2025 Ecommerce Planning: The Framework That Could 10x Your Profit

I am excited for this today. This is a little bit different Bridges Live than maybe we have done in the past. Normally these conversations have been me doing an interview with a marketing and finance leader or a CEO and finance leader in order to illustrate and discuss what are the topics that we've gone through in bridges.

But today we wanted to stop and try something a little different because we know that this time of year is really unique for us in the e commerce space. And that's that we are all likely in some form of 2025 planning and setting the expectation for your organization, whether you are ahead of marketing, whether you're just trying to get to the media budget, whether you're a CEO or CFO is a critically important.

Part of the business planning. I would even be willing to say that as I've, I've reflected on my own journey as a CEO, is that the expectation that I place on the organization often has dramatic impacts on how the organization has the potential to behave. That's really what I think about a plan as a plan represents the boundaries of actions that all of your people will take inside of your organization.

And so you want to be thoughtful about those things being possible. Okay. It is very discouraging to repeatedly fail at goals as an organization. If you are constantly showing up and your key people every month, despite amounts, amounts of effort are losing, it is a negative it becomes like a disease and a virus inside of your organization.

So understanding what is possible, but also ambitious and challenges people to constantly improve is really the art of great leadership. But one of the ways that we think that we can. Help because there is sort of a a qualitative version of uniquely understanding your organization and the potential of your business is for us to provide a point of view, at least in the beginning, or what we think is likely to occur.

And when it comes to great planning, we like to say that you must begin with what is likely to occur. It is important to anchor yourself in the reality of your present state, and then to begin to wrestle with what you would like to occur, and then to understand the gap between those two points. Strategy to me is really just that.

It's the bridge between what is likely to occur and what you would like to occur. And so if you as a business owner know that next year I want to do 5 million in EBITDA, It is important to understand if you were to run a disciplined model and an exercise of if nothing changed, if your business continued as is, if the future was like the past, what is actually likely to occur.

And then in the best case scenario, that likely outcome actually is producing. The one that you would like to occur. And then that's the the easiest of all the scenarios, but it's also the least likely to occur. Often there's some gap between the present reality and the desired state. And then you need to begin to solve for the problems.

You need to begin to understand the inputs that affect the output of revenue. And this is where we must avoid the temptation to simply say, Oh, well, I'll just change the numbers in a spreadsheet. I'll just make my revenue expectation bigger because it's what I want. This is what I watch. A lot of people do is that their forecasting is an actually an expression of desire more than it's an expression of any sort of data exercise.

And this is really damaging for companies as we go today. What I'm going to give you a live process of is basically a system that we have spent a decade really building, which is what we call the profit system at CTC, P R O P H I T. It's a clever pun that combines the word profit as in prophetic, the ability to predict and provide insight into the future.

And of course, the thing we're all after profit, a financial measure of the success of your business. And the idea is that we wanted to create a system that could help brands produce predictable, profitable products. The ability to look out into your business and have an expectation of future revenue that you know, can be somewhat accurate is so important because at the end of day, what e commerce is, is e commerce is simply an attempt to buy inventory.

Based on some expectation at which the time in which you think you can sell it, and then to try to float the margin capture against that timeline, right? That's really in many ways what e commerce is. It's this constant, repeated bet on a cash outlay against a cash return in some time frame. And so the ability to get good at this exercise is key.

Often deeply and intimately connected to your ability to produce profit. Now, so are the terms of your supplier. So are the overall margin and all of those things that, you know, and have heard us talk about often. But getting good at this exercise is important. 

All right. As we're busy here, taking you through the profit system, CTCs operating system for managing marketing and finance. There's a critical component that we don't touch. And that's the actual forecasting planning as relates to your cashflow, perhaps the most important part of this whole process.

And that's why we have partnered with final loop to bring you a solution because I know how critical this is. And I wouldn't want it to be excluded from the way you think about planning next year. So I'm going to show you just how cool final loop is and how much they can help you with that. This cashflow forecasting to sync and really tie in that third leg of the stool of building a true operating system for e commerce, the financial plan at a P and L level, your marketing calendar and planning, and of course your cash.

So one of the beautiful things about final loop is that you can see at any time all of the outstanding bills that you have. You can export an aging report of all of your customers. You can check on and see exactly.

Which customers you have unpaid invoices for when they are due. You can export them into looking at exactly when those billables are going to come paid or chase them down. You can also obviously track all of your cashflow in terms of total growth as well as trends on a more consistent basis, divided up by operating activities, investing activities, financing, et cetera.

And then you can look at your individual cashflow, break it down by month. And there's really cool features they've built, which allows you to export into a Google sheet and get an automated output of a 13 week cashflow forecast, including all of the billables and suppliers and people that you have paid in final loop and recognized and ready so that you can start to pull in your starting cash as well as your.

Revenue expectations. And this is where you can integrate directly with the CTC, , planning process to get to the revenue expectations. Then you can go through, understand all the people that you have payables due to get this into really the core operating document of an e commerce brand, which is that 13 week cashflow it's tab one on my browser as an agency and as a brand.

And it should be for you as well. This is truly what you want to see grow is the bank account. So the more that you can build a system that ultimately lends to this document right here, a 13 week cashflow forecast, the better you're going to be, the more effective you're going to be as an operator. So find a loop is our preferred partner for doing that.

They've built real time accounting that is so essential to helping you get more clarity around the financial realities of your business. So check them out today, find a loop. com.

 so the profit system to me combined two things.

[00:07:10] video2233884062: When we think about an operating system for e commerce, it has to bring to bear. Two really important parts of your organization. Number one is a financial plan. The financial plan lives in a spreadsheet. This is an output of a P and L expectation, a profit and loss statement, an income statement that shows you total expected revenues, total expected costs, total expected profit of the organization on a monthly or yearly, monthly, and even daily basis.

And that is the beginning of a financial plan that's measured in terms that that your finance department would use. But that by itself is wholly insufficient because the actual levers of the generation of that revenue come out of a different department. They come out of the marketing department.

And so the second part of the profit system is a marketing plan. And this is where I think what we've created is really, truly unique in our space. It's the intimate connection between the marketing calendar And the financial plan in so much that the actions in each space affect the other. And this is where, what I see inside of most organizations is these things exist independent of one another because the people responsible for each exercise don't totally understand exactly how the other behaves.

So oftentimes you'll get a finance department. That's ma asked. To provide a forecast of the business without understanding exactly where the business might be in relation to its marginal frontier. They may not have clear view into the trends of efficiency in your core media spend channels, or they don't know the exact planned dates of promotions and campaign launches and PR moments and influencer activations.

And so their ability to peg which month that big peak is going to come in. It's actually entirely dependent on somebody else, giving them that information and providing a point of view. So when we built this system, we wanted to bring those things together. And so what I'm going to show you today is how our planning process actually brings to life, both a marketing calendar and the qualitative portion of your planning with that financial plan.

