A Live Demo of How to Forecast Your Q4 Against Tariffs

[00:00:00] Taylor Holiday: Ultimately, at the end of the day, you're gonna get your new customers, your returning customers, total revenue expectation, all the way down to the profit expectation.

So we wanna be able to consider. What is the full burden cost of that revenue, and how does that relate to the profit that you want as an organization or business? Is this the right number? And then that can help us to throttle up or down the efficiency expectation. So by bringing in the full p and l and having a dynamic way to interact with the revenue forecast all the way down to the p and l in real time.

Allows us to just like play with scenarios that customers constantly are asking is what are my choices? Like if I spend X, what is the Y result? If I spend A, what is the B result? Like? How do I actually think about the right spot here on this curve to maximize whatever your business objective is? Is it to maximize profit?

Is it to maximize revenue growth? Is there some blend of those two that you want to consider? That's our work. That's what we're after. And why these models become so effective as the core inputs that drive this kind of discussion is that otherwise you're just sort of guessing at these relationships in a way that is really hard to determine.

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[00:01:38] Richard Gaffin: Hey folks. Welcome to the Ecommerce Playbook Podcast. I'm your host, Richard Gaffin, Director of Digital Product Strategy here at Common Thread Collective. And I'm joined yet again by Mr. Taylor Holiday, our CEO here at Common Thread Collective look, and slick, no hat Taylor.

What's going on today, man?

[00:01:54] Taylor Holiday: Nothing. Yeah, just trying to you know, give your head a little room to breathe every now and again.

[00:02:00] Richard Gaffin: yeah, no, I was saying I need to do that more, but it's like there's about two weeks post haircut where I'm no hat,

[00:02:05] Taylor Holiday: That's it.

[00:02:06] Richard Gaffin: Eagle Eye watchers of the YouTube channel will maybe notice that at a certain length, probably over an inch and a half, two inches, the hat has to go on.

[00:02:13] Taylor Holiday: There's also like a very real experience is that like when you get your haircut and you've got some hip cool man doing your hair, you walk out of there feeling great and then you can just never quite recreate it the same way. You know, I just gotta get some better tooling.

[00:02:27] Richard Gaffin: I find myself touching my hair all day if it's not covered. So

[00:02:29] Taylor Holiday: Yeah.

[00:02:30] Richard Gaffin: I gotta do, but alright folks, well, today what we want to talk about a little bit is continue our conversation rather about our spend forecasting models that we've been giving away for free over the last couple of weeks to select eight and nine figure brands.

And obviously we wanna do that for you too. So if you're interested. listening to this pod, go ahead and hit, hit us up, comment thread co.com, smash that higher s button, let us know that you're interested. Anyhow, what we wanted to talk through today is a couple of practical use cases for this. So, mere hours or really minutes before we hit record here, Taylor tweeted out an interesting video on his ex account around. Basically how these forecasting models help with some of the uncertainty around tariffs that's going on right now. So what we wanna do is have Taylor walk through a couple of examples. There's gonna be a video element to this too. So go ahead and check it out on YouTube if you're not already, and kind of yeah, exactly.

Walk through what these spend and MR models can do for you in very specific use cases. So Taylor, I'll turn it over to you. What you got for

[00:03:27] Taylor Holiday: Yeah, so, so we're talking about these free spending power models, but I think since they don't exist for a lot of people, they're hard to understand how I could practically create value out of this. So I wanna, I'm gonna give you a couple of examples today that are ways that you can create value using this free spending power model.

So one of the big challenges that many brands, so I put out this poll the other day on Twitter that asked. What percentage of you have had to raise your prices due to tariffs and it was 50% of brands.

So let's just assume then that's an indication that about 50% of brands are experiencing some change to their underlying cost of goods today. Well, that means that there's likely some change , in the gross margin profile of your business. So usually pricing changes are some attempt to offset, the increase in duties that you're paying because of tariffs. Now, I've also seen data that as of today, the absorption of those changes is about 14% manufacturers eating those costs on behalf of their customers. It's about 60% the brands eating the expense and 20% the customers in need expense. So how much you're able to pass that on is gonna obviously differ from industry to industry, but.

