How We Diagnose Every Ecommerce Problem (The Hierarchy of Metrics)

[00:00:00] Tony: every month our profit engineers go through an extra, a planning process that in ingests a business's cost profile through their p and l and pairs that with our data modeling. So modeling like the spend in A-A-M-E-R model and the returning customer revenue model. And the work is to produce a plan that outputs a certain amount of contribution margin. There's no, there's no more simple way that I could say it like this. This is the goal. This is the, the existence of a profit engineer is to produce a certain amount of contribution margin.

[00:00:36] Speaker: This episode is brought to you by Red Stag Fulfillment. Most fulfillment issues aren't big, obvious problems. They're small things, wrong orders, late shipments, inventory that's slightly off, individually manageable, but over time, they add up and you are the one dealing with the refunds, the complaints, and the hit to your margin.

[00:00:53] Most three pls treat that as part of the cost of doing business. Red Stag doesn't. If something goes wrong, they take responsibility for fixing it and covering the cost. Check 'em out@redstag.com.

[00:01:06] Richard: Hey folks. Welcome to the E-Commerce Playbook Podcast. I'm your host, Richard Gaffen, director of Digital Product Strategy here at Common Thread Collective. And now it's a wonderful day on the pod today, not only because it's actually 70 degree, 70 degrees in Portland, as you can see behind me, which is unusual, but we also have Tony the Chopper Chop on the podcast for the first time in a long time.

[00:01:26] So, Tony, what's going on man? You're looking to relax. You got the old sunglasses around the neck thing.

[00:01:33] Tony: I'm doing, I'm doing pretty good. We, speaking of warm, we never really had that much of a winter here in Southern California. I was talking to my wife the other day and thinking about how the fact we, we never turned our heat on. Our heat never came on like all winter. So, but yeah, we're in what, early April when we're full on

[00:01:49] summer here in California.

[00:01:50] So I'm glad some of that Nice weather's getting up to

[00:01:52] Richard: Yeah. Yeah. Made it up this way. And, and for those who are listening who are not from California, California does have a winter where it's 50 degrees, which is chilly for us down there, but unfortunately they didn't have that this time. But all right. Let's, let's jump right into it. So our topic of conversation today, which we're gonna be jamming on a little bit, is a topic that we've we're allude to on the pot a lot.

[00:02:11] And have done episodes on in the past. But I think in light of kind of the way we're shifting our focus to this new profit engineer framework might be worth resurfacing again. So what we're talking about is the hierarchy of metrics. And the hierarchy of metrics is the framework by which our strategists identify, diagnose problems, and then.

[00:02:34] Create a plan of action against them. So, which is really a way of saying this, this, this is sort of the framework by which we kind of, it is the thing that guides a lot of our thinking. So what I'm gonna have Tony do here is kind of walk us through what the hierarchy is, the various tiers, and then speak a little bit to the way our profit engineers use that hierarchy to generate action.

[00:02:52] And then also we'll talk a little bit how that connects to things like incrementality budgets, bids, media buying stuff and we'll go from there. So Tony, let's start. With talking about the hierarchy a little bit, so what is the hierarchy of metrics here and why is it so important?

[00:03:07] Tony: Yeah, so we, we tend to think about it in three sort of big chunks at the top of the pyramid, if you will. Business level metrics revenue contribution margin, which is super important for everything that we do. Media spend, media investment, so cost. So this is the very top of the pyramid. And. I think I'll actually skip down to the bottom of the period and then maybe come back to the middle,

[00:03:30] because I think this sort of represents kind of the, the interesting thing. So at, at the bottom of the pyramid is channel level metrics. So media spend on Google or Meta and the roas, or probably more accurately the IROs of those things and the revenue from the channels and throw email in there as well. So there's, there's a pretty big. Delta between these two points in the pyramid, the business level metrics all the way at the top, and the channel level metrics all the way at the bottom. And the, the, the idea of like connecting the dots between a business level metric like contribution margin, and what we go and do down at the channel level is the. It's the whole spirit of what a profit engineer does. Okay. So just to kind of set, set that up, but back to back to your question, what are they? So business level metrics at the top, revenue contribution margin media spend, channel level metrics at the bottom in between those sits customer level metrics, so cohorts around new customers and existing customers.

