CFO Summit: Board, Budget, Bonus - The Forecast Framework for 2026

[00:00:00] Speaker 4: Alright, well welcome everyone. We're gonna get started. We'll have people trickling in and a recording. As always. Today we're gonna be going through the first of what we hope to be a more frequent series around, um, what we're calling the CFO Summit. My hope is that what we're going to attract is, uh, for this space amidst a lot of the content we create targeted at marketers, um, that we create some, uh, additionally focused messaging towards founders, CFOs and those that are dealing specifically with the financial forecast.

[00:00:30] One of the unique positions that CTC sits in is that we handle fp and a for every one of our customers, um, as a foundational part of our service. And that brings, uh, a lot of emphasis to that exercise in this time of the year. And so we wanna discuss today a principle that we call, uh, board budget bonus.

[00:00:49] And it's the how and why of scenario planning. So through most of the year, we're running a rhythm of working primarily through what we would call the latest estimate, the most real time expected view of performance in a very narrow window. But every now and again, uh, we zoom out and usually on a calendar rhythm, whether you're on a fiscal or annual calendar.

[00:01:06] Uh, and we begin to look on longer horizons and we ask our ask ourselves questions in partnership with our customers about how various scenarios may affect different environments. So that's what we're gonna chat about today. We're gonna give some real examples of how this plays out and why I think it's such important part of the workflow.

[00:01:24] Before we get started, these events are brought to you by, uh, our sponsors Fulfill and Tax Cloud, both of whom I think provide complimentary supplementary services to, uh, what we are here to do in the environment. And we care a lot about choosing these people with intention. So fulfill, when I think about what the foundation of this process is built on top of its quality data at the financial and product level, uh, in a way that they fulfill is doing the Lord's work of unifying some of the most complex data structures and systems in an ERP environment that's actually, uh, delightful to use.

[00:01:57] Uh, and then Tax Cloud, having just gone through a transaction ourselves, I can tell you that the implications of clarity and confidence in your tax history, um, is incredibly important in e-commerce. It gets really complicated. Lots of states, lots of points of fulfillment, lots of nexus issues. Uh, tax Cloud is an awesome way to ensure that you feel really confident that you are in compliance, that you are paying not more than you need to, but what you need to.

[00:02:20] Uh, and so I'm really appreciative to both of them as being the kinds of sponsors that illustrate, uh, the information, uh, and consistent position that we wanna have in the market of helping brands produce predictable, profitable growth, uh, which is definitely an exercise in great data integrity, clarity of financial obligation.

[00:02:36] So thanks to both Fulfill and Tax Cloud. Um, so why are we having this conversation today? Uh, because, uh, this moment I think is generally crowded out by a primary focus on the next 10 days. So, uh, 2025 BCFM. Our BFCM, excuse me, is obviously the topic du jour for almost 99% of the content that exists in the world.

[00:02:59] But what I've experienced as a CEO and founder is that I'm very disassociated actually from what my job is in this moment relative to the execution function of the team. This moment for me and my team is actually governed almost exclusively by 2026. Planning is that most of my energy, attention, and effort as a leader right now, and I, this is true for my VP of finance, Brian and I, all of our time and energy is spent around, okay, in light of where we're at now and what we anticipate to close the year at, what does that mean for 2026?

[00:03:33] And there's a reason that there's an impetus for that conversation happening today besides the just, uh, annual rhythm of the calendar. And that's that. Yesterday, literally we had our board meeting. So we have quarterly board board meetings. We were acquired in, uh, uh, back in June by a private equity firm.

[00:03:50] So it's like a much more formal structure and setting than we've had. It was our first one with them, uh, and this was on November 18th. And in that space, we are expected in obligated to come and bring not only just a reflection of our performance in 2025, but the goal is to leave with an approval of the budget for 2026, an alignment of expectations between the shareholders and key stakeholders and the organization about what we're gonna go out and attempt to execute.

[00:04:15] So that means that right now, the last two weeks, the core leadership of our organization amidst tons of people doing tons of things for Black Friday and Cyber Monday, which we are as deep in the execution of a, as anybody, we have a simultaneous obligation to long-term planning. And that's the reality as I experience it for myself as well as for many of our partners, that there's this duality of action in this moment, which is deep.

[00:04:37] Uh, uh, present attention to the next 10 days, which are critically important as well as a zoom out to the future where you're beginning to compile story and narrative and financials and information to build for yourself a forecast of the future. So that's why I believe that part of what we can serve in this moment is to provide a space for people to zoom out and to support in while you're deep in the day-to-day execution, or your team might be allowing us to help to support, uh, in that broader position.

[00:05:06] But I want to talk about why this idea of scenario planning and specifically what I think are sp three very specific forms of scenarios are so important for e-commerce and then how to go about building them. We're gonna give you some very practical, specific examples that you can take away with today.

[00:05:22] So I would contend that the forecast is the root operating system of an e-commerce brand. That the process of building a financial plan in as much detail as possible is actually the mechanism by which you need. To work to align expectations toward a common goal. If you want everyone in your organization in the next year to feel like they know how their individual role all the way down to the lowest level individual contributor can affect with clarity, the company outcome, that you as the owner or you as the shareholders, or you as a representative of the shareholders want.

[00:06:00] If you want that alignment from shareholder all the way down to the most entry level junior employee, it begins with an alignment of expectations towards a common goal, because that then creates the opportunity for you as a leader to design a aligned incentives towards that goal. And when aligned incentives exist, that's how you actually generate aligned behavior.

[00:06:20] So if you experience anywhere in your organization the sensation that people are not driving towards the actions that you, the owner, you the shareholder most desire, I would simply challenge that it's a lack of clarity and alignment between the organizational goals, the incentives that exist, and then the behaviors are simply an output of that system design.

