We Tested Meta’s Incremental Attribution on $200K/Month: Here's Why We Turned It Off.
This episode is a tactical deep dive into two of the biggest measurement questions in DTC right now, capped off with Zach Stuck joining to break down how MarsMen sets ad budgets off forecasted recurring revenue.
The first half is a real client case study on Meta's Incremental Attribution (IA). Brad walks us through an apparel brand, with an AOV just over $100, strong organic and influencer traffic, and $150-200K per month in spend. A conversion lift study at the start of the year showed their one-day click ROAS almost perfectly matched their true incremental number, so 1DC became the account's benchmark. When they tested IA head-to-head against one-day click (each with its own holdout), IA looked terrible in week one, then dramatically better in weeks two and three, and the lift study combined with Triple Whale MTA data validated the win.
Then Meta changed its click attribution definition in March (only outbound clicks now count as clicks, with profile clicks and comments moved to "engaged"), and IA performance deviated hard from one-day click, Triple Whale, and product-level MER. IA also stopped responding to bid adjustments.
The takeaway: they reverted to one-day click, with a plan to retest IA in three to six months as Meta feeds the model more conversion lift data.
The second segment covers creative cohort spend analysis: breaking down each month's spend by the month the ads were launched. If most of May's spend is running through ads launched last year, your new creative isn't earning spend and something is broken in the pipeline. If nearly all spend is from ads launched in the last 30 days, you are over-reliant on a few recent outliers. Splitting the view by evergreen versus promo ads shows whether new creative is compounding month over month, which is what actually earns the right to scale spend. Zach adds Homestead's layer on top: tracking "breakthrough" ads ($2K spend in 7 days at target) versus scaled ads every month.
The final segment is Zach on how MarsMen scales: if you can forecast next month's recurring revenue, you can spend into that full amount, as long as your blended MER covers OPEX and your new-customer ROAS holds at target. He walks through the cash-flow traps (90-day subscriptions deferring revenue, promos shifting cohort behavior), MarsMen's rebuilt forecast model that applies retention curves at the individual customer level, daily new-customer targets by channel, and a reactivation play: offering discounted one-time purchases to list subscribers who never converted because they didn't want a subscription, which he estimates is worth hundreds of thousands per month.
Key Takeaways
Why Meta's Incremental Attribution passed a conversion lift test, only to then fall apart three weeks later.
How you can REALLY spend 100% of next month's forecasted MRR on ads this month, and when that math breaks.
The one thing that might be the real reason as to to why your account performance suddenly shifted after March 2026.
The percentage of this month's ad spend that should come from ads you launched this month, and what it means if the answer is almost none.
Is your "dead" email list quietly hiding hundreds of thousands in monthly revenue?
Why one-day click beats seven-day click for brands with heavy organic traffic, and how to know which one your account should trust.
What a "breakthrough ad" actually is and why your creative team should be measured on them every month.
Caution: How adding a 90-day subscription offer can put your cash flow at risk 60 days later.
Should subscription brands hold email, SMS, and organic to daily new-customer quotas the same way they do paid?
Is the creative volume game dead, or are the people abandoning it just measuring hit rates wrong?
Chapters
02:32 — Should you test Meta's Incremental Attribution in your account?
07:30 — How do you design a fair test of one-day click vs. Incremental Attribution?
11:32 — What did Meta's new click attribution definition change, and why did it break results?
14:02 — When should you turn Incremental Attribution off and go back to one-day click?
26:30 — How do you run a creative cohort analysis to see if your new ads are earning spend?
35:31 — How does a subscription brand use next month's MRR to set this month's ad budget?
46:02 — How do you turn a "dead" email list into six figures of monthly revenue?
This episode of the Scalability School podcast is sponsored by NorthBeam and they just launched Northbeam Incrementality. Northbeam Incrementality gives you easy, automated, self-service incrementality tests, while protecting you from the major mistakes so many people make while running incrementality tests. Your MTA handles the daily tactics, your MMM guides the long-term planning, and Incrementality provides the causal truth. It’s a closed loop that allows you to scale what works and cut what doesn't. Right now when you head over to www.northbeam.io/incrementality, they’re offering Scalability School listeners 50% off unlimited tests for a year when you join. Just tell them we sent you!
To connect with Andrew Foxwell send an email Andrew@foxwelldigital.com
To connect with Brad Ploch send him a DM at https://x.com/brad_ploch
To connect with Zach Stuck send him a DM at https://x.com/zachmstuck
Learn more about the Foxwell Founders Community at https://foxwellfounders.com
Learn more about the The Hive Haus Creators Community at http://HiveHausUGC.com
Full Transcript
(00:00) Because just due to the nature of how one day click and incremental work, kind of like we said earlier, one day click is only giving credit to people who click within 24 hours. By default, it is substantially more conservative than incremental attribution. So we don't want to be misled by one day click.
(00:17) What should we do? That third data point that I'm alluding to is split it down to evergreen and promo ads. It still ended up being the case that evergreen consistently is stacking up month over month. And that's what compounds, right? It's like if you can make more ads every single month that earns spend in the ad account, then that's what compounds over time and allows you to increase spend.
(00:35) I think a lot of brands actually kind of forget about that. They're like, hey, they didn't buy. We did all these like whatever flows to get them to maybe get a bigger discount over time. But then there was probably one reason why they didn't buy. So talk to that customer about that one reason why they didn't buy and give them a strong discount to still close.
(00:51) Because like it's very likely better off when you still have contribution margin on that customer to be gained if you just give them even a slightly bigger discount to like bring in those dollars versus them just sitting on an email and SMS list. And now let's take a listen to the Scalability School podcast.
(01:14) Welcome back to another episode of the Scalability School podcast. Andrew is allowing me to have the absolute honor of introducing the podcast, opening it up for reasons that he will describe shortly. He sounds beautiful, but he's going to let me open this sucker up. We have a couple of topics to jump into today.
(01:32) We're going to talk about incremental attribution. We're going to talk about creative cohorts. And if we are blessed and lucky, Zach Stuck will be joining us for the back half of this. We're going to talk about scaling ads, man. It's going to be dope. But look, as you can hear, I'll sing you a little with my husky voice. I'm in the mood for love.