[00:04:33] And then cohorts around paid revenue and organic revenue. So that's, that's the three business level metrics, customer level metrics, channel level metrics.

[00:04:42] Richard: Right. And so the idea here, like one, one of the insights at least that we had when we rolled out the hierarchy of metrics is this idea like. There's almost the if y'all remember the food pyramid once upon a time back in the nineties, the food pyramid essentially said, although with the caveat that it's reversed with the bottom being the most important, the top being the least, but the point was in the nineties they said that essentially like grains and processed carbs.

[00:05:07] Should be like the majority of your diet and fats and protein stuff were at the top like they needed to be. They were less important. Right? Now, of course, medical science or nutritional science has completely upended that. And now we understand, of course, that vegetables, protein, whatever needed to be on the bottom.

[00:05:24] And that essentially bread is just cake. And so it should be all the way at the top now. With this. Part of the reason that we introduced the hierarchy of metrics is that there's a similar issue with a lot of the way agencies approach this and a lot of way brands approach this, where they take platform metrics as being the sort of ultimate source of truth and don't think to connect them, not to say the contribution margin, those are the things that aren't important, but don't think of ways to connect them to those bigger picture sort of north star metrics.

[00:05:52] And that's kind of, the purpose of what we're doing here. So I think it's like helpful maybe Tony to guide us through. The sort of series of deductions that you kind of have to do, or that might not be the right word, but like sort of the, the thought process that has to happen to get to contribution margin from something like, I dunno, Facebook roas.

[00:06:12] Tony: Yeah. Yeah, that's, that's exactly right. And to your point about like. You know, maybe in the past or other agencies or whatever, like treating platform metrics as a, as a proxy for actual revenue. I think, I think that's where the profit engine and the profit engineer exists to, to not be that simplified.

[00:06:33] Right? So at the end of the day, our customers hire us for a, a couple different reasons, but they all sort of orbit around, helping them produce profit and, and really helping them, helping them understand like how to do that predictably. And so that, so that's the, the spirit of what. Profit engineer does using the profit engine.

[00:06:52] So, and so the every month our profit engineers go through an extra, a planning process that in ingests a business's cost profile through their p and l and pairs that with our data modeling. So modeling like the spend in A-A-M-E-R model and the returning customer revenue model. And the work is to produce a plan that outputs a certain amount of contribution margin. There's no, there's no more simple way that I could say it like this. This is the goal. This is the, the existence of a profit engineer is to produce a certain amount of contribution margin. So. The starting point of any given month is, okay, cool. We want to have, we have targets for the top level hierarchy of metrics, our business me level metrics.

[00:07:42] We have targets for ad spend for total sales represented in various different ways, whether it's net sales or gross whatever, whatever the revenue definition that our partners use. And then. Ultimately contribution margin. And then they'll, there's some ratio metric in there around whether it's MER or ACOs, advertising, cost of sales.

[00:08:04] Right. Okay. So now the profit engineer works their way down into the second level. Group of metrics, which if we have this total goal for revenue and contribution margin, how much of that do I need to get from my new customers? And how much of that do I need to get from my existing customers? And what are the historical patterns that inform that? Okay. Once we have the shape of that specifically, I'm, I'm gonna talk a little bit about, I'm gonna talk primarily about new customer acquisition, but that, that isn't the only purpose of media. I'm just gonna use it as, as the lens to kind of get down the hierarchy of metrics. So I have my big picture goal.