[00:06:41] So the financial forecast is a mechanism towards aligned behavior. That's actually what it's, it is a, the, the mechanism by which you create the day-to-day actions that you as the leader want out of your people. So the more clarity that you can walk into 2026 with about what the financial expectation is and all of the core inputs that drive it, the more likely you are to get the actions from your people and employees that you want.

[00:07:10] And I believe that scenario planning is the exercise of aligning expectations with different parties. Inside of an organization, there is likely different stakeholders with different interests that need to move to in different directions. So we're gonna use the illustration today of three specific parties that I think many brands deal with.

[00:07:32] And these might all three be true for you. It might be true that there's only one that matters. You might need two. And either way, you're gonna have an opportunity to take away a perception of how you would interact with it in the event that you had to serve one of these interests. So when I think about these three different groups, your board, this could also be substituted by bank, right?

[00:07:53] This is some governing authority by which you need to represent and over deliver and under promise to execution. The budget is what I would describe as the most likely scenario to occur. It's what I would work with my CFO to plan cash against. And then the bonus is the plan that I would use to drive towards an upper bound outcome for my team there likely in the bonus scenario, should be problems that are not yet solved.

[00:08:20] And so if we think about these three different things, so board, it's a conservative, external facing, designed to under promise and over deliver it. The simplest way to phrase a board or bank interaction should be the lowest acceptable number. If you think about that group of people and their interests, your job should be to represent the highest probability number that they are likely to accept.

[00:08:40] And every interest. Some of you, if you have a bank relationship, which I've managed for many years, even have now, there's likely a covenant that governs the obligation there. There's some relationship between the financial performance and a commitment that you've made to them that represents the lowest possible bound, acceptable outcome.

[00:08:58] A board requires a little bit more nuanced of understanding expectation that they have for their return, and shareholders over signed time period. But you can similarly back into this expectation of understanding the lowest acceptable number that is the most likely to occur. It's not a space where you wanna overpromise and under deliver.

[00:09:16] You are there to do what you say you're going to do to build consistent, reinforced trust with a group of people that you can deli design and deliver against an expectation your budget. I decided, I think that this should be your 50th percentile. This is the most likely forecast to occur. You're going to use this for your cash planning.

[00:09:38] In many ways, the tension ex between budget and bonus, uh, lays in how aggressive you want to be on some of the inventory purchase decisions that you make. But in terms of ensuring that if the revenue were to pass out and the, and the 50th percentile outcome, you can survive, you will live through that.

[00:09:58] That's where I would manage my costs and my cash against that budget. It's a plan that likely only you in your CFOC. It's not something that's widely distributed, but it represents an outcome that you feel very confident will achieve. In some cases, you may even push that down to a 40th percentile outcome, something that you feel really sure will occur.

[00:10:20] And then the third scenario that I think is critically important and is probably in many ways, the one that most of us are used to building, is what I would call the bonus plan. This is a stretch goal that your team can chase a bridge from what is likely to occur to what you would like to happen. This is the number that if you were to pay people bonuses, this is the achievement of that outcome.

[00:10:42] Usually this lives somewhere in the 75th to 80th percentile outcome. Again, depending on how good your 50th percentile outcome is as a business. We're gonna talk a little bit more about that later. But each of these scenarios are an important tool in your tool chest as a leader to design and align interest with various shareholders and various stakeholders in the organization.

[00:11:07] So this process, and here is where I think most people go wrong in building scenario planning is that far too often we start with what we want to happen, and then we try and build the outcome and numbers towards it. And so often when CTC gets brought in to a business, what we have to do is we have to re-anchor them to reality is that there is likely some process over a long period of time through external pressure, through a lot of different changes where the expectations of the organization have become disassociated from the underlying data of the business.

[00:11:48] In other words, what is possible has fallen away from what is probable, right? It's like these things. Begin to get a chasm too wide where there's just this repeated miss of expectations they've missed, forecast seven months in a row, eight months in a row. They're minus 30% to their annual plan, whatever it is.

[00:12:05] And our job is to come in as an external third party and anchor the organization back into what is likely to occur to root them in the data of the present reality. And sometimes what I'll say is that this is often easy as done as an outsider because there are all of these incentives of your internal team to not represent significantly negative outcomes.

[00:12:29] And that's not to say that the 50th percentile or the most likely outcome is going to be negative in every case. The one we're gonna look at today, that's not uh, actually the reality at all. But I would caution you as a leader to allow exclusively your internal people to present to you the likely scenarios that are going to occur.

[00:12:47] There is just too much risk for them to represent negativity to you. It will come off no matter how hard they try as capitulation, as a declaration of failure or incompetency. And so, so often allowing an external eye to say to you, this is purely what the data says is an anchor in reality, that you can move from it.

[00:13:12] By no means is the says that we have to stay here, that we have to like it, or that we have to remain with that as the expectation. But it is really important to begin this process with what is likely to occur. So when I think about building these scenarios, I think it's a four part process that we have to engage in that I'm gonna walk through today.

[00:13:31] And that's step one. That anchoring into reality is about quantitative modeling. It is about extracting away narrative about what we want to happen in the future, extracting away narrative about all the good things we're gonna do next time, and just simply asking the question. If we extrapolate the history of this business using good data and research, what does it tell us about what is likely to occur in the future?

[00:13:59] Because in that honest position, we can now begin to design strategy that is the bridge between that present state and that desired future outcome. So quantitative modeling is step one in this process. Step two then becomes qualitative planning. I would contend that strategy or planning is simply just that bridge between a present state and a future desired outcome.