(01:50) That's where I'm at right now with my bad voice. I've lost my voice, but it's not totally gone. I just sound like I'm struggling to breathe, which is true. So and my laughs sound like I've been smoking for 40 years. Other than that, doing great, feeling great. These are aura points. You're just stacking aura points right now. So much aura.
(02:12) Yeah, people are going to actually require me to do podcast appearances with this voice from now on. So I'll have to 11 labs it and figure that out. I think that, you know, one of the things we love is tactical episodes. And that's what we're trying to get into here, right? Which is around incremental attribution and versus one day click. I'm really excited to talk about it.
(02:34) So let's go through this. Like, I mean, we have some data from the founders community as well, which we'll talk about. But Brett, frame it up for us. Yeah. So we've been thinking a lot about incremental attribution since meta rolled it out. And I have an N of one kind of sample of a recent experience that we've had where we went through kind of the disappointment, excited, back to disappointment and trying to figure out where we go with incremental attribution.
(03:05) So if it's something that you think, if it's something that you've been thinking about testing, maybe you've already tested and you've had a similar experience, hopefully you can lean on this to kind of decide, is it worth testing in our account? And should we expect that there's going to be meaningful performance swings? Because, you know, everybody's always looking for, I mean, I'm kind of the guy that always asks everybody for a hack at the end of the episode because I like to hear the tactical nuggets.
(03:29) So hopefully you walk away with a kind of an understanding of what to do with incremental attribution from here. But I can walk through, I actually have a bunch of notes in screen shares and I can just walk through a timeline of a test that we ran through with a client and their experience. And then Andrew, you can always interrupt me and we can ask questions and dig into anything deeper. Does that sound good? Yeah, definitely.
(03:51) I mean, I think I'll just mention the community stuff out of the gates, which is people had seen it underperform thus far utilizing IA. Right. So it depends. Like one member said, he saw that it was especially on remarketing review through attribution on past purchases was inflating numbers. Sees IA aligned well with seven day click.
(04:18) Another member said that incremental ROAS and ads manager often lands around 90% of seven day click ROAS when you break it down. And Barry Hot legendary said he thinks it's good 90% of the problems he sees because it solves those problems, 90% of the problems he sees, especially on bigger accounts. So it's interesting. It's like, it's interesting.
(04:42) It's kind of like people said hasn't been as good, but then like overall we're like, I can see where it's happening. And it's going to be better. So yeah, I'd love to walk through it. Sweet. I will flip this over. You're watching this on youtube.com, which is a website on the internet that shows videos.
(04:59) Brad is sharing a screen, but if you're not, you know, and you're just having it in your ears, then, you know, I'm sorry, but you can go and look at the visuals on youtube.com. Somebody the other day I met at a conference. This is true. And said, who is the other guy when he met me on the, on the podcast.
(05:20) And he goes, I love his, I love his voice. I've listened to every episode twice. So some people just come for the audio. Hey, if this helps you fall asleep at night, that is an acceptable, is an acceptable listening method. Kind of an honor really. Yeah, we're just trying to, you know, we're just trying to smash records around here. So, okay.
(05:41) I will also try to explain what's on the screen for everybody that's, that's listening. So let me, let me frame this up with a little bit of context ahead of time. First off, this brand is in the apparel space. Their AOV is over a hundred dollars, not quite one 50, but it's kind of in that range. Depends on the seasonality and what products are popular.
(05:57) And they have actually a fair amount of organic traffic. They influencer like posts, things like that. So if that's helpful for context for, for brands that are thinking about this. So the beginning of the year, we went through a bunch of different things with this, with this brand. And we started to test meta conversion live studies directly in the platform to try and tease out how incremental is meta, et cetera, et cetera.
(06:19) So here's what I can tell you. So their average one day click row is, which, which shows up, shows up here as a 3.12 for, for the account or basically year to date up to this point is very, very close to the actual conversion live data results that we got back from meta. So there's like almost no difference between their one day click and the incremental number that we got back from, from meta.
(06:41) So that's encouraging because that means that we can use one day click as a, as this, I don't know if barometer is the right word. We can use it as a temperature check to say, Hey, if we're going to hold our performance accountable to something, this one day click number is reasonable for them. And one day click is probably, you know, like I said, for the organic traffic reasons that I mentioned, one day click is probably more applicable to them for them than the average account where seven day click.
(07:05) You know, I think there's this, this, this stat that's floating around recently, which is 70 click kind of under reports by 15 to 20%. We generally see that except for this account. So just, just kind of framing that up. So we ran a conversion of study to start the year, came back and basically said, Hey, your one day click row eyes number is basically your, your, your, um, incremental number, which is different than incremental, uh, attribution.
(07:24) Those are, those are different things. So just framing that up. So what we did is we ran through that conversional test and we started to get through the rest of, um, the, the, the Q1 Q Q2 rolls around beginning of Q2 rolls around end of March. And we want to say, Hey, incremental attribution. We're starting to see some success in other accounts. We're hearing about it.
(07:41) Meta started to suggest it a little bit more. We want to actually test if this is going to be successful or not. So what we do is we launch, um, we take our top performing campaign on a one day click basis and we split out and have an additional cell for incremental attribution and throw them both into the conversion of study. So that's the test.
(07:58) It's one day click versus incremental attribution with a holdout for each of them. Um, so that's kind of test design. Uh, we go through the first week of performance and incremental attribution itself looks terrible. Well, I, I have a, I have a tweet thread about this. If you go to my Twitter, you can find it. And it just looks miserable.
(08:18) And I'm like, should we turn this off? Like, I'm really, I'm really like nervous. This is not going to pan out. Um, but no, we decided to commit to this and set up the test. So like, let's give it more time week two, three roll around and the conversion lift results inside of meta are reporting much, much better now in favor of incremental attribution.
(08:33) So I'm starting to feel good. Uh, I see, Hey, one day click on it looks good. Incremental attribution looks good. The commercial lift study results look good. They use MTA data as well. So they use, they use triple oil. Um, uh, but your, your North beam would be a comparable. It's like that also looked good.