[00:08:45] I know what I need. I know what I need from a revenue and contribution margin standpoint from new customers. Now I'm down in the media channels. Okay. And the, by the time I get to the media channel, there is the way that, the way that the profit engineer thinks about it, and the way that CTC thinks about it is there's a series of math factors that we apply to the channel level Ross, in order to. Be confident that when we spend dollars, they are producing contribution margin above and beyond, or ideally at our target. Okay? So the the important math that goes into that is the unit economics, the contribution margin, the business after all the variable costs are backed out. So the cost of delivery including cogs and pick, pack fees, et cetera, et cetera.

[00:09:39] So that, that gives us a ratio. And the incrementality of the channel as measured by our series of geo holdout tests that we do for a brand uniquely or by our baselines. Okay, that's a very long-winded way of saying if I need to produce, if X amount, $500,000 in contribution margin from new customers, from meta, for example, for a brand well. I need to factor. I need to back out what my cogs are and I need to understand what the incrementality is of the new customer. Advertising on meta and the result of those two things will give me a target, an on platform target, and it, it's. There's a lot of math that goes into it. The math isn't like, we're not, we're not putting people on the moon here.

[00:10:32] So it's, it's not like the most complicated math. But it sort of surprises me like how few people I think do this actual math. But once, once we get to it, we get to our A MER target which folds in the contribution margin math and our IROs factor. We get to an on-platform target on meta, and then by the time we have that, Richard, it's really pretty straightforward how we actually set a target in meta like, 'cause we've done all this math ahead of time. Sorry, this last thought. 'cause like when I say it's like really straight, it's pretty straightforward to set a target on meta that is coming out of like. 20 years of doing this work we, we have like a funny question that we ask people sometimes in the business development process. Like, how do you, what's your meta roas target and why, and, it's sort of surprising, like how, how many different answers you get to it. But by the time we get to it, by the time we say our meta rose target is our meta acquisition, RO target is 2.0 or 2.5 or 3.0 or whatever it is. We're saying that because we've backed into that, through incrementality, through A MER math and when we go and spend that you know, $200,000 on meta. Or whatever it is, we're confident that that's gonna help us produce the $500 in contribution margin from the example that that I referenced.

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[00:12:39] Richard: All right. Okay, so I, I was gonna say that maybe it would be helpful to. Actually I'll here, I'm gonna share my screen and I'll, I'll kind of try, try to demo this and you can kind of, we'll kinda walk through this together. Chrome window. Sorry. All right. There we go. Okay, everybody see this? So here we have a brand, this is last seven. And so this is in our, our obviously our sta interface here. And what STA does is kind of in the main dashboard. The set of metrics that are being reported on are kind of broken out into this hierarchy, or at least into this sort of sequence, right?

[00:13:18] So for this brand and for all of our brands, essentially the North Star metric is contribution margin. So that's, that's the one that we're targeting. Now, we see here over the last seven days that they're, this brand is missing by 7.5%. Right? Which isn't a huge deal, but Okay. That's what they're missing by, so.

[00:13:37] There we have, there we have the problem. Let's say we go down to this next layer here, which is sales. Discounted sales is how they're defining sales. Ad spend, MER, which is overall efficiency, a OV and the number of orders. And we can see their ad spend is significantly behind their dis, their sales number is like a little bit low, but the ad spend being particularly low may be contributing to the overall problem.

[00:14:01] Again, seven percent's not terrible, but let's just say that that's the

[00:14:04] Tony: Well wait before you go down though, so

[00:14:06] this is great. So, because we, we wanted to talk about like diagnosing of the problem, right? So

[00:14:10] in this example that you showed me, something is just screaming to me

[00:14:14] like. That's like informing everything that I go, when I go look further down, I'm like informed by this thing.

[00:14:21] So you mentioned ad spend being under where we had forecasted,

[00:14:25] but there's another sort of inverted piece of this that's jumping out. What, what is it?

[00:14:29] Richard: This MER is 16% over, which is also a bad thing 'cause that's not what we're planning.