[00:14:20] It's the gap between what is likely to occur and what you would like to occur. It allows you to begin to go, okay, our spend efficiency, uh, expectation if we continue on the current path is this. Here are the things that we're gonna begin to do to improve against that. The reasons to believe the future will be better than the past.

[00:14:39] And this is where organizations often I think skip step two, which is that the finance team becomes responsible for the financial for. And it is disassociated from the marketing calendar exercise. And I'll just tell you, this is the number one flaw I see in forecasting is the disassociation between qualitative marketing, calendaring, and planning of events and quantitative modeling from the finance department.

[00:15:04] The calendar is the root source of revenue. The actions that you take as a marketing org are the units of growth. Every day you send an email, you launch an ad, you do a PR push, you assign that influencer, you launch that new campaign, whatever it is, that is the mechanism by which the curve changes in some direction.

[00:15:24] And so without that understanding of when those things are gonna occur, what the expected amplitude of those events is, we will not get to a place of being able to clearly understand the flow of our revenue, the expectation of changes or why there might be, uh, any effect in the future. Once we do that, the quantitative modeling, the qualitative daily planning of the entire marketing calendar, then we can begin to flow the revenue, we call it mapping the revenue now turning it into a daily expectation of over 35 different metrics across every day of the year, so that we can begin to build an expectation of how it will occur.

[00:15:56] The overarching expectation broken down into the every dollar, every day, and every channel. And then ultimately the part we'll get to the real magic of forecasting comes not in the modeling, not in the planning, but in the execution. It's the visibility to the expectation every day that allows you to plot, pivot, and profit to under, to identify where I am off course and course correct.

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[00:17:38] Speaker 4: With that, I'm gonna take you into, um, our process for how this works. We're gonna use a real brand here. I'm gonna show you how these three different scenarios can come to life and how then they get applied into the future inside of our data platform. So, stat List is the proprietary data tool that we have built, uh, that enables the service here at Common Thread Collective.

[00:17:59] It is anchored as a first step by our profit engineers in the process of building a financial plan. And that financial plan is supported by a data science team that builds four critical models that underpin. The expectation of the future. The first is what we would call a spend and a MER model. This is a relationship between the, the media budget and the efficiency of expectation with a consideration for seasonality.

[00:18:23] There's a retention model that looks at the expectation of returning customer revenue cohort specific LTV model, um, OLS regression, sort of that's the foundation that many people are comfortable with. We also have an event effect model that looks at the effect of different marketing actions over history and begins to use them to drive day-to-day changes based on your marketing calendar.

[00:18:43] And then we have a creative demand model that helps you answer the question about how many ads do I need to make to bring this budget to life? All of these are tools that are brought forward to help you create a dynamic ability to adjust plants. So what do I mean by this? So I'm inside the plan section here.

[00:18:59] You'll see what's really cool is that we have the ability to build as many variations of scenarios as we want. So if you're somebody who likes to look at a thousand different ways that it could possibly happen, this is the tool for you. So you can see I'm in the 2026 plan section here inside of Stats.

[00:19:13] This is a business, this is a real set of scenarios that one of our profit engineers is working through right now to help build for our customers. Um. You can see they already have five different versions of 2026. We have a board plan, we have a budget plan, we have the bonus plan, which is the primary one that we'll usually manage to.

[00:19:30] They've done a couple different scenario modeling around what if we went after max new customer revenue at breakeven versus max new customer profit. So some different, uh, variations on efficiency. Um, and if you look, let's just quickly glance at the numbers here. You'll see the budget plan, or let's start with the board plan.

[00:19:47] The most conservative view, 72 million in revenue, 22 million in contribution margin. If you get to the budget plan, you've got 78 million in revenue, 24 million in contribution margin, and then the bonus plan has 81,000,020 7 million in contribution margin. You can see the sort of stair step if you were to look at a probabilistic distribution.

[00:20:07] As we move further up the curb around what the expectation is, it gets a little bit higher, a little bit less likely to occur, a little bit more work to be done to solve the problem. Now, the key with this business and what I'll say is that they have the advantageous position that the board plan, the likely to occur scenario.

[00:20:26] The one that is consistent with the model that we would put forward is actually still growth. It is a good sign that this business is in a healthy place where their 50th percentile outcome in the future is still growth year over year by about 11 to 13%. So being able to take that to the board is not going to them.

[00:20:44] After coming off of a great year of growth and going, we're gonna go backwards. You're still able to say this is what is likely to occur. We can represent that with confidence and with data, but the expectation of growth is still built into it. For many of you, that may not be the case. And I do just want to acknowledge that in some cases, if the most likely thing to occur is, uh, a negative trend, the talk track to explaining that and the path out of it become really, really important to manage to, but you cannot, uh, hide from the fact that that may likely be the case.

[00:21:15] So I'm gonna go into one of these and show you how the vari the variables get adjusted for the different stages of the plan and how we can think about building these and creating effect. So as you see here, we go into the plan section and we have these three models. New, uh, new revenue model retention model of an effect model.

[00:21:34] And if I go into the new revenue model, what you're going to see, and you probably have seen some of this published, uh, from CTC, is, uh, a curve that models all of the historical. Let's take this out here. We start by plotting every point of the relationship between spend and efficiency over the history of the dataset of the brand.

[00:21:55] So we look at every month, how much did you spend? What was the efficiency? So we would measure this as, uh, ad spend and new customer revenue produces a MER. So on the Y axis we have a MER acquisition, marketing efficiency rating, new customer revenue divided by ad spend. Uh, and on the X Act we have how much spend occurred, and then what our team does is for every month of the year, because your spending power is not the same in every season.

[00:22:22] Every brand has some variation in their spending power by seasonality. Some of you, it's very extreme in terms of how much money you could spend in November and December versus June. If you sell swimsuits, you're gonna spend a heck of a lot more money in June than you are in December. For this brand, it's actually a moderate deviation.