(08:47) So everything was checking out that this was looking really good. And then, uh, we get to the end of the test and the results basically say incremental attribution is substantially more incremental than one day click. So there's a thrown around the word incremental, like a million times. Um, so if it's worth digging into that further, I can.
(09:04) But, um, basically, uh, incremental attribution was performing way better than one day click rise. So we say, okay, we feel pretty confident in these results. Um, now it's only one test. And so maybe it's worth repeating. It's on one skew and a subset of hundreds of skews. Uh, so before we roll this, you know, maybe before we roll it out aggressively or just like blanket account wide, we kind of want to go campaign by campaign.
(09:25) So we start to roll it out to several more campaigns. And what has ensued over the last couple of weeks is I think what's, what's really the visuals behind me. Um, and I can go into that in a second. Andrew, do you have any, any questions or like anything I can clarify before I dig into like what we've been seeing more recently? I mean, I think the big thing that you mentioned is basically like it was the patience that sort of paid off for you.
(09:49) Like, and that you're using incrementality as like a main measurement guide. You know what I'm saying? Like it's so much around like, is this truly incremental? And, and you waited, which is really hard for a lot of people to do both of those things. Yeah. Yes. Yeah. Very, very difficult. Especially after that first week started to look, look miserable.
(10:09) And when I say look miserable, it's like triple whale looked bad. One day clicked, looked really bad. Um, and it just seemed to take some time. And that's the sentiment that I've heard in the Foxhole group from people is like, Hey, either, uh, generally speaking at first, it's not going to look great, but over time it starts to get better.
(10:22) Uh, and then the sentiment I've been hearing lately is that it starts to fall off. Uh, and that's where these, these graphs start to come in. So I'm going to try to zoom in a little bit here and I'll kind of show you the change. So you can see kind of Q1 and this is the one day click versus incremental. The dotted line is incremental. So they're like very close in line.
(10:38) Like that's the thing to take away from this visual is that they are tightly correlated, not only correlated, but they're actually pretty close together. So one day click looks increment, looks like incremental attribution incremental, maybe parts a little bit higher, which I think you'd expect because one day click is very conservative, but then we start to have things trend off aggressively over time.
(10:58) So, uh, I'm highlighting on the graph, March 25th, we start to see this deviation from one day click and incremental attribution. They still follow each other from like a correlation standpoint. They're still pretty close, but the gap between those two lines starts to grow substantially. Um, and there's a couple of things that happened around this time.
(11:17) And that's where we're like, uh, from, from then to now is what we're trying to, trying to dig into and understand. So the first thing that happened, um, which was at kind of the beginning of March meta says, we are changing the way click attribution works. Um, we are going to say an outbound click is click attribution, but engagement, uh, the kind of engaged version of, of that is going to change.
(11:38) Even though a click to a profile, a click to comment, whatever things that used to historically you've been counted as, as click conversions, that's now moving to an engaged. Um, so that changed, um, and then, uh, so that changed and then started to roll out to other accounts that they announced at the beginning of March.
(11:52) I think later, later in March, it started to roll out. Um, that was one of the big things. Uh, we ended our test probably at the beginning of April, uh, and we started to roll out the test, uh, then as well. And that's kind of, since, since those things have happened, um, the kind of ensuing results have been, there's this deviation in one day click and incremental attribution.
(12:10) Uh, so that's an insight. Uh, and our question now becomes, uh, well, is there, is there a third data point that we can look at or a couple more data points that we can look at and ask the questions like, well, which one should we trust? Should we go back to one day click and use that as our guide? Or should we lean into incremental attribution? Because just due to the nature of how one day click and incremental work, kind of like we said earlier, one day click is only giving credit to people who clicked within 24 hours.
(12:37) By default, it is substantially more, uh, conservative than incremental attribution. So we don't want to be misled by one day click. What, what should we do? That third data point that I'm alluding to is triple whale, um, uh, MTA tool. Uh, historically triple has like largely followed, uh, the one day click.
(12:54) And so we've built up a lot of confidence in one day click in the platform because, um, we've spent three years using one day click. It's worked extremely well for those reasons. Um, and that is now also deviating very substantially from incremental attribution. Um, not to continue this yap, but I'll pause in a second, but, um, you would also expect that because triple is using click based it's, it's click based.
(13:13) So like it's not completely unexpected. Um, and at the same time, the deviation leaves us a little bit concerned. The final data point that we used, um, to kind of look at this more recently is, well, how did the actual new customer sales for these products look over particularly the last couple of weeks? We've had Memorial Day recently, so that, that certainly throws it off, but we've definitely noticed a downward trend in the overall efficiency.
(13:37) So something that we like to do for brands that have multiple products or product categories is take, um, as much as we can look at product level sales with product level spend. It's not perfect, but it allows you to see trends over time. Um, so if I were to take new customer sales of this, of SKU a divided by spend for SKU a, what is that? What is that product level MER that has gotten less efficient over the last couple of weeks? So what that's leading me to say is, okay, well, where do we go from here with this information?
(14:05) We are actually going to start deviating back to one day click in the short term until we feel more confident in incremental attribution. Maybe we need to run a new conversion of study. Um, but all of that to basically say, I don't really feel confident in incremental attribution for this account at this point in time, because it just seems to be heavy, heavily deviating from, um, the performance that we did see.
(14:25) Even though we started to validate it with an actual conversion lift test inside of meta. So, um, that's, that's, that's my personal anecdote. One other anecdote on incremental attribution, another account, same thing. Like it started off really slow. It got really strong for two weeks. Last couple of weeks, it's been miserable.
(14:43) So we're just turning it back off, going straight back to the seven day click over there. How much are they spending by the way? They'll spend 150, 200 K a month. And what's your hypothesis? So this is what it is now, but what's your hypothesis about like how this is going to change in the next six months? Yeah, I have this, this brand because of the organic trend of this brand.
(15:06) Um, that's why I've thought that one day click is, and we've, we've kind of experimented in one day click has been the more reliable thing to use. Seven day click for them. It's a little bit too wild because if they have an organic moment or something pops off, meta does not react quickly enough to bid back down.
