Understanding Seasonality of Media Buying by Month and Day

[00:00:00] Luke Austin: Every brand has a really distinct day of week effect.

That's a combination of those factors. I one of the best examples to illustrate this point is, we've worked with a golf apparel brand that many of us would know. They could spend. two to two and a half times as much media spend over the course of Saturday and Sunday that they could compare to a Tuesday and a Wednesday. And , there's all sorts of factors that play into this, but some of the , notable ones would be on the weekend. Folks are golfing more, there's golf on tv. just, it's just more top of mind.

And so this is, this is really typical and what I'll say is. What this tends to illustrate, similar to the spending power model, is that brands naturally have this sort of wave of opportunity that this illustrates certain days of the week, certain months of the year in which they can realize so much more demand so much more volume at their efficiency expectation. And it begs a question, okay, should we. after the weakest days or the weakest months and try to improve those. Or should we ride the wave of the biggest months and the biggest days of the week? Right. And. Outside of the like throwaway answer of do both,

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Perks they actually care about and smart segmentation that inspires action fueling outcomes like RMS Beauty's 66% boost in repeat purchase rate. And third, love's 56% lift in revenue per user Interested in learning more yacht PO is offering 10% off annual reviews and loyalty plans through September. Just mentioned common Thread Collective on your demo.

[00:02:41] Richard Gaffin: Hey folks. Welcome to the Ecommerce Playbook Podcast. I'm your host, Richard Gaffin, Director of Digital Product Strategy here at Common Thread Collective, and I'm joined today by our VP of Ecommerce Growth Strategy here at Common Thread Collective. Mr. Luke Austin, our war reporter, my my co-star on the DTC Hotline.

Luke, what's going on, man?

[00:03:00] Luke Austin: I'm doing great. Had a very eventful wedding weekend. My oldest sister got, got hitched as it were, and,

[00:03:07] Richard Gaffin: Wow.

[00:03:07] Luke Austin: it was, had two kids up, four hour drive, all packed in an Airbnb. Couple nights, 2-year-old, got car sick on the way back, threw

[00:03:17] Richard Gaffin: Nice.

[00:03:17] Luke Austin: seat. So yeah, it was like, it was, it was everything you'd hoped for in a, in a,

[00:03:22] Richard Gaffin: Of course.

[00:03:23] Luke Austin: in a road trip wedding weekend.

[00:03:24] Richard Gaffin: Yeah. Well, as I understand it, between all that you had the time to really like, cut a rug or whatever on the dance floor, huh?

[00:03:30] Luke Austin: Yeah, allegedly, I, I was not informed anything was being recorded, I I, I, I, I won't admit to anything.

[00:03:38] Richard Gaffin: All right. Yeah. Well, the evidence is in Slack, so I can see it. Our listeners can. All right folks. Well, today what we wanna talk about with Luke is continuing our series that we've been on for a little while here about our spend and a MR models. Just real quick, if you haven't heard already, we are giving those away to free, giving those away for free to select.

Eight and nine figure brands. Basically what this provides for you is kind of a breakdown of, I believe it's the next 12 months, right, Luke basically forecasting out using our modeling kind of process exactly what we think is going to happen on a day-to-day basis over the next 12 months. So if you want us to build something like that for you for free.

Like we said last week, there's no reason not to do it. So, hit us up@commentthreadco.com. Hit that high risk button. Let us know you wanna chat. We would love to talk to you now, specific today or rather specifically today what we wanna talk about is something called, or, or rather how this, the forecasting model and how our general system here at Common Thread collective.

Helps you think through seasonality in a much, much, much more precise way other than just, Hey, it's summer, things are down. Hey, it's holiday, things are up. Hey, it's Father's Day. That's a gifting moment. Whatever. This goes far beyond that. So Luke has a few examples here that we're going to dig into that demonstrates both how granular seasonality can get and how much or how much opportunity is revealed once you understand that, and then how different different businesses can be.

Vis-a-vis seasonality and how these forecasts reveal that to you. So Luke, let's let's turn it over to you. Why don't you kind of walk us through, just give us some definitions here about day of the week effect, and just overall some thoughts about seasonality.

[00:05:16] Luke Austin: Great. Yeah. So we're gonna look at three different brand examples here today. We're gonna pull up their spending year models and their day week effects and talk through. How this comes to light for each of them, specifically three different verticals and categories that really illustrate how different the output is and, and should be when you take all these things into consideration.