What is likely true is that there's some underlying change to gross margin year over year. For most brands in e-commerce that is like likely true. So this begs a question, if my gross margin goes from 60% to 55% or 60 from 52%, how does that change how I would approach setting my budget? Right? Because what it functionally demands is that you are taking a higher efficiency expectation on your media.

And we all know that there is some relationship between efficiency and volume. So as you go to set your Q4 budget with a different cost profile, the question is. How does that change how you need to change and set your budgets? This is a really important question, and you cannot just assume that you'll be able to spend the same amount of money at a higher efficiency than you did last year.

So let's, let's think about this problem. This is, this is the kind of problem that the spending power bottle is intended to help you solve, and let me sort of illustrate how we can do that. So the spending power model, what it allows us to do is look at all of the historical spend and efficiency relationship over time for every month.

So, seasonality we know is a obviously a really. Big component of how e-commerce brands are able to spend spending power is different in November than it is in August. For most brands is, is sort of the illustration. So you can see here I've got a bunch of different dots in this graph and I'm just gonna show you.

So this initial set of dots shows me the historical spend and efficiency for every month for the brand. And our model then will tune the August expectation or build a curve for the relationship between spending and efficiency in August for this brand. Okay. That's what this yellow dotted line represents.

It's a curve, an algorithmic line that shows the relationship between spend and efficiency. Then we can look at all the actual for last year in August. We can look at the selected point on the curve that we have here. And then we can see different dots for different periods of time, right? So because August has actually happened here, we have the actual, as well as the last year.

So let's say we're gonna look out into November, and you'll notice, right, that the spend power goes up, we can spend more money. You'll see the curve goes up. But the question we're asking today is, let's imagine that there's a change to the underlying cost. 'cause what the model's taking into consideration is.

Also contribution margin. It's considering the costs. Here you can see the cost percentage applied against the model to determine how much we can spend and we can optimize for different outcomes. We can optimize for maximum contribution margin. We can optimize for maximum lifetime contribution or max new customer revenue at break even.

All of those have cost as an input. Okay, so the question becomes, if this cost changes, what change happens to our spending power? So this is a brand, this is a real example that I'm giving you that shows that right now at their current cost profile in November, if they wanna maximize new customer revenue at breakeven, they can spend $1.918 million to generate 3.463 million in revenue at a 1.8 a MER.

And that gets them down here. You'll see basically to contribution margin neutral. Okay. But obviously if the cost change, the point at which their contribution margin neutral will change as well. So let's go ahead and do this. Let's assume there's a 20% change in the cost. So that would be about eight points, right?

We've got a 44% cost here. So cost of delivery is about 44%. In the example I'm looking at, if that goes up 20%, that's eight points, right? So 10% would be 4.4 20% would be 4.8. Let's call it five points. Of margin. And to make it simple, we're just gonna go to 52%. Okay? So we're gonna change the underlying cost.

Remember this number right here, 1.91 million at at the previous, and we're gonna recalculate the curve based on this adjusted cost. Boom. Okay. So Richard, for the folks at home, what just happened? What did our budget go down to?

[00:08:37] Richard Gaffin: It went down to 1.6 mil.

[00:08:39] Taylor Holiday: That's right, because now our new break even point is about a 2.0 8:00 AM ER, right?

We've got a 52% cost of goods. So that means we can spend less, but how much less? Well, the model tells us $300,000. So we can literally model the exact expectation of the change and come up with a new goal, which is we're gonna now set our budget at 1.63 million at a 2.08 MER to generate 3.4 million in revenue.

We could do the same thing looking at contribution margin and the point on the curve will change every time. This is really important is to help you understand what is the trade off, and this is when we think about the impact of cost of delivery. This is, this is a, a really under considered point

[00:09:17] Richard Gaffin: Mm-hmm.

[00:09:18] Taylor Holiday: when gross margin changes your revenue potential changes.

Lemme say this again. When gross margin changes your revenue, potential changes. So we went from 3.6 million in revenue to 3.4. Why? Because you can't push as far out on the curve as you can when the gross margin is greater.