(15:23) They use cost controls as well, pretty heavy on cost controls, cost cap and min row as, um, meta doesn't throttle back down quickly enough using seven day click. It still doesn't with one day click either, but we can manually make some adjustments there. And so, um, one day click for that reason feels better.
(15:38) The reason we'd like to incremental because it opened up, it was the theory was it would open up maybe the delivery a little bit more and be a little less conservative on who we're bidding for. Now, obviously we could change our bids. We can increase the budget. We could just force more spend into one day click.
(15:52) Um, but the idea, and I think this is why Barry likes it. Barry had a great explanation on Twitter. It's like in theory, it should solve all of the one day view weird issues that, that you get. Um, but I think the only hypothesis that I can really think of for this brand right now is that incremental attribution is not, it's not doing a great job for this account at teasing out what's actually incremental.
(16:13) And it's still conflating that with some of the view based conversion, some of the engaged conversions, uh, and some of that are organic demand. Uh, and so what it thinks is incremental is actually purchases that, um, I guess would have happened anyways, which are not incremental. What I think will happen, what I hope will happen is, I mean, credit to meta.
(16:31) Um, they, they allow you to do conversion of studies and don't make you spend absurd amounts of money. And they want this product to solve the problems that people are having. Like they're, they're, they're the ones that are saying, um, they're pushing people to test these things. And so I think as more conversion, uh, studies are run, um, and as, uh, more brands are doing this, I think incremental, incremental attribution should only get better.
(16:52) So I think three months, six months from now, we should be retesting it, um, for this brand. It's going to be a wait though. It's that with IA, they just don't have the data now, like, or as much data as they could have. It's like, it's still too limiting. Or is it sort of one of those things that we see on meta where it's good initially.
(17:13) And then like, as more people adopted, it goes down like, or, you know, like, or do you see that happening in the future? Um, or like, what is it, do you think mechanically that was holding it from not being not performing the same way? Or is it too generous? Is it the opposite? I mean, I, I kind of, I'm reading what you're saying, but there's a little bit of both.
(17:31) It's like some of it's limiting, but some of it's also just like, seems like it's kind of taken, putting it out there too widely. And that's what you thought was going to happen, but it didn't. Yeah. It's a, it's a good question. I, it would be interesting if somebody from meta, um, unfortunately they just had a bunch of layoffs.
(17:46) So hopefully none, uh, no people in the incremental attribution department, because, uh, it would be very interesting to hear from meta how. It actually works. So my understanding of how incremental attribution works is that there's tons of brands at any given time that are running conversion of studies on, on, on.
(18:01) Meta and they meta use those to inform building this new optimization algorithm. I don't, I don't know exactly how they would describe it, but conversion of studies were used. Historical conversion of studies were used to build this model. And that's how it optimizes. I think the tricky thing about that is like, there's, there's so much difference.
(18:20) And this is true of one day click and seven day click as well, but like, there's so much difference and uniqueness to each individual brand, depending on how much organic traffic they have, how much spend they're getting from other channels. Um, the industry, like there's, there's so much nuance in between those that maybe for this brand, the, the mean of what they built conversion, uh, incremental attribution on top of just doesn't apply yet.
(18:43) Um, because the other, here's the other thing that I didn't, I didn't say like we've continued particularly over the last, before we made the decision to shut off, shut this off. We've continued to dial back the bids and tell meta, Hey, we don't, we're not happy with performance. Like we're, we should dial this back and it's continued to basically spend regardless of that.
(18:59) It's not, it's not adjusting the way that one day click or seven day click does to bid changes. Um, so my, the only hypothesis that I really have at this point is that they built the model on kind of the, the average of the conversion of studies. And just, that's not working for this brand at this point in time.
(19:14) Um, I know a couple of brands that spend the majority of their budget through incremental attribution. So it's very possible that it works. You know, I'm curious if people are, uh, spending on multiple, uh, uh, attribution types. Um, and the, this, this needs to be one of the subsets of them. Um, and if they're reaching a different pocket of audience, but, uh, to the different pocket of audience, like we're not actually seeing really different delivery.
(19:38) There's a lot of overlap between one day click and incremental attribution. And the, um, so there's a lot of overlap and reach. And then the net new visit rates was basically the same between them. It's not like we were reaching a bunch of new people by using incremental attribution. Um, it was about the same, the same rate.
(19:52) And for this brand, that's like 70, 70% pretty consistently. Yeah. It's interesting. I mean, I think it's, it's, it's sort of in this weird, interesting phase where you hear a lot of people talking about it. There's a lot of hope in it. You know, do you think if you made this like one day click and you made it seven day click versus incremental attribution, what do you think would happen? That's it? Yeah, that's a good question.
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(21:13) Do you think if you made this like one day click and you made it seven day click versus incremental attribution, what do you think would happen? Yeah, that's a good question. I mean, that's what I'm kind of like struggling with, generally speaking, right now is the difference between a one day and a seven day click obviously changes the way that meta delivers.
(21:32) Because we use cost controls, generally speaking, the question that I would go back to is, okay, if they're not reach, if there's, there's overlap between the different attribution methods and who you're going to reach, then there's like not, not a ton of value in that. Because if I wanted to use seven day click because it was going to bid more aggressively than a one day click, well, the alternative thing I could do instead of switching from one day click to seven day click is just open the bids or spend more forcefully. Like I could just be more aggressive.
(22:03) Um, now you can make the argument that, okay, more data in a seven day click, maybe makes Facebook optimized a bit better, meta optimized a bit better and allows them to be more efficient. Um, I've just not really seen that to be the case, especially when you have a brand that's consistently getting through learning.
(22:18) And in this case, it's just been, it's just been more effective to use one day click because it doesn't go for those, uh, especially with the way that they changed click based attribution to be actual link click, as opposed to on all kinds of clicks that are, uh, whatever you could qualify as a click.
(22:35) Yeah. I don't know if that answers your question, but, um, I have my hypotheses around all of it. I mean, what's your, what do you think about the overall change with like the definition of clicks and like link clicks and stuff? Like, do you think it's been a net positive or do you think it's a net positive overall? What's your thinking? Cause it seems like it's, I mean, I think it should be too, but it seems like it's in the short term, like really screwed up some results because people were, it was
(22:57) optimizing on one thing and it's like almost had to relearn, you know? Um, I would assume that through like gem and lattice and all this, like one of the signals is this definition and that's changed. Right. Um, that's, uh, that's screwed, screwed things up to a degree. I don't know. What do you think? Yeah, I think that's, I think that's right.
