Meet PAM: The Profit Allocation Model Revolutionizing Ad Budgets

[00:00:00] Richard Gaffin: Hey folks. Welcome to The Ecommerce Playbook Podcast. I'm your host, Richard Gaffin, the Director of Digital Product Strategy here at Common Thread Collective. And I'm joined today by two people who've been on the pod before. They've been on the pod a few times. We've got Luke Austin, our VP of Ecommerce Strategy joining us and also Tony Chopp, the chopper himself, our VP of Paid Media here at CTC.

What's going on fellows?

[00:00:22] Tony Chopp: Hello, Richard.

[00:00:24] Richard Gaffin: Hi Luke. What's up?

[00:00:26] Luke Austin: Getting ready for the biggest time of year, the super bowl of e commerce. Somehow we're here in the 20th of November. Probably a good precursor for, I'll be sipping on a red solo cup that does just have water. It's what we had left in the office, but we're, we're very optimistic, right? This is not an indication of my current my current scent, our current sentiment or mood.

But yeah, we're, we're in a lot of early word sales and holiday collections have launched pretty positive early signs across the board and, and anticipating what the FCM holds.

[00:00:57] Tony Chopp: to

[00:00:57] Richard Gaffin: you go. Well, red solo cups represent a party. So maybe we'll still look at it from that framework. So we were talking about, before we, before we hit record here, we're talking a little bit about the GeoGuessr world championships and the Forklift world championships, but essentially what we're talking about here, BFCM is coming up, this is the e commerce world championships, like you're saying, the Superbowl.

And so what we wanted to talk about today is a tool that we've put together as part of our profit system. That helps you answer a very specific question, which is essentially what it does is it builds you a system to figure out how to allocate budget. So this system is called PAM, which is the profit allocation model.

And essentially what we want to do or what you wanted to do, Luke is to jump on here and kind of walk our listeners through exactly what this is, what kind of questions it answers, and then kind of how we incorporate it into our day to day work with our clients in order to make a very specific decisions around spend around budget allocation and around. So I'm going to just going to throw it over to you, Luke, to kind of start walking us through exactly what the profit allocation model is and, and kind of how it might be relevant to our listeners.

[00:02:02] Luke Austin: Yeah. Yeah. So this is, this is really a project between that, that Tony and I have spent a lot of, a lot of time on over the course of this year and clarifying the framework methodology and the tools surrounding how we most effectively allocate media budget for the brands that we work with.

We have the opportunity of seeing A ton of different DTC Econ brands of all different sizes within our data set. And we get insight into what works really well, what doesn't work as well. That's that's used to be able to refine the optimal framework for setting a budget allocation. And what we've done profit allocation model.

We've shortened to Pam intentionally personifying this as a lady named Pam. She is the person that gets to set our budget. It's not up to me. It's not up to Tony. It's not up to Richard in terms of this is what my budget should be. This is where it should be spent. These are what my daily targets are.

Pam is who gets to make that decision. And Pam represents a framework of a few different tools and the methodology around how we most effectively Media dollars for the brands that we work with. So what we wanted to actually start out with for everyone to make this real is three simple questions that we, that are in relation to how media dollars get allocated across the board for DC comp brands.

And we'll pose those so that we can all answer those for ourselves in terms of how we as marketers or operators are setting these budget allocation and making these decisions. What I'll say is that all of us, whether we're doing this internally or audibly in answering these questions are going to have different answers.

I'd be very surprised if any 1 person had the same exact answer for each 1 of these 3 questions, because in our experience, people are making budget allocation and target setting decisions. All sorts of different ways. It is. It is incredible how many different frameworks there are out there. How many different tools are being used?

Triangulation of all sorts of different metrics between MTAs, incrementality tools, MMS, last click, and then the platform attribution, the difference between click and view. Like, you can just go down the line. No one is doing it consistently. So that's what we're going to walk through and start out, Tony.

I don't know if there's anything else in terms of sort of the context or impetus in terms of what we've seen over the course of this year in developing this and anything else that that's worth highlighting or bringing out before we start getting into how we, how we do things and what we've landed on.

