Want Profits? Here Are the Exact Steps to Ecommerce Success

[00:00:00] Richard Gaffin: Hey folks, welcome to 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 our two Directors of Sales, Matt Axline and Peter Hassan. We talked with with both of these guys, probably a few months ago kind of generally speaking about what they were seeing in terms of the problems that everybody in the industry was struggling with.

And they're probably the guys who are most equipped to kind of give us on the ground insights about what's happening, both in the industry and then what we're doing specifically to solve some of the problems that they're seeing. And so, Matt and Pete are here are to walk us through. Basically walk us through the Prophit System which is of course CT system for creating a consistent profitable growth. But first, before we get into that, Matt and Pete, what's what's going on today, guys? How y'all doing?

[00:00:43] Matthew Axline: We're doing we're doing well.

Look, there's always something to complain about what I can find, but all things considered very good.

[00:00:51] Richard Gaffin: There you go.

Wow. Love that attitude. Pete, how you doing?

[00:00:54] Peter Hassan: Good man. Happy to be here. Glad to be back. It's a big sports week here in Los Angeles. We got the 

Dodgers, Yankees on Friday night, baseball is in full swing. And I have no virtual background. So for those watching on YouTube, this is where I am. I live in Echo park and Echo park, Los Angeles. And yeah, just excited to be here.

So thanks 

for having us, Richard.

[00:01:14] Matthew Axline: and I would say please know please no comments about the early christmas lights that are that are up here. Ken

year round year round mood lights, but yeah, we got The what do we got? We got the Dodgers there on Friday. We got to the biggest high school football games. We got USC playing in LA Friday, and I'm supposed to drive up through to a wedding on Friday afternoon evening.

[00:01:35] Peter Hassan: Enjoy it. Enjoy it.

[00:01:37] Matthew Axline: But 

[00:01:37] Richard Gaffin: you go. 

[00:01:38] Peter Hassan: He might need, he might need to leave right after this podcast.

[00:01:40] Richard Gaffin: Yeah. Yeah, 

[00:01:41] Peter Hassan: Yeah. Yeah. 

[00:01:42] Richard Gaffin: of days drive there for sure.

[00:01:44] Peter Hassan: Yeah, exactly. 

[00:01:45] Richard Gaffin: and also guys, don't worry about the background. I mean, you see what I'm working with here. I've been doing this for months and months. So, all right. Well, so let's, let's dive right into it. So, Basically, like I was saying, kind of in the intro that last time we talked a little bit about problems that the industry as a whole are facing, what people are bringing to you as their kind of core pain points when they're interested in our profit system and in our services. So let's talk a little bit about kind of leading into selling the profit system to people or walking them through the pitch or whatever, what are the problems that you're seeing currently that people are facing? Why are they coming to us? With these particular issues.

[00:02:21] Matthew Axline: Pete, I'll leave this one off and then you can you can piggyback off that because I know you got some unique insight on this. One of the the biggest Areas and items that I'm seeing at the moment with brands is that there was, and I don't think this is a novel idea for a lot of people that are out there.

They may be experiencing them, this themselves, which is great. 2020, 2021, good tailwinds into 2022 from COVID. 2023 things start to decline a little bit. But. You know, they're still holding on and they're still managing well and then 2024 things nosedive quite significantly for the brand or in a way that was much less than it was in the past couple of years and this has ramifications, whether it's from just the internal structure of the brand that they have, whether they've Hired a lot over the past couple of years and now have a pretty high opex billable or line item whether it's they took on money from investors and now that they see that start to to nosedive and there's a lot of questions coming around in that and so we get into 2024 when They went through a season in 2023 where they acquired a lot less new customers They're feeling the ramifications of that on the returning customer side in 2024 and they come and it's We need to get at least on a path Back up into the right in a way that shows whether internally for us, because we have plans to sell this business.

So future, future buyers of this, this business is healthy and growing again, or whether it's with investors that, hey, you can still have faith in this. You don't need to write this down so on and so forth. So that's in a broad sense, a very, very common theme. And it seems to be really I've encountered multiple brands, 7, 500 million is where they got in their peak, and then since then it's kind of tailed off, and there's unique things for each and every one, whether it's, like I said, higher opex in some sides, whether some areas, it's the customer acquisition fronts struggling, but that's sort of a common theme or trend with a lot of the brands that are coming in at the moment.

[00:04:26] Richard Gaffin: Yeah, that makes sense. And, and I think one, one kind of takeaway that I had from a conversation that we had before we hit record here was that idea that like brands of all sizes are struggling with essentially the same thing, which is that they don't have. They're not equipped to understand, or they're not equipped to face this particular moment.

They have a series of maybe operational structures that they've put in that really helped win the moment over the COVID period. But of course the COVID period was very unusual. In history, like it hadn't happened in a hundred years, maybe hopefully won't happen for another hundred. But the idea being like that, those particular operational, that operational standpoint isn't going to function anymore. And so maybe, I don't know if you guys have some without getting into some specifics, but some examples of the ways people or brands are coming to us unprepared for, to kind of handle the current moment.

[00:05:15] Peter Hassan: Yeah, I mean, I think there's a question a lot of times for businesses of the decision making framework in which they operate. And I think Taylor, our CEO for those listening talks about this and, and we talk about this a lot internally is just. Everybody has a different incentive. In a lot of cases, they have some metric that they look at.

It could be Facebook ROAS. It could be customer acquisition costs. Maybe if you're on the email side of things, it could be the returning customer rate of purchase or the LTV of the customer over time. And I think what seems to be a common seems to be a common through thread a lot amongst a lot of brands is how do we actually sequence down the decision. What's the metric that we look at every day and we decide, yes, we're going to, and in the case of performance marketing, let's just take that as an example. Yes, we're going to spend more dollars or no, we've got to go in and fix some of the current infrastructure because it's not producing what we want. And so I think that exists amongst a lot of businesses. There are some businesses that have achieved really aggressive growth and maybe there are, you know, high eight figures or low nine figures. And then there's lots of businesses that we talked to that are maybe approaching kind of this mid eight finger range. And they struggle with a lot of the similar challenges. And so part of hopefully what we can provide to folks is an outside perspective. Obviously, the benefit of an agency is that we have access to a lot of different businesses amongst, you know, various stages of their growth, amongst various industries, amongst various business challenges.