(23:19) Um, it's, uh, it's definitely, uh, it's definitely. Pausing my, I think more confusion in the short term and especially for, for brands that are on cost controls, right? Like you, you went from bidding on a very specific type of, of, of click and that changed. And if, if the amount of conversions that were coming through changed by 20%, you have a completely different bid that you need to adjust for now.
(23:42) Um, and so again, I think like short term volatility, but in the longterm, I think it's the right thing to do because one, it adds like way more clarity for an advertiser setting up a campaign from scratch. It's like what they're actually giving meta credit for. Uh, and then two, you know, I think people will, I think generally people will agree that if you had to go rank order, what's actually incremental.
(24:02) It's a click is probably the most incremental, most commonly, uh, of an engagement of some kind. So a click to a social profile. And so I should redefine that an outbound click is probably the most incremental. The next range of the, what they qualify as engagement, you know, it's, it's video views of some sort.
(24:18) It's clicks to profiles, it's likes, it's whatever. It's probably the next most incremental. And the view is the thing that's least incremental. Um, but you know, how's that changing with short form video content? And apparently, um, zoomers don't click ads or, you know, that that's the, that's the story.
(24:32) So, um, yeah, I mean, in theory, it all sounds great. It's just a TBD, how it all continues to, uh, to play out. Oh, Zach's here. Hey, what's up, dude? Hi guys. So let's talk about the creative cohort spend analysis, uh, that you pulled, like, what's the context of this, Brad? Um, I'm, I'm interested to hear about it.
(24:53) So creative hit rates, creative volume. That's been like a topic of discussion. Motion put out an awesome, I don't know if exactly, I don't remember what they called it, but it was like the creative. I don't know. They put out this 2026 creative PDF. It was like 80 pages long. And it was awesome because it showed like, here's what the biggest brands are doing from a creative volume perspective and by industry.
(25:12) And then also it said like, here, here are the types of ads that brands are running. Cause they have this AI tagging. That's awesome. Um, and so we've been thinking about that a lot. We've been looking at a lot and kind of using that as a benchmark for, okay, how much are our brands launching on creative on a monthly basis? And, um, what does that mean? But I think there's been, I think we talked about this in a recent episode.
(25:31) I feel like there's like this sentiment building in the Twitter sphere for DTC where it's like, people are kind of getting frustrated with the volume game a little bit. And they're, they're leaning back towards hit rates, making better ads, spending more time, being more thoughtful. And I think that's a fair exercise.
(25:44) I'm still definitely in like the creative volume is important camp because you have to take swings. Um, and at the same time you want those swings to be calculated, but I don't think anybody would say it shouldn't be calculated. Uh, but that's, that's a, that's a separate topic. In any case, the, the thing that we've been thinking about and trying to map back to is this idea of viewing our creative through a cohort lens.
(26:06) And I think I'm not sure if this is in, um, there might be like a creative kind of report in, uh, what you and Kurt built, Andrew. Um, but it might be more of a CPMR related thing. It's like the creative cohorts in their CPMR, which is sick. Um, it's a really cool view. Um, but what we basically want to know, the question we're trying to answer is like, where did our spend come from this month? So in May, was it ads that we launched in May or is it everything that we've had in the ad accounts historically?
(26:30) And then I can go back and I can look and I could say, okay, if our entire ad account is made up from spend, ads that were launched last year, all the new shit I'm making is going to waste. Like I, we are not doing a good job at making new ads that are resonating. And then you can start to dig into the reasons why.
(26:45) So this is kind of a, this is a bit of a surface view of like, okay, um, are we making the right things? Are we actually progressing? And I have an example of what this looks like. So I can, I'm sorry for the non YouTubers, but I'll flip my screen over again and we can look at this. Um, I realized that I didn't update the previous document properly. So I have a different looking one here.
(27:03) Sorry, you blame, blame, Claude, Claude for the visual. The data is accurate though. Um, so I'll try to describe what's here. Can you guys, guys see this? Okay. Yeah. So what you'll see is we've got spend by month. So January 300 grand, February 400, some grand, March three, 300 grand, April 289, May 258.
(27:21) So far we've got some time left in the month. Um, so this cohort view is what we've been finding really valuable. So if we look at January, we can see how much spend in January was made up from ads that were launched last year in this case and ads launched in January. And in this case, about 10% was from ads in January.
(27:39) And I guess what you're looking for when you're looking at this is that the month that you're currently in and previous months are making up like a reasonable percentage because there's, there's two things that would be an issue. If January here, which is 90% pre-January, like if we're looking at May and it was all ads launched last year, be like, fuck, what have we been doing? What have we been doing this entire year so far? We haven't made any ads that are working.
(28:01) Um, and then that's, that's the trigger to go look into that. It's, it's, uh, it's using this. You shouldn't just look at this and not do something with it, but you're trying to find insights to actually go and do something with this. But the other way, the other thing to consider is, well, if all of your spend is from ads that you launched in this month or in the previous month, I mean, there's some caveats to this, but you might also be concerned that like, okay, you're really over, over, over indexed on stuff you've launched recently.
(28:26) Uh, and you might want to continue to get new things in the pipeline because you don't want to become overly reliant on just a couple of, uh, outlier ads. I mean, all our ads are good, but at the same time, you don't want to become overly reliant on this. Uh, and so this is just a view that we've been looking at to make sure, okay.
(28:41) Um, are we, are we launching new ads successfully? Um, are there any pockets that we can look into, right? Like may, this is the biggest, this looks like one of the biggest months, um, where, uh, in the same month that those ads are getting to spend almost 20% and it's not even over yet. So like, did we do something recently that's hitting really well that we should be leaning more into? Um, so just kind of sparks that.