[00:04:30] Tony Chopp: No, I, I think that's a pretty good overview. I, I guess maybe the 1 thing to add is what the, the CTC system, quote unquote, sort of from planning and forecasting down to execution. When it comes to my team on the media team, we, we take our direction and guidance from from growth. And as far as like, how much money we're going to spend and how at what targets and or to say it a better way.

Growth is taking that direction from Pam from the overall system. So I think the thing to add is that what happens here in this in this system is that we have the best possible information around allocation and targets that lead to a business outcome. Which then sets my team up to go and execute.

Right. And when my team has super clear information about on meta, this is the acquisition ROAS target we need. It enables and empowers those individuals to go seek that outcome. And there's a bunch of stuff they have to do to get there. But what they don't have to do is ask themselves the question, is this the right target?

Or sort of go, go into all of that thought space. It's, it's pure. Action. How do I take action to get to this thing I'm trying to get to? And I think that's, that's where the magic really happens.

[00:05:55] Luke Austin: Yeah. Yeah. And it's a great call it because what, what this, this impacts most every role within the DTC e commerce organization in terms of the forecasts being set in terms of the folks executing on the media channels, in terms of the amount of creative that you need to be able to support those like this, this has ripple effects across the the majority of the organization as a result of what you're using to answer these three questions, Specific questions as it relates to budget allocation.

So let's let's run through them. We may pull up be able to pull up these visuals as well as we go through but I'll but I'll try to articulate them. So there's three core questions in this priority order that every d2c ecom brand owner operator marketer. Has to contend with first question, what should my total budget ad budget be to maximize profitability within my desired time window?

What is the amount that I should spend in full this coming month based on what my business goals are for what I'm for when I'm trying to maximize profitability for the organization and realize enterprise value. So what's my total budget be? Second question, how should that total budget then be allocated across each of the individual channels?

For the most incremental outcome. How do I go from that total budget then to a channel level allocation that's going to result in a most incremental allocation of those dollars on driving revenue and profitability for the business. And then the third question, which is what are the campaign level targets?

An expected profit outcome for every single day that I'm going to track against to make sure that this the plan is coming to fruition. Really important campaign level targets campaigns are where are where the settings exist within the core ad platforms, right? On meta, we're setting a campaign level, budget, campaign level, optimization, a campaign level target.

If we're using cost controls of some sort, same on the Google side of things, we have to translate. Total budget to allocation across channels to what should my target be in this specific campaign in this ad platform in a way that connects these, these three things altogether. It's, it's a really challenging progression for sure, but it's necessary because these are the decisions we're all making every month, every week, every day for the brands that we are all involved in working with.

So take a moment, think about those questions for yourself. How am I setting the total budget on any given monthly basis? How am I then deciding on, do I spend 50 percent of it in this channel and 50 in this or 60, 40, what's the channel location look like? And then third, what then is the campaign level target within the platform that I'm going to operate against that somehow connects those to those first two Questions.

Try to define that clearly for yourself of where you're at right now, because what we're going to propose is what our answer is in order to answer each of these questions in the most optimal way

[00:08:52] Richard Gaffin: All right. Well, let's look, yeah, once, once we give the folks a chance to think about the answers to those questions, and let's just roll straight into what we think the solution is here around media allocation. And the first slide that I'm looking at here says incrementality is the gold standard.

So it's a word we used a billion times, but we're, we're jumping back into it right now. So tell us a little bit more about how that kind of relates.

[00:09:12] Luke Austin: Yes. So let's let's take one. Let's take one step back, actually. So incrementality.

[00:09:17] Tony Chopp: just

[00:09:19] Luke Austin: it plays into the second question here, which is how should the total budget be allocated? But first, we have to answer the question. What should the total ad spend budget be? Based on what my, my business objective is now, the tool that we have developed for that at CTC and spent a lot of time in building out through Steve Ricketts work and the data team is the

[00:09:40] Tony Chopp: Bye.