And so hopefully what we can provide to folks is number one experience in the problem that they're facing. And I think a lot of the compounding value of our client base, you know, that can be an applicable thing as we talk to new brands. And then number two, an outside perspective on what we believe is possible.

And so as we get deeper into explaining what the profit system is, I think a lot of it at a high level, yes, there's a lot of kind of tactical things, data modeling, forecasting work that goes on once we actually get started, but a lot of it, the, the high level is to help brands understand where can we go? And what's an outside perspective on the business. And so I think those two things helping brands understand how do we, how do we build a decision making framework is really critical at the challenge that a lot of brands face and then number two, you know, having exposure to this journey at various stages for lots of different businesses, hopefully we can provide them with an unbiased third party POV on what we think is possible.

And then there's always collaboration, right? There's always collaboration in terms of how do we beat the model? How do we improve? And that gets more into the tactical structure of the Facebook ad accounts or the, excuse me, the meta ad account in this case, or the Google ads account or, you know, creative testing or email and SMS.

And so I think that's really the entry point for a lot of these conversations. And then every business is a little unique, maybe in terms of their decision making framework or challenges that they're experiencing uniquely. But Matt, I don't know if you'd add anything onto that, but that feels like kind of a common way that these conversations really arise.

It's less about. Hey, we're doing a lot of things wrong, necessarily. It's just the lack of maybe clarity in terms of how do we build out the decision making framework?

[00:08:18] Matthew Axline: Yeah, yeah, I think I think that's well said with the decision making framework. One of the reasons I believe a lot of brands. At least engage in conversation around the profit system when they see it is, it is a sense of clarity. To them on every dollar, every day, every place, what's the expected return from that across the 35 plus metrics that we forecast out, whereas their system at the moment, while it's worked over the past couple of years, all of a sudden, it's not working.

And. What I say is, there's not really a novel problem, at least from what we've encountered, like, a lot of what I've seen over the past 6 months is. More or less the same thing, just kind of twisted in a, in a separate way. Cause each brand's its own little special snowflake in regards to their own specific problem, but it's all very similar.

And we always take the approach when we work with brands of when forecasting what is likely to occur. Based on the current set and parameters of the business. And if we want to change that, what needs to be true in that? So we do a lot of scenario planning as well when we build these out. And the idea behind it is that we get really, really clear on what is our North Star.

That each and every day, whether it's us, whether it's the brands who are doing it. What is that North Star that we are all tracking towards so that our behavior can move forward from that? Because the idea of Maybe the paid side it the brand has a certain metric that they're chasing down, but finance really only cares about contribution margin or EBITDA at the end of the day.

Those are two kind of competing interests that can occur. And people internally at brands are bonused differently based on different things. So it is a sort of a restructuring and reframing towards this is what you want to achieve. Everything needs to be pointed in that direction.

[00:10:02] Richard Gaffin: So I think like a way to summarize, like what the profit system does is it, it's a way of reorienting your organization around a single goal around a single business goal, rather, and then making sure that every sort of. Moving part in your business is pushing towards that thing. Generally speaking for most of our brands, that's profitability.

It kind of needs to be profitability. And a lot of the time it's, it sounds like people are coming to us with the belief that what they have is fundamentally a Facebook ads problem, but it turns out it was really a cost problem. That seems like it's pretty, a pretty common thing. So the, the profit system actually, part of what it helps do is just clarify where's, where are things going wrong? And then what needs to be done? On the broad organizational level, and then minutely in every little part of the organization, what needs to be done in order to make that goal happen. So that's, that's the outcome. The profit system produces. But what I want to talk about today is. What's the mechanic by which the profit system does that. And that's kind of like, I think the value of this conversation for our listeners and for our viewers is having you guys who are the experts on this, walk us through kind of how the profit system does what we say it does. So let's say, pretend I'm coming to you saying like, Hey, our businesses you know, we're down year over year. And we're not really sure why Facebook feels like it's falling off, but we can't fix it. We've heard about you guys. We hear that you can do all these things for us. How does it work? So I come to you with that. What's what do you say?

[00:11:26] Matthew Axline: I'd hit you with in six months, if this doesn't change, what do you expect your business to be? No, I'm just kidding. Classic, classic, classic sales maneuver. So I think we, we want to, we want to screen share

a little bit here as well. Pete, if you want to, if you want to pull that up and we'll, we'll kind of walk through the profit system.

But if a brand typically comes to that, like comes to us and talks about it, and Pete, I'd love to have your opinion on this as well. If a brand comes to us and says that, Hey, we're down year over year, obviously I want to get a sense of why they believe that is true. And then from there, understand like, where do they want to go?

Like, what is the goal and objective of the business? I think that's something that as we talk about kind of unique, not unique problems, but issues that brands I've, when I've encountered them some of the things that they have, which is the, the, the larger overall organizational goal has become kind of muddied and unclear.

And that has to be set. Where do we want this business to go? What do we want to do with it in a one year, two year, five year window? Because everything else kind of streamlines down from there. So, Pete, you got this up. If you want to just talk through this component and then we can kind of piggyback and the the other components of the profit system. 

[00:12:39] Peter Hassan: Yeah, for sure. So Richard, back to your point, I think, tactically or practically, how does this how does this all come to life for those that are maybe listening on audio? What we're showing here on the screen is the data integration process through CTC's data tool, data visualization tool called statless. And so this can be maybe a boring part of the process, but I actually think it's really critical. Whether it's a statless tool or whether it's a tool brands use internally, maybe a tool they use outside the CTC system. Obviously, we're here talking about our system, but I think data integrity is really critical, making sure that we're all looking at the same data and the same numbers. Is step one before we can make any hypotheses or strategic decisions or tactical decisions in platform. So the way that our work starts with every brand, and I'm using Bamboo Earth as the example. I know that's probably been used previously in marketing materials from CTC and the podcast. But the way that our system works is we start by integrating all the data sources that a business has primarily. For their DTC business. So you can see in this example, for those listening and for those watching, the first data connection is an API integration into Shopify. So we're going to pull all of the historical revenue information and data out of the Shopify system. And that makes sense, right? That's where the customer transaction is happening, right?