And, and what you're gonna hear from us over the course of this month as we talk about the span day mural model, which is sort of the core first starting point to our profit system here at CTC is we're gonna be talking about different aspects of the spend a ER model, different inputs and considerations that the spend a ER model has has in it.

We'll be talking about things like. The how we account for cost of goods and LTV and how those impact the, the long term forecasting. We'll be talking about seasonality which we're gonna dive in today. And specifically under the lens of seasonality, we're gonna look at. How different the optimal budget allocation should be over the course of October, november, December, for, for your brand through the lens of these three brands.

So just over the course of this Q4, again, we're building these spin MER models and the forecast in our profit system through Q4 of this year, and 20 and all of 2026. So. 15 month forecast at this point into this year, through the end of next year, but we're gonna look at October, November, December, these three brands, how disparate the budget allocation recommendation is between just those three months and then within those brands. How we take the monthly budget allocation and degrade that into, or decompose that rather into daily targets through the lens of the day, a week effect being one of the inputs to get there. And how your spending power and, and the efficiency of your spend varies substantially on a daily basis.

And every brand has a different day of week effect inherent to them.

[00:07:00] Richard Gaffin: Right. Okay. Well let's then, let's jump right into an example here. Let's, let's go into our first brand. So tell us a little bit about this brand. What's the vertical, what's the business? And then kind of dig into the model.

[00:07:10] Luke Austin: Great. So for those watching, we'll have the spin a UR model examples pulled up. For those listening, we'll be chatting through them. And the first example that we have here is fitness apparel brand. And what we're looking at first here is the recommended optimal budget allocation for October, November, and December. And how it changes between those months. So as a refresher we've talked about this in some of our recent, recent episodes, but our spin a ER model has three different optimal budget allocation selections that we build outta the box so we can optimize for. Maximizing contribution margin from first time customers in month one, we can optimize for maximizing contribution margin over defined LTV period, 60 day, 90 day a year. From our first customers. First time customers, or maximizing. First time customer revenue at break, even contribution margin in month one. So those are the, the, the different points that we look at. And what we, what we do is in the building of the spend a ER model, which is an ensemble model of a number of models that looks at seasonality, degradation of historical efficiency Google search trends, categorical and competitive impacts.

We take all those ensemble model to help us understand what the degradation curve of efficiency looks like for every. For every month for you. And so what we can see here is we build this out. We can optimize for those three different selections, maximize contribution margin, maximize lifetime cm, maximize revenue, and then we can also see plotted every single point of spend, every $5,000, every $10,000. What is the incremental new customer revenue that I get with that incremental? Spend and what is the trade off in terms of the contribution margin to help us understand the trade off between these business decisions and really quantify the principle that we all know, which is my CAC increases as spend increases.

Yes. But how much specifically for your brand and your curve, your spending power is unique. Everyone has a very unique. Model. And so what that, that does for this brand example, again, fitness apparel is what we're looking at it. We're looking at October, November, and December. And we'll look at the maximize contribution margin scenario.

First, just to illustrate how this comes to life. So in October, if we were trying to maximize contribution margin, the recommendation would be to spend $170,000 in October to spend $488,000, and then in December to spend $341,000. Now let's compare that to maximize first time customer revenue at breakeven cm within each of those months. To look at how different that looks. At October, maximize revenue optimization, the recommendation is to spend 680 5K. November. Recommendation is to spend 2 million, December 1.32 million. So the, the difference between these spend scenarios, November's recommendation is to spend three times as much, three 3.2 x as much as a as October. And December is about two x the, the budget allocation recommendation compared to December. And what I'll say is conversations around budget allocation, forecasting and targets that we have on a daily basis with the customers that we. to work with is the output of what the, around this time period, specifically in Q4 and November and December, around Black Friday, cyber Monday, what the budget allocation is currently set to as we have these conversations around the span, a ER model. Is, is, is all over the place. Like we, we see this being really close where November the current plan has spent about, you know, 1.5 x, maybe two x as much as October. And that feels really aggressive. Like, oh, it, yeah, it's two x as much as November compared to October. So we're really scaling up. We see it go the other direction where. We're gonna kind of keep it simple, kind of keep it more flat and we're just gonna realize a bunch more efficiency in November and kind of keep it you know, it's, it's gonna be pretty close to October's budget allocation, slightly higher, but we're really going for efficiency and everywhere in between.