So today, what we're going to do is we're going to go through a demo. That demo is going to be inside of our data platform statless. That's going to show you how we will actually do. And our teams are currently engaging in planning. For 2025. And what are the inputs that we would go after? And I want my hope is for two things for you today.

One is that whether you work with CTC or not. What I want you to focus on in this demo is I want you to think you can explore the tool and the exact exercise, but I want to want you to really think about as the inputs, what are the inputs of a model and how could you get a point of view in your business about these inputs and how they relate to one another?

I want us to think in this modeling exercise, far less about the output. The output is less important to me than the clarity of the inputs. So. That is the way that all of you are going to be able to take away something and action it for your business is to understand how you could think about the inputs that inform your 2025 plan and how you could create clarity of them and how they relate to one another.

The second is for those of you that are considering finding and using a partner like CTC. And specifically to help you with this exercise and to understand what you would get if you were to work with us to build a 2025 plan for you. And one of the beautiful things about the profit system that we've built at CTC is that I don't care if you have an agency currently in place.

I don't care if you have an internal team, we don't need to actually execute against any of this to provide it to you as a point of view, we are happy to help you build a really robust and clear plan and tools of expectation, whether we execute it against, against the actual plan or not. So just keep that in mind that we don't, it's not a necessity that you use CTC to execute the system in order to work with us and grant, just put in the chat.

If you go to profitsystem. com, you can get access to exploring that for your business individually. But now it is time to go through our demo. One of the beautiful things 

about CTC is that, Because we own or have ownership in our own brand, we're able to use real data to give you views into how this exercise and business planning can happen.

So I'm going to take you through let's go to last year. Bamboo earth. So bamboo earth is our skincare brand. You can go to bamboo earth. com. You can see exactly we are just North of eight figures as a business, about 11 and a half million dollars this year. We'll end up hopefully somewhere close to around 12 million in revenue.

Oh, sorry. I'm looking at 2023, but go back this year. There we go. There we go. So 9 million so far will end somewhere around 10 million this year in pursuit of a little bit more efficiency as we go. So you can see where our individual businesses in transparency. I don't own and operate CDC or a bamboo earth.

I'm not involved on a day to day basis. I just get access to the information to share publicly. Dave's been really gracious. You guys know Davery cook on Twitter. He is the president. Yeah. Our business partners and Josh run and operate that day to day. So we get to show you some of this cool information.

They've been gracious enough to share how we would do this. So what you're seeing right now is the inside view of statless. If you go into the settings section, you'll see all of the integrations that feed the database structure here. So we have all of the classic integrations that you might expect from Shopify and Amazon and Facebook and Google and GA4 and Klaviyo And some manual tracking of influencer spend and our own first party pixel and lots of other data integrations that allow us, including most recently app Lovin to allow us to get a view into a client's historic performance over time.

And all of our work in this exercise begins in this section here that we call the plan section. This is where we begin with all of our customers to build the financial and operating plan for the organization. So we're going to jump into here. And one of the things that's beautiful about this is it allows you to do scenario planning.

So we can build lots of versions of plans over time. You can build an annual budget that never changes. Then you can also build a monthly LE estimate, which is usually what we do. We'll build an annual plan and then we'll reforecast every single month. We'll keep both of those versions in existence. So you can track your performance to both at any given time.

You can create an aggressive scenario. You can create a conservative cashflow estimate. Oftentimes there's a great episode that bridges that I have, that's called a bank board budget bonus. And the idea is that every business is often creating variable financial plans relative to different people that they're communicating to in the organization.

So this sort of scenario planning becomes really helpful. But for the sake of today, we are going to work on building a 2025 plan, and I'm going to show you exactly how this would work. And we're going to do this live together on this webinar. Our planning system involves three core models. That generate the expectations of the data.

So we have a data science team here at CTC that oddly enough is led by Steve recode, Dave's brother who's a data scientist and helps to build all of our models. Every client gets a custom model built for, and this is a really important differentiation. There are some tools in the market that help you with forecasting, but because they are software businesses, they are forced into building standard models.

That allow every customer to use some basic regression or trend. And here's what I'll tell you about that as an exercise. It is massively flawed. E commerce datasets come with all sorts of qualitative exceptions that need to be considered in the process, whether that's related to seasonality, related to the marketing calendar, related to inventory positions, related to times that there were unique challenges in your business, where you need a human that is able to interact with you about those to make sure that the model considers.

What data should be removed from the data set, what data should be under considered or over considered. And all of that happens. So you'll see up here where it says model 1. 0, 1. 01, 0. 2, 0. 3. For every one of our customers, we update their models every quarter. So you can see this is a version of the model that was just built and released on December 10th.

So that's the most recent update to this first model, which is the new customer acquisition efficiency model. At CTC, our forecasting process is what we call a revenue layer cake model, which is that there is two different models, your existing customer revenue and your new customer revenue that stack on top of each other to create your future forecast.

And this first model, the first thing that we have to do is to model the expectation of new customer revenue. And so what that begins with, if I just sort of take you through this in a step by step basis is we begin by looking at the historical relationship between spend every one of these dots is an XY plot of spend on the x axis and AMER or new customer acquisition efficiency on the y axis.

Okay. And if we look at that historical plot, what the data scientist is going to do is have a consideration for seasonality. And like I said, any extractions to then create a modeled relationship between spend and efficiency over time. So the gray dots here, you can see are all of the historical points that the business has produced.

The yellow line represents the modeled expectation for the month of January. Every month is going to have a different curve that has to do with the seasonality of the business. In our case, bamboo earth is a more, their primary product that we sell is a moisturizer. January is actually the best month of Facebook media spend for the whole year.

It's because CPMs are lower and the demand in winter and cold months is higher And so our curve and how much we're able to spend at different efficiency levels is different for January than it is in June. They're very different. I'll show you that in a second. So this modeled line represents the possible potential outcomes for the business.

And then this if I look at this dot here shows me what we were able to spend last year. So last year we spent 206, 000 at a 1. 428 AMER. So you can see how I'm starting to build the composition of possible choices that I have to make as the growth strategist in this case for the month of January. And our job here, if we're going to build a 2025 forecast is to start by setting the spend and efficiency expectation of every month of the year.

I'm not going to do all 12 months for now, because I just want to illustrate this, but that would be the task at hand. So let's talk about some of the other data points that we have here in consideration of which, what my spending efficiency should be. One, I need to know the cost of my customers on an all in what we call cost of delivery basis.

This includes. Your cogs, this includes your payment processor fees. This includes shipping and fulfillment. This includes returns. This is your fully loaded cost of delivery. Now, when I build my January cost settings, I have some choices. I can use the number from the last 30 days. I can use an average of the last six months or I can use January of last year.

Why would that be different? Well, the product mix that you might be selling in that month can change. You might've made marginal improvements to the business. There's all sorts of ways in which the actual number might change. Or I might know, Hey, we just got a great new deal with a supplier, something that the dataset could never know.

