The Marketing Calendar Is Your Real Revenue Model

[00:00:00] Luke Austin: You need a model for determining your budget.

That accounts for seasonality, historical impacts, but roots. And identifying how the marketing calendar understands impacts each of those, each of those months, and then gives variable recommendation in terms of the budget allocation for that time period.

[00:00:18] Richard Gaffin: 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 in our brand new studio, well, it's not really brand new, but we've taken it over. This is our brand new podcast studio, our VP of ecommerce strategy.

Mr. Luke Austin, our war reporter, is joining us again. Luke, how you're doing, ma'am?

[00:00:36] Luke Austin: I'm doing great. Yeah. A group of us, were out in Dubai last week for Black Friday, Saturday, Monday with Joy Sharma and the accelerator crew. Getting back into the normal after the BFCM prep, BFCM, Dubai Time Zone wildness, getting back into a normal swing of things, feel feels good, feels good to get back into the routine.

[00:00:58] Richard Gaffin: Yeah, absolutely. And we talked at length about the city of Dubai in our other podcast, the DTC Hotline, if you wanna check that out, wherever you get your podcast, the DTC Hotline with me, Luke Austin, and Mr. Tony Chopp. But anyway so what we wanna talk about today is to kind of frame this upcoming next week on Wednesday the December the 17th.

We are holding what we're calling the e-commerce CFO Summit. And the idea here is we're gonna have a bunch of CFOs jump on with Taylor and he's gonna talk through something that we've talked about a lot here on this podcast. And at CTC in general, which is the connection between marketing and finance.

And so this is gonna be from the perspective of the CFO speaking directly to the CFO. Like your job is to build a forecast. So generally speaking the premise here then is that generally speaking, you may not have the information that you need to actually build one accurately, and that information comes from the marketing side.

So, what Luke is gonna talk us through here is how to take a marketing calendar and build a forecast off of it, because obviously revenue is generated by actions. The marketing team is the one that takes and executes and plans the actions. Therefore, planning revenue has to be based on that behavior. So, Luke, why don't you kind of set up for us how this process starts and then kinda lead us into some examples.

[00:02:14] Luke Austin: Yeah. I think to, to frame it up, it's important that we all share a similar understanding of the level of, of importance of this topic around. The marketing calendar and its importance as it relates to driving the business outcomes and the forecasting process. And for direct to consumer e-commerce brands for the.com businesses that we're all focused on working on marketing.

Calendar actions are the primary thing that drive revenue variability, the things that you have planned on the marketing calendar. The actions associated with each are the primary lever levers that are gonna drive the variability in revenue. There are, there are some underlying there are some underlying pieces related to yes, seasonality dynamics built in, depending on your business that are going to play a role.

There is obviously the ad spend investment that's gonna play a role as well. But the largest impact on fluctuations in your daily and monthly revenue outcome is gonna be related to the marketing calendar actions that you have planned. And it's fascinating in the, in the conversations we've had with hundreds of direct consumer e-commerce brands that we engage in, in this process for how disconnected the process is between the marketing and finance teams and the and the approach.

In, in across different brands as well in terms of building a forecast all the way from all the way from we have a daily run rate that we build and we have an evergreen baseline day that's non promo, non-New net new product launch. And that runs at. This much revenue and this much spend typically.

And so we build that up and then we have a promo day and that runs at this level. Two what we're gonna do is set sort of a fixed ACOs target or add to sales ratio or MER for every month. So we want a 20% add to sales ratio. We're gonna apply that flat. That's what we expect the ad spend to be.

And then we're gonna adjust that for some days based on discount rate we expect if we're running a promotion. And so the variability that exists in the forecasting process and the level of disconnect from the marketing calendar is something that we're passionate about solving for that gap because of.

The because of the behavior that then follows from that from that forecasting process. So the marketing calendar being intimately tied and actually informing the forecast is is the core of what we've built in our system and in our in our service execution on the brands that we work with.

Is building a forecast that integrates all those data points that we. Can leverage the marketing calendar to inform the forecast output. And then we can track against the actual input of the actual impact of those marketing calendar actions on a day-to-day basis to understand if they were having an anticipated impact or not.