[00:09:42] Luke Austin: The spend a mirror model is an ensemble model of about about 18 different models that take into account degradation of acquisition efficiency at different spend intervals by looking at factors like seasonality, historical performance and we could go down the line. It looks at Google search trends for your specific category.

But the spin name, your models, the tool that we have developed for this what the spinning mirror allows us to see is. At different spin levels for different months in the year. What is my expected new customer revenue contribution margin from new customers? Am er new customer cack at that specific spin interval and then be able to optimize for Three different scenarios maximizing contribution margin from new customers in month one maximizing contribution margin from new customers in a given ltv Time period.

So against a 60 91 21 year LTV time period or maximizing revenue from new customers at break, even contribution margin for that month. And so what we're doing is defining the business objective and then using the spending mirror model to set the spend level and the total ad spend budget for that month based on what the, Business objective is.

So that is the tool that we have developed to answer that to answer that first question of what's my total budget allocation be because again, we, we have seen this being done in all sorts of different ways. Many brands are spending past their efficiency frontier or say that another way.

Spending much more for a negative incremental AMER in any given time period than is necessary. There are brands who are below their efficiency frontier, and then many other brands who just want to be able to see at different spin levels, what is the trade off between my top line and bottom line, bottom line outcome.

So, the spin AMER model is the answer to question number one. Number 1,

[00:11:29] Tony Chopp: How much it Luke, I've heard you say this before, too. And I love it. Like, you said it earlier. Like, Luke doesn't decide how much your budget is. Richard doesn't decide. Tony doesn't decide the spend in our model decides based on the business outcome that's being sought, whether it's maximizing profit in a certain window, or whether it's maximizing new customers in a certain window.

[00:11:53] Luke Austin: yeah, yeah, that's exactly right. And what we're doing each month is we're actualizing the model and then we're updating it based on the actual months that we have in the history with the goal of beating the model. Because the model is a representation of what our current trajectory is based on historical performance.

Our goal is to beat to beat that model outcome. And so each month we're looking at how do we land against the model? Do we land over under under the expectation? And then what does that mean for us setting next month's target and allows us to quantify that really quickly. Really specifically. So spend a mirror model says the total budget.

Then we need to go from here's my total spend for next month. How do I allocate that to each of the core ad channels in the most optimal way. That is where incrementality. Comes in as the gold standard of measurement. So Tony, I'll, I'll kick it over to you to walk through why we're using capitality as our core framework and then how we use it.

[00:12:53] Tony Chopp: Yeah, I think The easiest way to describe it is a really simple premise that everybody's familiar with. If you take the reported conversion value or revenue in meta in Google ads and TikTok, for example, and add those three numbers together, you're going to get a number that's Bigger than what you see in your Shopify store for any given point in time.

So the, all of the platforms have, and there's all the, the attribution and all the various questions that go into sort of the, the mixture of why this is the case, but we understand that there's a difference between what the platforms are saying their contribution is and what shows up in our bank account on any given, in any given month. The purpose of incrementality is, is really, really simple. It's for us to get to an understanding of the, the platforms reported contribution connected to the actual business outcome. So we do this with really simple studies geo holdout studies, very, very simple stuff. We hold out the media. In a specific region.

Now there's a bunch of data science that goes into how we get there. And we partner with all the major platforms like house and measured, and we have our own tool that we use as well. But the idea is really simple, hold out media in a specific area. That's one piece of the idea. Another piece of the idea that's really, really super important.

And this is a thing that I Is really, is really important that we have to latch on to the measuring stick for these studies. It has to be store revenue. Very, very important. All of the channels offer some sort of incrementality or lift studies. Meta does conversion lift. Google has the same thing, but they all use platform reported revenue.

Okay. And that that's actually the thing that we're, we're attempting to interrogate and understand more deeply. Okay, so when we do the purpose of doing any of these incrementality studies is to understand the difference between the platforms reported contribution. And the IROAS or the ICAC, which is our interpretation of that channel's contribution or that tactics contribution to revenue.