That's where you actually check out and buy items. The other critical pieces to note here are the data integrations into the marketing channels. So for bamboo earth, in this case, meta and google ads are their primary vehicles of performance marketing spend. And so we A. P. I. Connect into those sources. To ensure that we're pulling the marketing spend data out of the places that we're spending for those maybe watching, we'll see a couple other connections in here. We have some influencer spend that we want to pull in. We connect our ESP, which in this case, our email service provider is clavio. And we have a few other connections to ensure that the data integrity setup is right. So this is step one. Step two. And Matt, I want to go back to you in case I'm missing anything here. Step two is ensuring that we have the product cost of goods sold accurate at the skew level. So when we define cost of goods sold, we mean the actual amount it costs to make the product. So we're not necessarily looking at shipping or pickpack or variable expenses outside of yeah. What would traditionally be defined as cost of goods sold, but in the actual back end of statless, if you were to click and for those folks that get you know, that we worked with the profits on the profit system with in the cost setting section, you'll see that we have all of our skews here. I'll try to zoom in a little bit. We have all of our skews here, and then we have a associated cost of goods sold to actually make that skew. So this is a really critical component. Of the data integration and data foundation that we're building is that we not only understand the marketing spend information from the media channels that we're active in, but that we also understand, obviously, the revenue data flowing in from Shopify and then the cost side of the business, and we can get to some of the additional costs here in a minute, but I want to pause here.

Matt Rich, in case you guys have any, any thoughts or commentary on this piece. This is really, really critical. I don't want to say often gets overlooked. But what I will say is sometimes I've experienced like, yeah, it's 95 percent there. Not, not from, not from the work that we do necessarily. We're going to, we're going to pursue the ruthless, ruthless you know, validation of a hundred percent data accuracy. But what I've seen across really large organizations is like, yeah, it's mostly there. Or yeah, it's like, it's 98 percent correct. But like these SKUs don't have the right cost of goods or like our shipping is not that accurate. It's not, you know, it's not what we're actually pulling in which is an understandable answer, right?

There's a lot of complicated things happening every day. There's a lot of decisions, a lot of meetings, a lot of conversations. But I would challenge all of us, our agency included, and any brand that engages in, in growth marketing as a, as a vehicle to drive new customer acquisition, to really, really, really make sure that you don't overlook spending time here because those dollars that bleed out because we forgot to add that extra 2 percent for, you know, the credit card processor fees. Those will catch up to you at some point, and there's always going to be a mistrust of the data. So for us, really, step one in this process is making sure we have a really solid and clean data infrastructure to work from. And that's really how we begin this, begin this project.

[00:17:03] Richard Gaffin: Well, I was going to say, and it kind of kicks off a complete, or rather like a real, a real reorientation of thinking happens almost immediately in this process. It sounds like, which is that, of course, it makes sense. Like you were saying, like, when you ask, say a CMO, hey, or, you know, is every SKU updated or is COGS on every SKU or shipping on every SKU updated correctly? It's like, it makes sense to me that the answer sort of be like, ah, maybe, or not, it's not quite there because generally speaking, that's not, Really their responsibility, or it's not something that they're thinking about on a day to day basis, making sure all those little things are in place. But, but what we're saying is from a growth marketing perspective, actually that stuff is, is, critical and actually where you have to begin. And so already at the very beginning of this process, the profit system is reorienting how you think about 

the job, generally speaking. 

[00:17:47] Peter Hassan: And I think if we go back to the idea of eight and nine figure and even seven figure ecom brands having large teams, right where you maybe have a media buyer or you have a CFO or you have a head of growth.

I think what this allows us to do collectively is to understand how everybody fits into the system,

right?

So Richard, to your point, right? If the CMO is not responsible for cost of goods sold being accurate in the Shopify product pages, no problem. But whoever is internally for the brand, their thing needs to be accurate first before we move on to all the other 

dominoes, right? And so now that person has a place and a way to participate in the system that actually brings kind of this like cohesive energy. And really kind of this team mindset to say like, Hey, the thing I'm responsible for needs to be accurate to make us successful as a business versus the feeling of like, yep, I'm not responsible for that. So I don't really know. But that person is going to pick that up and they're going to make sure that their thing is accurate because all all these pieces have to work together to make the system work.

So I think that's part of the value here, too, is trying to get everybody to participate in their kind of component of it. And I think teams really have a lot of fun when they understand, like, all right, cool, this is my job responsibility. How do I actually participate in this thing?

[00:19:03] Richard Gaffin: All right. Let's let's keep rolling.

[00:19:06] Peter Hassan: So this is step one. I don't want to, I don't want to spend too, too much time here. I want to get to the planning section and maybe Matt, I can turn it over to you to kind of walk through the first. Piece of that, but the main summary here for those listening and for those watching building a good data infrastructure where all the data is accurate, pulling in correctly. We have an understanding of revenue, marketing spend, and then cost is really, really, really critical. There's some other things we get into in this system. So in this step of the process, so this is not all encompassing, but I think this covers the kind of meat and potatoes for now. So step two is the planning process. And so Matt, I'll, I'll jump into the planning section here for Bamboo Earth, and we'll go ahead and look at this official official one that feels like the best place to go. Maybe 

[00:19:51] Matthew Axline: for just two, two seconds. Cause I want to, I want to touch on this real quickly. So one thing just to wrap up the last section around the data integrity that so critical and so important as Peter said, and is. Surprisingly a difficult endeavor, which makes sense considering there's a lot of things everywhere.