And I can manually input the cost expectation there, but you can't select. The point of your media spending efficiency without understanding the actual cost of the business. This is like, why this connection between finance and marketing is so important, is that you cannot make this decision without this data point.

Because the marginal break even point is different relative to where I, where, what my actual cost of delivery is. Other things that I have to understand here. I have to understand both my AOV, As well as my LTV. Okay. Why is this important? Because when we go to think about how we want to select the media budget, it's predicated on the business objective that we want.

I'm going to talk more about this in a second, but in order to help me with this, what, because we have every business is LTV. I can look at both the AOV. Again, I can take the same idea. What do I want to use last year? Do I want to use the last 30 days? Do I want to use the last six months? What period do I want to use up to me to select this or manually override it?

Cause I know I have some different impact to AOV. I'm increasing prices, 10%, whatever I want to do with this number. I'll often use in a, in a very seasonal business, I'll often use, Last year's AOV, unless there's some change that I know about, then this multiplier gives me a factor for LTV change, depending on the period of time that I care to use.

So LTV, not a very helpful phrase because it references an ambiguous period of time. We'll often talk about a one year LTV or a 60 day LTV or a 90 day LTV. For many brands the best starting point for recommendation in my mind is to look at maximizing the lifetime contribution margin within a year.

Or if the, if you're a smaller business with less cash consideration and smaller existing customer revenue, I might even go shorter from there. So in this case, if we just pick as an example, a one year LTV for this business, now I get the multiplier factor and the LTV as a consideration in my model.

So now I've got my inputs. Over here, we're not going to do anything with this right now, but this allows me to tweak the model if I want it to outperform, or I thought I was going to underperform the model for any reason. This might be because if I know, let's say, I know I'm out of stock on my best two SKUs.

I can't reasonably expect that my new customer acquisition acquisition efficiency in the next month is going to be as good. I might like actually reduce the expectation of performance of the model, et cetera. So this is just an ability to edit or tweak the performance. Now that I have this in place, what I'm going to do is I have to make a choice related to my business objective.

Here are the choices that exist for you. I could choose to try to maximize contribution margin in month one, maximize contribution margin in month one. Of the customer for the customer acquisition efficiency that I have. Now, obviously this is most likely to recommend to me the lowest amount of spend.

You can see that if I wanted to maximize the first month's contribution margin off of my new customers, that's net sales minus ad spend, minus cost of delivery in month one. That the model would tell me that my budget should be 80, 000 and my target should be a 1. 8 AMER. You can see that's way up here, very little to spend, very high efficiency.

That's also not very much revenue. That means my top line revenue on my new customer acquisition would be 141, 000. Now, that may or may not be the right thing for my business. If I needed to, let's say, maximize cash flow because I had some pending liability that I needed to pay, that might be the right decision.

But in most cases, this is probably too conservative of a view of acquisition. The next most conservative view would be to go to max lifetime contribution margin. So this takes into a consideration, not just the efficiency on first order, but the efficiency over the lifetime of that customer cohort. So if you acquire a hundred customers, they obviously continue to produce value for an additional period of time.

So higher volume. Over a longer period of time will often yield more total contribution, assuming there's not a huge trade off in efficiency. So in this case, if I select max lifetime contribution, and again, I'm using the one year multiplier here, you'll see that it changes the budget target to 133, 000 at a one five six AMER that produces 208, 000.

Of new order revenue. So here was the point I had previously. So I'm spending an additional, let's say 60, 000 to produce roughly 67, 000 more dollars in revenue. So that's actually in the short term going to create less first order contribution, less cash in my bank account immediately. This is really important.

This is why these trade offs are so important to understand. But over the lifetime, within one year, I will have produced 93, 000 or 18, 000 more dollars of contribution. This is where I think brands all the time are considering the trade offs and risks associated with these numbers. Your confidence in the realization of this future margin without anything that would potentially degrade it.

You spend money on existing customers without meaning to your margin profile changes in the future. This would yield the best return on invested capital, but it often comes with additional risk associated with it. So there's no right answer to this game. This is where it really is a business decision.

So you can see this is a little bit more spend for some more revenue for greater lifetime contribution margin. But oftentimes it's not actually what people want. They'll actually want max revenue. For the business which is to maximize first order contribution at breakeven. Now for Bamboo Earth, it just so happens that the max revenue and max lifetime contribution margin, when you select to a year are actually the same point in the curve, but if I go back and do it this way, let's say max lifetime contribution at a 60 day basis, you'll see that the point on the curve for max lifetime contribution margin actually moves further up this curve than does The other one, and if I go to max revenue, now it's see, you'll see it moves more to here.

In most cases, BambooWorth pushes the max lifetime contribution point to the point of breakeven on first order. So these, these points tend to be fairly similar. That's not the case for all brands. But this point right here is maximizing first customer revenue without at breakeven. Now I could, I could change the, there's a, there's a constraint in the model that doesn't allow you to generate negative first order contribution margin, but you could, you could change that if you wanted to, to see if there are changes in the model to what that illustrates.

Okay. The other thing I can do is I can just put in my own expectations. If I said, well, last year I spent 206, 000. What if I did that again? So if I spend 200, 000, you'll see, it's going to suggest a 1. 35 on the model that's going to generate 270, 000 in order revenue, but it's going to create negative 26, 000 of contribution margin.

So I've got a choice to make at that point. Do I want the incremental revenue at a cost to my bottom line? Well, that's up to you to determine in your business and the expectations that you have around growth, et cetera. So you can play with all sorts of individual scenarios here, but for the sake of this exercise, I'm We're going to go to a one year LTV for the month of January.

We're going to pick the max lifetime contribution point. And we're going to say that, all right, our budget is going to be 133, 000 and my AMER target's going to be a one, five cents. And there I have my G I've done the first step of this process, which is building my January expectation. So I can easily just apply this to all future months.

And you'll see that I've done that ahead of time is to get to max lifetime contribution and give myself the budget in each of those months to do so. And you'll notice that the spend in each month is a little bit different in the model relative to those expectations. But this is step one. I've now got data point one, which is my new customer media budget.

And now I've got to move on to the second part of this model, which is the returning customer revenue. Okay, you're returning customer revenue as an e commerce business is actually the easiest part to forecast. I'm sure many of you have heard of cohort specific LTV models, where you can take every curve of customers that you've ever had.

Let me just isolate this down for you to show you what this looks like. This is what's referred to often as a spaghetti chart, is that you'll see for every cohort of customers, they have some. Percentage increase in value over time. You can model those curves. They're going to be off by different periods relative to seasonality moments.

Right? And when you stack all of these curves up, you can very easily begin to model out the expected future value of your existing customers. Okay? So if we think about the first thing we're doing is getting to a new customer expectation. The second thing we're doing is getting to an existing customer expectation.

So right now, Bamboo earth has a little bit of a challenge. You'll, you may have noticed in the beginning that 2014 was actually, or 2000, sorry, 24 was down from 2023. And it's because there has been challenges for new customer acquisition. We're moving into product development to help to solve for that because we've really grinded out the core funnel of acquisition.

