Understanding Spend, aMER, and Spending Power

[00:00:00] Richard Gaffin: Hey folks. Welcome to another episode of the Ecommerce Playbook Podcast. I'm your host, Richard Gaffin, Director of Digital Product Strategy here at Common Thread Collective, and I'm joined today by two very special guests. One, you've ha you've seen him before on this podcast, many a time. It's our VP of Ecommerce Strategy, Mr. Luke Austin joining us to kinda report from the trenches. 

And then a second special guest we haven't had him on. He actually, he hasn't been on with me at least in a long time. Director of Data here at Common Thread Collective, Mr. Steve Rekuc. What is going on guys?

[00:00:26] Luke Austin: Decided to jam on some spend aMER models. I think the last, some of you may have seen Steve on the operator's pod not, not too long ago, discussing this topic at a, at a high level. So, we, we've got some things going on that I think will be interesting to, to share with you all. But we're gonna, we're gonna do a deeper dive into spend a r model spending power, how this fits into the CTC system and what the, what the output of these things actually look like.

Walk, walk through a couple of these models in, in person live.

[00:00:55] Richard Gaffin: Yeah, exactly. Yeah. And the point of this really is to give you guys a detailed sense of like what the actual concrete impact on your business can be of setting one of these spend and a MER models up.

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[00:02:33] Richard Gaffin: So just a, a couple words quickly about like why we're talking about this now. So currently as you're listening to this, this will probably have launched the previous day, but we're running a special offer for brands that are in the eight nine figure range. And the idea here is, is we wanna talk to you and if we determine that it's, it's a good fit, we wanna build one of these models for you for free. So if you're interested, let us know. Click the highest button, common thread code.com. Let us know that you're interested in getting one of these free reports. So. Without further ado, let's jump into exactly what one of these reports can do for you. Starting with some definitions here. So Luke, why don't you tell us what is a suspended A MER report? Just so we cover our bases and then we can roll from there.

[00:03:17] Luke Austin: Yeah. So for, for those following along in a, in a visual format, we'll be sharing,

we'll be sharing along the way as well just to give us a, a better sense of how, how these tools live within our system. And you can follow along that way. And then we'll, we'll try to describe it as well for those, for those listening in.

So, the spin a year model is really the starting point of our forecasting and planning process at CTC. First comes data integration, getting all the costs, right, getting every, getting the marketing calendar, ingested, et cetera. All the data points that are required to be able to build a forecast in the modeling off of.

But the spin a e model is the first step. Once we have the data ingested all the costs, right? In being able to project out. Future expectations for the business outcome and what the budget allocation should be for the business. And so what you're seeing here is an overview of the planning process and on the left hand menu what we have in sort of sequential order is that we start with our customer models, the new customer model, returning customer model of then effect model.

Then we go into the marketing calendar and event planning and the impact of the email and SMS sends against each of those days. Then we go to budget decomposition, really channel level spend targets and then spend targets by day based on day of week effect, the event effect model and incrementality and forming the ROAS targets for each of the channels.

Then we ingest all the sort of cost level details on the p and l side related to taxes and shipping confirming discount rates and returns, et cetera. Then we get to a full p and l of output. So we've talked about this in some other spaces and some other podcasts. Of a high level of the planning process and what those other components are.

But what we're gonna deep dive into is the spend MER model or the new customer model today and what and what that is. And it's the first step in this planning, in that sequence of planning process that I just talked through. And what it does is helps to set the optimal budget allocation amount. Based on your business objective for becoming year so that we build out a year long p and l level forecast.

What the first question that we're needing to understand is, what is the optimal budget? Total budget allocation across all channels against your specific business objective. That's gonna set what the new customer revenue expectation is. It's gonna set what the contribution margin from new customers is.

That's gonna feed into how many new customers we're gonna acquire each month that will then be returning customers and feed the returning customer model. But this is the starting point. And the what, what we've seen over the years of doing this is the spend a U model output is. Shocking for almost every brand there, there aren't a lot of spin, a Euro model outputs that we'll look at and, and be like, oh yeah, that's like, you know, that's 5% different than what our previous budget expectation was.

So we, this helped us sort of fine tune and calibrate it. The output tends to be much more dramatic than that in showing brands. They're substantially overspending the expectation and way past the efficiency curve in terms of the degradation of their media spend. Or that there's, they're leaving meaningful opportunity on the table without taking 'em to con the context of LTV repeat purchase against the new customers or some of those other factors.