Because that feedback loop and level of visibility is is also a really challenging problem to solve. Or we set the forecast based on one of a hundred ways of forecasting that. Your brand may deploy and then the sort of feedback loop and uncovering when we're off forecast, why are we off forecast is another quagmire that's solved in a hundred different ways as well.

That's, again, disconnected from sort of the inputs that actually lead to the marketing, the, to the revenue variability that's tied up in the marketing calendar Actions.

[00:05:19] Richard Gaffin: Right. Okay. So sort of like a stated, I guess the, the kind of core problem. And I know that you have like a specific example that you wanna walk through that kind of demonstrates for us, like the, this connection or the way that maybe it can go right? When you actually can bring these things together.

[00:05:36] Luke Austin: Yes. So when I'm pulling up now for those following along visually, and then for those just listening, will will describe it. Audibly as in as much depth as possible is an example for a brand where we see. Where the marketing calendar integration plays a critical piece in the forecasting process and how this actually comes to life for us.

So where this starts in the process is in our spending power model, which the output of this is spend your is giving an optimal budget recommendation for every single month of the year based on factors around seasonality, historical degradation of efficiency, et cetera. And so this is the starting point, allows us to know what the optimal budget is, what the new customer revenue and the following contribution margin is based on that level of budget.

And in the spending power model. The, the core insight from the spinning power model for every brand is that there is a different, there is a differing level of degradation, of efficiency of your spend at every different time during the year. Right. So there's some curve that exists for every brand of the efficiency of their ad spend and the impact of that ad spend on driving revenue and profit dollars for the business.

That degradation curve changes every single month of every single year, and then gets decomposed on a daily basis as well. And so the primary challenge with fixed budgets or even setting budgets based off of. MER or ACOs targets is, it doesn't account for the variability of the of the spending power of your ad spend in any in any given timeframe.

That's really critical. And so to illustrate this, what we have up here is for a brand, we took the first six months of 2025, and we plugged in $250,000 in total spend, just as an example, right? So for this brand, you can see by month, January, February, March, April, may, June of 2025. $250,000 in spend is what we sort of override in the model.

And what the spending power model is giving to us as an output is what is our expected new customer revenue contribution margin from new customers, and then our A MER efficiency against that same $250,000 level of spend over the course of each of these six months. And what we can see is it's a widely different outcome in terms of what's anticipated from it in in January.

Against $250,000 in spend for this brand, the anticipated outcome is 3.9 4:00 AM ER to drive 980 5K in new customer revenue and 111 K in contribution margin 250 K, spend 9 85 in new order revenue and contribution margin of 111 K February one month after. 215 K in spend, same brand, three six 7:00 AM ER nine 18 in order revenue and a contribution margin of 57 K.

So almost half, so almost half as much contribution margin in the month just following January on the same level of spend. And then this widens right in every in different months, whereas in May, as an example, 250 K and spin for the same brand. 5 9, 6 A MER is the anticipated efficiency, new customer revenue at 1.49 and contribution margin at 200, almost $270,000.

So two and a half x more contribution margin than in January at the same level of spend because the, the spending power, the, the effectiveness of that ad spend varies. And we can see this, we, we build this out sort of visually in the spinning power model. We can see the degradation efficiency curve.

For every single month. And we have the slope of that curb for each of the month. And you can see in different months how it cha how the slope of that line changes. It gets more dramatic in some months. It gets flatter in other months. And that really illustrates what the, what the, the spending power and how that fluctuates at a, at a, any given point in time.

So why is this important? So the spinning power model. As an ensemble model, we look at a number of different factors. And it accounts for seasonality, of course, historical degradation, efficiency. We'll look at categorical and competitive search trends and, and we'll look at the broader consumer sentiment, the D two C index, D two CC, I will integrate those data points.

But the core thing for us, going sort of back to the framing of this conversation is these factors around seasonality and the category. The, the main, the primary thing driving the revenue variability in these time periods is what was happening or not happening on the marketing calendar related to those time periods, right?