And so you'll notice that if this is my returning customer revenue for 2023, Here's what the model is telling me is going to happen in 2024. Now I'm going to stop here because this is beginning to illustrate what I promise you, Dave does not like, but this is what is likely to occur. This is what is the model is telling us is that the end result for the year is that 2025 revenue would just be up very slightly from 2024 returning data.

So in all these gaps and versus times being ahead yields to. I want to talk about another reason why data gets complicated and why this is still a very human process. I know something about BambooEarth that the model itself is very difficult to surface. And when you blend out averages becomes very challenging.

So if you look back at the AOV for returning customers in 2023. You'll notice that it was 90. That's what this column here is showing me that in 2024, the AOV on returning customers goes way down, but the order volume goes way up. Okay. So what's happening here is that we introduced membership into bamboo earth.

And what that did was It functionally processes a bunch of low dollar value orders that are the membership renewals. So what that, what that creates for you then is a big change where you have higher order volume at a lower AOV. And you'll see what my model starts doing is it sort of starts middling those averages where it gives me an 83 eight AOV against an 81, 000 order value.

A business. Now, what do I know? I know that that's not going to be the case that the AOV is not going to be some average of these two things. So I can actually begin to override this process with what I know as a human. And this is again, why having a growth strategist can begin to actually apply the qualitative understanding of the business to be much more accurate.

And that these are going to be much closer to, let's just say January is going to be 20 to 70 in that month, but I'm also going to see this order count rise. So I'm going to have to do some manual work here to help inform this. I may go back to the data science team and help them to improve it, but this is just an illustration of why this cannot be a process that happens independent human analysis.

So that's step two. Okay. Is getting to a returning customer revenue expectation for the business that's beginning to build my model. Now you'll see, again, I just built a very quick version of my new customer model. So it actually is showing me that returning customer revenue order is going to be down year over year.

When I go back and through, do that on detail. That's where the next step of going, okay, we've got to probably outperform some of these things comes into play. But here's what I want to talk about. That is actually the really important part of. Where our planning exercise goes from here. So after building these first two things, what I have now is I have an expectation of my spend, my new customer revenue, my returning customer revenue, and therefore my overall revenue for the month of January.

So I have my new customer revenue, my returning customer revenue, and my total revenue. And I have my media budget, right? So I have all of those things for the monthly view for the month of January. But that is actually insufficient to get me to a level of executionary planning that allows my team to go and make things happen.

And it's missing some really important data points. And that's what I talked about earlier, the marketing calendar. So the next step that we do in this process is we actually take and we build one, a historical view of every marketing moment that has occurred for the business. So in this case, I went back and sort of illustrated what this looks like is that last November, we launched a new product, the Bilberry and snow mask on November 8th.

We ran some key activations for about eight days. We, it was, we labeled this as a product launch. We obviously had a black Friday. We had cyber weekend, those moments now become endpoints in a database that when I go to do a product launch in the future, it will reference back to this period for what the effect is on my revenue.

We call this the event effect model. So I can go back and look at what happened during this product launch in the past. Did it improve my efficiency of acquisition? Did it increase my existing customer revenue? And you'll see here as an example, In most cases, the primary thing that happens when you launch a new product, say, is that you get an increase in the impact of your existing customer revenue.

So when we launched the Bilberry snow mask, you'll see that our existing customer revenue expectation for that day was up. And so there's some effect on the daily expectation of expected returning customer revenue when you do something like that. So as then I build out my marketing calendar for 2025 in the month of January, we have a seasonal event that Dave's running called non toxic 90.

And then as I go into February, you'll see, we have a product relaunch that's starting in February called an exfoliating mask. We have a promotional day, all of those future plans based on the event effect model. Okay. We'll actually alter the daily flow of revenue of new and existing customer revenue based on how those effects happened in the past.

And this is really where we are beginning to apply some machine learning and qualitative connection between what your marketing plan is and what the impact is on your financial plan. So we'll fill out all of this calendar. We'll save and apply the event effect model relative to where we're at. So we might say that, Hey the expected impact on AMER when running a seasonal event is a 259 percent improvement of AMER.

And if we were to apply this. Then when I go into my daily expectation for the month of January, you're going to see that the AMER on the dates when the events are occurring are higher than the AMER expectations on other days. So this is really important to understand of how that affects us down to a daily revenue expectation.

The next thing we also have to get to is the, the, the spend and effect model only gives us the, The budget in total for the month, we now have to actually turn that budget into a channel allocation. So we have an MMM, inside of statless that is built on top of Robin, which is Meta's open source MMM library.

I think it is. Very good. It's as good as any MMM that you're going to find out there. We can also continue to adjust it based on the desired outputs for this individual business to see what the recommended budget is for the month. So I could come in and say, okay, if I wanted to use the marketing mix model for this month, it's going to give me these channel level recommendations for bamboo earth, and that sets my channel level allocation.

Now I can go and make adjustments to that. If I said, Hey, we're actually going to do an app love and test this month. We're going to take 5 percent of our budget. We're going to allocate it there. Then you could do that accordingly if you wanted, or you could stick to the exact model output. Again, often this is a dialogue and conversation with our customers.

We also take into effect what we call a day of the week effect. Many stores you'll see for Bamboo Earth, it's a very dramatic improvement of revenue on the weekends. This is very common for e commerce businesses where you see the midweeks being the lowest revenue days and the weekends being the highest revenue days.

And so that model, the daily effect gets applied. And the end result is you have an expectation of your marketing calendar, Your channel level budget, your new and returning customer revenue and contribution margin every day for the whole month. So we've gone from having a monthly budget. Down to a daily channel level expectation and a daily expectation of new and returning customer revenue connected to my marketing calendar every day for the whole month.

Now we're not done there, right? We still have other things that we want to consider. Now, if I want to override the expectation of my returning customer revenue. So as an example, we talked about why it might be lower. Every data point we have offers you the opportunity to provide a manual override or expectation.

So if I said, Hey, I actually think this is going to be 523. I think your model is light, or I want to create a greater expectation. I can do that. And then we begin to model out a bunch of additional inputs that form the foundation for our system. So email revenue. As a percentage of total revenue. This is going to be the thing that sets up the goal for the email team and every month you can see that Bamboo Earth has about a 24 percent of their revenue.

This is going to be using a the Klaviyo reported seven day click revenue as a percentage of total revenue is the calculation here. That's what I would encourage you to use. I don't think you should use last click GA. I think you should use a Klaviyo seven day click reported revenue. We try to normalize most of our channel revenue reporting down to that metric to start.

This gives you a good goal to work against. That will set an expectation of how much revenue needs to come from that channel. And then we're just going to work down the line. Same thing with organic. We use this for SEO clients and help to think about non direct clicks. The discount rate shipping costs.