So the out the output is really dramatic. And so we're gonna show here two, two individual brands, actual a ER spin, a ER models, and and someone of what that output looks like to illustrate this really specifically. But before we do, I think it would be helpful, Steve being the the brains, the mastermind behind spin a r model and the creation of them to sort of give us a backdrop, if you would, Steve, on what, what, how do we build spin a ER models?

What are they, what are the data points we're pulling into it? What type of models are, are they? Let's get a little let's get, let's get granular on what the work that goes into building these out. And then we can share the, these two brand specific output.

[00:07:06] Steve Rekuc: Vu. think one of the things that. It's important to point out in that is that it's not necessarily the same each month either. So it might be that you're o underspending in one month and underspending in the next month. And that's one of the things that I focus on in, in building the spend versus a MER model.

So we're kind of establishing curves for every previous month that we have good data for, that you've kind of spent in the same sort of region. And had the same relative amount of new customers. So we're working within kind of bounds of what you've done in the past. Establish what you've done in the past and see how that spending power has looked like.

How much can you spend with your A MER degrading? How quickly does it degrade in all the past months? Then we're creating multiple models for the future. To 40 different models that we utilize then pair down using an ensemble model. So we're gonna throw out some of the worst models that either say, oh, you're going to grow exponentially, or you're gonna crash exponentially.

So we're looking at like the, the reasonable considerations and then. Using the ensemble model, we're taking a combination of those to determine what is most closely reflective of the past, and then is most closely reflected then of what's going to occur in the future for your brand.

[00:08:24] Luke Austin: Great. So couple couple things to to pull out and clarify there. Ensemble model, I think is the most helpful way, at least for me to think about this, which is this is not just one model, it's an ensemble. So it's a combination of a number of different models. I think it's like, Steve, is it 15 or 17 different models that will look at and potentially integrate across each of these outputs?

[00:08:47] Steve Rekuc: It, it's closer to 40. 

[00:08:48] Luke Austin: Okay. I was way.

[00:08:50] Steve Rekuc: I'll pare it down and it'll, it depends on the brands. 'cause some, some brands will have models that if you're projecting out 18 months into the future. and in this case we're, we're doing through 2026 when we model. if you're projecting that far out in the future and it's saying erroneous things for December of 2026, then we're kind of removing those models from consideration.

So it might get paired down in the ensemble to only 20 you know, some cases 12, depending on how volatile the brand's performance has been in the past.

[00:09:19] Luke Austin: Yes, yes. Okay. So, maybe we edit out the part where I, I was way off and guessing the number of models doesn't, doesn't look great for us. But the, so. Ensemble model. There's 40 different models that we're looking at to potentially include into this. Looking at things like seasonality factors, the historical degradation of the efficiency.

We even look at things like Google search trends right around the category and take these things into account and building the model that is most predictive of what the future, what the future months look like. And then, in terms of our confidence level in the models, we're combining these models together to create what the best output is.

That could be up to 40 models that we're including in this, in this one on ensemble model output. And then what are we doing, Steve, in terms of back testing the output of these models or understanding how confident we can be in the output that they're generating.

[00:10:12] Steve Rekuc: Yeah, I'm checking all the previous iterations to see if in using that ensemble, how closely we would've come in the past to predicting the spending power and then the cac. Or, and the A MER of previous months. So we're trying to kind of aim and get within a very reasonable bound typically we're within like five, 10% of what they've done in the past with those models.

[00:10:36] Luke Austin: Great. And then let's, let's talk about another concept, which I, I think is it, it, which is important to this discussion, which is the idea of spending power. So spending power is is a way that we're able to back into. Brand's ability to increase media, spend velocity or volume and the relationship of that to the degradation of that media, spend the efficiency related to that at different levels.

And every brand has a different spending power available to them. Some brands, media just degrades more quickly than others. It falls off the quick it falls off the cliff faster whereas others are able to continue to spend without, with lower degradation of the efficiency. So Steve, talk to us a bit about spending power and, how we think about that idea in in this output.

[00:11:23] Steve Rekuc: That was a great concept that we kind of developed to convey how quickly a brand's ability to spend without degrading their. Efficiency kind of, came about and it does vary significantly from brand to brand as well as from month to month. So some brands are a little bit more seasonally agnostic.