[00:09:35] Richard Gaffin: Mm-hmm.

[00:09:35] Taylor Holiday: So in this case, the brand can now reset their own expectations. It's gonna change their financial forecast.

This isn't just about changing your media, right? This isn't just about changing your budget. This is about, this is going to affect the entire financial forecast of the organization. When tariffs create change to gross margin, it changes your top line revenue potential. At the same goal This is, this is, I think the under considered part of this is that the reason your revenue year over year won't be quite as good.

You either have to choose to sacrifice efficiency or sacrifice volume. And if you wanna hold this constant, that's gonna be the case. So this is a very real dialogue that we're in with our customers all the time around what is happening to your underlying costs and therefore how does that affect what the budget's going to be and where the media plan is gonna go, and therefore the financial forecast to where all of marketing finance are aligned.

[00:10:23] Richard Gaffin: Let's quickly talk j just because to make sure we kind of redefine all of our sort of phrases here, contribution margin neutral.

[00:10:30] Taylor Holiday: Yes.

[00:10:30] Richard Gaffin: that mean specifically?

[00:10:31] Taylor Holiday: Yeah, so that's a great question. So that means after all costs are considered, so your product costs, shipping, fulfillment, variable expenses related to payment, processor fees and add spend. Contribution margin is. Net revenue minus all of those costs equals neutral, zero contribution margin, meaning you're, you're making no money on your new customers.

Now, for most people, that's not gonna be a desirable effect because they, they need those customers to produce. Some amount of contribution margin. So alternatively, we could optimize for a point on the curve where we're maximizing lifetime contribution margin in some window, and we're gonna talk about that example in a second.

Or maximizing first order contribution margin. So you can see at this point on the curve, spending 470,000 outta five to 1:00 AM ER, you're gonna produce a million dollars in contribution margin. So why would a brand ever take a neutral contribution margin? Well, because we're just looking at new customers right now.

A lot of brands. Every month are taking, when you, especially as you get to maturity, like this brand is, you can see they're doing about $50 million of revenue a year. They're gonna make a bunch of money off their existing customer base. And so therefore, they wanna drive as much new customer acquisition as they can to neutral.

And so we talk a lot about this idea that your existing customer contribution margins should cover opex. And if it does, then you can be aggressive in new customer acquisition. And so there's, there's a, there's a, there's a. Dynamic here where we're interplaying all these things at once to help set how aggressive we can be in new customer acquisition.

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[00:12:47] Richard Gaffin: Gotcha. And then also to, to be clear as well, like when you go down to that that spreadsheet or rather table that you had below,

Yeah. what that's representing is sort of the co contribution margin neutral point or whatever, is that the contribution margin on that specific tranche of spend is.

[00:13:04] Taylor Holiday: On those new, on that new customer revenue? Yes. On your new customer?

[00:13:07] Richard Gaffin: margin,

[00:13:08] Taylor Holiday: No, it's it.

[00:13:09] Richard Gaffin: 20 K of spend, like the difference between 1.4 million and 1.42 million or whatever, that additional 20 k of spend, dunno if I have my math right, is that would be contribution margin neutral, and then any spend over and against that would be

[00:13:24] Taylor Holiday: Negative.

[00:13:25] Richard Gaffin: negative,

[00:13:26] Taylor Holiday: You can actually see that. Like, actually what what's happening is that you're actually, this trench of spend down here is like wildly negative in contribution margin. The, so this is a good question, is like what we're saying is that at this point, the incremental dollars in every incremental dollar is generating 20 cents.

So you, this is actually like the point at which the next dollar is is actually incrementally negative, is gonna be this point of the max cm, right? It's gonna be like right around here. You can see the incremental. So we have a column here for incremental, A MER, meaning the next dollar you put in. What is it gonna generate?

Well, you can see how quickly you reach a point where the next incremental dollar is producing negative. So like if this is a 2.08 is break even based on 50% cogs, then literally like, the max cm point should be that. So we're still looking at the 44% cost. If I go to back to 52% here, like we had earlier, that max cm point is gonna be the point at which the next incremental dollar is negative.