Taxes, return rate for new customers, return rate for returning customers, cost of goods. Variable expenses, OpEx expenses for the business all the way down to a channel level ROAS targets, which are built on the incrementality factors that every business has either through the measurement roadmap that we've worked on with them together or the the default benchmarks.

If it's a new customer, we're taking the channel level targets and taking the incrementality factor times the AMER. For the channel equals the channel level target. This is really important. I'm going to say this again, the AMER that comes out of the model times, the incrementality factor for the channel equals the channel level target for the client.

Okay. That's a really important, easy way to operationalize this exercise. The end result is you get to a full P and L level plan. We'll see the month of January here. That gets me to gross sales, discounts, shipping, net sales, hogs, variable expenses, paid media, contribution, margin SG and a profit or quarter accounting display, key performance metrics, all of the above.

And then what that does is once this plan is published, there's two additional things that happen in a CTC universe. Number one, we're going to publish that plan into a growth map. A growth map now is a place where a media buyer is going to operationalize that information and put it to work. Because we still have work to do to get to a plan in even more detail.

We now have to take the channel level budget. Has to spend 55, 000 and we need to turn it down into campaign level expectations every day. So what you can see is we are just taking a plan and chunking it down and chunking it down and chunking it down and chunking it down until every day there is an expectation of every dollar.

So on Facebook as an example, what might happen is that the, because our statless database, Is connected to grab one of these. We don't use bamboo earth all that often as a map. I'm going to show you a different one here because this, because bamboo earth is not actually a customer. This is not an active one.

Let's do this. I'm going to show you guys something real fast. The target number that we create in the statless planning process gets pushed automatically and synced into this spreadsheet. We call it a growth map. And then that media buyer receives that target number. So if I look here, what you'll see is this yellow column is what a media buyer would get at the beginning of the month.

They would see 415, 000 as my budget. Here's the current expectation of spend by day. And now their job is to translate that target into a media plan at the campaign level. So on Metta, where is this 415, 000 going to be spent? And what you'll see here is every campaign in the meta account, what the expected spend is every day on that campaign by offer type for the entire month in true CDC fashion.

We have lots of campaigns. We run everything on cost controls and there's two different kinds of campaigns that they get to use to plan. One is what we call the previous concepts. These are the campaigns that were already live. When you started the month, if you're building a media plan as a media buyer, you need to know a few things.

You need to know one, what's my budget. What's my target? What campaigns am I starting the month with that are going to last me throughout the month? So we start by pulling in all of the results from the previous campaign. So this is like, if I'm looking at, if I was building, let's go back to January, 2025.

Okay. Here are all the campaigns that were live in December. I can choose to say, all right, I'm going to bring with me this leggings campaign. It's going to be live from the first one, one 2025. And I'm going to, it's going to be live all month. Leggings is a standard campaign. It stays with me as we go, et cetera, et cetera, et cetera.

My daily spend might be 2, 000. And now I'm going to start with all the campaigns I'm bringing with me. That's going to be this first set of columns here. All these are existing campaigns that we started in a month, but you'll see on day one, they were already spending, but inevitably there's going to be some gap between the campaigns that I already have live in the budget that I want to spend.

And so that's where all of these new campaigns come in. And that comes out of the concept log. This is a creative planning process now where the creative strategist and the media buyer are working together to plan net new campaign launches that are going to help them get to the budget target. And because they know the constraint of what is my media budget?

How many campaigns are, how many campaigns do I already have? How many new things do I need? They can actually build a plan to get to where we're going. Sorry, this is taking a second to load here. There we go. You'll see what a concept log is. So you can see how the financial planning process sets a budget, sets a channel allocation at the growth strategy level, that a media buyer and a creative strategist can now begin to build a media plan that includes go live dates for every campaign. What the campaign is, who the audience is, what the angle is, what the offer is, the format, the landing page, the copy, and the budget expectation, the bid that's set on every campaign.

And then this planned campaign going live on nine, four, Looking backwards here shows up in the media plan tab with an expected budget and spend every day. So the end result here is what you're going to get to is an expectation of performance every single day of the month in every campaign. So if I go back and I look let's pick a month here where I did a bunch of this.

Let's go to, let's look at like, let's go back to that September. Did I do this? So here you'll see the exact expectation of performance across every metric. Every day for the month, I can look at this, you know, and just pick an individual day and say, okay, on nine 11, how did we perform relative to expectation all the way down to the returning and new customer level, all the way down to the channel level of performance.

And so what this allows me to do is as I now have my plan, what you can see, I can compare against any one of these plans that I've built any against the scenarios in any period of time. The key here is With any modeling, here's what I want you to understand is that the only thing I know for sure is that all your numbers are going to be wrong.

And the point isn't about being right. It's how close can we get to the expectation by bringing clarity to where I'm wrong, so I can fix it. So let's imagine that let's go back and pretend that it's September 8th and you are a growth strategist working for bamboo earth, or you're the team working in bamboo earth.

Okay. What you would see is something like this. I'm ahead of my contribution margin target by 1%. I'm behind on the top line by 5. 6%. And I'm under my ad spend by 30%. So if I'm in charge of this, what this helps me to understand is what kind of problem do I have? And I like to say in e commerce, there's only three kinds of business or three kinds of problems.

There's volume, efficiency, or both. Those are the only two kinds of problems. The both one is the hardest to solve. But in this case, what I can see is, wait a second. I'm ahead on my efficiency target. I'm ahead on contribution margin. I'm underspending the opportunity. And then I can go a layer further and I can get a little clearer.

I can say, Oh, my returning customer revenue is actually ahead of expectation. I'm beating Miami AMR target, but I'm just not generating a new, enough new customer revenue. I am actually massively underspending. A channel. So if I was running bamboo earth in this case, again, we're not, but I would say, Hey, why aren't we spending on Google?

We said we were going to spend 3, 000. We're way ahead on efficiency. Something is obviously being suffocated here. We need to go fix this problem meta. Now it's a little bit harder, right? Cause we have a, both problem. We're not actually ahead of Ross and we're behind on the channel spend and revenue. But given that I'm ahead of AMER, it becomes very obvious what I need to do.

First, is there room to fix the Google problem and get the spend up? I'm way ahead on efficiency. I clearly need to loosen the controls or go after more non brand here. Step one, solve that problem. Step two, I may push into this scale, knowing that I have a little bit of room against AMER and some buffer for my returning customer revenue, because the hierarchy of goals here is achieve this goal.

Then this goal, then this goal, and the channel level ROAS target is subservient to all these other expectations. So this, what it does is by building a daily plan that has clarity of expectation, I now know how to exercise an action against the opportunity with clarity as every individual team member.

And what our teams do is they communicate and what we call map notes. So if you came in and it was nine 11, the growth strategists would be sending out a message that said, Hey, here's what, here's what happened yesterday, here's where we were at in performance to expectation. Here's anywhere that we missed.