So the, those are the primary actions that are going to lead to higher or lower spinning power. At any given point in time, that'll combine with your seasonality and your categorical impacts, et cetera. But the marketing calendar actions are going to drive it, the product launches that are planned, the promotions that are planned, et cetera.

And so that's why starting with the spending power model is the most important. Spot. And the thing that it illustrates is that at different months in the year, your spend should be approached very uniquely for, for that time period because there's a unique spending power curve that exists for your brand at that point in time.

That just cannot be captured when you use a fixed budget, an A to S ratio. An MER ratio because what you're going to do is you're gonna overspend some months relative to their opportunity and you are going to underspend some months relative to their opportunity. By approaching it in that way, you need a model for determining your budget.

That accounts for seasonality, historical impacts, but roots. And identifying how the marketing calendar understands impacts each of those, each of those months, and then gives variable recommendation in terms of the budget allocation for that time period.

[00:11:13] Richard Gaffin: So, so real quick on the on what we're looking at here,

[00:11:16] Luke Austin: Yep.

[00:11:17] Richard Gaffin: model, the, what is, what is accounting for the variability between months? Now, you'd already mentioned seasonality being part of it, but it's, so when, when you're, let's say this, we're looking at 20, 20 fives. Model, but let's say you're constructing it for 2026, may of 2025 had a, what we're looking at here is like, what is that?

A 5.9 6:00 AM ER. Considerably more spending power, let's say more potential contribution margin than the other months or, or than at least the previous four. And so when you're making the building forecasting model for 2026, what factors So. And, and maybe you're predicting that May is going to have a similar a MER 5.96.

What that's taken into account is May is a good month generally for some sort of seasonality purpose for this brand, but is it also there was a set of actions taken in the previous May that if taken again, might result in the same result.

[00:12:11] Luke Austin: Yeah, so the, the context around the, the marketing calendar is important in this case and what I'm. What I am sort of asserting in this conversation is the, is sort of to invert that where we, yes, we know May is a stronger month relative to January. And I think like in the forecasting process generally most folks for most brands would have a sense of that for each month.

Like, oh yeah, may May's May's a little better. Like, we can usually spend a little more right, like that, that sense of the, the variation in the time period Now. Why is made better? So for this brand there's, you have seasonal impacts Yes. That happen based on the buying behavior. But this isn't, this brand isn't very seasonal in terms of like selling ski and snowboard gear, right?

Where it's like people are buying it in the winter. There's there, there's less variability in the seasonality. It's more related to the marketing calendar actions that just happen to come up in those seasonal periods. So may you have Mother's Day going on and this brand being focused on home home goods and home decor naturally that's a moment for them to lead into.

And so they have marketing moments that they've built around that seasonal moment, but the marketing calendar actions are really like riding the wave. They're like, they're the thing that's propelling this forward. Rather than it just being, oh, ma is a good month for the brand outside of, you know, the marketing calendar actions that, that impact that impact the outcome as a result.

[00:13:34] Richard Gaffin: So it's not just that. So, I guess is what you're saying, it's not just that Mother's Day lands in May, it's that the set of actions taken around Mother's Day were significant or, or is it, is that, I mean, I guess, is that the kind of thing that you're talking about? There happens to be a moment that makes sense and then it was taken advantage of and that's why it had such an outside impact.

[00:13:52] Luke Austin: Yes. That's, that's, that's, that's right. And in 2026, if we're not planning to do a big moment around Mother's Day, then may will be different than, than May in this case. Right. Like the marketing, the, it's not gonna, the, the higher efficiency in May is not going, going to continue outside of the marketing calendar, actions that inform it, but the most, the most important piece in the spinning power model.

And then I think as we move into the. A VIN effect model and look at the dailies. It'll tie this together as well. But the most important piece with the spinning power model is that it helps us to quantify how much better is May than June. Right. Specifically based on those historical factors, rather than an assumption around, yeah, we know May is better than June historically, and we can spend more, but how much more?