So you can see that that's only $400,000 of spend and we're, if you, but if you want to break even, you can spend all the way up, down to like 1.61 million, but, but you, all along that way, from $500,000 to, to 1.6 million, you're trading a dollar for less than a dollar. This is really important, like this is where I think everybody wants to know is like, what's the return on the next dollar in?

And that's what this model is helping us to see is that like the second you cross this point, you now are trading dollars for negative dollars. You are, but you, but you're trying to maximize revenue. And that's why we always talk about this idea of lifetime contribution margin as being the best point on the curve because it considers.

The total return on those dollars and the point at which over some period of time you're gonna maximize your return on investment. But, but even at this point where it's saying $800,000 would be the right budget, you can see that that last dollar in is generating a 0.83 return, where 2.0 is break even.

So you're still way out beyond the marginal frontier on that individual dollar, but the total cohort return over time has maximized at that threshold.

[00:15:39] Richard Gaffin: right.

[00:15:39] Taylor Holiday: That's like, I think a really important thing for people to consider is that these dollars are gonna be very negative in terms of their impact individually, but collectively, that total value is gonna be greatest.

[00:15:49] Richard Gaffin: Yeah. No, I, and I think, yeah, the point being that like this, this goes into very fine detail about.

[00:15:54] Taylor Holiday: That's right.

[00:15:54] Richard Gaffin: The kind of efficiency of your spend. And you can imagine something as huge as the, the effect of tariffs on your margin or whatever is going to affect, have like this ripple effect throughout your spend, which is why it's so important to have such a detailed forecast.

And I think most people are going into this just saying like, well, our margins or, or rather our margin is shrunk, but we'll still try to maybe kind of hit the same numbers and see what happens. You know

[00:16:16] Taylor Holiday: That. That's right.

[00:16:16] Richard Gaffin: allows you to see much more clearly.

[00:16:19] Taylor Holiday: That's right. And, and this is where I wanna talk about, so something else here, which is so we're asserting then like this consideration for the LTV of customers, right? And one of the big questions that gets asked. Or, or considered, or should be considered is what is the LTV of your Black Friday, cyber Monday cohort of customers.

Okay. And so what I wanna show you right now is a very common thing that we see amongst cohort curves from gifting periods. This is especially true if your brand is subject to gifting at all. So, what I'm looking at right now is a revenue over time chart. Okay, so this is sort of your traditional cohort view of customers worth X in day one.

How much are they worth on day? 30, 60, 90, et cetera. It's called a cumulative LTV report, so it shows you over time what a customer is worth on a revenue basis. Okay? And so what I wanna show you is like if we look at this line right here is my November cohort. And what you'll notice is that on a first order basis, it's like the highest initial a OV of almost any of the cohorts, right?

Like if I start layering in the other months, and if I just look down first a OV, you'll see that it is the highest of any month. Okay, so this is often true when you, when you do discounts and bundles and things on Black Friday, cyber Monday, you often get like higher AOVs. You're often also trying to like sort of set off offset gross margin, that sort of thing.

But what I wanna show that happens is that if we look at the value over time, so you see the slope of this line illustrating how much incremental revenue I'm getting over time and we compare it to a different cohort. So I just added in for those of you watching on the video, the February February cohort curve.

So what do you notice, Richard, about this line versus the gifting one?

[00:18:08] Richard Gaffin: The, and sorry, the green being the February

[00:18:11] Taylor Holiday: Green is February. Blue is November. What do you notice about these?

[00:18:14] Richard Gaffin: the growth the growth over time is better for the February cohort.

[00:18:18] Taylor Holiday: That's right. The slope of this line. In other words, how much additional value does it capture is greater. So even though its starting point is a $132 a OV versus 144, you'll notice that by month, by day 150. So that's month five. It's actually surpassed the cohort value of. The black Friday one because those customers continue to increase in value at a better rate than the gifting customers.

You'll see that like by month eight. If we compare month eight to nine. You're at $200 for the February cohort, and then it jumps to 2 0 6, so a $6 increase, whereas day two 40 of the other cohort we're at 1 92, and it only increases to 1 94, so a $2 increase. So there's a widening gap over time of the value of these customers.