There's, you know, cost of goods, maybe for this and not for that and so on and so forth. But that is something we spend the first couple of weeks and very, very, very diligently going through because. Candidly, if someone doesn't trust the numbers that they're seeing in statless, they're not going to trust everything else.

That we do in here. So it is really important that we hammer those hammer those nails if they do 

pop up. But what you see here on the planning section. So. If you followed CTC content in the past, some of this stuff is going to look familiar because it was all actually in our growth map. We've now taken the majority of that and move that here in the stat list.

And what you'll notice in this for Bambi. You'll see this if you look. You know, for any of our other branches, there's a couple of different plans in here. This is scenario planning. We always forecast, like I said, what is likely to occur in the business. That's official. and then we'll go through and we'll, we'll plan out other scenarios of, Hey, what would this look like with X revenue growth and Y margin growth?

So, Pete, why don't you go into official and then we'll start talking through the first couple of steps here. So data integrity is important because it feeds everything else. In this system here with our models with the spend and so on and so forth, but everything for us when it 

comes to building out the 12 month PNL level forecast and operating system that is the profit system starts here on the custom data models.

We have data scientists here at CTC who built them out and it's across the 2 areas. Brands make money. as a company, which is

new revenue And returning revenue. The new customer revenue side is the AMER to CAC model. So historical relation, a study of the historical relationship between AMER spending CAC for a brand.

And it's actually. 14 or 15 different models that are all kind of aggregated into one large model, taking things into account like seasonality and others. And then the retention model which is a cohort specific retention model, looking at two separate things, which is one, the customers that you have in your customer profile today, all the customers that you've acquired in their value over the subsequent months, and then future Customer 

cohorts that you acquire in their value.

So these two items here give us kind of the foundation for the forecast, which is new customer revenue, new customer orders, returning customer revenue, returning customer orders inefficiency specifically on the new customer side. Peter, is there anything you want to add here before we jump into the, to the models themselves?

[00:22:41] Peter Hassan: Yeah, I just want to highlight the new revenue model is one that's, I think, really they're both equally important, but I think there's a lot of, as you know, as we talked to a lot of businesses, one of the challenging things to understand especially in a volatile environment where covid may have boosted performance, right?

1. O. S. Was a moment where performance was more challenging for brands. Is to understand at what point does the spend actually degrade in efficiency. And I kind of think of this, I'm a big golfer. So I kind of think of this as like, as if when you start golfing, right, it's a lot easier to start, you know, it's a lot easier to go from shooting maybe 110 to a hundred than it is once you get really good, right?

For the folks that are shooting, maybe even par, maybe, maybe they're scratch golfers, it's really difficult to get to the next level, right? Because there's just diminishing returns, right? At some point to get to the next level becomes much harder. And so I think for a lot of these eight and nine figure businesses where they've produced, you know, historically. Strong meta performance, maybe customer acquisition costs that they were happy with, and they continue to push into higher levels of media spend. you know, up into the high six and seven figures on a monthly basis, it becomes more challenging to drive better efficiency. And it's harder to answer the question of, you know, when is the spend less incremental or when is it not producing the contribution dollars that I need? And so this model can really reorient organizationally, both us and our partners in terms of a baseline expectation that we can all work from. And then it's our jobs together. Once we get into the work to say, how do we improve, right? We don't just want to set the model and hit the model. We always want to beat the model.

That's, that's the goal. Collectively. Those are things like new account structure, new creative, maybe giveaways new things that we can introduce into the mix that haven't previously been enacted upon. But I think it's really important. Similarly, the data integrity that start with a fundamental belief of what is the data actually telling us at what point should we set our media spend before we start thinking about all the fun marketing ideas.

Sometimes that can want to feel like we're seeing bad performance. So let's just think of a bunch of marketing ideas. I think the inverse should be true. What should the expectation be now? We've all got to get comfortable with that. Sometimes we may not like that outcome, but we've got to get as close to as comfortable as possible with that.

And then we can kind of dive into more of the fun marketing ideas. So that's the only other piece I'd add there before we jump into the models.

[00:25:07] Matthew Axline: Cool.

[00:25:08] Richard Gaffin: All right. Well, let's, let's dig in.

[00:25:09] Peter Hassan: Cool. So I'll click on this first one, Matt, if you kind of want to

[00:25:14] Matthew Axline: Yes. 

[00:25:15] Peter Hassan: of what you're seeing here

and feel free to direct me wherever you want me to go.

[00:25:18] Matthew Axline: no, I think, I think this is, I think this is a good start and then we'll scroll down in just a moment. So as Pete noted, the the idea of what we want to get to here is sort of what is the marginal frontier of a brand spent in any given time period. And we're obviously looking at 2024 for bamboo earth.

And based on that, we have a couple of different optimizations that we can select from Pete, and if you want to scroll down just right there. So. Click on that. Yep. Perfect. So when you see this essentially what we want to Optimize for is dependent on what we want that specific month to be So we have options of max contribution margin on first order purchase max revenue lifetime contribution margin And then a couple other ones in there as well so 

essentially What this is giving us too and let's go to pete you want to go up and just click november just so we can kind of You As we're looking into November, we can choose our specific optimizations there.

So essentially what this is telling us is that for bamboo earth, if they wanted to maximize lifetime contribution margin, which candidly, that's what they do a lot, their skincare brand, they have pretty darn good LTV. It's something that they buy into a lot. They would need to spend. Roughly 329, 000 at a 1.

5 AMER. And the next tranche of spend from that, their lifetime contribution margin begins to degrade and play the same thing for new customer revenue and for contribution margin. We can go up and select those toggles there. So this begins to build a framework for us of, Hey, based on what we want to optimize for.

How much should I be spending and what should my overall new customer efficiency be in this scenario? And it's going to be different. And when we go through this and we build this out, there's different selections for each and every month. Scroll up just a little bit here, Pete. One of the things we talked about as well is you'll see the percentage over model on the model tweaks there.