Right. And so in our spending power model, if we're going for. If we're going to the max contribution margin scenario in May, the spending power model is gonna recommend spending 280 5K to drive 1.6 million in new customer revenue. That same optimization In January. The recommendation is going to be to spend 160 2K for 800 K in new customer revenue.

And so we're able to, we're able to tease out like. So May, should we spend 20% more than January or 30 or 40, like what's the right point on the curve? And this helps us to quantify what that point is based on the changes in the spending power that exist on a monthly basis.

[00:15:17] Richard Gaffin: Yeah. Okay, cool. Let's then let's pop over to the event effect model and, and dig into a little bit of how the spend, spending power model then kind of like cascades or plays into this, and then how you go from there.

[00:15:28] Luke Austin: Yeah, so, what we have up here is the marketing calendar that's integrated into stat that we build out based on historical marketing moments and then plan out future marketing moments. And what's and then the email SMS that we have planned against each of these days if there are some. And then the novel thing here is that we have tags associated with each event.

That's in the marketing calendar. So rather than, than these just living as individual marketing moments in their own silo, right? We have the, we have Black Friday, 2025, and we have product launch, see that happen in August of 2025. What we do is we categorize each of these historical events by the event type that they were and so.

For all historical product launches, whether that be product launch C in August, or product launch B in in June those all get tagged as product launch type events. And then for our promotional events, labor Day or 4th of July or whatever they be, they, they don't live in isolation. They get tagged as promotional events.

And then on down the line, we have tags for influencer drops. For VIP early access drops and then each of our sort of major days as well, black Friday, Saturday, Monday, historically Labor Day, mother's Day. And so we can, we categorize the historical events into buckets around the event types that they are, and what that does is feeds into our event effect model, which helps us to quantify exactly how much impact that future event is going to have based on the aggregate of the historical events of that same type.

Right. So, it, for this brand as an example there are 21 historical promotion events that we have tagged in the event effect model. So we've built, we, we build over time a sample size of event types for promotions, product launches, et cetera, that help the event effect model give us even better recommendations in the future of what those, those same events are going to do.

So we have 21. Historical event effect types and then what we look at after tagging those events. The model helps us to understand the impact of that specific event. Type on two specific areas for the business. One is the impact on our A MER or new customer acquisition efficiency, so for promotional promotional events, what is the impact of that event?

Type on our A MER for the time period that we are running that event we all know is that different events have a different impact in terms of how much we can spend into those moments and how much they improve our efficiency. And typically if you have. If you have a a sale or product launch, right?

Like, you know, you can spend into that moment to some extent, but by how much? Right? And so this helps us to quantify the impact and the second metric. The second area that the event effect model helps us to understand is what is the impact of that event type. On our, the returning customer revenue related to new customer revenue.

'cause what we all also know is that there's some impact on increased returning customer revenue or repeat percentage on days when you have a product launch as, as an example, versus an evergreen day. So when you have an a marketing moment that's planned into the future, where you're trying to understand is.

How much more can I spend into that moment while holding my level of expected efficiency? And then what is the increase going to be on my returning customer revenue relative to a normal sort of baseline day when I do that launch as well? And that's really what we're after in helping to understand.

What is the revenue expectation that we have for each of these moments? And so the event effect model, we tag, we integrate the marketing calendar, tag, the historical events, and in the future events we have planned that share a similar tag as the historical events. Then we can model the behavior of those events and the impact on the a MR efficiency as well as the returning customer revenue efficiency for each day that that specific event is running.

[00:19:22] Richard Gaffin: So the idea here is that like if you're, if the let's say the forecast for 2026 is predicting a 5.9 6:00 AM ER, whatever in, in this upcoming May,

[00:19:33] Luke Austin: Yep.

[00:19:34] Richard Gaffin: essentially what you have to do then is go into the event effect model and make sure that the. There's enough events to actually bring that to fruition.

Right? So you would go in here and sort of understand like, actually we need to do these, you know, dozen things or whatever over the course of the month of May in order to make that happen. And then presumably if you wanted to beat the model, and let's say that that's something Yeah. You, you decided you wanted to, I don't know, grow X amount year over year in May, you would have to add more.