And so again, what, what the spending power model helps us to do is just helps us to quantify what the right range should be to help quantify those. What we're sort of expressing as. What we wanna do is realize more efficiency in November. Okay, well how much, what's the trade off? And then top line revenue versus the cut contribution margin. Or we wanna be as aggressive as we can be. Okay, based on your discount rate in that month, based on the cost of goods impact, how aggressive can you be in a month, like November when you have those at play? Versus some of the other months surrounding it. And you can see for, in this specific example again, the budget allocation for November is three x as high as October for the same business objective.

[00:11:59] Richard Gaffin: Right. Yeah. Yeah. So I was gonna say like in terms of, of how this, let's say, reveals something about the seasonality for this brand. So the, the fact that you might, that obviously November and December are up months is, that's not a huge surprise. That's kinda the way for a lot of brands. One thing that seems to pop out here is that October's significant or is a down month by quite a bit, which wouldn't necessarily, is that something that necessarily makes sense for this brand?

Is that unique to this brand? Yeah, well maybe dig into that a little bit.

[00:12:28] Luke Austin: Yeah, yeah, yeah. Ex exactly. And I think the, the October allocation is, is. It's pretty similar. Yeah. Down, down from September more in line with what, with what August looked like. But is is sort of September's, September's higher, pulling back into October and then ramping back up into November and, and December.

And for this brand there, there is there is a gifting angle based on the product and so that's why we're also seeing December being. Pretty, pretty high in terms of the recommended budget allocation lower than Novembers. But still a good amount of budget that we're pushing in December relative to time periods earlier in the year because the gifting angle provides the additional volume and efficiency during that time period that we can, that we can lean into.

[00:13:14] Richard Gaffin: Gotcha. Okay. Yeah, let's let's, let's keep rolling here.

[00:13:17] Luke Austin: So the, the other piece we'll look at in, in addition to the monthly budget allocation with the spend spending power model is day of week effect. Day of week effect allows us to understand based on the customer behavior for the brand, specifically the demographic breakdown the product purchase behavior, and then as well as the, the, so sort of the marketing. Comms on a, on a weekly basis for that brand, what are the strongest days of the week and what are the weakest days of the week? And when should we expect to realize higher spend volume, higher revenue volume as a result? And when should we expect our, our, our lower volume days? Every brand has a really distinct day of week effect.

That's a combination of those factors. I think gr one of the best examples to illustrate this point is, we've worked with a golf brand, golf apparel brand that many of us would know. They could spend. two to two and a half times as much media spend over the course of Saturday and Sunday that they could compare to a Tuesday and a Wednesday. And there's, there's all sorts of factors that play into this, but some of the notable, notable ones would be on the weekend. Folks are golfing more, there's golf on tv. just, it's just more top of mind.

[00:14:31] Richard Gaffin: Mm-hmm.

[00:14:31] Luke Austin: and people are looking at polos that, that other, that their friends are wearing, thinking about getting a new polo for maybe next week's round, whatever it might be. And then. The, the brands that understand this dynamic also play into that, right? So they'll they'll, they'll push into more email and SMS comms, they'll think about their product launches and new marketing drops around those moments. And so the Saturdays and Sundays actually then become bigger relative to the Wednesdays and Thursdays over time. And and that's an important, that, that's an important one to illustrate because it's a really, there's a really substantial dynamic in terms of the spin, in terms of the day week effect for, for that brand. Everyone, everyone has it. So this is the day, week effect that we're showing for the same fitness apparel brand that we just talked through in the spending power model. And what you can see is Fridays are by far their best day,

[00:15:20] Richard Gaffin: Hmm.

[00:15:21] Luke Austin: above the mean. So what we do here is we look at each of the days of the week and we have positive percentile, values and negative percentile values that, that reference against the average, against the mean for that time period. What, how how much higher or lower is that day in terms of. terms of the volume, volume that we can expect against our efficiency expectation. So, Mondays are the worst day for this brand. Negative 14%, or, sorry, the second worst day Tuesdays are negative 17%, but Mondays and Tuesdays are by far the, the two worst days of the week. Then it flattens out Wednesday and Thursday. Wednesday minus 3%, Thursday plus 2%. So really close to the average. Fridays are the, are the best days, 15% Saturdays are pretty strong, and Sundays at seven to 9%. And then again, it drops off on Monday. And you can sort of see the day, week. We have a, we have a, a chart here sort of visualizing what those numbers look like, but on a Friday plus 15% above the mean versus a month versus a Tuesday, which is 17% below the mean. that, what that reference is, is in either direction. If you have 15% above and 17% below, there can be as big of a, as big of a swing as 30, 32% over the course of those, those periods, right? In terms of the amount of ad spend that you should expect to get through at the same efficiency expectation in terms of the revenue drop volume driven, especially new customer revenue volume, right, which is gonna come primarily from your paid media and acquisition spin. That's a, that's a wild that's a wild swing. That's a really large outcome. 33% difference in terms of the volume that you can expect between those, those two days. And, and this is outcome, how it comes to life in, in the day of week effect.