If we think we can do better because it's historically been, you know, A bad structure. There's been a variety of things that have been going on. We have a couple of moments coming up. We could think we can build into the creative is a large opportunity here. We can add a percentage over the month as well and say, Hey, we actually think we can do a little bit better than this in this scenario.

So the idea here in the framing that we want we want to build is for each and every month. What are we spending? What is our goal? In that month, whether it's revenue on new customer acquisition, revenue margin, and then what should our efficiency be towards that? And that's going to be really important in the next step of that.

When we start talking about incrementality, allocation of dollars and so on and so forth. So anything you want to add there, Pete

[00:27:54] Peter Hassan: No, I think he did a nice job summing it up. I would just say for those listening that may not be able to see the screen. Really, what we're illustrating here is just a table of all the spend levels. So how much we're spending this one's looking in specific increments to this business. And then, as Matt alluded to A.

1. E. R. So for those that are not familiar with A. M. E. R. A. M. E. R. stands for acquisition marketing efficiency ratio. So we're looking at revenue from new customers divided by media spend.

So M. E. R. Exactly. MER is a very common one. That's a metric we look at too, but that's looking at total revenue. You can imagine returning customer revenue is included in that. And so for new customer acquisition, we often look at AMER and we just want to look at, at what spend levels does it start to get worse in the simplest form? So you can see, as we start, or I guess for those listening, you can't see, but for those following along as we spend more, generally the efficiency is going to degrade over time. And so really as part of this process, as Matt alluded to, we're trying to understand what's the financial outcome we're after contribution revenue or lifetime contribution margin. And then how do we set a spend amount and an efficiency target that's appropriate to create that outcome? And then like, like Matt alluded to over model is always better, right?

We want to implement marketing strategies to beat the model. So really a solid step one here. And maybe we can jump into step two, which is our returning customer model.

[00:29:13] Matthew Axline: Yeah. 

[00:29:14] Richard Gaffin: Yeah, let's do it 

[00:29:15] Matthew Axline: just say on the model real, real quickly is like, no, in my experience, no CMO CFO is yeah, likely to occur. Great. Fantastic. Let's go chase that. It's always cool. Let's do more. So the idea behind that, whenever we forecast it, I'm just going to continue to reiterate this for people is set the model, beat the model.

Is always how we want to operate. And when we go through this and go through this process and build this out, it's, it's a, yes, we build it out in the profit system at the very, very, very beginning of 12 month view. But if we do end up working together, or even if we don't hand this off to a brand a recommendation is that should always, it should continue to be updated because things change.

In the business as we go throughout the course of 12 months, things are always going to change different inventory, different moments that come up and so on and so forth. So, we won't spend too much time here on the existing customer model. I don't know if you scroll down Pete. Yeah, unfortunately bamboo earths.

I don't know if they have the cohort view that I really like, which is each and every month broken out in kind of a table that shows us exactly how many returning customer revenue. customers we're going to have and what the returning customer revenue is. But the idea is that we want to build a base for that returning customer revenue so that we can understand each and every month, what is actually flowing in that's going to be supporting the business.

And I've ran into examples with brands in here where they basically covered their OPEX with the returning customer revenue. And then that means everything on the new customer acquisition side is gravy. Right? Like we can go out and we can play as much as we want, not play, but we can push pretty heavily because we have a very good tranche of returning customer revenue and the inverse being true also leads to, Hey, the new customer acquisitions hype really, really has to support all the efforts on this business because we can't bake bank on returning customer revenue here.

So the level of not usefulness, but importance of the returning customer model is highly dependent on the business. and the industry that the business is in, whether first order is what they need or to maintain things like something like, like, like wallets or single item purchases, large purchases, or if it's something that's more relevant to them.

So Pete, go ahead and add anything you want there.

[00:31:34] Peter Hassan: Yeah, it probably goes without saying for the listeners of the podcast. But. I think it's always important to know if the new customer growth slows down, your returning customer growth is eventually going to slow down. And I think this is a bit, this is a challenge that a lot of businesses have faced coming out of COVID, where new customer acquisition was up and to the right for many months on end. What inevitably happened is coming out of COVID, the returning customer value and volume was really strong for a lot of businesses. And so it created a false kind of narrative that, Hey, we're going to continue to always see this returning customer. Value over time, but when new customers slow down, right?

There are less potential returning customers. So it's important that these two things work together. Now to Matt's point, every business is different, right? Some rely more on new customers to drive the growth. Other businesses like bamboo earth, they get a lot of returning customer revenue. And they get a lot of value out of those customers.

And so the dynamics are a little bit different for that business, but just, just, important to understand that as new customer revenue growth slows, the returning customer growth will be impacted at some point. So. It's really critical that we always have an eye on both of those things so we don't get caught in a trap where we feel like everything's going fine.

And then all of a sudden our returning customer revenue is smaller and our new customer revenue is smaller and our OPEX stayed the same. And now we're just putting more, putting less dollars to the bottom line. So, that's the only other piece I'd add there. But yeah, this is the step, step 2 in the data modeling process is taking a look at the returning customer. Model after we model out the new customers, a

[00:33:11] Matthew Axline: that I'll add to that, like a real life example, Speaking, I've been speaking with a brand that's at their peak, they're about 80 million. They're down from that somewhat significantly at the moment. And they experienced a very, very, very, they're experiencing a very, very similar problem to what Pete just noted, which is 2023 new customer revenue was down.

2024, the entire business is down. Now, as we go into 2025, we have to accept the reality that, hey, we're going to be improved, but it's still not going to be close to the levels we were at before because we do not have a big bank of new customers from this year that can help kind of float or grow us and the returning customer revenue side for next year.

So it's more of like a two year project to begin to get back to those, those upper levels.

[00:34:01] Richard Gaffin: I was just going to jump in and say, like, just to kind of summarize that, like, the purpose of both of these models and you mentioned sort of the whole phenomenon of, of needing to beat the model, like the purpose of both of these models is to give you a baseline understanding of what is true right now, I think, and that's, and that is the thing that most businesses are, are missing.