Events. And it's in some senses as simple as that. If you do more things, more things will happen. If you do fewer things, fewer things will happen. But yeah. Does that, is that roughly accurate?

[00:20:11] Luke Austin: Yes, that's right. And there's going to be, in the forecasting process we land on, so the spending power model. So it's the overall expectation. And in that process, typically what we're doing is not just saying, cool, here's the current trajectory of the business. Let's sustain that over the course of the next year and just try to hit it, right?

Like what we're all trying to do is improve the pacing of the current business. And so how we'll do that is in the new customer model, we have an input around percentage over model, so we can set an expectation and we work with. The operators of the, the customers that we work with, the decision makers around, what is the expectation?

Where do you, where do you need to get to for next year? That's the expectation that informs some percentage over model. So we're gonna inform some expectation around beating the baseline model. And then to, to your point, what that's going to do is it's going to cascade down well to be able to get to that level of outcome that's different than what the current baseline of the business suggests.

You are going to need additional marketing actions to, to bring that outcome about. Because if you just have the same actions that you did last year and approach it in the same way, you're gonna see that through the event effect model. You build up to the revenue forecast and you're gonna be short of that expectation.

Delta. And so you need to, you need to slate in additional marketing actions is gonna be the core thing that's going to drive the gap between between that. And this is where like, we're, we're going through a lot of these conversations right now related to 2026 forecasting and, and and do on a consistent basis where, many times the initial question, Hey, we were just having a conversation yesterday with, with brand where the initial sort of 2026 forecast a month ago was being worked through. We were getting close to a scenario. There were some. Meetings with the key leadership and the board and came back based on the underperformance of a couple other sales channels.

Did the, the expectation for the.com business was raised by a pretty meaningful amount. And so the sort of line of questioning and conversation initially became around how much more ad spend do you need to be able to bridge this additional revenue gap that we have? And the reality is that for most brands, you're already, they're already sitting in a place that's close to the level of.

Degradation on the spending power curve, right? Like you can just keep spending and expect a similar rate of efficiency. And in most cases, that that degradation drops off really precipitously. Where many brands are already at a place where the next incremental $10,000 of spend is actually at a negative contribution margin or a sub one incremental.

A MER, right? Like dollar in is gonna produce $0.60 of revenue out, right? Like we, we see that really consistently. And so pushing more ad spend to drive. 1,000,002, an additional revenue to bridge the gap. Like isn't, isn't going to be the thing that does it. What's going to get you there is net new marketing actions that you can actually tie an expectation to of their impact.

And then drive, drive the business forward in that way to bridge the gap to the expectation.

[00:23:09] Richard Gaffin: Yeah. Okay. Well, speaking of bridging the gap and also about, you sort of mentioned a scenario with this conversation with the sort of leadership team. Let's, let's put ourselves in the, the boots of A CFO. I don't know if CFOs wear boots, but in this case they do. Right? So let's put them, put ourselves in their boots and, think through, let's say you're in like a traditional scenario where as CFO you don't really have any control over calendar. What, what role ought you to play in this conversation? Like as a, as a CFO, who's kind of like awakens to this sort of way of thinking? Like how, how do you feel like your role in that conversation would play out?

[00:23:47] Luke Austin: So what I'd say is, I think to do, to do this, to do this, task to do this expectation well as it relates to forecasting, setting expectation and tracking against the actualization of the business against it. To do this well necessitates a, a deep understanding and connection and ability to have input in relation to the marketing calendar and actions in the same way that to be a really effective.

Head of growth or leader of the marketing department or even media buyer, necessitates a deep understanding of the financials of the business. He goes both ways, right? So that, that is sort of like the system that we've built and the workflow, I think helps to enable that connection, but requires a deep understanding and an impact from both sides on the finance team to the marketing calendar and the actions going on, on the marketing side.

The marketing team to the finance side as well, and to speak a similar language in that way. So what I'd say is like that, that should be the expectation of whoever's involved in this process, is that there's a deep understanding of both the financial and the marketing impacts as they both have substantial impact on what the outcome of the business is going to be.