So that's the basic premise of how it is, how it happens, why it happens. And I'll add one more thing, which Luke, I know we're kind of getting to the next stage of like target setting this, this idea of IROs or incremental rows or incremental CAC is the piece of information we need in order to get to stage three of the puzzle, which is how do we, how do we set a target in the platform for our media team to go and take action on

[00:15:49] Luke Austin: Yep, exactly. So the, the incrementality percentage or incrementality factor is the key that we're looking for here, which is what Tony's talking about is the It's the relative measure between what the platform is reporting versus what the geo holdout test is reporting and the difference between those, those two things expressed as a percentage, right?

So if, if if meta is for a certain campaign set saying it's operating at a seven day click ROAS of 2. 0. And then we run a geo holdout test and it confirms that I Ross of meta is a 2. 0. Then that has 100 percent incrementality factor against seven Click. Yep. So that is the what it is reporting on meta is very aligned with what the true incremental impact is.

And then the other way. If it's seven day click on MEDIS 2. 0, but the IROAS result is a 1. 0, then that's a 50 percent incrementality factor, right? It's, it's over reporting by 2X in this case, what incrementality is. And the incrementality factor is the key here because what the incrementality factor allows us to do is connect step one, question one with question three in this process, which is we need to go from business goal definition To platform target, right?

The, the platforms have a limited number of options that they can optimize for, right? Meta, you have 7 day click, 1 day click, and then 7 plus 1 as your optimization settings within, within the the campaigns, all based on the Meta reported revenue that it's, that it's operating on against the signal from the Metapixel same on the Google side, right?

We have a little more flexibility in terms of the time window, but we have on platform. Is optimizing against the conversion value that's being passed through the pixel. So we need to go from business objective to watch my on platform target B and the incrementality factor, having clarity on that is the key, because what we're able to do is going back to step one, that's where we set your total budget allocation.

So total level of spend, total level of new customer revenue, that's business objective, what's going to show up in Shopify and your AMER. So we have an AMER target for. The for that specific month. Now, what we need to do is go from AMER target to meta acquisition campaign target, Google P max, non brand target, Google brand target.

And the incrementality factor is what translates those things. So how that works. Yes. Go ahead, Tony.

[00:18:16] Tony Chopp: Can I use a really simple example? Sorry to interrupt you, but so that the Google brand search one is my favorite example of like the illustration at this point. So I'm going to use like Luke comes to our team and says, hey, we need a 2 to 1. In order to, in order to be profitable. Luke doesn't say it, the spend in AMER models says it, but then Luke, Luke ultimately says it Google brand searches is such a funny little cohort of traffic because it's people who already know about you.

And as it turns out, it has a really consistently very low incrementality. Okay. So, just for the sake of simplicity. Let's say that Google brand search, we find the incrementality factor to be 20%, which is actually pretty consistent to where we see it at across our, across our studies. Really, really simple math.

We take that two to one AMER target. We divide it by 20%. To get to the platform target. So, I'm doing the mental math here. I think I think my numbers are right. 2 to 1 divided by 20 percent equals 8 to 1 or is it 10 to 1? I don't know. I'll do the math. The point being 10 to 1, right? So the point being in platform in order in order for.

The Google buyer to help the business achieve their goal to help get to what the spend in AR and the, our model is telling us is possible. That middle step of the incrementality percentage is the critical piece. We take that. Now we go run that Google brand search at 10 to one. And I use this one as an example, because it's such an extreme example of a, of a piece of traffic that has really low incrementality, right?

[00:20:00] Luke Austin: Yeah, there's, there's, that's the channel that usually has the most Disparate connection between the on platform reported versus what the true eyebrows read is. The, the other, the other channel set that is even lower than that would be like coupon site affiliate. So like rock 10 capital one shopping that that can get, that can get lower, but usually that's where that's kind of the lower end.

And then higher incrementality channels would be like, Meta acquisition only optimized on click only basis. I think, I think that's what we can talk through now as well. So what we're after is the incrementality factor. That's step we're using incrementality, set the budget allocation. And so, there are two two main processes that are happening sequentially that are helping us to get to incrementality factors.