[00:16:58] Richard Gaffin: Mm-hmm.

[00:16:59] Luke Austin: is another way of illustrating, I think like the spending power model that we looked at.

November's budget allocation recommendation was a little over three x as highs coming out of October's. seeing even on a daily basis Fridays versus a Tuesday, you have a 33% swing in terms of the, the volume that you can, that you can expect which is these, the optimal budget recommendation and the opportunity available for brands tends to be, there tends to be a much wider range of outcomes than than, than typically comes into conversation around, around these things. And what it allows us to do is to better plan against what the business outcome is because we're, we're able to expand the range of outcomes, look at all of them plotted, and then have a conversation within that context around, okay, now based on everything we know, what do we want to go and accomplish together?

It it, it widens the band of the range of outcomes rather than having it more narrowly subset around, okay, what did we do last year? What's our sort of year over year? Okay. We're spending about 15% more year over year. So, okay, what if we did. It, it, it broadens what the, what the potential outcomes can look like.

[00:18:07] Speaker 2: Want more conversions, more repeat purchases. Of course you do. The trick is picking a provider that helps you do more with less yapo reviews help shoppers say yes faster with AI powered summaries and filters that answer their questions before they bounce. Driving real results like pers 25% jump in onsite conversions and yapo loyalty keeps those shoppers coming back with rewards that feel personal.

Perks they actually care about and smart segmentation that inspires action fueling outcomes like RMS Beauty's 66% boost in repeat purchase rate. And third, love's 56% lift in revenue per user Interested in learning more yacht PO is offering 10% off annual reviews and loyalty plans through September. Just mentioned common Thread Collective on your demo Want more conversions, more repeat purchases. Of course you do. The trick is picking a provider that helps you do more with less yapo reviews help shoppers say yes faster with AI powered summaries and filters that answer their questions before they bounce. Driving real results like pers 25% jump in onsite conversions and yapo loyalty keeps those shoppers coming back with rewards that feel personal.

Perks they actually care about and smart segmentation that inspires action fueling outcomes like RMS Beauty's 66% boost in repeat purchase rate. And third, love's 56% lift in revenue per user Interested in learning more yacht PO is offering 10% off annual reviews and loyalty plans through September. Just mentioned common Thread Collective on your demo. Want more conversions, more repeat purchases. Of course you do. The trick is picking a provider that helps you do more with less yapo reviews help shoppers say yes faster with AI powered summaries and filters that answer their questions before they bounce. Driving real results like pers 25% jump in onsite conversions and yapo loyalty keeps those shoppers coming back with rewards that feel personal.

Perks they actually care about and smart segmentation that inspires action fueling outcomes like RMS Beauty's 66% boost in repeat purchase rate. And third, love's 56% lift in revenue per user Interested in learning more yacht PO is offering 10% off annual reviews and loyalty plans through September. Just mentioned common Thread Collective on your demo..

[00:20:27] Richard Gaffin: Yeah, well, it creates a situation. This has been a theme over the last several episodes here. It creates a much more sort of precise clarity around the decisions that you have to make upcoming. So again, like you were mentioning, just sort of doing a general like year over year. Sort of analysis and then saying, well, we'll just do that, but a little better.

This gives you the opportunity to actually break down what's likely to happen based on historical data about your consumer's behavior over across the days of the week or whatever. So, how, so how co, I mean, it sounds like it is, but how common is this sort of like breadth of fluctuation across brands?

[00:21:01] Luke Austin: Yes. Yeah, it's, it's very, it's very common. We'll pull up a couple other day of week effect here is just while we're on the topic of this conversation to look at them. So, pulling up a second example here, which is gonna be a brand in the. Any the home home goods category and what we see for them is basically the opposite of what we just saw in the previous example.

So, previous example, fitness apparel company. Fridays were the strongest day. Tuesdays were the weakest days for this home Goods brand Fridays are the worst day,

[00:21:30] Richard Gaffin: Hm.

[00:21:31] Luke Austin: negative 10%. It gets the mean, and then the strongest days are Mondays and Sundays. And then it sort of tapers off between each of those, between each of those days.