If you're selling something like an auto part or supplements or medicine, those tend to be a little bit more. Stable over the year where you're not seeing as much variance. Whereas if you're selling something that's a little bit more outdoor specific like skis or or goggles, like, they're gonna be heavily concentrated in that late fall and winter timeframe. so it, it's important kind of to establish this for your brand, and that's one of the reasons why we want to offer it to potential clients. In that we get to kind of see what's going on with your brand, and you get to see what's going on with your brand over, over time and into the future.

[00:12:17] Richard Gaffin: So actually maybe, maybe to summarize that real quick, like the idea behind one of these spend A MER models, if you break it down to like the most concrete definition is this gives you the most accurate possible, at least as far as we can tell, right? The most accurate forecast of what is likely to happen for your specific brand. The specific, the seasonality of your product. So, and that's, that's the thing that helps you kind of make those judgment calls on on budget allocation, on spend, whatever, because you have some understanding of, of what's expected to happen, and that's much more clear and it's, it's not just sort of an arbitrary guest, which oftentimes is what people are building their forecasts off of.

So, just wanted to make sure that we kind of clarified that specific reason. But anyway, I'll, I'll throw it back to you guys.

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[00:13:27] Luke Austin: That's, that's great. So to close the loop on this, on this piece of conversation around the methodology and approach for building out these on ensemble models. We've also got, got up here on the screen visually for those for those looking, I'll describe it for those just listening. Is the workbook on the backend of what it could look like for, for one of these ensemble models for a spend a MUR model?

And what you're seeing is for most of us, not something we'll be able to track along with much, but is lines and lines of code that goes into. Applying each of these various models onto the dataset, seeing what the output in terms of the predictability of applying that model against the dataset is, and then deciding whether to include or discard that model and working through each of the models in that way to get to the best ensemble model.

Output. That was probably the highest level way of describing what's going on here. Steve, anything you'd add in terms of what we look at or what goes into the workbook behind the scenes before we get to the actual model output examples?

[00:14:28] Steve Rekuc: Yeah, I, I think there's a lot of consideration to kind of remove outliers and to see performance that's indicative of what your brand has been doing most recently. we're kind of throwing out some of the more maybe the high points that you might have had back in 2020 even some of the lower points that happened, as well as a brand's starting to grow or a brand's kind of diversifying.

[00:14:52] Luke Austin: Yeah. Yep. Yeah, so the, the, the data set, having a data set that's clean and gonna have data points indicative of the future is really important. And then applying. Various models and seeing what the output is. So that, that's what the spin, how the span and the a ER model fits into our system from a forecasting planning standpoint is the initial point in the process.

Steve walked us through what we look at in building the spin a ER models to get to the highest confidence output possible. And then we'll land here with two brands specific spend a r model outputs and what some of those sort of surprising results that we referenced earlier how that can actually actually take shape.

So what we have up here is an example of a spend a R model in stat stats for a specific brand. And we can see a couple things which is we have every month of the year spend. A MR new customer revenue, new customer contribution margin as well as the core sort of inputs and settings that we're taking into account that are being applied onto the model.

So our full, fully loaded cost of delivery percentage I is an input here that we're taking into account. We're taking into account the A OV taking into account LTV multiplier over different time periods so we can look at 60 day LTV ninety, a hundred twenty one year LTV. To get into what that expectation will be.

And then what we're able to see, which is the core output here, is what is the model's recommended spend level. So total budget allocation based on the stated business objective for that specific. Time period. So what we can see here is, this is a good example of a brand that we started work working with earlier, earlier this year.

But to start out the year they were they had a, a really different sort of approach in terms of their budget allocation. They were spending a lot more money than we are spending with them now. And one of the big reasons for that was the spin, A MER model giving us a clear indication into what the optimal total budget allocation.

So this is a, an apparel brand. They sit within that category and what you can see is we're looking at March of 2025. So earlier on this year they spent $2.14 million total in ad spend at a 1.6 3:00 AM ER. And. What we can see is what the model would recommend. We have three different sort of business objective output selections.

One is to maximize contribution margin from first time customers in month one. So I want the maximum amount of contribution margin from my new customers this month. The second option is I want to maximize. The contribution margin for my first time customers over a set LTV defined period. So lifetime contribution margin.