[00:14:34] Tony: Well, yeah. So I mean this, this particular example that you're showing me is a business that's behind us. It's top line behind us, it's bottom line. And the media spend is, is behind the pace, but it's more profitable than we anticipated.

[00:14:46] Ergo, everything that I'm gonna go look at, I'm gonna, we're gonna go down to the customer me metrics next and see if we can spot whether that gap is from new or returning more so,

[00:14:55] But once we get down to the media channels, we're gonna be looking at where are we behind on media pacing, because that, that's almost certainly a culprit.

[00:15:03] Richard: Okay, so let's all right, so there's clearly like spend, spend being low also contributes to a higher efficiency. So that, that's the problem here. So let's go down to the next level here, which is where we're looking at returning versus new. So let's see if I, if I can diagnose what's popping out. Paid.

[00:15:18] I revenues low, paid orders are low. And these are these new. What is this?

[00:15:27] Tony: Well, yeah. So we've

[00:15:28] flipped. We've been on this journey of like flipping everything that we report on, on the paid side

[00:15:33] to to incremental. So whether it's revenue or orders or CAC or roas everything is being lens through the, the incrementality factors that we have.

[00:15:45] Richard: sorry. Gotcha. Okay, so, we go to this level, we can determine what can we determine here? Ultimately, new orders seem like they're lower. New revenue's, a little low Return revenue's a little low. I dunno. What, what are you seeing here, Tony?

[00:15:58] Tony: Well, yeah, I mean, so it's kind of the same story. So I'm, I'm sort of like circling in on the paid revenue piece of the puzzle, right?

[00:16:05] So paid revenue's behind and when we scroll down, a little new re re returning customer revenue is also behind. So

[00:16:11] there's, there's a gap on both sides. So. As I'm thinking about diagnosing this, you're talking to the media guy.

[00:16:17] So I'm gonna get really anchored

[00:16:18] Richard: Okay. Please do.

[00:16:20] Tony: revenue being behind and the media spend being behind. I'm sort of like flagging in my, in my head that our, the paid CAC is like ahead of where the model wanted it to be, but one, one problem at a time. I'm gonna make sure we fix the media spend pacing first and foremost.

[00:16:35] Richard: Okay. All right, so then we'll roll down to the channel performance level here. So we can see that Google spend is significantly low.

[00:16:43] Tony: Yeah, but let's, let's, before we go further,

[00:16:45] so let's, let's, just go like, sort of across that whole row, right? So

[00:16:49] Google spend 50%, 44% behind where we would have planned it to be at this point in the month. But what, what is that last column telling you?

[00:16:59] Richard: The Google ROAS here? Yeah. Yeah. It's absurdly inflated. So 75% over, which again.

[00:17:06] Tony: yeah, no, sorry.

[00:17:07] Richard: What's that? Well, I was gonna say, my, my interpretation of it as the layman here, of course, is that the, the fact that spend is low is artificially, if you will, inflating the Google roas. Is that

[00:17:19] Tony: I would just, yeah, so I just, the words, the words are like, I'm very sensitive to these things, Richard,

[00:17:26] Richard: Please? Sure.

[00:17:28] Tony: I, the artificially I don't know if I would say that inflated. I don't know if I would say that either.

[00:17:33] What, what, what is for sure here is we are under investing on this channel. Like we have significant headroom. So back to the thing that I told you about, right? We did a bunch of math to arrive at that. Target that, see the number 3.52 on the Google IROs thing, right below the big numbers 6.19.

[00:17:53] Richard: Oh, yeah, yeah. Right here.

[00:17:54] Tony: Okay. So all of our math, all of our math says if we run the Google Channel at 3.52, we are producing contribution margin.

[00:18:02] Richard: Mm-hmm.

[00:18:03] Tony: 3.52 IROs. We are currently running the Google Channel in this example, at at twice that, and we are underspending. So if this was a media account that I was looking at, I'd be like, what in the world are we doing, fellas?