One are what we call incrementality starting points. And then two is your incrementality testing. Road map. So let's so let's start with the starting points. And then Tony, maybe you could walk us through the testing road map. So what we're trying to get to is. What are the incrementality factors that you should use for your business to get from AMER goal to platform ROAS target for each of the specific channels?

And we have to start somewhere, right? We're going to, we're going to conduct geo hold up tests in a specific roadmap way. Tony's going to walk us through that. But right now, today, we need to start somewhere in terms of assuming some level of incrementality between the different channels and tactics that we're running.

And so what we've done over the course of this year is. Created a a catalog, a database of the incrementality geo holdout results that we have run for the brands that we work with based on data from partners like measured and house that Tony mentioned. And we've created incrementality starting points that are within a range of the incrementality factors we tend to see for the core channels and tactics that DTC e commerce brands work with.

And what's fascinating is. That is how close the geo holdout test results land to these incrementality starting points for a large number of brands. And we're adjusting these somewhat as we go and get new test readouts. But these factors are really solid starting points in terms of thinking about the incrementality of certain channels when you have them set up in a certain way.

So I'll walk through a few of these and what and what they look like. So, starting at the top in terms of highest incrementality percentage, we tend to see meta. Acquisition campaigns, so excluding existing customers and optimize on a click only basis. On a seven day click only basis specifically.

120 percent is what we use is the incrementality benchmark or incrementality starting point, which means if my target is a two. Then what I'm going to do is divide that by 120%. This, this is the math to get to my meta acquisition target. So Amy, our targets are to my meta acquisition target. In this case at 120 percent in brutality is going to be a 1.

67. What this is saying is that meta. True acquisition optimized on seven day click only tends to under report its incremental impact by about that 20%. So Amy, our target two on platform, a one, six, seven, moving down the line meta retention tends to be about half that. So 60 percent incremental. So your ROAS target on meta at a two AMER would have to be a 3.

33 Google non brand. So when you have true clean non brand campaigns without brand search terms or brand adjacent search terms pulling in. So Google non brand PMAX or non brand search 75 percent incremental is the, is the starting point for that. YouTube. Conversion optimized acquisition or prospecting oriented.

Also 75 percent incremental. Google brand, Pmax, brand Pmax or brand search 30 percent incremental. And then we could highlight some others here. I mentioned like affiliate coupon sites, Rakuten et cetera, about 25 percent incremental. And then what we tend to see is that higher like awareness optimized campaigns, depending on the platform, tend to have a much more disparate incrementality factor where we can see, you know, a CTV channel at at 230 percent incremental, but the on platform reported Ross is a 0.

3. So like the I Ross is still really low. So that bucket gets a little more disparate, but for our core channels, meta acquisition, meta retention, Google non brand, Google brand. Those are the incrementality starting points. We'll start out with to assume what the incrementality factor is to get from AMER to the on platform target.

[00:24:31] Tony Chopp: the 1 thing to add here is in both meta and Google, the incrementality difference between acquisition focused campaigns. and retention on meta or brand on Google. The difference between those being so large is the reason why it's a really important reason for why we do account structures the way that we do, why we separate these two types of traffic in your account, because The difference in the incrementality percentage means we're going to need to have a much different target for a meta acquisition versus a meta retention campaign.

Same thing in Google. We're going to need a much different target for a Google non brand campaign versus a brand campaign. The other thing that I want to add to this piece of the conversation is there, to me, in my mind, there is no right or wrong answer when it comes to Attribution windows or blah, blah, blah, blah, blah.

Any of those things, right? Think about it this way. On one hand, you have incrementality percentage. And on another hand, you have attribution stuff. So as your attribution stuff. Goes wider. So longer windows, more view as this line goes up, your incrementality percentage goes down. Okay. So if you want to run display advertising with a 60 day view through, for example, I'm going to say, okay, cool.

We're going to run it through incrementality. And it's likely going to be very, very low. And what we would need in order to create profit from that sort of setup is a ROAS target of 40 to one or whatever it is. Right. So. Bigger attribution, wider attribution, lower incrementality. Not necessarily right or wrong, but this is why this question is so important so that we can get to the platform target.