And you can look at this in comparison to a lot of these curves. So this is March, this is April, this is May, this is June. So in most cases, in most cases, not in everyone, but in most cases. The Black Friday Cyber Monday cohort is worth slightly less over the lifetime of the business than others. So again, as we go back and say, okay, well what does that mean about our spend and a MER model?

Well, what it means is not only do we have the ability to think about the adjustment to the costs, but we also can think about the LTV multiplier that we're using and we can say, okay, let's go to a year. And what we're gonna do is we're gonna actually bring this down a bit. We're gonna calculate when we think about Max lifetime contribution margin, we're gonna suppress the lifetime value of this cohort.

Over time. Now we may move the A OV up. We may say like, okay, but actually we don't wanna use la. We wanna use last year's number. And we're gonna use that as being 1 44, let's say slightly higher. And then that's gonna give us a relationship between the A OV and the the lifetime value multiplier that we're using to get to this max lifetime CM number to really think about the right spot of setting our budget.

[00:20:23] Richard Gaffin: Right.

[00:20:24] Taylor Holiday: So all of these things are like, if you really want to be effective in setting your budgets for November and December, you need to consider your gross margin changes. You need to consider the, the value of these cohorts uniquely over time. And this is all the work that our growth strategists are doing to help our brands build the appropriate budget and forecast and identify the right point on this curve to maximize the value for their business.

[00:20:46] Richard Gaffin: Right, exactly. Yeah, no, and I think just to kind of re reiterate my earlier point, like this, the idea here is to get as like detailed and clear a sense of what is going to happen or as accurate a po a model as possible of what's going to happen so you have some understanding of how to execute against it. Again, like we talked about last time we spoke, and, and honestly every single time we talk about this, it's like a model is really about creating a plan of action, not about prediction. But there's an element of being able to finally, or predict as closely as possible what's going to happen, gives you a sense of what the plan of action is going to need to be in order to make what you need to happen happen. And I think like this provides like a ton of clarity around, particularly like the long term effect of Black Friday is something I think a lot of people don't have the capacity to

[00:21:32] Taylor Holiday: That's right.

[00:21:32] Richard Gaffin: and that's exactly what this gives you.

[00:21:34] Taylor Holiday: And of course we're not stopping there, right? 'cause we just, we're gonna go and model the existing customers. We can do that on another episode and talk about it. But ultimately, at the end of the day, you're gonna get your new customers, your returning customers, total revenue expectation, all the way down to the profit expectation.

So we wanna be able to consider. What is the full burden cost of that revenue, and how does that relate to the profit that you want as an organization or business? Is this the right number? And then that can help us to throttle up or down the efficiency expectation. So by bringing in the full p and l and having a dynamic way to interact with the revenue forecast all the way down to the p and l in real time.

Allows us to just like play with scenarios that customers constantly are asking is what are my choices? Like if I spend X, what is the Y result? If I spend A, what is the B result? Like? How do I actually think about the right spot here on this curve to maximize whatever your business objective is? Is it to maximize profit?

Is it to maximize revenue growth? Is there some blend of those two that you want to consider? That's our work. That's what we're after. And why these models become so effective as the core inputs that drive this kind of discussion is that otherwise you're just sort of guessing at these relationships in a way that is really hard to determine.

[00:22:42] Richard Gaffin: Yeah. Okay. Let me ask you a question that I asked Luke and Steve our data scientist a couple of weeks ago, which was like, when, when you present these models to clients, what is generally speaking the, like, the moment that people's jaws drop, or the moment that people like, really get that sense of like, oh, something's different here.

[00:23:00] Taylor Holiday: I, I think they're often surprised to see how over much they're overspending. And where the actual optimal curve point on the curve is. That's, I think the, the thing that is always like, whoa, like we're way out beyond our, our marginal return. That the challenge with that. Is that oftentimes there's a year over year business expectation that if you overspent last year to drive last year's revenue, the problem is you often.

Are stuck in this loop

[00:23:30] Richard Gaffin: Mm-hmm.