[00:18:17] The first thing that I would do, ladies and gentlemen, not just fellas, the first thing that I would do is make sure that when we go and look in the media account that all of our targets are set and aligned to. This marginal outcome that, that we're going after, right? A

[00:18:34] 3.52. It's very, it's very likely that the targets are set too tight, potentially, or the budgets aren't big enough either way. This is a place right here where you wanna talk about. We, we wanted to diagnose the problem, right? We have a contribution margin problem.

[00:18:48] We have a top line revenue problem. We have more efficiency than we planned for. On the Google Channel specifically. And this is actually broken out in a non-brand and, and brand in the, in the way. You have the view set up right here. So on Google

[00:19:00] non-brand or Google acquisition specifically, go pump the brand.

[00:19:04] Go pump the gas pedals fellow, ladies and gentlemen.

[00:19:08] Richard: Right. No, that makes sense. And yeah, just to, just to make sure that I know what I'm talking about here. So the idea here is not, of course, that this number is inflated. This is actually not inflated. It's accurate, right? Because it's an IRS number. It's just way too high for where we plan to be, which is an indicative of underspend, which then, like you're saying, triggers us to think through, okay, we need to be pushing on Google more in order to get kind of the numbers to where we need to be.

[00:19:38] But anyway, so I, I think like that's a useful way of particularly, 'cause this is sort of a, a stark example of saying we started here, okay, with contribution margin being slightly down. Really noticing that overall MER, so efficiency is, is 16% over ad spend is 19% below. That takes us to this layer where we can sort of determine that, yes, we're missing on paid revenue overall, and then come down here and see, okay, we can diagnose that.

[00:20:06] The core problem that leads all the way back up to this one is that we're underspending on Google. And or, or, yeah, sorry. We're underspending on Google, and if we were to push there, we would be able to bring the numbers sort of back up into balance. Is that more or less accurate?

[00:20:21] Tony: Yeah, Yeah, yeah. I'm so, I'm so annoying with these language things.

[00:20:26] Richard: No, no, please.

[00:20:28] Tony: There is additional opportunity for volume

[00:20:31] available on Google, Google non-brand specifically. And, and then if we go to one, one more row down here. Sorry. You went, you went two more

[00:20:41] rows down. So the Google brand spend,

[00:20:43] Richard: I see what you're saying. Sorry. Yes. Right here.

[00:20:45] Tony: Same thing. Exact same thing. So, and now I want to connect some dots here between the, the hierarchy of metrics levels, right? So, we had in the customer level metrics, we have a paid revenue sort of gap, but we also have a returning revenue gap, right?

[00:21:01] Richard: I.

[00:21:02] Tony: One thing that we know about Google brand search is it tends to, it tends to be more existing customers, right? So we have a gap in existing customers. Right.

[00:21:12] And we did a bunch of IROs math to get us to our target on Google brand search. We give it a huge haircut. We basically say like, this is very low incrementality because it's, it's returning customers. We, we have to run it at a very high target, and yet still it is well above that IROs target.

[00:21:30] Okay, so cms, the big problem, but contained within that is an existing customer gap, returning customer gap. And the opportunity here is same as the non-brand side unrealized opportunity, uncaptured demand in Google brand specifically.

[00:21:50] Richard: Right. So, sorry. I was just trying to think like, so this, this is overall Google spend, this is just branded, broken out?

[00:22:00] Tony: They're, they're actually two separate things. So

[00:22:02] one is, non-brand and one is one is brand. So

[00:22:05] we, this is, this is, a new addition to status, Richard. So you could, you could take it off if you wanted to. You see the little checkbox up there that says breakout non acquisition

[00:22:15] and that'll just smash it all together.

[00:22:17] Richard: Cool.