[00:22:42] Now you can still see that November can spend $3 million at a 1 99, whereas February can only spend 1 6 5. So there's still the traditional seasonal impact built into the model. But what I want you to notice is this right here, this model tweaks, okay? Where, what our growth strategist is doing, our profit engineer is doing in this case, is we are building the board plan.

[00:23:08] This is the conservative expectation to reality. So you'll notice over here where it says percent over model, and you can see he's actually taking the expectation of the model and reducing its outcome. So this is an example of how we can take the curve, okay? That exists. The model expectation of the relationship between spend and efficiency, that is the output of this tool.

[00:23:40] So you can see at every different spend level, what's my efficiency? How much new customer revenue does it generate? What's my marginal outcome of cost? How much contribution margin do I generate? What's my LTV multiplier over different periods of time? I can look at it and then therefore, what's my lifetime contribution margin?

[00:23:55] And I can pick different points on this curve, whether it's max lifetime contribution, max contribution margin, month one max revenue at breakeven. I can find the different points on the line that would illustrate those different moments. But what we've done here in this case is for the sake of the first model, the one that we want to go to the board is we're not just going to take the base model at 0% adjustment, which if we look here, let's see what it adjusts.

[00:24:19] This 1.35 million would go up to a one eight 5:00 AM ER. If I were to go exactly on model, the curve would actually raise up a little bit, right, in terms of its efficiency expectation, but it's actually keyed down. We're saying, actually we wanna reduce the expectation here. We're building a board plan. We wanna look out and go, okay, how low below what is expected to occur?

[00:24:40] Can we get and still get growth? So this is, this is part of that exercise. And you'll notice part of what they do that they layer on the qualitative understanding is a model doesn't understand necessarily all the time, all of the unique dynamics that occurred in the past that are not going to occur in the future.

[00:25:01] And this is why humans actually have to interact with this process is that I, I would contend that, that it will almost never be possible for a model of, that is an extrapolation of the past data to work as an exclusive outta the box solution. For e-comm. It will almost always require human intervention, uh, to create the highest level of fidelity.

[00:25:22] And this is a perfect example of why last February and March, this business, if we look at their historical, excuse me, sorry. Um, points on the curve. You'll notice that way out here. Last year, we were able to spend 1.68 million at a two to 1:00 AM ER in this month. Okay? So the model sees that and goes, wow, February was really fantastic.

[00:25:46] But what we know, because we analyze their entire marketing calendar from the previous years to understand the relationship between the qualitative actions, the quantitative outcomes, is that there were influencer moments unexpected from really big names that drove big spikes in daily revenue on new customer acquisition.

[00:26:02] And so because we don't have those in the plan again this year, we're able to take the model down in those months even further than we are the rest of the time. So over and over, they can go through and build the expectation of new customer revenue against these various effects. Pick a point on the curve, reduce the effect of the model for the sake of the board plan, or giving a conservative estimation relative to what the data is telling us and build that expectation.

[00:26:32] We do this for every month. Every month there's a different curve. There's a different expectation of the relationship between spending and efficiency. How far can we go at various budgets? And the good news is these models, you'll notice here, 1 0 1, 1 0 2, every quarter we update the model based on the most recent data.

[00:26:48] So one of the conversations I had on X was, uh, with Krista from, uh, Kovas, CMO there, who I'm good friends with. She's awesome. And she's saying it's really hard for me to do my planning until BFCM occurs because it's such a variation in swing. Well, when you have a dynamic set of tooling where that data's being absorbed constantly and those models are being updated, that'll all happen for you in real time.

[00:27:11] You'll be able to get that effect and be able to benefit and move faster through that process because those data connection between the planning process and tooling and the realization of actual data, uh, uh, happens. So, um, all of this can change and will be updated at the end of every quarter.

[00:27:27] Consistently, every model will be improved. Over time, we'll reflect on the deviations. What were we missed? What were we wrong about? To give us the best view of where we can get. What I love about this model too is that it shows us at every dollar what the incremental A MER of the next tranche of spend is.

[00:27:44] So this helps you to think about like if I were to optimize for maximum contribution margin, in other words, in month one, this column becomes the point at which I'm generating the maximum amount of contribution margin. It's actually at 2 million at a 2, 3 5. 'cause you can see that every incremental dollar from this point forward is below break even.

[00:28:04] So that means when I go to spend the next dollar, I'm actually generating negative contribution margin. And you'll notice that from 1, 5, 4, 3, we just see it start to go down. And so I'm now generating less and less short-term contribution margin. But why would you wanna do that? Well, because for some of you, you have fantastic LTV, and if I look at the point at which I would generate maximum lifetime contribution over whatever time period you wanna define lifetime, as I can see that, ooh, I can actually spend a lot more if I'm after the highest L lifetime contribution.

[00:28:35] But you'll notice I get substantially less contribution margin in month, month one. But is that trade off worth it? That's the kind of business strategy discussion you have in this planning process. So you get to decide between all the various points on this curve, understanding the financial objectives of the organization, how important is top line growth versus bottom line expectation, what's your, uh, uh, your, uh, realization of value as an organization?

[00:28:57] All those things can contribute to the conversation. So this is the first variable that I have, the tool that I have to build various scenarios where I can go from, okay, reduction of the modeled expectation for the board plan, and I can get to a new order revenue of 44 million on 23 in spend. Okay? Now if I go back to my planning section, go back here.

[00:29:21] Okay? And let's move from the board plan to the budget plan. You'll notice that I spend more money, 24 to 25 million to generate 50.143. So as I go into the model and you see, you'll just see slightly less reduction. Now, this was 20% in these periods, we still think that's a year over year decline. That's gonna happen regardless of whether it's border budget.