So I want to maximize life, my contribution margin over 90 days or 120 days or over the next year. So that's gonna allow you to be more aggressive than maximizing CM in month one, right? 'cause you're willing to accept the longer time period. And then the most aggressive business objective scenario, which, which is gonna be maximizing revenue in month one.

Had a break even first time customer contribution margin. So I'm willing to go break even on first time customer cm. I'm gonna get plenty of cm from my returning customer cohort, so I'm good there. I'm pushing for top line revenue and growth and the and so what we see here is in March they spent 2.14 million in total ad spend.

The spend a ER model recommendation based on a maximized. Contribution margin stated business objective. So maximizing CM from first time customers. The most conservative scenario is 1.06 million in total ad spend. So 1.06 million to 2.14 was the actual, is the gap that exists between the, those two recommended scenarios.

And we can see this sort of come to life. And the different levels of de degradation of the efficiency for this brand as well. We're able to see across di different levels of span, different intervals of span, how they efficiency degrades over time for, for this business. And so what we have up on the screen here for those just listening along, is, every $20,000 interval of incremental spend and what the efficiency is associated with that $20,000 of incremental spend. So as you spend $20,000 more from set level, what is the incremental A MER associated with that spend? The incremental new customer revenue and incremental contribution margin from from that.

And so what we're able to see is. For March of 2025. The break even point on first time customer contribution margin for this business was right around $1.6 million in spend. So right around $1.6 million in spend, or 2.3 4:00 AM ER is where this brand would be at break even first time customer contribution margin.

So anything past $1.6 million in March of 2025 starts to yield. Negative first time customer contribution to margin. And, and increasing at, at increasing amounts because the efficiency degrades more quickly as, as you scale up in that way as well. So the drawing this back to, they spent 2.1 million, $2.14 million in March, 2025.

That was a negative $400,000, little over negative $400,000 in first time. Customer contribution margin within that month, and what the model was stating is that we could have spent instead of 2.14 million, the break even point being 1.6 million, we could have spent 1.6 million instead of 2.14, and the trade off in terms of the top line revenue against that incremental media spend, would've been about $200,000.

Right around that amount, $220,000 would be the difference in that trade off between

[00:20:52] Richard Gaffin: I,

[00:20:53] Luke Austin: those spend those spend gaps.

[00:20:54] Richard Gaffin: to zoom in on this because I think this is, like, this is kind of where the, the sparkle is when you show these

[00:20:59] Luke Austin: Yes.

[00:20:59] Richard Gaffin: types of, of of models to people where you're demonstrating that like each additional tranche of let's say 20 K in spend, is actually losing you money over a certain point. And

[00:21:09] Luke Austin: Yes.

[00:21:09] Richard Gaffin: let's say hypothetically this brand were spending when they were spending two point whatever million. And they were getting 1.6 8:00 AM ER. Let's say that, that was like, that was their efficiency target or something like that.

[00:21:21] Luke Austin: Yep.

[00:21:22] Richard Gaffin: of the reason this happens. It's like, we wanna continue to push spend. As long as we're at performance, they're at performance. So they're like, let's keep going. But what you see, scroll up here in the in this, let's go back to the top.

Go back to their actuals. Yeah, so we see, yeah, a spend of 2.14 mil yielding revenue of 3.4 and then go to the maxxim. Version of this. Let's pop back to that. Right. So actually what we're looking at is, is a difference of maybe like a hundred thousand dollars in top line revenue. And so what this demonstrates is like they could have spent a million dollars. And gotten the same amount and it got much, much wider efficiency and that would've produced the contribution margin that they wanted. And so this is, this is exactly what these spend and A MER models reveal to you is like just if your spend overall is efficient or not, but what specific tranches of spend over a certain point are actually becoming inefficient, how far could you dial it back to actually attain more efficiency and actually even hit essentially the same top line revenue number.

[00:22:17] Luke Austin: Yep, that's exactly right. What we all know is that there is, there's opportunity cost in either direction, right? I'm gonna pull back media spend and there's gonna be some impact to my top line. Revenue. Abs, absolutely like that. That relationship is gonna exist to some extent. And the other direction, the same thing.

I'm gonna push more media spend, drive more top line revenue, but my profitability at some point will, will be, will be impacted. My profitability percent will for sure be impacted, but the. What the spending a mural model allows us to do is to quantify what that trade off is ultimately, at the end of the day, to, for us to be able to quantify what that opportunity cost is between the decisions.