Some understanding of what is likely. Most people, yeah. Are coming in or setting goals without an understanding of what's likely at all. There's just an arbitrary expectation that, Hey, we need to hit this number. What this does is provides you with an idea of what might happen. And once you have an understanding of the baseline, the ground that you're standing on, you have some idea of how far above that, that you can rise.

And that's kind of the idea behind both of these models. So, okay, let's, let's keep rolling to to our next section here. 

[00:34:47] Matthew Axline: Very quickly, what's also really, really, really important when we think about, okay, we have what we're spending in a given month, we have our new customer revenue returning, so on and so forth.

All the information that we get from the two data models, custom data models, is, is the marketing calendar side of things as well. And what you're seeing here is, is actually something that just rolled out that we're pretty darn excited about. It's an event model. For the marketing calendar, so essentially looking at historical marketing calendar moments their revenue and percentage of revenue and applying that as we think about adding for example in February of 2025, we want to add something on the marketing calendar.

What can we expect from that moment in terms of revenue coming in overall expected efficiency on new customers, so on and so forth, so that we can begin to plan a little bit better. So just a way for us to get a little bit more predictive about the marketing calendar based on historical evidence of similar similar type events.

[00:35:46] Peter Hassan: And I think going back to the idea of getting everybody involved in the process the collaboration between those folks that may be you know, most more closely connected to brand. This could be a way to kind of bring that into the ecosystem, right? Because if you think about a brand marketer that maybe has the ability to say, okay, cool, what's a really awesome marketing moment that we could implement in 2025, or maybe a product marketer that says this is a new product category we want to get into, they can collaborate with the growth marketer that's setting the plan and setting the forecast and setting the targets and say, and say, all right, Well, last time we had a product launch, we saw this impact on revenue and A.

1. E. R. Our efficiency got better. Our revenue went up. And so there can kind of be collaboration between those different parties. So just another way to get different people involved. And then, you know, all this stems from the marketing calendar. So maybe we jump there next. Part of this process for those following along via audio.

What we're looking at here is the marketing calendar side of stat So what we'll actually do for brands as part of the profit system is we'll ingest their marketing calendar and we'll plot all the key marketing moments that they had. So if you take April, for example, for Bamboo Earth those watching will be a little easier to follow along, but we really just have every day of the month plotted out and then we have an input for all the key marketing moments for that business. So in this case, a really simple illustration of this is on the first day of the month, April 1st, April Fool's Day, Bamboo Earth ran an April Fool's sales promotion. So we have that noted in the calendar. As you move to the right, we have a drop down menu where you can select what type of marketing moment was it?

Was it a product launch? Was it a promotion, influencer, PR, seasonal event, VIP drop or other? And so as we go back to that event model, we can categorize all the product drops, all the promotions, all the key moments by product or by type. And then we can see what kind of impact that had on the business historically, and that can really inform us for the future.

So there's kind of this one part data modeling, looking at all the historical information. And then there's the second part, which is more of the kind of marketing moments I want to say more kind of qualitative pieces, but we always tie that back to how did it actually impact the numbers. And so this is a really critical step.

We'll do this work and we try to get it, you know, looking historically as well. So there's a little drop down here. You could see ideally we want all the marketing moments from 2023 as well as 2024, as well as 2025. 'cause you can imagine if we ran a bunch of product promotions in 2023 and we didn't do that in 2024, our expectations can't be the same. Same with product launches, same with maybe our PR hits or influencer moments. And so this is not to be a forgotten about piece of the puzzle. The marketing calendar is really important. And then I think we've talked on this pod previously just about how the best brands are telling stories all the time. It doesn't necessarily mean that they're on sale all the time. I know that's a common challenge that a lot of brands face is that the easiest way to drive a bunch of new customer and returning customer revenue is to go on sale. But the best businesses in the world, if you think about Nike as an example, right?

Brawny jr and lebron took the court last night and they had a marketing story where lebron filled his car in the In the driveway with I think it was fruity pebbles or some cereal As they were on the way to the stadium. And so that's Nike's way of every time there's a, you know, a big sports moment, they have something to talk about.

Now, again, that's obviously, you know, Nike's one of the best brands in the world. They have a huge team, huge resources. So the expectation is not to be at that level all the time, but I think it is important to say for your individual business, what are the stories that we can tell. Now some of those are gonna be based on the calendar.

Some of those are gonna be based on seasonality. Hopefully some of them will be based on product development. And some newness and new introductions for your potential clients. But this is a really good way in which we can see how much do we have planned for the month and hopefully tie it back to the event model which shows a little bit of the impact on revenue.

[00:39:42] Richard Gaffin: Yeah, so that, that kind of serves then as a pivot point between the sort of historical data modeling, which is the beginning of this process into the forecasting planning piece, which is sort of the next part. So again, like, we've built out a model of what types of event drops work. So we have, or sorry, product drops or event types work, and now we're sort of planning for the future and kind of categorizing them by event type so that we have some understanding what to expect. So now let's, let's move on to. Sort of the rest of that process of, of planning out what's going to happen.

[00:40:14] Matthew Axline: Yeah, so this has been sort of a subset of a problem when we talk about what is kind of incoming to CTC when people are just generally interested in the profit system or just chatting with us in general, it's like a kind of a downturn here really need to get up into the right in a, in a, in a sooner rather than later fashion in the subset of that is.

The allocation of my dollars, there is still a mystery around that. I know we've went through kind of the attribution wars of the past couple of years with a bunch of different sources there. What is my centralized source of truth for brands? And so there's still a real kind of clouded thought process around how do I best allocate my dollars?

Like, Real life example speaking to a very very very large well known brand that has both stores and An e com site and they're using a last click model at the moment. They don't have a better They want a better that's part of the reason why we're speaking They want a better model, but they don't have a better model at the moment.

So What we want to get to the bottom of with this is where should we allocate our dollars? On a channel specific basis, how should those dollars be allocated on a day by day basis in relation to a day of the week effect? As well as the marketing calendar and then how does that break down into each and every channel?