(29:02) And if you wanted to run this, like motions, not sponsoring this, but I can give you the prompt to go. You can just drop this in motion and, uh, in their little runneth chat and it'll produce this for you. Um, HTML graph and everything. Um, just kind of a cool way of, um, looking at this and breaking it down.
(29:17) So basically what you're saying is instead of doing cohort or creative sort of like by batches or like, Hey, this was this batch. You're doing it by a monthly cohort. And that's how you're looking at them and categorizing them in order to make sense of the performance by each of those cohorts by month.
(29:37) Like it's, it's a more linear package. Makes it easier to understand what we did, what we shipped in February is still good. And what did we, and then you were able to dig in and look at February and say, these are the things that gripped that are helped to scale. How do we go back and replicate some of the unlocks we had? Is that, that's essentially what you're getting at with this. Yeah, exactly.
(29:59) And there's, there's, this is, this is a view in a certain way that you can look at it. Uh, if you're a motion user, you can go to their new launches report and that'll give you a weekly version of it. It doesn't look exactly like this, but it'll give you a weekly version where you can expand and contract.
(30:12) Uh, and what you want to do, I mean, we've talked about, um, I feel like, I feel like scalability school needs to take a lot of credit for like spend velocity as a, as something to lean into. I know Zach, that was something that you mentioned a ton last year, like spend velocity and things picking up quickly.
(30:25) But like, if you launched ads in the last week that became the top spender, that's the signal to lean into is like, okay, um, did we hit on a seasonal moment that's relevant? Or is there something lasting in here that we can go recreate, um, and lean into and do more of. And this is just another way of, of looking at that. So there's the weekly view of it.
(30:42) Um, but it's, it's basically just like forcing you to, to reconcile. If you're, if you're a brand owner and you are hiring create, like you, let's say you have your brand owner and you have a creative agency, uh, and you hired them in March and you pull this report and all of the spend is going through shit you've created.
(30:55) Like that's, you know, there's obviously different ways of looking at it, but like, that's the, that's the question to go ask and say, it's like, Hey, is anything you're making actually, um, producing like net new net new spend in the, in the, um, this one has seasonal, um, seasonal swings. And I feel like that's why this is changing so much, especially recently. I love it. I don't have any questions on this. Zach, do you have questions on this? No, it makes a ton of sense.
(31:16) Um, we do this kind of like end of month report every month that, um, our like creative ops manager and our VP of performance creative put together. It's similar to this and then just goes a bit deeper where we see the difference between what we call like breakthrough ads versus scaled ads. Um, which breakthrough is like, does it hit $2,000 in spend within a seven day period at the North beam when they click target goal.
(31:42) And so we kind of look at month over month over month where we're saying like, okay, how, how are we performing? How many ads are actually breaking through or scaling this month versus previous months? And it's, it's somewhat, it's almost like a, maybe one step deeper, maybe one step above. I don't really know like the best way to describe that, but yeah, I mean, this is super important.
(32:03) It's like, what do we miss? And like, what is the longevity? It'd be really interesting to look at it, um, over time too. So you're like, this is what it looks like now, but then what does it look like going forward? I don't know. I don't know if I'm like explaining that right either, but the progressive like swing, like does the, does each month end up turning into 10% of a spend? Or does it actually eat up like, ideally, is it a quarter? Is it 25% of spend for the first month and then kind of degradates down to like 10 and then holds at 10? That'd also be interesting too, to see. Yeah, that's a great point.
(32:30) I mean, the reason that like the, the thing that inspired me to do this in the first place is we had a brand who was like, they were like generally bought into the idea of creative volume is important. Um, but they wanted to like, see the proof that it was working. Um, and so they started, they, they probably quadrupled their, their output, um, through a bunch of like very thoughtful adjustments on the way that their internal team operated.
(32:52) So they took inspiration from kind of like ideas that we were sharing with them and then they converted it into actual output. And it was just important for them to make sure like, Hey, did what we, we, we produced, uh, a hundred ads this month. And you know, that's up from the 20 we were previously locked into.
(33:08) Did those do anything? And so it allows us to look back and say, yes, they worked. But here's another way to split it. It's like 50 of the ads that you gave us for, were for a promo that lasted three days. And so like, that's, that's useful, but like, and it makes sense because promo, promo spends harder, uh, and over a shorter period of time, but you want to make sure that those evergreen ads that are still live in the ad account continue to actually produce.
(33:30) Because then, you know, you've actually hit something and it's not just like a one hit wonder promo ad that you can actually repurpose. And so it was just like validation for them that what they were making was working. And when you split it down to evergreen and promo ads, it still ended up being the case that evergreen consistently is stacking up month over month.
(33:46) And that's what compounds, right? It's like, if you can, if you can make more ads every single month that earns spend in the ad account, then that's what compounds over time. And allows you to increase spend, uh, over the, over the year. Talk about, there's this guy, Zach Stuck, who has this brand called Marsman. Um, and it says, we're going to go into now how Marsman scales on next month's repeat revenue.
(34:09) Uh, which is kind of a hot topic. I feel like everybody wants to know. I mean, headline, I mean, Zach, I want to go into this. Sure. Sure. I mean, Brad, do you want to like tee this up anymore? Do you, is that, is that enough for me to. Yeah. I mean, the, the, the frame, the, I remember we were having a conversation one time.
(34:26) And one of the things that you mentioned is like, one of the things that we think about for pushing spend aggressively. Now you guys are, you guys have hit an insane, um, growth rate and, and, um, uh, MRR. And so, and you're pushing growth, growth aggressively. And I look in brands that have solid repeat rate all the time.
(34:44) And maybe they have different profitability goals. They have different growth goals, whatever. Like often it's uncommon that the brand is not being aggressive enough in scaling. Cause they haven't done the financial, uh, homework that you guys have done to understand, like, how much can we spend into this? Um, and one of the things that you mentioned that you use is like a guide for this is like, well, okay, if we know, and I might not be framing this perfectly exactly the way you phrased it, but, um,
(35:06) you said, Hey, if we have this, an idea that our EP customer revenue next month is going to be, I'm just going to pick a number. It's going to be 500 grand. I'm probably selling that very short for Mars. Like it's gonna be 500 grand. It's like, well, we know we're gonna have 500 grand in cash. We can spend into that, like at least that much next month.