But again, you can see Mondays are 12.58% above the the average outcome. And then Thursdays are minus 11%, Fridays minus 10%. So, the range between those is 23%, right between the high of a Monday versus the low of low of a Thursday. And it'll pull up a third example here. Just to drive the point home even further.

This is a personal care personal care brand. Their strongest day of the week is a Wednesday and followed by a

[00:22:11] Richard Gaffin: Hmm.

[00:22:12] Luke Austin: with their weakest days, Saturday and Sunday at minus 15% and minus 16%. So Wednesdays are plus 20% above the. And Sundays are almost minus 17%, minus 16.9%

[00:22:25] Richard Gaffin: Hmm.

[00:22:25] Luke Austin: below the mean outcome. So 37% swing from the high of a Wednesday to the low of a Sunday.

Which relative to the first brand we looked at they were having their best days over Friday and then into the weekend and the worst days sort of midweek which were much, much slower. And so this is, this is really typical and what I'll say is. What this tends to illustrate, similar to the spending power model, is that brands naturally have this sort of wave of opportunity that this illustrates certain days of the week, certain months of the year in which they can realize so much more demand so much more volume at their efficiency expectation. And it begs a question, okay, should we. after the weakest days or the weakest months and try to improve those. Or should we ride the wave of the biggest months and the biggest days of the week? Right. And. Outside of the like throwaway answer of do both,

[00:23:19] Richard Gaffin: Mm-hmm.

[00:23:20] Luke Austin: our, our perspective based on, based on what we have seen, is that you are going to realize more upside for the business by focusing on riding the waves

[00:23:28] Richard Gaffin: Right.

[00:23:29] Luke Austin: and amplifying the waves for the business that already exists.

And the, the, the example that we talked through previously of the Gar golf apparel brand, Sundays did not. that same day a week effect. When we started working with that brand, it was much lower. It's closer to this sort of a swing that we've been

[00:23:44] Richard Gaffin: Hmm.

[00:23:44] Luke Austin: at for these brands where it was maybe a 30% or, or maybe closer to a 50% swing between the low and the high of any given of, of any given day, What we saw is going into Saturdays and then peaking in Sundays. There's natural sort of gravity that exists in the consumer behavior. There's natural awareness that comes from people watching a bunch of golf and being out in the course as well, where we are actually going to intentionally lean into this consumer behavior by launching. Creatives more specific to this time period focusing on product launches. During those, we ran some weekend specific ad campaigns as well based on who was playing on golf and and, and just some unique moments around the consumer behavior and then really made sure that we are opening up. The spend and giving all the volume that we could at our efficiency expectation for those days. And this started just to like the, the waves of these weekend, one weekend and be like, oh, that wave is a little bigger than last Sunday. And the next weekend is like these waves just sort of kept piling on and. The Tuesday, Wednesday down days, they sort of, they sort of stayed the same. They didn't get worse.

[00:24:53] Richard Gaffin: Hmm.

[00:24:53] Luke Austin: weren't pulling a lot of demand. They weren't getting better. We weren't intentionally focusing there. They stayed the same Saturdays and Sundays compounded, and that helped to lift up the overall of the week.

Now there's a time and place for sure for let's go after the Tuesdays and Wednesdays. Think about ways to draw this down. But the, but the brand had a natural demand wave oriented around this, that this helps to identify where can we lean in to amplify the already existing momentum that exists for the business.

[00:25:21] Richard Gaffin: Yeah, that, no, that's good. Like the way I've heard it phrased is like this, this definitely seems like an illustrated illustration of the phenomenon of, of the sort of like a market forces being basically like trying to control them as like trying to control the sea or something, or the weather. It's like there's just ways that humans on aggregate behave, and what this allows you to do is identify those and then amplify what's already happening as opposed to.

Say doing something like, well, oh, because I don't know, in this case, Monday's down, we should make Mondays better. Somehow it's like that, that, that doesn't really matter because people buy these types of products on Wednesdays for whatever reason. Now, in terms of like how much variance is there between businesses within the same vertical, so what we've seen here is obviously a fitness apparel brand.

We've seen a home Goods brand. We've seen, and this is like a skincare wellness brand, right? So. They have radically different kind of curves across the, across the day of the week between the three of them, but between brands in the same space, what do we see?

[00:26:22] Luke Austin: Yeah. So we're able to look at industry industry breakdown

[00:26:28] Richard Gaffin: Mm-hmm.

[00:26:29] Luke Austin: For the day a week effect specifically. And so what we're doing here is I'm toggling between the store specific day a week effect versus the industry aggregate day a week effect for this

[00:26:40] Richard Gaffin: Interesting.