So we actually got a bunch of new customer revenue, but we were a little, little under efficient on that. So we might go take a BNC action. So what. So what now, what, here's what happened, here's what it means. And here's what we're doing about it. And every day we're tracking that progress, both on a yesterday, as well as a month to data expectation and going to solve against the plan that we created.

So this workflow is a combination of build the plan, get to the details of execution of every individual team member in every channel, track progress to expectation horse, correct. When you're off course, that's the key to building a great financial plan. As you go into the year. Is you need to get a financial plan that you believe in, that's rooted in good data.

You need to go out and execute against it every day with context of expectation. And you need to constantly course correct. So the other thing that I will do here when I build this 2025 plan is I know for bamboo earth, one of the big problems that we have to solve in terms of whether or not this revenue number is ending in an output for the overall annual, P and L that excites me.

And I promise you eight, seven, four is not going to be exciting to them is now I have to go back and do this harder exercise about, okay, what are we going to improve in the calendar exercise? As we go now, I know that Dave and I talked this through and this promotion that they've come up with. This non toxic 90 is a messaging and invitation to the program that we've had a discussion about what the goal is of how this campaign is going to affect the performance of the business.

And we think it's going to do two things. When we did this, let me pull up the quick reference to the dialogue. So it's a bundled set of products that we think is going to improve AOV and allow us to be more aggressive on acquisition. So to illustrate how I might bring that to life is I'm probably going to go in here and say, I actually want a higher budget expectation for new customer AOV in the first days.

I also think with some of the new creative that we have, we're actually going to outperform January by 4%. And now I'm going to get to a number. If I go after max revenue or max. That allows me to drive a little bit more revenue at the same efficiency to get to a bigger goal in the month of January.

That's closer to the new order revenue that maybe Dave wants for the month of November. And you have an underpinning of why you believe that's going to occur. Additionally, on the returning customer revenue side, we talked about why the model has limitations to where we're at. And so we might override that with an expectation for where we're going to get for that month.

There's lots of tool, other reports that we could use to help build that expectation. And off we go to get to the January level P and L, and we would go through this and do this for every month, make sure their op ex is pulled in, make sure all the costs are right, et cetera, et cetera. So that's everything that's happening.

And you can see that they're like the amount of effort on the front end is a lot to get this right and to have a very clear point of view on a budget that you can be confident in, that you could defend, that you could put in front of your board, you could put in front of your boss and feel really confident is accurate and defensible.

What I like to do, because clients will often push us is what having a baseline model allows you to ask back to somebody who wants more from you is which input is going to change. Are we going to be able to drive more efficient acquisition than we ever have before? Are we going to, and if so, why, what's the justification for that?

Is returning customer revenue going to be better? Is AOV going to be better? What's going to change to change the outcome. And then you can, as the person who's, if someone says to you, Hey, I think we're actually going to improve our gross margin to 31% that's going to allow you to spend more money because you can take a lower efficiency.

Great. Well, now I know that I can drive to one four five and somebody else is responsible for delivering this 31%. And if that happens, the business is going to be successful so you can get into a dialogue about what is going to change in order to produce the outcome for the business. That's going to lead to better results than anything that you've seen in the past.

And that's really where the dialogue around great planning can occur is getting everybody onto the same page of what we need to improve as a business to get better and having this immediate available tooling where you can instantly see the effects of like, okay, if we were able to create four points of margin, how much more volume could we get?

What would that be worth to us as a business? How important would that factor be? Is really critical to why this exercise is so impactful. All right. I'm going to stop. I know there's a lot there. I hope what you see inside a stat list. There's so many more things that we can do along the way to once we get these plans in place.

I want to show you guys something cool here is that we can actually track. Let's try and do this in a way that. Again, I'll just do this one makes these comfortable with me sharing a couple of these things. One of the cool things that this allows us to do in peak moments, once we have these plans in place is on an intraday read, we're able to see how we're performing on an hourly basis against the plan by hour across every metric.

So what the beautiful thing is, once you have the data structure underpinning your business, that shows you exactly how much revenue, how much spend in every channel, every day that you expect, you can get to really cool reports like this, where on these peak days, we can say, all right, I want to compare my hourly performance to target.

Okay. Using the revenue pacing from this same day last year. And that's what this report shows me is like, all right, I can see my total sales, my total sales target by hour and where I'm at so far today against the expectation for sales, spend, Facebook spend, Facebook efficiency, Google spend, et cetera, every day, every hour.

So I can now keep chunking this system down so that I can get a read in any given moment every hour to understand how I'm performing the expectation. That's the power of the underlying data expectations so that you can, if you know, and often what this has the most to do with is like Facebook spend pacing.

So I'll give it, here's an example where today I look at this and I go, wait a second, I'm a hundred and I'm 40 percent ahead. Of my revenue pacing, my AMER, which is right here, target is actually a little bit behind expectation. My new revenue is about 17%. What this means is I'm way ahead on my existing customer contribution margin for the day.

So if I wanted to, I could make a recommendation to go be more aggressive because this is still a highly profitable AMER. And because I'm getting some boost out of my existing customer revenue, I think there's a real argument that somebody should make a recommendation today to go push harder into these channels of efficiency, given the strength from the other business.

So it's the clarity of the information that allows me to action to go and produce the best results. So we have tons of cool reports like this. We can also back this out to a little bit wider view into something we call the calendar report. Where one of the most powerful things about statless is that it allows us having a plan of expectation of every metric allows me to look at data in the future.

So as an example, if I go into a monthly view I can add this little dot that shows me project projection that now says, okay, if I pull up a date range of the first through the 31st, I can now look at, okay, based on what has been so actual so far in terms of my contribution margin and my revenue, if I were to hit my target for every day for the rest of the month.

Where would I land at month's end? And I can actually see, and then I also can see what marketing moments I have planned. So the calendar actually shows up here. I can look at what marketing actions occurred in the past to create the revenue. I can see that. Okay. That was cyber Monday. Okay. That was cyber Monday.

Sale extended. Here is the Chrome launch. Again, you see this little shows me the events from that day. What Facebook campaigns that I launched on any given day. And so I can look at, all right, for each of these metrics, where am I at relative to expectation? So this is my actual, here's my goal for every day of the month so far.

So all of this underpinning enables all this unique reporting that allows me. 

To get to day to day execution of a system that is highly effective. So, I recognize that for many of you, some of this isn't immediately available. And so the simple version is to start to get an expectation of your business, of a new customer level, of a returning customer level, of a relationship between your spend and your efficiency, to begin to model the basic inputs of this output.

And here's what I'll tell you. The good news about forecasting is that it's very hard to actually outperform a basic trend forecast. So if you just rooted yourself in the trend of your spending efficiency over time, you could get to a starting point in this exercise. If you root yourself in the trend of your returning customer revenue over time, you can get to a basic level of expectation here.

And then you can start to chunk it down. You can look at the flow of your revenue by day. You can look at your media spend across channels to start to get to a system that allows you to operate every dollar every day in context of expectation. Every email send, every SMS send. We all have an expectation of every dollar to make sure that we're delivering on that expectation.