(35:21) Um, because we want to be, want to be aggressive. Um, so I'm sure there's a lot more context and things that go into making the decision, but it's, it's, it's, it's. It's at least, uh, maybe safe. Cause you have the cash to be able to spend into, uh, into that. So that's the framing. Yeah. So at the end of the day, like this is where these MRR businesses or subscription businesses scale so quickly.
(35:40) It just comes down to this simple model, which is if I can forecast for this example, 500 K in reoccurring revenue. And I know that my OPEX is going to be a hundred thousand for the month. Um, I know that I need to maybe run at like a 1.8 or two, two, let's just call it a two, uh, MER, uh, or two blended rows, whatever term you want to use.
(36:02) Um, to still maintain profitability with a hundred K OPEX. I then know I can spend that full MRR into it. And as long as I know if I'm trying to achieve a two MER, I have to be at a one new customer ROAS. And so if I can stay at a one new customer ROAS, I can still hit my target and I can keep going. The nice thing about this is like with time and with more scale that MER can actually go down.
(36:27) Um, as long as your OPEX doesn't offset it too much, meaning that like, let's just like ratched up those numbers a ton. So let's say, um, we're, our MRR is 5 million a month now, for example, and we know what our OPEX is. Maybe our OPEX is a million a month now. I don't know, for example. And so with that, because we have such good margin and we know we're going to do 5 million in returning, we might be able to say, Hey, we can run a 1.5 MER now in total.
(36:54) So I can still spend that full 5 million in revenue and do only 2.5 million in revenue to average out to a 1.5 total MER. So again, like this is where with more scale plus the returning customer revenue, when you can forecast, um, that returning it like just, that's where things really start to compound.
(37:12) I think the thing that's been really interesting for us is, is candidly like, there's so many things that you do in these subscription businesses that will have like a, uh, fundamental changes on like forecasting that returning customer revenue that could have big impacts on like how you think about the business.
(37:29) So let's say you run a sale at the end of a month, one month, and your cat goes down and you scale into it. The next month you could assume, depending on how many percentage of those customers are 30 day customers versus 90 day customers, you might see a big spike at the end of it. But let's say like you run an offer where your 90 day subscription versus 30 day subscription is a bigger discount.
(37:49) So out of nowhere, that becomes a larger percentage of your customers at the end of that month. You now are deferring all that revenue for 90 days on the back of the month where then you're like, Oh shit, I better plan my spend accordingly around those pockets. Also, like if you're testing different bundles, you're testing all sorts of stuff, like this can all have huge impacts on it.
(38:07) So I think like as much as reoccurring revenue is like a blessing, it can be a curse too, because there's so many different variables. If you are really doing a bunch of testing, like we have been with Mars, where we're trying like different offers, different price points, you know, 30 day, 90 day, you know, 180 day subscriptions to kind of see like, what is the sweet spot? All of those changes will then have an impact down 30, 60, 90, 180 days from now that you really have to like, be careful with.
(38:33) And there's a lot of like assumptions that go into the model until you have like longevity. So, I mean, the smartest thing you can really do is like, just be like, cool, we're going to sell a 30 day subscription. That's all we're going to sell. We're going to scale into that because then there's no other crazy nuance that happens.
(38:48) The second you had a discount, additional discount or whatever, like that could actually impact your cohorts, which then resets your, all of your learnings on what's expected for returning customer revenue. But the simple math on it is when you know what your MRR is going to be and you have a good, good model built around it, like you can usually spend into that fully.
(39:04) And most of these brands that are, that are scaling the greens of the world, uh, IM8s, whatever, like they're looking at the returning customer revenue and saying, Hey, if we're going to do 5 million this month in MRR, we're definitely going to spend at least 5 million in ads. Yeah. And the, in the issue, the point that you made that's, you have to be careful with is when you, you add the 90 day as you've maybe, maybe you rip a bunch of 90 day.
(39:26) And all of a sudden you went from a hundred percent is 30 days, a hundred percent of your subscriptions are 30 days to 50% of them dramatically are 90 days. Well, if you're, if you don't change your cohort math and your four year cashflow forecast to account for that, you're going to spend into all of that, but you're not, you're actually not going to realize that cash for another 60 days beyond that.
(39:44) And that's where you put yourself into a funky cash position. So that's what you're saying about keeping it simple to avoid. The cash positions, plus then like running different discounts throughout different times of the month too. So like maybe you run a discount, like 4th of July is in the front half of July.
(39:58) Father's day is on like the back half of the month or like mother's days in the back of the month. So like, depending on when you're running those, those sales, it's also going to have fluctuations on the returning, which then ideally you're looking at, okay, I did, you know, 500 K of MRR. I can spend 500 K.
(40:12) You have to really be cautious of when you're spending into that 500 K around all of those moments that you've made historically. Otherwise you can overspend in the front half of the month and then you don't have enough budget on the back half when CAC is actually really good. Can we talk about the forecasting and how many times you're, how often you're reforecasting? Like, is it built on subscription base, repeat purchase cohort curve? Yeah.
(40:37) Like, is it a simple percentage? Like what is it? Yeah. So we just rebuilt our entire model this week because we had some issues candidly for May of us like missing, but all learning opportunities for us. But we, we just rebuilt it where it actually goes down to the individual customer level and then is now starting to pull in all sorts of moments around that individual customer.
(40:56) So we basically look at who that customer is, what they paid that first day, if there was a discount or not. We factor that into the retention curve of that individual based on us knowing, Hey, sometimes when they use a discount, they're much more of like a, you know, they're not as the longevity of that customer is less because they're more of a discount shopper and they're more likely to cancel.
(41:14) So we're like, we're applying retention curves based on the decisions they made and the sales price point or whatever they did on the front end. Plus then we're starting to pull in other things. Like we're pulling in, we're going to start planning things from like gorgeous or a CX team to say like, did this customer reach out through our portal to cancel? But then they stayed on because we gave them an additional discount.
(41:34) What does that mean for the longevity of that customer? So now we're getting down to the per customer basis where candidly, like prior to that, we were looking at it more holistically. We were saying, okay, how many customers bought on this day, 30 days ago, look at that monthly core, look at the churn for that cohort and just kind of make a lot of assumptions.