And to my point earlier, the difference between acquisition and retention or brand is an impetus for why we structure media campaigns the way that we do.

[00:26:42] Luke Austin: and so, yeah, connected to that, the, the incrementality starting points are a really good starting point. We have to make some assumption. Right. And, and I think what, what what pains us the most is when we, when we see an account where.

It's mostly Google brand in the PMAX, for example, that we know is lowering incrementality, and it's being run at the same ROAS that a meta seven day click is, and we know that there is a much there's a much larger difference between those. It's, it's based on the incrementality starting points. It's about a four X difference, right?

120 percent incremental. For meta day click and Google brand at 30%, where you could probably be, you should probably be spending quite a bit more on meta in that scenario and increasing your ROAS target on Google to drive more incremental revenue. But incrementality starting points get us there.

Now, all that said, starting incrementality, starting points are great, but these do change on a brand event brand basis. Absolutely. Based on category and then the brand specifically, how much competition you have, et cetera. So. A testing roadmap is crucial because we need to validate these for your specific brand.

So, Tony, walk us through how we think about prioritizing a testing roadmap schedule.

[00:27:48] Tony Chopp: Yeah, so, so 3 core buckets 1st bucket core channels. So I'll use the example of meta and Google as, as a, for example, but anywhere where we're spending significant investment now, and the first piece of the puzzle is to test the core channels around acquisition and retention or in Google brand and non brand that's step number one.

That needs to rely on an incrementality testing tool that uses store revenue as the measuring stick. Measured, housed, we have our own. The second piece that we have is new channel activation. So any exploration that we're going into or any of the smaller spending channels. We want to expand our reads to new channels that we want to test or smaller spending channels, core channels first.

New channel activation. The third stage, the third bucket is this versus that tactical ideas within an account. Okay. So for example, seven day, click versus one day, click on better or seven day, click versus one day view on better. Bidding strategies on Google. So anything that's this versus that within, within an account ASC versus BAU standard shopping versus performance max performance, max feed only versus performance max with all the assets, all of those sorts of questions, step number one, core channels, the big stuff needs to use incrementality measurement that uses store revenue.

Step number two. smaller spending channels or new channel activation. Same story needs to use incrementality testing that uses store revenue. Step number three, this versus that questions. And here's where you can actually open up to using in platform conversion lift studies. Okay. So if you wanted to test ASC versus conversion campaigns in meta, but you already have your overall channel incrementality read, you can use.

Metas conversion lift study in this scenario because you're going to, you're going to apply the same factor that you have from step number one. So these tools that meta and Google offer for conversion lift are valuable and useful, and sometimes they can be really fast to execute. So we do, I do have a place for them in where we use them.

It's just important where it sits and it's, it has to be this versus that question inside the channel.

[00:30:16] Luke Austin: All right, so I think we we can recap at this point. We walked through all the steps as it relates to the three core questions that were Trying to answer what should my total budget be to maximize profitability within my desired time window to spend a mirror model is what gives us the answer to that question?

How should that budget be allocated across channels for the most incremental outcome? Incrementality is a measurement framework that we have seen the most effective to answer that second question, starting with incrementality starting points, and then running a testing roadmap to validate the factors for your business individually.

And then third, What are the campaign level targets and expect to profit outcome for every single day so we can track against whether this plan is actually happening or not and what adjustments. We need to make. So the campaign level targets to state it very directly are the on platform ROAS targets for each channel that are set based on incrementality.

And then we're running those in a very specific structure that Tony's chat about a bit and tracking within our growth map tool. And within, within statless as well, how we're performing against those, against those expectations. But the key is that the platform ROAS targets that we have set.

And are assessing whether this campaign is performing above or below the expectation. Those are tied to my AMR target based on the incrementality factor that is set. Those are the three questions. That is how we answer those three questions at CTC and the framework that we've developed through our friend, Pam, to help us make the most optimal budget allocation decisions.

[00:31:48] Richard Gaffin: Love, Pam.

[00:31:50] Tony Chopp: Hey, sorry,

[00:31:51] Richard Gaffin: us.