And that is the key to building a great 2045 plan and how you connect finance and marketing into a shared set of common language around a contribution margin goal that every day you're executing against that has a consideration for all of the costs, the variable expenses, as well as the revenues in your system.

So I was five minutes late. I'm going until now. I'm going to do some quick Q and a, I'm going to try and respond to some of these for you. So I'll hang out for another 10 minutes here. If you all are good, throw some more Q and a in the chat. And I will begin to see what I can do to answer as many of these as possible.

And of course go to profit system. com. Fill it out. We'll talk to you about it. Let us show you what we could offer you, where again, I can hand this system to you and your team and you guys can execute it, or we can support in executing against any of it. So whatever is most helpful to you in terms of whether what role we're playing and just providing you the tooling and the data and the analysis and the point of view on your business, or if we actually are going to take it and help you day to day execute against every dollar.

So I'm all right. How would you this apply for an online retailer such as Walmart? See, it is viable. How dynamic is the model against variable market conditions? Does it plan to forecast account until profit act their tax balance? Cash flow statements leads to production time. Okay, one like man, you're looking for a job.

I want you to come come work for us because you're basically describing our product roadmap here. So an online retailer is perfectly fine because at the end of the day, you still have a unit cost and a unit sale. Your unit costs and margins are just different and usually the SKU bases are much larger.

But in theory, it's the same principle. This is the same system we use to manage CTT, right? There's a service that we sell. It has some underpinning of cost and you can track the relationship of those things over time. Market conditions. This is a great question. As you guys know, we published the DTCCI, which is a consumer confidence index.

We also have a large benchmark data set in cases where an individual businesses data is not very predictive of itself. We will often use that as well as keyword volume. And so we're beginning to find Other inputs into the models that include macroeconomic factors, because if there's anything that coven taught us, it's that maybe the biggest impact on our on our performance as a business is related to the macro condition.

So we are continuing that's on the road map. And then in terms of everything you're describing, how does this integrate with a balance sheet or a cashflow or inventory systems? Those are all things in integrations we're working on. In the meantime we love partners like final loop, who is a partner in this webinar, who I would say like, When we think about our role in this, we don't actually think of our role as your accounting platform.

We would like our information to be directly consistent with your accounting platform. But we, our job isn't to be dollar for dollar right in the way that something like Final Loop is going to allow you to actualize and be specific about getting the accounting side of this right in real time as well.

So those things are constantly being reconciled to one another. As you go, we don't think a marketing system should spend the energy or effort around the product to create that kind of specificity that something like final loop is going to offer you. And that's why we partner with them as often as we can.

All right, Michael Lorenzo. So if I have a business that doesn't have a high purchase frequency, maximizing contribution on first order is the right strategy. Abso freaking lutely. That's it. Now, if you are not, if you produce no LTV, the only value that you're going to capture off the customer is maximizing first order contribution margin.

That's right. That's exactly right, Michael. The disparity in day to day spend levels is linked to the marketing calendar previous years or both. The day to day spend levels are affected by the marketing calendar, number one. Number two is the day of the week effect that we see, especially because we use cost controls for everything that tends to Maximize the available profitable volume, which tends to be more spent on the weekends than weekdays.

How do you factor in non paid channels? So email and SMS is a non paid channel. We also have a consideration. You heard me talk about it pretty quickly around organic. So we would define organic as any non paid click based channels. And we'll often use that for setting up goals around SEO right now.

The email effect and SEO effects don't actually alter the model. We're working on thinking about the relationship between those things as we go. But there is goaling that comes as an output to the system around what your organic revenue goal is for, for your business. When we're offering SEO as a service and what your email revenue goal is from there.

How would you approach this? Oh, sorry. What program do you use to measure what true ROAS there is? So there does. Okay. So we are big fans of incrementality testing. So when we start with every customer, we're going to build a roadmap of incrementality testing. We use a bunch of different partners for that measured.

Work magics house. We also have a white label solution that we're, and one that we're building for ourselves. So every one of our clients, what we're going to want to do is work right through your biggest spend channels. Usually that's meta to start to get an incrementality read on the relationship between the platform reported number and the incremental impact of that channel, that's going to inform our targets and channels.

I talked about to get to the day to day execution function. And then also we'll, we'll use that data to inform the MMM as we go. How would you approach this if you were the founder of a new brand? Very challenging. I would build some baseline assumptions and assume that you would be very wrong early on.

The ability to forecast accurately is usually related to the quality and consistency of the data that you have. The more data, the more consistent, the easier it is to forecast, the more variable and smaller, the data, the less easy it is. So in that case, I would use some expectations of things like my media budget and the performance about benchmarks and what we know in the channel, in your category to set my new customer revenue, and then Good news is in this case, your existing customer revenue is zero.

So in month one, I would also then use a model of the expected LTV for your product category to build the existing customers. So if you think about it, the less data an individual brand has, the more I have to depend on aggregate data like benchmarks, but you can still build a model. And someone like CTC could be really good at that because we have lots of data.

So modeling LTV curves in different product categories is pretty easy for us because we have lots of data from lots of different brands. Okay. Continuing on. How do you look at sales out? Relative to sales in regarding forecasting. So, we don't participate right now in forecasting for any wholesale portion of our client's business.

We take on the responsibility for all of their digital enterprise. We are launching Amazon as part of that process right now. It's one of the core things we're focusing on. But we don't participate in forecasting the wholesaling because that revenue is very human. It's not modeled in some predictable way.

It's all about the sales cycles that you have with customers. So we don't participate in that part of it. How much of this is also aided by CTC for the accelerator program? All accelerator clients get this full exercise. Everything I just described, you will get as part of the accelerator program.

Is the AMER shown only platform data or is it blended? AMER has no platform data. It is new customer revenue for the business divided by ad spend. So it is not platform reported data. Why Klaviyo 7 day click as opposed to default 5 day open 5 day click? So I want to try to think about revenue when it's being reported by a platform as standardized as possible.

I don't think I think about opens like I do view through and that like the incrementality of that revenue reporting is generally overstated. So if I'm reporting that on a seven day click and we're standardizing everything to that, I like to do the same with Klaviyo. I think it's a more conservative view of the revenue, the email impact.

But what I'll say is my, what I care about the most with your email revenue goal is that it's consistent. That what you're doing over time, what I see sometimes happen is that In particular when you use opens or anything is that you'll tend to see the behavior tell you that the signal would be that you should spend more time on flows because especially browse abandonment and card abandonment.

That's like very high view through conversion rate. Very high. Rates of revenue assignment into those things that when you obligate to a click, it tends to move your behavior towards campaigns, which is where more revenue creation I think really actually happens. But I, again, what I care about the most is that you use a consistent measure of email revenue over time.

I have recently heard you speak about blending Amazon into your advertising spend and overall and they are, yes. So we are launching in January and you'll see a bunch of public messaging about this from us soon. An update to the profit system that allows you to model the entire business inclusive of Amazon and Google.