If I spend $500,000 less this month, what is the, what is the trade off? How much less top line revenue am I getting, and how much more bottom line am I getting and be able to assess the optimal point of budget allocation based on the state, the stated business objective and. To this example. There, there's a much wider range of a, of possible outcomes that I think many brands understand in terms of their, the choices that they have, right?

It's many of the conversations related to media budget allocation tend to be okay. We've, in this example, 2.14 and media spend right in in January, or sorry, March of 2025. The conversations may be around. We spend around 2 million a month on our core DR channels. It kind of scales up some in November, you know, and, and maybe into December, black Friday, cyber Monday holiday, but like 2 million a month in sort of a normal month.

So if we're trying to be more efficient, could we cut back, you know, 10%, 15% and be a little more efficient for these next months? What the spend MER model is saying is like. Zoom out. You could, you could assess this in a completely different way. What if you cut media spend in half, right? What, what is the impact to your top line and your bottom line in different in different timing outcomes?

What if, what if it's a 15% difference, right? What if it's 30% difference? And then what if you went the other direction as well, right? For this brand, it's sort of indicating we should be more efficient and pullback, but we'll show the other example here. Which is a brand where it's indicating the other thing, because of the LTV impact, there's more room to push, there's more ability to lean in and drive more volume.

But what this does is it helps us to quantify the trade off and the opportunity cost between incremental levels of spend every five, 10, or $20,000. What is the trade off in top line and bottom line? And then for us to be able to zoom out and have a much bigger conversation around what are we trying to do with the business and what is the total budget allocation that's gonna, that's going to get us there rather than.

Orienting things just around, okay, here's our, you know, sort of trailing 12 month media budget. Here's our MER or our percentage of revenue, our aco. That's what we're gonna set it as. So whatever the percentage of revenue is, that's what the media budget is going to be. That misses 40 different models of input and being able to assess what your potential outcomes could be for for the business.

[00:25:12] Richard Gaffin: Cool. Let's, let's roll. I'll let you kind of click over to this next one here. Okay. So just to then, to recap what we're looking at here is essentially a brand that when they came to us, what we've determined via the spend a MER model is that they were underspending, they were just sort of making, making the opposite. Mistake. So let's dig into that.

[00:25:30] Luke Austin: That's right. And the main, the main impact here. So this another reason this is interesting to look at is this is a supplement brand with very high LTV, that there's, top top five percentile in terms of the LTV outcome against this brand. And so what that does is it shows up in the disparity between the maximized contribution margin and the maximized lifetime contribution margin business selections.

So, what, what we're doing here, we're looking at September, 2025 maximizing contribution margin in that month for this business. Looks like. Spending $316,000 at a three eight 3:00 AM ER for $1.2 million in new customer revenue. $316,000 for max maximizing lifetime CM for this brand in September, 2025.

Looks like spending $855,000 at a 2:04 AM ER to drive 1.74 in first time customer revenue, so $316,000. Versus $855,000, both with the objective of maximizing contribution margin, right? They're both after the same thing, maximizing contribution margin for the business, but they're constrained by different time balance.

The first is maximize contribution this month. The other is I actually wanna maximize contribution margin from this cohort of new customers within a year. So how much can I push to be able to, to be able to get there against against those, that customer cohort and we can see the breakdown. So of the trade off of these different levels of spend in the spend MER data points here that we have on the screen where we're able to see how much the lifetime contribution margin starts to separate right?

In month one. The difference in new customer revenue is really small, right? There's max, the cm lower spend scenario is gonna drive $1.2 million in new customer revenue. The max lifetime contribution margin is gonna drive. Five $1.7 million in new customer revenue. So it, it's just a difference of $500,000 in new customer revenue in month one, which after you take out the additional ad spending cost of delivery, like it, it doesn't seem like it would pencil to be able to maximize the contribution margin outcome for the, for the business.

But when you apply. 350% LTV increase within one year, you're, it's a, it's a whole different ball game. Because though that additional volume of new customers that you're acquiring in month one is going to drive an outsized impact within a year in terms of the broader contribution margin, so the lifetime contribution margin, even though the new customer contribution margin, there isn't or new customer revenue, there isn't a.