[00:26:41] Luke Austin: For the home goods, for the home goods brand. That, and the first thing you'll notice is that when you toggle to an industry level, just like with any aggregate metric. The, the slope becomes flatter. There's less, there's a less variance because the, the data points it, it regresses to the mean. Once you add in more of, more of that, and, and that makes sense because. Although the industry might share common traits, you could have in this example, a home good goods brand that, that caters to an older demographic versus younger demographic, right? You could have home good brands that gets a lot more distribution via TikTok versus one that is

[00:27:18] Richard Gaffin: Hmm.

[00:27:18] Luke Austin: focused on meta or Criteo or whatever it might be.

And so the distribution channels. Home, good home goods brands that currently they're marketing, they send an email a day every day of the week, and it's consistent that way. Versus one that's sort of started to notice this trend for themselves. And they started to amplify the waves like we're talking about, and they said two or three over the course of Saturday and Sunday, and they tone back midweek.

All those things play into it. So as we'd expect, the industry aggregate tends to, tends to shift down. Now we've noticed we've noticed some, some things in terms of looking at, you know, personal care. Skincare brands versus apparel and how there's sort of this natural, like personal care and skincare tends to have this sort of midweek Tuesday, Wednesday pickup.

But again, it's like, it, it really ties to what's the consumer and demographic that you are that you're going after. And then how much this brand has the brand played into this. But the, the long and short is. On an industry aggregate level the numbers become less disparate. And then when you start to drill down on a store specific level is when you start to see much much more interesting variants that highlights, oh wow, this is a month where we could push in a lot more.

Or This is the day that we could push in a lot more. And we should actually not, not try to think about right now making improving the losers, but making the winners even better.

[00:28:37] Richard Gaffin: Interesting. So let's talk real quick about like how the. Some examples of like how seasonality affects these brands. And stepping back to the kinda like the month, month over month view here. So obviously we had a, a quick look at our apparel brand in terms of sort of how they fluctuate into November.

They have a good December. Let's talk about some, how some of these other brands kind of, play out there.

[00:28:59] Luke Austin: Great. So we're looking here now at the Home Good Brands spending power model on a monthly basis. So we're looking at day of week effect, and now we're jumping back into the spin spending power model. It's the monthly optimal budget allocation. Looking at Max, max revenue. So. I'll just read through here September.

The max revenue recommendation is 4.8 million in spend. October 5 million in spend, so pretty close September to October. Interesting point to illustrate. October here is slightly higher than September in terms of the spending power that exists for the brand. The previous example we looked at, which is the fitness apparel. October was lower which is a, an aggregate tends to be the case just depending on the marketing calendar. What, what brands do, where do kind of a push in September or maybe around Labor Day, right? And then you pull back in October and then ramp back up in November. But this brand's interesting because October, their spending power is actually slightly stronger than September, so they can kind of wrap ramp up there. Then you have November at 13.3 million in spend. So we jump from October at 5 million to November at 13.3. December at 9.7 million. So down from November, but still easily their biggest second biggest month of the year, spending twice as much in December as they are planning to or, or as they have in any, in any historical month in in the year. So similar broader principles as the fitness apparel brand that we looked at. But relative to November, more spend being pushed into December because of the seasonality at play within this home, good category versus the fitness apparel. And then the other unique thing is October is actually slightly higher than September, rather than being dropped down in terms of the efficiency expectation and the spending power available in that month for them.

[00:30:46] Richard Gaffin: Hmm. What do you think the reason is there that, that the December for this brand is, is so significantly better than the fitness apparel brand, just given that they're both potentially giftable items. What's your, what's your analysis of like the behavior there?

[00:30:58] Luke Austin: Yeah. So, I think home goods are, are easier to gift than, than apparel.

[00:31:05] Richard Gaffin: Hmm.

[00:31:05] Luke Austin: is, is likely one of the reasons there. Don't have to figure out, don't have to figure out the size as much as even like the, the colorway of the, the shirt or the shoe. And you could buy someone a really nice. for their home over Christmas and New Year's.

So I think that, that, that plays into it. And then again, there's a thing, there's a thing here where like under underneath this data, there's a there's the qualitative data. There's a story that exists on why this exists for them and a brand of this size, right? So this brand is spending. Like 13 million in November, 9.7 million in December.

The previous brand, brand is spinning a good bit less than this is. Likely this brand has sort of realized more of these peaks for them and help to amplify the demand similar to the day a week effect. And so

[00:31:54] Richard Gaffin: Right.