[00:31:52] Tony Chopp: I got to throw 1 last thing in here because it's sort of the topic. Did you were, applovin is all, all the rage in the D to C e commerce world right now. And guess what we're doing? Running a bunch of incrementality studies. So we're, we're about probably a week away from having some significant, some meaningful reads.

On the increment incremental contribution of the channel. It's sort of, it's interesting. It's built using seven day click. It actually reports with one day click. So there's a lot of reason Luke and I were talking about this the other day. Like what, what, how close do we think the channel reported return is going to be to the, the the IROs read.

So we, I think we, we haven't made any wagers yet, but

[00:32:29] Richard Gaffin: Oh, yeah. I was gonna say, place your bets.

[00:32:31] Luke Austin: I've yeah, I've got the, I've got the under on a hundred percent incremental against the seven day platform reported ROAS. So I'm going to bucket it in alignment with Google non brand incrementality factor. So that's 75%. So 75 percent of what the seven day click app level non platform reported is going to be what the IROAS is going to be.

That's the bucket I'm putting it in my mind

[00:32:54] Tony Chopp: Well, I, I think we're saying the same thing. We're just saying, we're just saying it differently because I'm, I'm at, I'm at 100 percent incremental rows on the 1 day. Click. So, yeah.

[00:33:03] Luke Austin: day. Yeah, it's good. Then that'd be pretty close to pay on the relationship for, for the brand. So we'll see. We'll see. We're, we're, we're hopeful. We are hopeful that it is a strong, strong incrementality channel. But regardless, every channel is incremental at some level of spend, right? It's just a, it's just a question of what, what should the ROAS target be adjusted to based on the incrementality factor.

So even if it's 10 percent incremental, we can spend some budget at 10 percent incrementality factor. It's just going to be a really high ROAS

[00:33:35] Richard Gaffin: So, I wanna ask one sort of clarifying question, because I think this, Probably we can pull and make a pretty good clip out of it, but just to sort of like dumb down, maybe thinking about incrementality to its sort of most basic level, because it's not just that incrementality provides a lens through which to view the goals you're setting.

It also provides a sense of what's actually happening as well, by which I mean, so let's say let's take meta acquisition on a seven day click as our example here, let's say you're getting a one. Row ass on your meta acquisition on a seven day click attribution window. That means in reality, you're getting 1.

2 because 120 percent is the, is

[00:34:12] Luke Austin: That's right. That's right. That's right. The 1. 2 is reality because the 1. 2 is based on a geoholder test that's based on Shopify revenue. So the 1. 2 is actual Shopify revenue showing up against that spend. And then inversely, yeah, if it's a, you know, if it's a one on Google non brand, then it would actually be a 0.

75. You're just getting 75 cents on that dollar

[00:34:34] Richard Gaffin: Right. So yeah, that's as sort of an. A way to begin to assess maybe like where you're at relative to what the actual incremental impact of the channel might be just doing some quick math on, let's say, because Google Brands a great one, because let's say you're gonna go like a 15 or whatever, actually, actually, what we're looking at is you're getting around a five, maybe less ROAS on that channel.

And it's important Kind of keep that in mind as you assess the performance of each channel.

[00:34:58] Luke Austin: Exactly. Yep,

[00:34:59] Tony Chopp: Probably closer to a three,

[00:35:02] Richard Gaffin: There you

[00:35:03] Tony Chopp: but, but yeah, exactly.

[00:35:05] Richard Gaffin: Yeah. Cool. All right. Well, I think that that's, that's super helpful. And again, Hey, if, anybody of you or any one of you are in a position where you want to hire us as an agency, we can help you with this. Go to comment for co. com, hit that hire us button. Let us know you want to talk.

We would happy to discuss putting Pam to work on your business. All right, Luke, Tony, any last words, anything else you want to share with us?

[00:35:29] Tony Chopp: Let's go have a big Black Friday.

[00:35:31] Richard Gaffin: Yeah, that's right. That's right. Go Pam. Cool. All right, folks. Well, thanks for joining us again, and we will see you all next week. Take care.