Every single day across the course of that month From meta to google to pick your poison in terms of a channel and then we take it one step deeper into our Across those channels each and every day. How does that break down on a campaign basis? We'll get to that point in just a moment for the listeners who are listening on screen basically what we have is an allocation of A budget based on the month, right?

So we built out what we're going to be spending, what our efficiency goal is going to be a budget of allocation by channel and then by day and then by campaign in there, which kind of toggles back and forth. But this is where we kind of get into the incrementality side of it, which is. Ecom's favorite buzzword at the moment, but for good reason.

So Pete if you want go ahead and take a walk through the incrementality how we apply that to the mmm that we have in here how that is related to The spend amr model and how we calculate it by amr as well. And then I think we can move on to them

[00:42:38] Peter Hassan: Yeah, for sure. I'll try, I'll try to wrap it up relatively simply. Just cause I think incrementality is probably a topic that we could have many podcasts about. But the idea with incrementality is to try to understand the incremental impact of each channel. So often the way that I like to think about it is a channel like Google branded search.

For example, if someone is searching for your brand. That's likely to have really low incrementality. The, the ability of, or I should say the, the impact of the Google brand search ad probably didn't necessarily drive the purchase. Right. Whereas if you think about something like meta acquisition, so prospecting on meta, not going after returning customers, but finding new customers that likely has much higher incrementality, right?

It's more likely that that actually caused a revenue growth from Shopify than maybe a tactic like Google brand. And that logically makes sense, right? If I go to buy a pair of you know, I got to buy a pair of Nike running shoes, we'll just stay on that example for a minute. And I searched Nike running shoes. Right. The Google ad that shows up is likely, I was likely to make the purchase regardless, whereas if I found on running, for example, from a meta ad, and all of a sudden I go down and I buy that product, well, that tactic is likely much more incremental. So the long of the short is that we're introducing incrementality into our work with clients.

Now, one of the ways for brands that haven't ever tested incrementality historically is to use incrementality benchmarks. There's a lot of benchmarks across the e commerce industry that we can apply across certain channels. So you think of meta and Google is channels. You can then apply benchmarks against certain tactic types.

So let's take meta acquisition. Let's take Google P Max. Let's take Google brand as examples of tactics, and we can apply those incrementality factors to the A. M. E. R. Target. So we can say this is the A. M. E. R. That we're going after for the brand. We can apply an incrementality factor. I won't get into the details or the math of how we do that.

That's for another pod. But that's how we can effectively arrive at what an in platform ROAS target should be. Now, a better version, you can imagine one step up from that is when brands have their own incremental reads, right? So when they run their own geo lift holdout studies, that's typically the way that a lot of this is run. They can get their own incrementality reads. So you can imagine for some businesses, they differ from the benchmarks. Now the benchmarks are better than zero. But a brand's own incrementality studies are often going to be more specific to their data and more unique. So, yeah, we're going down this pathway with a lot of businesses. It's been a huge unlock for brands. And really what I'd say is it's allowed us to allocate media dollars appropriately up or down relative to the incremental impact. The simplest way to think about it is if a channel is less incremental, we likely want to spend less dollars there. If it's more incremental, we likely want to spend more dollars there. And so it's 1 of the pieces that we're kind of weaving into how we actually allocate the media budget beyond just kind of what's previously been the case for a lot of businesses, which is either like a feeling that we need to spend. You know, 60, 70, 80 percent of our budget into meta or a lot of really complicated to Matt's point, attribution style tools that, you know, everybody has a different outcome or read on the numbers.

And so, incrementality is a really critical piece. If those listening are interested, Matt and I are happy to make ourselves available to chat specifically about incrementality here at CTC, how we use it. We can kind of jump on the, on a call and go through that. But that is an important piece here.

Not if I'm missing anything, please jump in there. I don't want to go too deep into a rabbit hole. Cause I think we should save it for a future podcast.

[00:46:09] Matthew Axline: Yeah, we'd be spending a lot of time there. The only last thing i'll say is once we basically figure out As Peter said, we apply the incrementality rates of the percentages. For example, Meta Facebook is usually 120 percent incremental. We apply that to the new customer efficiency target that we have per month.

And then that goes for each and every channel that we have those, then those numbers that are basically applied to the MMM that we have in here. They integrate into the MMM. So we have an MMM built on the back of, of, of Robin so that for each and every Month in channel. We have what our incremental return needs to be there for that specific area.

So that's just for those curious. That's how it all plays into our system. That's how we apply the incrementality studies and the whole balance. What I would say is just in once again, as Pete noted, probably for another podcast, there's a really interesting dynamic right now around trusting incrementality reads, which is like, I actually really don't like it.

That incrementality read, right? Like for example, you know, there's been studies done for larger brands that we've worked with that have been kind of, conversion lift studies on meta and it's come back incredibly low. And it's like, do I believe that? Do I believe this over here? So oftentimes there's kind of a stair stepper approach to it, but getting kind of the, the acceptance of knowledge of what are we going for here is a pretty big hurdle to, to tackle when we work with brands.

[00:47:41] Peter Hassan: All right. So the outcome of all of this, right. We started with data integrity. That's step one. Then we built the custom data models. Each brand's models are unique, the new and returning customer models. We also have the event effect model, right? How do marketing moments impact our efficiency and our revenue?

Then we built the, or we ingested the marketing calendar. Then we allocated the media spend. And then the outcome of all of that is clear metrics across both sides of the business. What you'll see here for those watching, for those listening the first section is our first time revenue. So at expectations of our first time customers, how much revenue are they producing?