[00:31:54] Luke Austin: the fascinating thing when we look at these, we build the spinning power model, build the day a week effect.

We look at what exists and then it highlights conversations around, okay. What have we already done to amplify gifting in December? And the answer to this question for this home good brand there might be a lot more depth to it than than the fitness apparel brand. May and maybe not. Maybe they both say, Hey, we haven't actually done much.

It's just sort of natural to us. Or maybe they both say, the past two years we've really focused on gifting In December to amplify what we expected was was a an existing wave for our brand during this time period.

[00:32:26] Richard Gaffin: That's interesting. Let's look at, okay, let's, let's go look at the third example then as, as a comp here, because I'm curious to see what their year over year looks like.

[00:32:34] Luke Austin: Yes. So, is the third brand. This is the personal care skincare category brand. And this spinning power model output is completely different than the first two brands that we will look at. So, recommendation so we're looking here at max lifetime contribution margin as the as the optimal budget allocation. In a side note for the three optimization selections, max cm, max revenue, and max lifetime cm. For any brands that have substantial LTV or consistent repeat purchase behavior like this personal care brand. Looking at max lifetime contribution margin is typically the optimal budget allocation and the starting point that, that we recommend, maximize revenue at break, even first time co contribution margin. Typically is not what we'd recommend unless the brand has a, a diverse distribution across sales channels and realizes some of the impact of this D two C media spend on their Amazon sales or other sales channels that, that, that it contributes to and look at their business whole more holistically in that way. And then. For a brand that has really little LTV impact whatsoever, the max lifetime contribution margin and max cm scenarios are gonna be basically the same. They're gonna be

[00:33:51] Richard Gaffin: Mm-hmm.

[00:33:51] Luke Austin: each other. So the, the simplest way of saying it is max lifetime CM is typically the right thing to do.

[00:33:58] Richard Gaffin: Right.

[00:33:59] Luke Austin: Outside of other considerations for broader distribution across sales channels cash flow considerations in the short term around needing to move inventory, inventory or generate cash flow, which is potentially gonna lead to needing to push more volume, even if it's at a lower cm in the short term. And then there's, there's all sorts of other. impacts as well where we don't wanna see new customer revenue declining year over year. So we're gonna push, we're gonna push more volume to make up that gap, even if it is going to be margin deteriorative in a short term and all, and all sorts of things that are, are novel to each business that need to be solved for.

But max lifetime, CM. Is is typically what is the optimal starting point for, for brands. And so for, for this one, max lifetime, CM is what we're looking for. Personal care brand in September 239 k recommended spend October 206 K, so slightly lower October. This is, that's sort of the general flow that we'll see is a little more efficient.

In October, relative September, November 289 K and December 3 0 9 K. So. November is not much different than Octo than September's budget allocation. September 239 K, November 2 89 K We're spending 50 grand more, but November is gonna be really similar to June. In terms of the in terms of the ad spend and pretty close to August as well, which is at 270 K, it's not a major moment for this brand around BFCM. It's personal care. It's very specific to what what an individual may or may not like for themselves. It's a risky, risky gifting item. And so you can kind of see like in November, December, the, the spin's pretty flat between those two months. 2 89 in November, 3 0 9 in December. Slightly more efficiency relative to the previous, the previous month, but, but not that much.

Like most

[00:35:47] Richard Gaffin: Hmm.

[00:35:47] Luke Austin: January, 2026 is gonna be the biggest, the highest spending month for this brand relative to any of these months. Whereas for the previous two brands we talked about, November is gonna be by far the highest spending month for them because of the industry that they sit in. And and how much of this lends to a giftable item and, and a promo item versus it being. A a, a personal care item that has a sort of consistent repeat behavior around it. And, and one other point to add on to that, which is LTV associated with these sort of promo period cohorts varies across the board as well, depending on how much brands lean into them, what the discounting, what the offer is on the front end. And so, the LTV can be lower for these brands percentage wise. Now, the trade off is you're gonna have much more efficient. Customer acquisition efficiency in, in a month like this, which is what we're seeing, show up here in the a ER, which, so I can do more in November. So, although the LTV might be lower for this cohort, the cost to acquire the customer in the cohort is really low as well.

And so it's actually going to be one of the most profitable cohorts. Some of brands may see, may see that be different where they can, where they can make up the LTV on the back end in a way. But the recommended spending power output for this brand. Completely different than the first two that

[00:37:06] Richard Gaffin: Right. Yeah, you can see it's clearly much, it tells a story of a brand that's, or, or a product rather, that's much more evergreen, essentially. I mean, that's also kind of what the day of the week effect shows. If their peak is on Wednesday, it's sort of like during people's work weeks, they sort of come to the hump day and they're like, oh my God, I need something to pick me up.