So where you would have one revenue goal that includes both channels spend with an an expectation of effect in both places. And I think that is really the future of what brands are going to do is they're going to think about their digital enterprise as being sort of agnostic to distribution. So whether it's your website or Tik TOK shop or Meta shop or Amazon is that the reality is that media spend like.

Conversion optimized meta ads or YouTube, they affect all of them. And so they need to be considered and realized on one single P and L. So I think that you're gonna hear a lot about that from us in 2025. You build your returning customer model based upon which month they purchase. Could you recreate a returning customer model based on the type of product?

Yes, you can. You could, you could, Paul, you could go as detailed in your returning customer model as you want and have the time to do. And if you have cohorts that have massive discrepancy, like usually the only time we'll get into a layer deeper than just general customer cohorts is for subscription bands, brands, you have to model the subscription customers and their curve and the volume and expectation of them very differently because there's such a dramatic difference in LTV, generally speaking, the distinctions in LTV between Products that aren't a subscription aren't as dramatic, but it's certainly an exercise that you could really go as far as you want in terms of how detailed you make the modeling on how you do your return customer model.

For sure. How do you build this for a company? That would be a third e com third Amazon and third retail. So I would treat Amazon and. com the same, and I would separate out retail. I have a client who is needing revenue to pay off debt, but we're struggling to hit the AMER required at the spend volume.

So this, this is, this is, this is actually really important language. Revenue doesn't pay off debt. It's really, it doesn't. Just imagine a scenario where I gave Meta a dollar to get back a dollar of revenue, and then took that dollar of revenue and paid off a liability. I haven't done anything. I've actually increased my liability in that process.

It's really important to understand that margin pays off debt. Free cashflow services debt. And so you have to make sure that you're actually driving marginal outcome and you can't just lower your cost control and take more revenue to pay off debt. Now here's what I'll say. I understand that sometimes there's a bit of a shell game that has to be played in terms of the flow of funds and that in sometimes the 45 days later, Is the less pressing debt than the payment that's due tomorrow.

I empathize with that. I understand that, but just to be clear, that doesn't actually reduce the outstanding total liability at all. I talked a little bit about launching the, I think, forecasting is always important because you have to purchase inventory and that's the decision that kills e commerce businesses.

Macro conditions. I would not spend a lot of time on this for most of you. I don't actually think the data is very good. And it's very hard to apply in a very specific level, unless there was something like, let's say you sold political apparel or like, we work with brands, like we work with Barstool sports.

And so, in, in a lot of ways, the world series is a macro factor that whether the Yankees or the Brewers are in the world series really changes the amount of inventory they can sell. And then I've seen some businesses, like we worked with a brand that sold, um, plants. And so weather was a big factor that we tried to bring into the forecast.

I think I would begin with Abby and expectation of what is the macro event that you believe, and can we prove that it has an effect? And if so, what data is available for it to help model it? Something like weather's really hard, right? Because weather's not predictive more than like three to seven days out.

So it's like. It's really inconsequential in some ways but that's what I'd say. Start with the hypothesis about what you think the macro effect is. That's driving the impact on the business. Can we source data to include in the model? And then let's design and get some people to think about how we would do that.

All right. Let's see here. Do you find any statistical significant test to find confidence levels of any of your findings? Yes. Every one of our models are back tested to a degree of accuracy and that confidence is expressed to the customer in the model. So, yes, we, we back test to create a confidence interval.

Robin also publishes a confidence interval on the output of their model. So the, the, the key here. With any modeling exercises to think always like probabilistically and in confidence intervals, there's almost never a model is never binary, good or bad. It has some degree of confidence and we want to be honest about the X, the confidence that we have.

Could you share any cohort specific retention model sheets for those just getting started? We have a product called the enterprise scaling guide. That is available to you if you want to buy that that's a good starting point. There's also a YouTube video that started all of this journey for us many years ago that myself and Coleman Verheyen did for Bamboo Earth, and if you go watch, we'll talk to you about that.

Shopify also has a cohort specific retention table, output that and average the columns and that's your blending curve starting point. That's the easiest thing to do. Export your historic cohort table out of Shopify. Average the month, one month, two month, three month, four percentage change and use that curve to start.

That's the easiest starting point. For a business that is seasonal in terms of hot versus cold, seasonal workwear is one year LTV. The best just going longer. It makes sense going longer. Almost never makes sense. In less, and I'll show you why is that let me give you, give you an illustration of this many for many businesses, For the vast majority of businesses the average time to purchase for customers is going to be shorter than a year.

So here's what I mean. This is an apparel business. And If I look at this right here is what we call the customer churn calculation table. We do this for every brand. And it shows me the average days between purchases for customers and when they become at risk or churned. So we would define a churned customer in e commerce as when they reach 80 percent of the purchases.

Of customers have purchased within that time period. Once they get out beyond that, we consider them churned because they have a very low likelihood of repurchase. You'll see for this brand, if a customer doesn't repurchase within 130 days, the likelihood that they repurchase is very low. And that's true for most businesses.

That doesn't mean that no customers ever purchased after beyond that day. It just means that the vast majority, more than 80 percent or 80 percent of purchases happen within 130 days between those purchases. That's different for every business, but I've never seen one that's out longer than a year. I'd never have.

All right. Three more minutes here. Let's keep going. Is there a good way to get access to the growth map or something to help with modeling or tracking a P and L that can be made? Yeah. So anonymous attendee, profit system, the bot com, we sell you this system. You don't have to work with CTC to on a service level.

You can buy the system, the technology that maps the tools you get the team to implement it once for you, and then you're off and running. So yes, you can. What outside of your system is the most important to the success of a business, for example, why do some of your clients fail versus succeed? Oh, in most cases, it has very little to do with our system.

The reality is, and we have a lot of content about this. I would recommend. Corey, can you drop one of the drawing videos? The most recent one about the genetic attributes of successful e commerce businesses. Paul, I don't know if you follow our YouTube channel, but I would encourage you to do it.

My most recent drawing video talks about what the attributes that I see of highly successful businesses and what they do really well. And what, what is true about them that makes them successful outside of our system. So especially for smaller brands. So now the lowest entry point into our system for the tooling is the enterprise scaling guide.

Then I would say admission is going to get you access to a lot of our sheets and tools and systems and then the global accelerator. That's sort of the buildup is the products that you can buy on a one off basis admission recurring monthly membership into our community where you get access to a lot of stuffs and then global accelerator.

That's sort of the buildup for you all. So I appreciate you all. You got nerdy with me today. I believe that great financial planning connected marketing calendars is the way to become a disciplined, successful e commerce business. I can't tell you how much people struggle with this and how big of a problem it is in our industry.

And why we're committing our lives to, to get making it better. So profit system. com. We'd love to help you out and see if we can't be useful in making 2025 the year that you walk into your next season, feeling more confident than ever that you have a great plan that you could lead your company in.

So appreciate everyone. Thanks for coming.