[00:23:31] Taylor Holiday: where you have now redlined the business so much that the next dollars in are so negative. Like, like we saw back to like that 0.19 incremental A MER, that you just can't actually spend more and get more. And so you suddenly feel this, this sensation that you just keep spending, but you're generating so little back.

[00:23:52] Richard Gaffin: Mm-hmm.

[00:23:53] Taylor Holiday: But like that is, that is, that is what is occurring now when you're, you're, if you're all the way out to a dollar and 20 cents, when you go to push the lever, it feels like nothing happens. And that's 'cause it's true, right? And so when you're in that place, often you have to step back, reestablish a base where you're growing, the amount of returning customers a little bit slower to then reach that next tranche of spend.

And that is not a thing that brands like to do. They want to go, go, go, go, go, go, go, go, go. Unless they change the underlying input somehow. New product, new offer, new something, new piece of amazing creative, like they're, they're ramming themselves into this really negative exchange of dollar for 10 cents.

That causes usually a lot of bigger business conversations. Now, in some cases, the opposite's true. There's an exciting opportunity that says, Hey, you're actually massively underspending the opportunity. And we see that too, that that exists too.

[00:24:44] Richard Gaffin: Right. Yeah. And, and I think Luke brought that up in the, in the context specifically of like lifetime value understanding. Like when you can get a sense of what, like maximizing customer LTV would look like, you realize like, oh my God, we could have been spending way more at a much tighter first order value because our LTV is so good and we just didn't understand that that was

[00:25:02] Taylor Holiday: Yeah. I also think that like with the gross margin change, I don't think that marketers are proactively seeking out understanding when that impact is gonna show up on the p and l ending cogs, and therefore, how does that affect my budget? I don't think those conversations are happening as much as they should.

[00:25:16] Richard Gaffin: Yeah, totally. Well, I mean I think that that's, that kind of is a good example of how like. It's really feels like what these forecasting models do is they clarify problems that already exist that are invisible. Right? And what'll happen is like those issues will become visible at some point, but it's going to be more catastrophic than if you were to say like, actually you can't grow X amount year over year this month, even though that feels really bad.

But if you don't do that this month, that means in several, or, you know, 6, 7, 8 months down the road, you're going to be able to grow in a way that you wouldn't have been able to if you had kept the pedal of the metal in the way that you're doing right now. Right. Yeah, essentially it's like surfacing problems that are kind of festering under the surface they come up and sort of cause a problem.

Right.

[00:25:58] Taylor Holiday: Exactly. Exactly. And so I think all of this tooling is just ways of getting a sense of what has been true, what that means, I need to try to accomplish, to change it, what realities we must consider. It's also a really, what I've found is that finance doesn't like to hear from marketing about.

Request for more budget without clear evidence of potential impact and incremental value or why they can't accomplish some financial goal. CEOs don't like to hear that, and so if you're a marketer in that situation, oftentimes we are a resource to help you with that discussion.

[00:26:37] Richard Gaffin: Hmm.

[00:26:37] Taylor Holiday: help bring validation to the potential bounds of possibility with to a board, to a CEO, to somebody in finance where there's this pressure to accomplish things based on some commitment made somewhere that you need more evidence to the bounds of limitation, like we can be helpful with that or vice versa.

If you're a CEO that's looking to represent to your marketing team why you think there might be more on the bone. We can help with that too. So I think data information is power. It gives you the ability to build a case for good decision making, and that clarity is what we want to help bring to any side of the organization that is looking for more understanding of what is possible.

[00:27:18] Richard Gaffin: Love it. folks, well if you want one of these for free and you're an eight, nine figure business, you know who to call, who to get in touch with Common thread code.com. Hit the highest button, let us know that you're interested in one of our free spend and a MER models. And if you qualify, we would love, love, love to build that for you. Taylor, anything else you wanna hit on this?

[00:27:36] Taylor Holiday: Nope, that's it. We're gonna keep, keep trying to bring examples and edge cases of this so that you can understand why it is a useful tool inside of your organization. Why I can't imagine trying to run an organization without it.

[00:27:49] Richard Gaffin: Love it. All right folks, well that's gonna do it for us for this week. Take care everyone, and we'll talk to you next time. See ya.