideation. So, you know, we wanted this idea of, like, okay. What's our next big thing? Like, what is what are the the big swings that we're gonna take as a business that is gonna, like, continue to accelerate our growth? And, like, being faced with that kind of question is just like, ah, it can be overwhelming. Yeah. But actually, like, we just took the first maybe, like, three layers of the metric tree and just said, look, a big idea is gonna drive one of these five things. Let's take them piece by piece and think, okay. What are the things that is going to drive this? What are the things that are going to drive this? And suddenly, like, it just gave some structure to the ideation, I think, helps us to come up with Your angry ideas, the highest highest levers of impact. That's awesome. Exactly. Yeah. So I think, like, that's an example of, you know, maybe, you know, if you're in a product team that has, like, a regular ideation, be it every quarter or whatever, like, take a metric treat to that. It doesn't have to be, like, six levels deep. Like, just take your OKRs at the top, break it down a couple of levels and think, okay. What are the things that are gonna drive this? What are the things that are gonna drive this? And suddenly you've got, like, that structure of thought, and it's the metric tree that's kind of facilitated that. That's a great answer. That's I'm that's awesome. Thank you so much. We've been talking about operational clarity today. It's been amazing. We've talked about the way it can help you drive your business. Value helps us see it itself. You can drive critical thinking. You can use it for ideation. There's a lot of value in how you can use, metric metric maps, operational priority in general to do it. Calum, I'm grateful for showing your wisdom today. Thank you so much for helping us all understand it better and think about how we can get started. Yeah. Appreciate your time. Thanks so much. No. Thanks very much. It's been fun.