[00:29:43] But you'll notice plus 5% only minus two, plus 10, plus 10, plus eight, plus 20 plus five. So this is a more aggressive, that gets me about $5 million of incremental revenue on about a million and a half incremental spend. This is my mid tier. This is like closer to the most likely expectation. Now again, that doesn't always mean model at zero all the way through because you have to hold in your head the calendar, the expectation of the novel actions that exist.

[00:30:13] And then on the bonus side, we see that we've got 51 point, almost 52 million on the same 25 million in spend. So all we've done in this case is increase the expectation of efficiency. You'll notice in this case, there are no months where we have a reduction of the efficiency of model. So as an example, if we wanna generate this, we have to have a conversation about February and March that says, okay, last year we generated, uh, influencer action A, B, and C.

[00:30:37] In order for us to go out and recreate that, what are we going to do to generate that disproportionate efficiency in that period of time? How are we gonna go out? And I know that in August and September they have some novel product releases planned that tie into that. But this is how we can use the new customer model to create variations of scenarios that exist that can serve different audiences.

[00:30:58] And so all this simply sets up is in the bonus plan. I now, whoever's in charge of my new customer acquisition efficiency and my paid media, we're collaborating around the expectation of, here's your spend goal. Here's your A MER goal. Here's your new order revenue goal. Let's go get it. And now you've created a clear set of incentive or a clear set of expectations.

[00:31:17] You can tie their incentives to these results, and now you're gonna align your behaviors towards those outcomes. The same thing then becomes true on the returning customer side. You play the same game. Now the beauty of the model is that in many ways, you don't have to do as much of the manual adjustment because when you sync your results from the new customer model, when you generate more new customer revenue.

[00:31:39] You by definition create more returning customer revenue in the future as well. That's that lifetime, uh, contribution margin adjustment. So by sinking the results from the new customer model, we're automatically gonna create an impact to the returning customer expectation. It's gonna layer in both the performance of all of your historical, historic cohorts.

[00:31:58] You can see we build this, it's an OLS regression model that looks at, uh, the performance of all of your cohorts over time to adjust the month one factor up or down by how much revenue realization your curve of customers will get. And then it's going to layer in the amount of new customer revenue that flows through the model.

[00:32:16] And again, you can actualize this in real time. It's why it's so easy to update this in a dynamic fashion to get you to an expectation. You can see there's our new order revenue expectation for 2026 that gets synced into the forecast. That affects the expectation of returning customer revenue for the year to get you to a 2026 returning customer revenue number for each model.

[00:32:39] So we rarely have to go in and actually make a bunch of manual adjustments to the returning model. 'cause the returning model is often, most likely a byproduct of the new customer acquisition. It's almost always the case that returning customer revenue is a lagging signal and output of the growth of your new customer revenue.

[00:32:59] And to illustrate that, I wanna show you, um, the easiest way to know if next year's returning customer revenue will be less or more than this year's returning customer revenue is the size of your active customer file at the end of the year versus the previous year. It's the easiest indication that, and then your annual new customer revenue for the coming year.

[00:33:22] Those two inputs will tell you whether you're gonna have more or less returning customer revenue. So, so this is, what do I mean by an active customer? So a lot of times brands make the mistake of just analyzing how many emails do I have? Like the, the, that your customer files. A is a, is a thing that grows up into the right forever, but that's just not true.

[00:33:41] Many of those people have lapsed, they have stopped buying from you and are never coming back. So what we do is we look at a period by which 80% of second purchases occur a time between purchases. So we can look in, um, a setting section. Actually, I'm be careful here. I'm not gonna open settings 'cause I'm sort of bugging it out.

[00:34:02] But what we can look at is the lapse period, the point at which after this period of time, if a customer hasn't repurchased, we consider them churned because they are very unlikely to ever purchase this again. And then we can map every month, how many new customers did we add to the file? How many are within that active returning state, meaning they're within the period of time where they're still likely to purchase, how many lapsed customers were reactivated, and how many customers are reaching at risk status, getting close to that churned period?

[00:34:29] And then how many churned? And if you take the amount of churned customers over the active added customers, you get a net net active change. In other words, did your customer file grow or shrink in active status? And so if we look right now, you'll see this count today, this is the week of 1109. You'll see the amount of total active customers that this business has.

[00:34:50] And if we go back and compare that, so this is for the week of 1109 through 1115, and we go back and look at

[00:35:00] 11 nine of 2024, you'll see 235,000 versus 247,000. Okay? So I have about 5% ish more new customers today. Than I did this time last year. So I shouldn't expect a massive amount of returning customer growth, but it certainly would grow. And I should flag for myself that in the last four months, I've actually had less than ideal net active change, where actually you can see it too.

[00:35:28] You can see the customer files shrinking a little bit for this business. They need to make some reinvestment in new customer acquisition in order to sustain their long-term growth. This is the canary in the coal mine. This is the leading indicator that sets up that sort of then gets outputted in the model where I can look at the expectation of returning customer revenue year over year.

[00:35:46] And you can see this is 25 million against 21 because we have an expectation now, once we get in to December and layer in that will be about a two and a half million more. It's gonna be really close. It's gonna be a five to 10% delta year over year. So I think it's those things are very often connected in the modeling.

[00:36:04] So as I get through these two things, my new customer, EE expectation of the model sets up my returning customer expectation. So did all of this year's work to give me this foundation? And I wanna be really clear with you, this is the biggest place where brands will start to make an expectation of their returning customer revenue that is disassociated from reality.

[00:36:23] So often what I see brands do when they build their financial forecast is they, they just assume that their returning customer revenue will go up every year because you have more total customers. It is the biggest flaw in retention modeling that I see brands make is that they do not account for the fact that many of those customers are not active.