Whatever. Like there's, there's a certain the, the, the behavior around the product on an individual basis plays out into the aggregate and is revealed by these, by the models that we're building. So like, one thing

[00:37:34] Luke Austin: test.

[00:37:34] Richard Gaffin: I think is worth pointing out is that. If you're running, let's say a health and wellness brand or whatever, you may have some general sense that it's more of an evergreen product and because of New Year, new you, new Year's resolutions, whatever, January is gonna be strong.

But what this reveals is. Confirming whether or not that's true and then revealing specifically how your brand interacts with that set of behaviors. So you have some understanding of like where your actual opportunities are, and of course, have some clarity around what's actually going on which is, yeah.

Yet another way that this model like really clarifies what's going on with your business in a way that just simple, I don't know, year over year forecasting just can't even touch. Right. 

[00:38:13] Luke Austin: That to that, to that point, which is

[00:38:15] Richard Gaffin: Mm-hmm.

[00:38:33] Luke Austin: their customer. That's why they created

[00:38:35] Richard Gaffin: Mm-hmm.

[00:38:35] Luke Austin: and, and, and created the product. Our role in this is just helping to quantify all these things that I think most of us are realize and understand of. Yes, we know the spending power to grades at, we know that c increases at a higher level of spend.

We know that certain days of the week are better and worse than others,

[00:38:52] Richard Gaffin: Mm-hmm.

[00:38:52] Luke Austin: certain months of the year are better and worse than others. But by how much exactly is, is really what we're after here in helping to build these models to then give the insight to be able to make the decision against, okay, now we know what the trade off is between this day versus that day, versus this month versus that month.

We know what the trade off is and the opportunity cost, top line versus bottom. Where specifically do we want to set the budget allocation and index against the business opportunity? And now we have a way to quantify all the qualitative data points that, that we have against what the business is, who the consumer is, and how that lends to each of these factors that are, that are at play.

[00:39:30] Richard Gaffin: . We are giving these away for free. Right now, four select eight, nine figure brands. So if this is something that you think would be valuable for your business and I think it would, this is worth checking out.

Comment thread code.com. Hit that hire us button. Now, one thing I have neglected to ask of the last few episodes, which I should be asking more is if you're in a position, if you're not in a position, let's say right now two sign up for one of these free spend and a MER models, what is do you think is like the number one thing that you can do to.

Begin harnessing day of the week effects or, or this more granular seasonality.

[00:40:02] Luke Austin: The main thing that the, the models outline is is help helping to illustrate the difference in. The acquisition efficiency in these different time periods, specifically the, the A MER, which the spinning power model orients around. and so there is going to be a higher A MER expectation that you have in November, December relative to any of your other months of the year. It's probably not 10 to 20% higher than your historical months. It's probably. It's probably 30% higher, 40% higher. And that's just the average again, to this conversation could swing in, in any direction. But for many brands that those that time period that we're coming up here in Q4 represents a meaningful opportunity to either drive a lot more demand than you could in any other period, or to realize a lot more efficiency than you could in any, any other period. And so this is my non-answer of saying. you're, if you're on Shopify and a an a nine figure brand, we, we'd love to build out one of these models for you and have a have a conversation. And we're having a few of these conversations a week right now for brands that we're doing this with. And it's fascinating.

We, we love having these conversations 'cause we're able to bring, here's, here's. The model that we've built that have helped to assist in these conversations for us. We built this because of trying to solve for these problems and solving these conversations. Bring those to the table, help to talk through the output, get feedback on what the business objective is and the things that we need to account for in that, and then orient collaboratively around a plan together.

So just encourage anyone out there, if you're on Shopify, if you're an eight, nine figure brand, let us build a u free span, a ER model and have a conversation with you around what the output is because. I, if nothing else, it will broaden the range of possible outcomes for your brand in Q4 and then into 2026 in terms of how you're thinking about the levers that you have at your disposal to be able to drive that business outcome.

[00:41:54] Richard Gaffin: It's okay. Alright folks, well you heard the man check us out common thread code.com. Let us know that you're interested in a free spend, an A MER model. We'd love to build one for you. Alright until next time thanks Luke. Appreciate you joining us. Appreciate your expertise for everybody else out there.

We will see you next time. Take care.