Now, what I would say connected to that is, is the entry point. And I think the most helpful, sort of the, yeah, the most helpful sort of entry point as it relates to this is being able to track the actual impact of marketing actions related to the forecast outcome and better understand over time if they're leading to the goal or not.

So what I mean by that is we've talked a lot about sort of the planning, the planning process, but our, our perspective on forecasting is it's more of an exercise in execution than it is in planning. Right. The planning is helpful because it sets the expectation for the business. It's not helpful 'cause it sets, like, we're not, we're not trying to predict what the business is gonna do five months from now, right?

We're, we're trying to set an expectation for what needs to happen to be able to get to the business goal, to be able to get to the objective set. And so the execution of it. It is the, is the most important part. Once the planning has been, has been done, and so what we have up here. As another visual that we'll talk through is the calendar report and status.

The calendar report and stat is once the planning process is done, we've locked in the spinning power model. We've done the event effect model, we've locked in dailies. There's other points of this piece of this process that we just haven't talked through, right? Related to returning customer model and.

The pulling in the cogs and the p and l level and the mm m setting, the budget allocation. But once the planning process is done related to these pieces, what we have is daily targets for every single metric for across revenue, contribution margin, new customer revenue, returning customer revenue, and then down for the marketing channels as well.

Facebook, Google, down the line. And what we can see is one, our tracking, our, our pacing against each of the targets for each of these for each of these pieces, which, which is helpful. But the the additional layer is the marketing actions that were taken each of those days and what the impact was on the financial outcome.

And this is the other piece that we see missing. Outside of the planning process being disconnected, the executional process being very disconnected, where there's a daily distro that comes out from the finance director that gets sent to everyone that's, here's our revenue yesterday, here's the spend and here's the ACOs right against the goal, and that's happening each day.

But then the marketing team is living in whatever their analytics platform is or looking at Meta meta's ad dashboard and looking at the actual span and ROAS for the day. And there's just no connection against like, are these how, if we missed yesterday or if we are ahead, what was the thing that actually led to that so that we can better understand the expectation is set in the future around these things.

In the calendar report, what we have up here is daily targets for each of these metrics, and then every single day of the month pulling in through the APIs from each of these plat, from each of the marketing platforms directly. So Facebook, Google, email, and then our marketing calendar that we have integrated.

We can see for each of these days what the marketing moment was, what the Facebook campaigns that were launched were, what the emails were that were sent out. The adjustments on Google, and we can see those actions that were taken and then what the actual outcome for the business was. That helps to uncover where we may have, where we may have missed in that way.

And so in this example, for this brand. On November 20th we beat the revenue goal pretty substantially. And and so you can sort of see like November 20th versus the expectation was a really strong, was a really strong day. And the main, the main rea reason that happened is the early access offer that went out prior to Black Friday, cyber Monday.

Was drove more revenue than expectation. The marketing calendar had more impact. The email drove more revenue than expectation. And so that is what led to the increase that day in terms of the revenue against goal. And then we paced really close to the revenue expectation over the coming five days up until.

Up until Black Friday. And then what you see on Black Friday is actually a a big miss to the revenue goal relative to the expectation. So you see revenue goal and then the actual revenue against that day being lower. And you can see that that day. In terms of the, the metrics and, and what we did, we landed under in revenue, landed under in spend.

Returning customer and new customer revenue were both beyond expectation by some extent, but the Black Friday offer and the strategy orient oriented with that day did not drive the outcome that we were, we were expecting. The email underperformed against expectation, and so we can see that the early access marketing actions that we took led to a higher revenue day.

That, that seemed to pull some revenue forward from Black Friday. But what this did is illustrate to us, based on the marketing actions we had taken, is we actually needed more, more marketing actions on Cyber Monday than we previously had planned to make up the gap. The email was underperforming against expectation.

We weren't seeing as much ad as much ad spend efficiency. And so what that necessitates is more campaigns and creatives on meta. And then additional emails with larger with larger broader lists audience segmentation than we previous had previously had planned on several Monday. We need to open, open things up to drive more volume because we saw the impact of those actions on the day of Black Friday relative to what the expectation was.