[00:36:41] They are not coming back. There is no data, there is no healthy set of ex of new cohorts that are gonna indicate. Another easy hack is if you generated more new customer revenue this year than last year, you'll generate more returning customer revenue next year than this year. Lemme say that again. If you generated more new customer revenue this year than last year, you're likely to generate more returning customer revenue next year than this year.

[00:37:06] And that becomes these signals that you can use absent these models to begin to help you think about, okay, what is my expectation of my returning customers? What is my expectation of my new customers? So this sets up then, okay, I now have these two core variables that help me build scenarios, right? I have new customer model that allows me to toggle new customer revenue.

[00:37:27] I have my returning customer revenue expectation in each of these scenarios. Now, as I think about that, the next step here, the most important thing, and I'm actually gonna go to 2025 to illustrate this, is actually, uh, this in much more practice. 'cause we haven't built out the entire marketing calendar yet for this business.

[00:37:43] But I wanna show you, uh, what it looks like to then layer in the marketing calendar. So if we go into November. In order for us to get an expectation of the flow of the revenue in November and to ask ourselves questions about whether or not we think we can outperform this year and last year, and whether the model might be wrong based on something we don't know from the previous period.

[00:38:05] We need to be able to look at every moment that we have planned, every email and MS that is planned to be sent and scheduled as well as a view into last year's moments. So we can see every date that E Black Friday was, we tag these corresponding promotions from the previous year. Those tags become the relational, uh, input for the event effect model.

[00:38:27] So we can look at all the different kinds of marketing moments that might exist, product launch promotion, influencer pr, seasonal event, VIP drop, other shipping, cutoff day, labor Day, et cetera, et cetera. And we tag them so that we can begin to analyze the historical effect on revenue, on new customer revenue, on, uh, returning customer revenue on a day where we run a promotion.

[00:38:49] Black Friday's its own specific tag. So is Cyber Monday. So is Black Friday weekend, right? Like all of these days have tags and we can look back and last year and go, oh, okay. Well last year we did a travel sets mini highlight DOPP kit. We're not doing that on this day. We can't really expect these days to comp each other, but we're also launching new things this year.

[00:39:06] We have cherry teaser and cherry early access SMS in ways that these might be reasons why I would expect us to outperform the model. Because the model is an extrapolation of last year's seasonal effects, and this year we have more planned. So by building the marketing calendar, working closely to both analyze the historical marketing calendar, integrate the new one, we can then take that to create an expectation of effect from those actions.

[00:39:31] So this is the event effect model that looks at all of those historical tags. Here's all the different tags. Here's the end, the number of events that we have tagged. Here's the confidence interval of high and low and average and median effect on various impacts. So as an example, when we run a promotion, we expect a 77% improvement on A MER.

[00:39:53] This is the recommended number. This is the low end, this is the high end. So these intervals also represent for you the individual builder of the plan. The range of good, medium and greats are bad me, uh, likely and great that you can use to say, okay, if I'm building the great plan, then I expect this product launch to be this upper bound effect, not this lower bound effect when I launch a new product.

[00:40:18] Or maybe it's even better than that. Maybe there's something that you know or anticipate that's different, but so often when we start with those things we have no anchor in, well, what did the historical promotions do? How much did Black Friday impact? A MER. How much did Black Friday impact returning customer expectation?

[00:40:37] And we can make these adjustments. We can say, Hey, we expect it to substantially underperform. We expect it to substantially outperform by two and a half standard deviations. Like you can come in and you can think about the marketing action you're planning to take, and then you can apply an effect on the revenue.

[00:40:50] This is really key is that it forces us if we want, if, if someone's dissatisfied with a forecast, you have to change it through the marketing. You don't just go edit the spreadsheet. You have to say, I'm taking action A, and the effect will be B. And so that is what this forces. It says, okay, here are all the past events that we've done.

[00:41:09] Okay, here's the new events that we have, and here's the effect on revenue that we anticipate. And that effect on revenue shows up in the daily flow of these outputted models. So you can see, like, let's just go to, um, let's just look at a different month. Let's look at October. Okay. So you see we have the media budget that came outta the spend in a MER model gets broken down through an MMN into a channel level allocation, a percentage of that spend onto brand, how much of it we spend on retention versus acquisition, the day of week effect.

[00:41:44] So we look at the store, how much does the flow of revenue alter Day to day, we take the marketing calendar and then we overlay that against the actions that we expect in the month. So you get to a daily expectation of revenue, uh, and spend every day in the model. And so each of these events that we have planned have some effect on the future expectation.

[00:42:04] So you can see the efficiency rising in Black Friday, right? You can see the budgets increasing in every channel, how much money you're spending in every place. So when we talk about literally every dollar, every day in every channel flowing out of that overall expectation, this is the work that's being done to give you a confidence of exactly what's happening.

[00:42:26] And we'll do this for every plan, right? So you get to a place where the goal is that when all of these things exist, okay, you are going to get to this level view, which is really the key here, is that the only thing that we know, just to bounce back to this discussion piece of it, um, we know that great forecasting is actually an exercise in execution more than modeling.

[00:42:52] Uh, George EP Box is a, um, mathematician that has inspired a lot of my thinking about forecasting, and he has this great phrase that I adhere to, uh, vehemently. And, and this might surprise you given how much of the conversation right now focused on modeling is that the only thing I know about every one of those models is that they will be wrong.

[00:43:10] And so George would say, uh, all models are wrong, but some models are useful and they are useful if they allow us to quickly see where we are, importantly wrong. So the key to all this planning, and this is, this is really the magic of it all, is that you'll be wrong. No one of them. The bonus, the board, the budget, none of these plans will actually be right.