What's our efficiency in which we need to acquire them? What's our recommended spend? Then we get to returning revenue, right? These are the returning orders, the returning revenue volume, the repeat rate of that cohort of customers. So now tying all the way back to the data models, we've covered both sides of the customer profile, the new and returning side, those are going to roll up into a holistic view from a revenue standpoint of the business. Right. So we're gonna look at overall revenue by month We're going to see our taxes and our refunds. And then ultimately, we're going to get to a profit level view, or more of a P& L style level view of the business. So we're pulling in the cost of goods that we talked about at the beginning at the skew level by product. So we're pulling in a general total number of cost of goods. And what we get to is a contribution margin expectation every month. This is typically our North star for our partnerships. These are really the dollars that sit above your operating expenses. Right. Operating expenses are things that are harder to move, right? They tend to be salaries.

They tend to be people costs. They tend to be office expenses, things that are generally more fixed and are harder to move. Right. We don't all of a sudden change those things overnight. But contribution is really important. We want to create more contribution dollars. Then we'll have a section for SG& A. In here and ultimately we'll get into it. We'll get to a net income expectation for the business every month. So this is kind of the, I would say, rolled out or roll up view of all the different inputs that get into statless and really kind of the PNL side of how we arrive at the forecast to make sure everybody's clear on the financial expectations of the business. So this is the culmination of a lot of that work and what it nets out into, which I'll let Matt speak to a little bit. And I'm going to look at, let's look at September. So let's look at a closed month for sake of, of the pod a dashboard level view where we see all the metrics that we've established and we see performance relative to those metrics. So Matt, maybe speak a little bit to what we're seeing here on the screen. What is the kind of day to day process and why is this piece of the puzzle important as we kind of wrap up the profit system conversation?

[00:50:29] Matthew Axline: Yeah, so this is part 2 of the clarity component. That I spoke about earlier, right? Part 1 is obviously the entire exercise we just went through. Part 2 is what does this look like in practice ongoing? So everything that we've forecasted out. Is now it's obviously instant lesson that we can track against each and every day for any given time period that that we have.

And so you'll notice the way it's just kind of segmented out for those that are listening. We kind of refer to it as the hierarchy of metrics, more business related metrics on the top. Side of this, and then in the middle is more customer related metrics, and then the bottom are more platform related metrics.

So the idea is, is everything in green is pacing above what we have forecasted. You'll see percentages there as well, plus or minus anything in red is below the, the forecast that we set out. So oftentimes this is a really good area to come through and take a look at how are we pacing towards our goals at any given moment throughout the course of a month.

And if we're off, where are we off and where do we need to make changes? So, I mean, bamboo earth looks like they had a fricking banger month last month. So there's not real sort of a, a situation of, Hey, here are things that you can improve. But there's a lot of times and we'll come through. And even if you just look at this year over year without a forecast in here, it can help kind of pull out insights around where, where, where's the gap here.

And so as we go through this each and every day with the brands and brands will get this tab. In an email each and every morning with the forecast to it. So they're very quickly able to see where the areas of opportunity here. But the idea is, is that we're very, very clear. Each and every day of each and every month across each and every tactic here.

How are we pacing towards our goal? If we're off, what needs to change? Are we down on the new customer revenue side? Why is that? Okay. It's because we're a little less efficient on the meta front. We need to think about adding more campaigns or whatever it may be. Or, you know, there's also a cross channel approach to this for down on the returning customer revenue side, because email has been down, but we feel like we can make up the gap to overall revenue.

From the paid acquisition side of things. Awesome. Like we're pacing above what our expectation is. This gives us clarity to do that. So in a nutshell, that is the profit system. We have not talked about a lot of other components of it for sake of time and wordiness here. There's aspects of how does creative Fit in to all of this.

There's aspects of the daily trackers for meta and for for Google here as well that we'd love to get into at some point in time. But there you go. That's enough of it. 

[00:53:10] Richard Gaffin: yeah, this is great. Like, I think, I think what this clarifies and to kind of summarize everything we've just discussed the, the issue, let's say like the beginning with the, the problem is like when you think about. What do I need to do next month to succeed? What goal do I need to hit? Generally speaking, a lot of brands are going into that with essentially an arbitrary number or a number that they've been given from somewhere else about like what needs to be hit. What we're doing is first assessing the data and building a model to give a much more likely expectation for what's going to happen.

So now all of a sudden you have some understanding of what could happen, right? Then we have a series of tools that help you build a plan towards reaching or exceeding that model. And then we have sort of a way to continually diagnose on a day to day basis across a number of different message metrics, whether or not you're getting close to that month. So, for instance, on the, on the dashboard, we just saw you have contribution margin at the top. That's the North star. If that number is red and you go to the next line, or rather, I should say, if that number is red, that means 1 of the metrics on the next line is probably also read. And then you can say, okay, so AOV is the problem.

Then you go down to the next line and say like, what are the contributing factors to AOV? And then you can continually think about all of the, it gives you a way to make decisions pretty quick. And that's, that's fundamentally what this provides. It provides a map. It provides clarity. It just provides a much more reasonable and grounded way of decision making.

[00:54:32] Matthew Axline: 1 last thing I would add in there real quickly is it's and we kind of touched on that. It's an operating system. Whether that's CTC is the one who's operating it, whether it's the brand who's operating it to where if I know I'm a media buyer that's running meta ads for XYZ brand, this is how much I need to spend on this day.

This is the creative that's going live on this specific day. This is the efficiency that I need to hold to. Awesome. Come in tomorrow. How did yesterday do? Am I above or below pace? Same thing again and again and again. Same on the email front. We didn't really talk too much about email, but each and every campaign and flow with revenue forecasted out for each and every one of them.

So everyone has a very clear and concise sort of, sort of marching orders to reach that end all be all goal, whether it's contribution margin, EBITDA, whatever it may be.

[00:55:18] Richard Gaffin: Cool. All right, fellas. Well, appreciate you joining me. I appreciate you going a little long here, kind of walking us through the profit system. Of course, if any of you want to talk to us about implementing the profit system for your own brand talk to Matt and Pete, you can just go commentary, co. com, click the high risk button, let us know that you want to chat.

And we're happy to have that conversation with you, but Matt and Pete, thanks for joining us on the pod. Everyone listening. Appreciate it. We'll talk to you next time.