[00:22:18] Tony: But we like to have look at 'em broken, broken apart specifically because, we, treat the media campaigns through, through audience segmentation around, man, let me start that over again. We structure the media campaigns. To be able to influence the customer cohorts as they are represented in the h Give metrics AKA. We have campaigns that are going after new customers specifically, and we have campaigns that are designed to reactivate existing customers that haven't purchased in some time.

[00:22:52] Richard: Yeah.

[00:22:52] Tony: A K, a Google brand and non-brand.

[00:22:55] Richard: Right. Okay, cool. Well, I think that that's hopefully a helpful example. You can show it's so easy even I can do it kind of. But the idea here is that if you, again, to illustrate the thought process that we go through, we have a big picture problem and we're able to. Bring it down to the platform level or really, which is the way to say, is the action level to the ground, which is to say we can now push on Google's overall Google spend or on non-branded specifically to try to find more volume or find more opportunity on this particular channel.

[00:23:28] So yeah. I, I guess anything, anything else you wanna say on this? I'm trying to like find like, so what's some other examples of like, the way that this maybe then leads to action?

[00:23:39] Tony: Well, so ultimately we, we haven't gotten down to the action layer yet. The action layer is do something in, in a campaign, right? So you're, we're still

[00:23:47] looking at the, at the channel level here, which is useful. But the framework goes all the way down to the campaign. so do we have,

[00:23:55] in this example that you have pulled up, I'm not sure if we have the, the Compass Tracker tabs, but let's go take a look.

[00:24:01] Richard: Let's have a look here.

[00:24:03] Tony: Go to reporting and then go to Google Trackers. Yeah, since we're using Google as our example. Okay. So what, what we have here, so Richard, to your point, like we've sort of diagnosed the problem, but we haven't gotten to action yet.

[00:24:21] Richard: Mm-hmm.

[00:24:22] Tony: What we have here is each of the campaigns in the Google Ads account, and this is typically where we're gonna go do not typically this is where we're gonna go, do. Action, bid adjustment, budget adjustment, something, and Google new keywords and meta new ads. Right. So I'm looking at, each column represents a campaign, right? Right. And the

[00:24:45] third one over standard shopping, non-brand, all items

[00:24:49] Richard: mm-hmm.

[00:24:50] Tony: is you see where it says actual IROs.

[00:24:53] Richard: Yep.

[00:24:54] Tony: Nine point something or other compared to a 4.0 target. And this is, if I'm going, if I'm gonna take action in this example account that you gave me, I all the way, I'm gonna take a top to bottom contribution margin problem, net revenue problem, ad spend problem, too profitable, returning customer gap, paid gap, Google. over target. Now I'm looking at a specific campaign in the Google Ads account that is. Well above its target, right?

[00:25:31] So now I'm gonna go into that campaign in the Google Ads account, and I'm gonna make sure my bid's right and I'm gonna make sure I have plenty of budget in there. And it looks like the, those little tiny little green arrows that you can see, there are actually

[00:25:45]

[00:25:45] Tony: examples of change history that our media, media team is, is making in the account.

[00:25:50] So it looks like our, our team is activating against this idea that this particular campaign is. It has opportunity to, to open it up.

[00:25:59] But that's something that, that the profit engineer has has at their disposal, like as part of this whole system because sort of this, we're always doing this, this is always happening over time. And being able to sort of have this kind of breadcrumb this tail the tape, like, Hey, this campaign was giving me a signal that it had more room to run.

[00:26:17] Kind of going back and looking, all right. We opened it up a couple days ago. We opened it up a week ago. Being able to see in that table, like, oh, the media spend is increasing through this particular campaign. Oh, and the IROs is starting to flatten out or come down a little bit. These are all the, this is, this is the whole sequence of how we go from how we went, all the way from we have a contribution margin problem at the

[00:26:39] very top to now we're making specific changes in a Google Ads campaign as a result.

[00:26:43] Richard: Yeah, totally. I mean, I think this is like a great illustration of, you had mentioned before, like when you ask people like, what is your ROAS target? And there's like such a wide range of answers, and I would imagine, and at least in my experience, a lot of those ROAS numbers come from just kind of. Nowhere like a feeling.