[00:43:34] You'll very rarely actually guess the exact amount. That's not the game. We're not playing guess m and ms in a jar we're playing, where can I see? How fast can I see where what I expected to be true was wrong so that I can fix it? And there is a process by which every month as you do this, you develop the skill where you force yourself to guess again.

[00:43:57] And to learn and refine and guess again, and update the model and guess again, and update and learn what were we wrong about? What changed it, what thing happened? That exercise where every month you try again and try again and try again. And we do this across hundreds of brands. You refine your thinking and your understanding of the levers of growth in your business.

[00:44:15] You begin to deeply and intimately understand the units of impact inside the thing that you're building and doing. And then every day you show up and we get this view of exactly where we're at today against the expectation that we created. So I can drop in here and I can compare all my different plans.

[00:44:33] Think of this as the, uh, the budget plan, the one that we use, we call it the CTC Profit system. This is the primary plan that we're gonna operate and manage to, right? So here we are today, we're beating the expectation across all these areas. We have way stronger than anticipated new customer revenue. We have great efficiency in our A MER.

[00:44:51] And we can use incrementality. There's a whole measurement system that we use to actually get to a channel level to understand where are we more efficient than we expected. In this case, it looks like meta is crushing. We're doing a great job in this channel. It's leading to better than expected performance of efficiency.

[00:45:04] Same thing in Google. So we can begin to understand, hey, there's more here than we thought the model was off for some reason, and we'll learn extra, fix it. But for today, good news, let's press, let's go. And you can see that visibility against a stretch goal. So even against the stretch goal here, we're ahead, uh, on overall contribution margin.

[00:45:25] We're able to drive more profitable volume that we anticipated. So even though it might be slightly less efficient, some of these moments are creating better than expected outcomes. We're still a little bit ahead of new revenue. We're right on the returning customer view. Email revenue's well ahead of anticipation.

[00:45:40] All of these metrics drive your team's day-to-day actions that allow you to understand what you need to do to make sure that you go achieve the goal. That's the power of the system. Various models that you can look at and compare at any time to. You can always see where am I at relative to my board?

[00:45:58] Where am I at relative to my budget? Where am I at relative to bonus? And then each metric has an expectation that allows you to know where to go when you're off course. What action do you need to take from here? And that is a powerful, powerful tool that allows us to create aligned expectations that generate aligned behaviors towards that common goal.

[00:46:19] The whole organization is laddering up every day where you, the CFO, or you, the founder, can ensure that your media buyer, your growth strategist, your email and SMS team, your agency, whoever knows the goal, knows the financial outcome that you promised in a boardroom somewhere and your entire team every day is executing towards it.

[00:46:35] There's nothing more powerful than that feeling that you know, that everybody understands where they need to get and they're looking at it every day and they're trying to drive the, the effect. It creates unified expectations across departments. Your finance team, because our planning process and the tooling, the financial forecast doesn't stop at a revenue level.

[00:46:51] We absorb all the costs. Lemme go back and just illustrate this real fast because I don't think I got to that. This planning process goes all the way down to the p and l level, okay? Where we absorb all of the costs of the organization, all of the g and a, all of the variable costs, all of the cogs, everything to get to a full p and l level forecast where you know that your marketing team is actually working towards an efficiency expectation that produces the cash that you need, the profit that you need, the EBITDA that you need.

[00:47:24] You get real time tracking of board budget and bonus performance. So those plans live forever, and usually what we do is we anchor them. So we'll build those three plans at the start of the year. They become the anchored permanent immovable numbers. And then we also build what we would call the le. Every month we go through and reforecast the thing.

[00:47:44] We believe that we are most likely to occur in the coming month. 'cause as you deviate throughout the year, the gap between the present plan, the initial plans on a daily basis widens. It's just the further out you go, the more error there is in any forecast. So we'll then have that, this is what we're driving towards this month and maybe we're way ahead of forecast.

[00:48:02] And so we need to go out and actually like reset our expectations of our behavior this month. So we do a rhythm. Every customer every month gets an updated LE forecast to go alongside the standing budget bonus and board expectations. It eliminates dozens of spreadsheets, gives leadership confidence, and creates a roadmap for every team member.

[00:48:20] It all shows up every day, and even more so during Black Friday and Cyber Monday, we build it and break this down even further down into an hourly track where you're getting an expectation of every hour, every metric. So great. Forecasting, like I said, is an exercise in execution more than modeling. It's a critically important process.

[00:48:38] Either aligns the behavior of the entirety of the organization and there's different interest to serve with it. And so if you need support in thinking through those scenarios, having an external third party put eyes on the likelihood outcome to remove bias or history or expectation or incentive, and just say, here's what the data is telling us.

[00:48:54] We would love to do that modeling for you. We can give you these tools. Your team can manage and operate the system, or we can do it with you where the modeling can be done for you, where you can walk away with a 2026 forecast that you go. I feel more confident in the plan and the thought work that I've done going into 2026, and I didn't even have to step away from Black Friday to do it.

[00:49:12] You as a leader can get more confident, clearer about your expectation of the future, a better plan to share with your team than ever before without having to lift a finger. Let us do that modeling for you and let us then do, go do the execution with you. The real magic of how this comes to life, the real way that you're actually going to achieve your goal next year is not from the the beauty of the model.

[00:49:33] It's not from the accuracy of the data. The only thing we know about models is that they will be wrong. It's about their ability to highlight for us where we are off course, so that every day we can wake up and have our friends scout drop in Slack. How our performance was to expectation and for your team to look at those signals all the way down to every dollar and every campaign and every ad channel, and go and make it better to go make the improvements needed to drive your business towards its end goal.