And so this is what helps us to connect the dots between, okay, we missed the target. Why. Okay. We can see it show up in the metrics but then we can also see where the marketing calendar actions had the intended impact or not, and then what that necessitates for the coming days or the coming month in terms of incremental marketing actions to bridge the gap between where we're pacing against the expectation.

[00:30:44] Richard Gaffin: Yeah, no, it's, it's interesting looking at that. You could pull back up again real quick. The so the purple line here for for those of who are watching is the the expectation line Correct. And then the gold one is actuals. So, what, what this is like reflecting to me anyway, is that there was, there was no expectation built in that on November 20th there would some be some kind of early access thing.

That would crush. Right? And then on the expectation was that on Black Friday, that's where all kind of the revenue would be realized. What we're seeing is the impact of action because on November 20th that spike is not just something random like Oh. Something happened on November 20. What was it? It was actually, we did something and made something happen, and that actually had a trickle down effect to Black Friday, which actually pulled revenue down.

Presumably it pulled revenue forward into November 20th, but then that necessitates doing something on Cyber Monday that'll fix the problem. So anyway, all that to say, it's like these, these lines or the gap between these lines is like the gap is filled in by, by a set of actions. It's not. Random. It's not like predicting the weather or something like that, which again, is something we harp on a lot.

But anyway, it's interesting. The, the value of this is to see that clearly reflected in, in data,

[00:31:51] Luke Austin: Yeah. And, and there's, I think a really good illustration of, of that point is related to the email, send calendar and cadence where there's, every brand, especially coming on Black Friday, cyber Monday, like the conversations that we have around how many emails do I need to send, how many emails is too many to send?

How wide can the segmentation and the audience size be to be for those days? Or how, or what is the risk and sort of trade off as it relates to the unsubscribed rates and the deliverability? I might like the, it's a there's not a consistent point of view as it relates to that and what this does for us.

Is taking the example of that Black Friday day marketing calendar actions are gonna have the, I have the most impact on and variability in your revenue. And particularly for promotional days like this. Sending more emails is kind of the lever you have left, right? As it relates to marketing actions.

You can change your offer, you can drive some more ad spend. Those are gonna have impacts, but. Send more emails is, is sort of the lever that exists there. So we had a plan for Black Friday that took into account what we believe to be the best approach based on historical performance balancing sort of protecting deliverability and, and the right segmentation and protecting against future unsubscribed rates.

And so we had a, we had a plan that we felt it was well balanced that took, took those things into account. Now was the plan, was that a good plan? And what this would say is. No, because we didn't hit the revenue target. So what that necessitates is the plan for Cyber Monday. We've gotta go wider. Now, we don't have optionality as it relates to protecting some of the future deliverability concerns, right?

We have to lean into the revenue obligation. And so this, this then becomes the scoreboard for all these things it relates to. Okay. Maybe we can tighten up our list a little bit. Maybe we can, you know, maybe we can do. 365 day purchasers and, you know, highly engaged customers instead of all list, all time with some exclusions, let's start there.

But then we can measure the actual impact of that, of the marketing actions. If they didn't produce the result, then that gives us the answer we need on what strategy we need to deploy in the future to bridge the, the remainder of the gap.

[00:33:58] Richard Gaffin: Yeah. Makes sense. Alright, well I think we're at time here, so I'll, I'll, we'll, we can go ahead and and wrap things up. But one thing I'll say is like, if, if you guys wanna dig deeper into this and kind of get a sense of how we do this in a little bit more of a hands-on way, the e-commerce CFO summit, it's coming up next Wednesday.

December the 17th, 10:00 AM to 11:00 AM Pacific time Now, we'll have a link of the show notes to the signup page for that. And then it will also be on our homepage, common thread code.com. Scroll down to sort of like just past the fold and you'll see the the signup link there. So check it out, all you CFOs and anybody else who's interested.

We would love to see you there. But anyway, until next time, take care. Thanks for joining us, Luke. And bye.