A massive difference there. The lifetime contribution margin is where we see the difference. Start to show up where by engaging with the max lifetime CM strategy, so eight 50 5K instead of three 16 k and spend, the brand's gonna drive $500,000 more of contribution margin within a year time period.

[00:28:27] Richard Gaffin: Right. So yeah, I mean, I, it, it's a good illustration of like nailing down. I mean, in order to make this work for yourself, you have to have some understanding of what your business's core goal is, like, what you are trying to maximize. But then also, like in this particular instance with the supplement brand, some understanding of the inherent mechanics of your business that if you want to maximize these certain things and you have a business that's, where, where your one year LTV is spectacular. when you're sort of sitting down and forecasting by kind of guessing what's gonna happen based on what happened in the past, that's the type of thing that's very difficult to have the foresight to do. But with the sort of the 40 different models system that we have here, being out actually algorithmically able to predict LTV over the course of a year really transforms the way that you behave now. And I think that's kind of, that's kind of what we're showing here is like your, your ability to affect. Your future a, a year from now

[00:29:22] Luke Austin: Yeah,

[00:29:22] Richard Gaffin: actually starts, starts here in kind of constructing this this forecasting model. 

[00:29:27] Luke Austin: Exactly right. And the other layer that is what we'll add on in, in addition to the LTV, is the halo effector impact across other sales channels that driving media spend here as a resulting in. Right. So Amazon be the big one where for most brands who sell on Amazon, there's a, there's a.

Substantial halo effect of your, the spend that you're driving towards your D two C business, on your Amazon business as a heuristic, like it's probably in the neighborhood of 20 to 30% minimum. In terms of the halo halo effect, it's diff different for every brand, but there is, there is an impact. And so what we see is not just for brands with some LTV outcome, but also more for brands that have a substantial.

Distribution across other sales channels is that many are underspending against the opportunity for their business by not having the full purview of the sales channels and LTV against the spend a MR model brought into the picture. Which is, which is what it, what it's all about bringing, bringing everything into the picture and then being able to see what the business outcome is against the total business perspective.

Otherwise it's, it's. It can be really easy to continue to overspend against the opportunity and just sort of accept like, well, I think we gotta spend $2 million a month like the first example to keep our revenue up. And that's just sort of like the range we'll be in, or in this case, to underspend meaningfully against what the opportunity could look like.

[00:30:50] Richard Gaffin: Yeah. Okay, so let's, for, let's say, say I am somebody who's sitting here. I'm a, the CMO of an eight nine figure company, and I'm listening to this. What, what problems am I, am I struggling with right now that this, that this solves? So obviously we've given some examples. There's, you maybe have some sense of what that would be, but just gimme a concrete picture of like the situation that the CMO is in, that this then comes in and, and clarifies a lot for him. Steve,

[00:31:20] Steve Rekuc: Sure. Yeah, this definitely helps 'em kind of figure out how their spend is impacting each month differently and how to kind of optimize the spend in each future month so that they can maximize their contribution margin or their lifetime contribution margin, or how much can they push revenue without de grad. Too much into their contribution margin. kind of really important to establish and understand that for your brand, so you'd at least know the map of what you're about to do rather than just set a a constant $2 million a month budget.

[00:31:54] Richard Gaffin: Yeah, that makes sense Luke.

[00:31:56] Luke Austin: Yeah,

it will. This will first help in any conversations related to forecasting budget planning. Which we all find ourselves in to some, some extent, either on a monthly basis, quarterly basis, or we're planning out annually for, for the year. So this tool is going to be essential and very helpful in understanding what the revenue outcome is going to look like for your business at different levels of spend and helping you to set what the total budget allocation should be connected to that.

And to build off what Steve said is it's gonna, it's gonna allow brands to think, differently than they likely have in terms of what the range of possible outcomes for their business look like, and the levers and options that they have in terms of the total budget allocation against those, against those things.

It's gonna, it's gonna widen the conversation. It's gonna widen the purview to think about to think about outcomes that might exist outside of the current set of, set of considerations that come up in the monthly quarterly annual forecasting conversations. It's like when we, we love looking at the output of these spin a ER models.

When Steve and team. Finishes them because it's, it's, it's always surprising. There's, there's a really dramatic output that exists with each one of these that leads to some challenging conversations and some really fascinating conversations that we have together with the brands that we work with and build these for.