[00:50:01] So if any of that feels distant for you, it feels overwhelming. I know for many years. I labored over the process of trying to build my company, a view of a financial forecast that I could feel confident in tying their financial objectives to that wasn't outlandish, that was within the realm of possibility that was defensible and thoughtful, and we run the same system at CTC.

[00:50:24] We have a realtime view of r, p and L. It's, we call it the sandbox. All of the inputs updated in real time that give me the ability to see exactly where we're at. It's how we went to our board. We have a board level view of our expectation. We have a bonus level view of our expectation. They're different.

[00:50:38] We dog food, this same thing, and I can tell you it has as the leader. My ability to see, oh, we're off course on new business. We're off course on retention. We're off course in paid social revenue expectation. We're off course all the way down to what upsell service line is deficient. It's the clarity of what the problem is or wow, this thing is actually way overperforming the expectation.

[00:51:03] Lean in. What can we do? Is there more gas to be pressed on? It's that it's the understanding of the inputs. It's understanding where you are wrong or right. That gives you the power to go and exert yourself on the organization for the sake of improvement. So that's the, that's the, that's the idea.

[00:51:18] Forecasting is your core operating system. It aligns the incentives that align the behaviors of your organization. It starts with quantitative modeling. You have to anchor yourself in the reality of what is true today. Where is your business now? And for many people we're coming out of a period where over the last 24 months, you have a decrease in the new customer acquisition of your business.

[00:51:39] And you're looking out to next year and you're going, I hope it's gonna be better. I hope it's gonna be better. And what I'll tell you is that if you have come out of a period of deflated new customer revenue, the problem is, is that next year's returning customer revenue will be lower. And your contribution margin, your profit expansion comes from an increase of the percentage of revenue that comes from returning customers.

[00:51:58] And so you are gonna have to be, have a longer journey to take people on and to get a board invested, to get a team invested that hey, we actually have to restart our new customer acquisition engine, which means that we've gotta go out and spend more money to acquire more new customers at slightly, uh, maybe even a slight loss, maybe very little margin, maybe break even.

[00:52:17] That's gonna drive our MER down for a while. It might actually drive our contribution margin down for a bit, but this is the two year way. This is how this gets better in the future, and this is why it's necessary. If you have to tell that story, you want the data to give people a vision for the journey they have to go on.

[00:52:33] And here's the truth for many eCommerce businesses, if you don't compound your LTV fast, this journey can take time. A hole in new customer revenue can take years to get out of. And for you to anchor yourself in that reality yourself and to to get the team not to a place where you feel like, man, we're so discouraged.

[00:52:51] 'cause we keep missing expectation. And missing expectation because there's this expectation of the returning customer file that does, that's not possible. It's some of the most discouraging action I see. Get that placed onto teams. So let us help, let us help you build that story in the boardroom. Let us help you build that story with your bank.

[00:53:06] Let us help you outline the bonus to help your team get the right bonus schedules and financial incentives that they can go hit. Tie to the clear actions that they're gonna build. So you walk into 2026 with a marketing calendar that's fully dialed every email, every day, every dollar in every channel. A clear expectation of the financial results that you want as a business and how you're gonna get there.

[00:53:22] That's what we're after. That's what we've spent a decade building a system to do. And the only reason we're better at it than you, it's not 'cause we're smarter, it's just 'cause we've been doing it for years. This is just a process of learn, refine, learn, refine. What did I get wrong? How did I fix it? What did I not consider?

[00:53:38] Oh, crap, I didn't ask about inventory. Oh no, I didn't know about X, Y, and Z. Oh, we moved the date of A, B, and C. Wow. That influencer moment was really impactful. Wow, that TV spot was really important. Understanding what are all the little effects that generate revenue across all these businesses? We get to see it all.

[00:53:52] Why does this email A generate so much value? Why does PR hit B generate so much value across so many different brands? We've just repped this over and over and over and over and over, and we track ourselves every month. How did every person do to expectation? How were our models to expectation? What improvements could we make?

[00:54:08] How do we continue to refine them? And that's why as an organization, we end up with, I think this year we're like plus 4% to revenue across over a billion dollars in GMV. Like the, we do this in a way that becomes highly accurate because we work to make it right. So. Uh, I hope this is helpful, whether you work with us or not.

[00:54:25] I hope there's takeaways here about how you can think through the levers and inputs that go into a forecast, the ways that you generate different scenarios, how to think about how efficiency could change and spend, could change how that new customer change affects returning customer. How you think about communicating to a board versus your cashflow planning versus your team.

[00:54:45] And, and as always, look, we're here to do this. We believe that this is the core place that we offer unique value in the world, and we're better at this than anybody we just are. And again, it's because we just messed it up more than everybody else. We've screwed it up more, and so we refined more so we're clear on the levers, the inputs, and then if you need somebody to sit at the center of it.

[00:55:04] To drive your team every day. This is where we're off course. What are we gonna do about it? I'm sitting right now next to Anmar, talking to a nine figure brand about how every day so far in the month of November, where are we relative to this expectation? What are we gonna do about it? How are we changing spread, and are we bringing it forward?

[00:55:17] 'cause again, it's not guess and see after 30 days whether you've got it right or not, it's make it right. That's the key with forecasting. So appreciate the time. Uh, I know I'm weirdly enthusiastic about this process. Uh, we really love to think about how to, to outcome. I, I've lived in a world where, as a leader, how disorienting it feels to be missing and failing and not know why I that happened to me.

[00:55:42] I lived that life and there's nothing that makes me feel more discouraged, uh, more burnt out than not understanding why things are going bad and not knowing what clarity, what to do about it. Clarity is empowerment. It gives you optionality, it gives you the chance to go again and win. So let us help you build that clarity, uh, and figure out how to make the financial forecast the center, uh, of your workflow please.

[00:56:07]