[00:26:58] Tony: vibes.

[00:26:59] Richard: Yeah, yeah, exactly. Was four is better, bigger than three, let's go for four. You know, that kind of thing. And what we're showing you is like, this is a completely different level of clarity. So what we're moving from is not maybe a little less clarity to a little more, a lot of the times what you'll be moving from with the profit engine system is.

[00:27:16] No clarity, all vibes to an actual systematized process to go from a big picture problem to a small picture adjustment that will then affect that big picture problem later.

[00:27:27] Tony: And something you said sort of jumped out at me. So four is better than three. Four is not necessarily better.

[00:27:33] Three. If Three. is the right number, if three is the right number based on the a MER math that folds in cost of delivery and the incrementality of the channel, then three is the right number.

[00:27:45] It's not four. If you're running a campaign at four, in that case, you are absolutely leaving volume on the table, which is, I think exactly what we, what we illustrated in this particular example that we walked through is we have we have, you know. Volume that's, that's being left on the table, on the Google side specifically.

[00:28:02] So, and there's, there's like all sorts of like, ways to poke holes in this. Like, for example you know, the, the incrementality sort of fold in is always challenging, right? Like it's based on the last test that we have around, or sometimes it's based on the benchmarks. But what, what we would say is it. We have to operate the whole system end to end with the best available information that we have. And if for whatever reason we don't, we find, we find ourselves distrusting the incrementality read, then we gotta go get another incrementality read. 'cause the whole system is predicated on having this end to end sort of measurement stack that connects like, oh, I'm going to, I'm getting signals to go fund the hell outta that campaign.

[00:28:44] I'm gonna go do it. I'm gonna do it. Really, I'm gonna do it with confidence.

[00:28:48] Richard: Yeah. All right, cool. Well, Tony, anything else that you wanna draw out here on the hierarchy?

[00:28:55] Tony: No, I don't, I don't think so. I think may, maybe just like some call outs.

[00:28:59] Richard: Sure.

[00:29:01] Tony: back, back to that call out around the whole system is predicated on. It's predicated on good data in equals good decision making.

[00:29:11] So when we go through our, when the profit engineer goes through their planning process being able to have really good data around cogs and opex and all the cost side of the business, it, that's the only way that we can get to the other side of this thing where we we're doing like, activity in media or whatever.

[00:29:29] So like that's really important on the media side. Related to the idea of incrementality, it's really important to have discipline when you are crafting media campaigns specifically around the audience layer so that when you're trying to reach new customers you're confidence, you're reaching new customers and you have incrementality tests that support what, what the incremental contribution of a new customer is and vice versa for existing customers. Without that sort of good data in good sort of structural premise, the whole thing breaks down. So it looks really magical when we have it all dialed. That all wired in correctly. But the, the point that I wanna emphasize is that all the majority of the work is in getting that structure and that foundation laid. And then once we're getting to like doing stuff in media accounts, we're doing it on, on top of such a solid foundation that. We're, we're very likely to produce the outcome We're after.

[00:30:34] Richard: Yeah, sorry. No, it's, it's, it's a good call that this, this is built on having. A just profound amount of data all brought into one place that's all sort of being crunched at the same time. Or that the profit engineer is able to then sort of pull from in order to actually identify what's real and then take action against reality, I think.

[00:30:54] Tony: Yeah.

[00:30:55] Yeah.

[00:30:56] Richard: Cool. All right, well, I think that's that's gonna do it for us. Yeah, as, as always, if you are interested in getting this level of clarity, having us build this rich data set for you and kind of showing wiring this up for your business, you know where to find us, commentary code.com, hit that hire us button.

[00:31:12] We would love to talk to you. But yeah, that's gonna do it for us for now. Tony, thanks for joining us, everybody else. We'll see you next time.

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