Because it highlights things in terms of the historical media mix that could have that could have been done differently potentially, but more, more importantly, it widens the conversation in terms of how we can approach the future and the context of the business objective. And it's fascinating.

[00:33:32] Richard Gaffin: Yeah, I think too, like the, the, I can sort of picture a situation in

which at like at your business is kind of going fine. But you're kind of at a loss to figure out how to break through to another layer of growth or whatever, right? Like if you continue doing what you're doing, you'll, you'll be okay.

You'll maybe grow a little bit, whatever, but you're just racking your brain to try to figure out like, how, how could I possibly like breakthrough or break away from this kind of current situation that I'm in? And what the spend and a MER model does by offering basically like insight into hidden information is it reveals to you exactly the levers that you could pull. To get there because it shows you what's actually happening and what's actually likely to happen. Steve, did you want to jump in with something?

[00:34:14] Steve Rekuc: Well, yeah, I think it also adds the opportunity in certain months where you're like, oh, we could have, we could press spend a little bit more in May and maybe not as much in June. so it points out some of those opportunities. I think it's important. To, to understand and set that as a baseline because it for, for future months.

Because then you can also determine if we need to do something more, if we a new product launch or have a special marketing moment one of the four peaks that we often re recommend in that you might want to, you can push these up a little. You can say that we're gonna do better than the model because we're going to do something.

And that's where like our growth strategy team comes in and to advise and, and suggest what they could do to improve on that.

[00:34:59] Richard Gaffin: no, that

[00:35:00] Luke Austin: Yeah.

One, sorry. Just that, that reminded me one other example. We, we could go through so many different examples, but work with the brand. Personal care brand middle last year that we built Spin, aim R model four, and then worked with and. The output of the spin a mural model was that for the consecutive months of the remainder of the back half of last year, they spent close to half the amount that they were spinning previously on their core conversion optimized media, and drove the same new customer out revenue outcome that they, that they what that they were driving previously.

And at substantially higher contribution margin, obviously, right? 'cause you have so much less media spend against it. And. That's, that's fantastic. The savings are great, but what that ultimately allows you to do is find new ways to allocate that media that's gonna produce incremental growth. Right.

It's basically showing you the current playbook that you have on your core conversion channels and the thing like. You can't just keep increasing the budget and expect it to produce incremental outcomes at some, at some, let's take those dollars and let's reinvest them into higher levels, levers that are gonna increase the efficiency of the business as a whole.

So additional creative output, channel expansion investment into product collaborations, marketing moments, all the things we talk about all the time, right? But be able to understand, cool, this is, this is the necessary budget level, but then let's take the excess and the remaining and invest it into things that are.

Into units of growth that are gonna produce incremental gains for, for the business. So.

[00:36:28] Richard Gaffin: Yeah. No, it makes sense. So, again, if this is something that you're interested in, again, if you're an eight, nine figure business. And you're curious to figure out how to break through to the next level of spend here to break through the next level of growth, to understand what's actually going on in your business, in your business, and sort of what hidden factors are playing a role in maybe a lack of efficiency that you can't fully see for yourself right now. We wanna talk to you, hit us up, comment thread code.com. Hit that high risk button, let us know that you're interested in the spend A MBR model. We'll get in touch with you if you think you're a fit, and we can move forward from there. Okay. Anything else that you guys wanna hit on this? Any sort of maybe key takeaways from, from this exercise in, in general, Steve?

[00:37:11] Steve Rekuc: Yeah, I think these are crucial to kind of get built. That's why it's a the starting point for our forecast is to establish what we think you can spend profitably and then work out the details from there. But that's it's almost like building the map of what's possible for your business in the future,

[00:37:26] Richard Gaffin: Mm-hmm.

[00:37:27] Steve Rekuc: In terms of profitability versus your spend in these typical ad channels.

And I think it's a necessity.

[00:37:34] Richard Gaffin: Luke.

[00:37:35] Luke Austin: Nothing I. Else to add?

[00:37:38] Richard Gaffin: Do it. That's right.

[00:37:39] Luke Austin: Yes, do it.

[00:37:40] Richard Gaffin: do it. Get in touch with us. We'll build this map for you. Alright folks, well Luke, Steve, appreciate you joining me today. And to everybody else out there, thank you for listening and we'll talk to you next time. See.

[00:37:51] Steve Rekuc: Thank you, Richard.