(41:49) But at the scale that we're getting to now, like we had to look at it as granular as we possibly could. The thing that's really interesting now is we're taking this forecast. We're basically saying, okay, again, simplified numbers, 5 million in MR this month. We want to do 10 million in revenue. We can run in a two blended ROAS, which would mean that we'd have to run.
(42:09) We'd have to, you know, run at a certain degree where we would still maintain profitability, officer OPEX, all that stuff. We're then saying, looking at our forecast, we're then saying, okay, new customer target is 5 million in revenue. How do we go hit 5 million in revenue? We're breaking down our targets by channel and estimated orders by channel every single day.
(42:29) So now we're looking at like not just orders or NCROAS or CPA from Facebook or one day click. Like we're literally looking at did today, did email SMS drive the correct amount of customers? Did organic drive the right amount of customers like by channel? Because again, like at the scale that we're getting to, if like one of those channels misses, but everything else is hitting, we could miss our target by, you know, six figures, potentially even seven figures a month.
(42:54) So it's been really, it's been really cool to actually like be able to start to diagnose it that way and actually look at it on a per day per channel basis. Because I don't think many brands get to the point where they're doing enough volume where then it actually validates like did email actually drive 300 customers today? Yes or no? Because it's just like too granular, too segmented based on size.
(43:15) But now that the numbers have gotten big enough that we can actually like keep an eye on the trends, like it's really helpful. Also, then we can plan, hey, if we're going to run a promotion, email can be flat for the first half of the month. But then during that sale period, we expect it to actually pop up.
(43:27) We also, you know, have the indication that like, you know, new customer cac is going to drop on these channels, but maybe it doesn't move on podcasts because that's linear throughout the month, things like that. So, yeah, I mean, forecasting is important for all businesses, but especially for subscription, like you have to really dive into it.
(43:45) Otherwise, like you will either not grow as quickly as what you possibly can or you'll actually inverse, which is like you're not thinking enough of how many net new subscribers you need to add and what performance needs to be on every single channel for you to actually add net new subscribers. Because for the most part, most of these subscription brands are only growing because they're adding net new because likely most of them are churning the same amount as they're adding in.
(44:07) So everything above and beyond that is like the real growth path. And the podcast point is interesting because it's like, those are the things you do. And like, I think a lot of people, it's like maybe they put together a monthly view, but the day to day can shift so much to your point for a promo. Even like week or day to day trends, like maybe your weekends are more aggressive, maybe they're slower, depending on the category that you're in. I have two follow questions on that.
(44:30) When you get when you see that channel level performance, like maybe you can use two examples. You could use a meta example or the email example or both. It's like if those are off, are you reallocating in the platform? Is it kind of like that's where the deep dive starts? Like, OK, meta is off. OK, well, is there somewhere in meta that we can reallocate? And if it's like, nah, probably not. This is all shit. We got to go to somewhere else.
(44:53) It's like, are you making that daily decisions? Are you zooming out over a longer period of time? And the mini follow to that is like, what are you using as the, I guess, like source of truth? Are you using just like MTA data or using platform data? Just kind of normalized. Yeah. So for the CAC by channel, we're using last click to dictate that.
(45:11) And then we kind of back into a last click and then an MTA to kind of like figure out for our paid channels, like what is the happy medium there? So that's kind of like the blend. At the end of the day, it's like, you know, we've had, so for example, like email and SMS has been falling short based on our daily targets.
(45:26) And we're sort of, we're trying to figure out, OK, like what's happening on email and SMS that's not allowing us to continue to like perform at the levels and the growth that we need to see month over month from those channels. And so for us, it was like, oh, well, we run a subscription offer, like a discount 50% off your first purchase and like for life on that subscription.
(45:45) Um, but we don't really have that many like unique offers to add into email and SMS to get those extra pops or get the people that weren't willing to purchase. So there's, there's one like tactic that we're about to start testing where basically if a customer is sitting in our ecosystem, in our own channel list, and they haven't converted over the course of like a 60 day period, we're actually going to start discounting single one-time purchase offerings.
(46:08) So normally we're not a one-time purchase brand. We're not trying to get any one-time purchases, but we have, we have such big lists now that like there's all these people that just, just don't buy simply for the fact that they don't want to subscribe or they haven't gone on Amazon yet to buy on one-time purchase.
(46:21) So that's just like a way for us to be like, Hey, we have this like dead list essentially of just people that have come into the funnel, but haven't bought for some reason that I think a lot of brands actually like kind of forget about that. They're like, Hey, they didn't buy. We did all these like whatever, uh, flows to like get them to maybe get a bigger discount over time.
(46:37) But then there was probably one reason why they didn't buy. So like talk to that customer about that one reason why they didn't buy and give them a strong discount to still close. Cause like it's very likely better off and you still have contribution margin on that customer to be gained. If you just give them even a slightly bigger discount to like bring in those dollars versus them just sitting on an email and SMS list and then just like dying off because you, you pull them from the list because they don't, don't interact.
(47:00) So yeah, there's like little things like that, that we found by digging into, Hey, email and SMS is off for that day. What, what can we do to actually get those extra pops throughout the month? Um, and yeah, same thing kind of with paid. I mean, paid is more of the typical analyzing. It's like Apple ovens off this week. Okay.
(47:17) What are we, what are we doing? Have we not, you know, have we tested a quiz funnel to Apple oven or are we just running listicles or whatever? Right. So it's like landing page creative, the whole gambit of normal funnel optimization stuff. Have you, so the thing you just said about like the retention, you guys running the same playbook then for people who have like lapsed as customers.
(47:33) Like they, they tried it for two months and they, they unsubscribed and now you have to do this whole win back to get them back on board. Are you guys good? Same thing. One time purchase or push them to Amazon. Yeah. Yeah. Same thing. Same thing. Yeah. I think the biggest thing this week was just like, Hey, we're not talking to these people that just don't want to buy it because it's just a subscription.
(47:51) So it's like, let's just go clean that up first. Cause that's probably hundreds of thousands of revenue, like every single month. If we just give them a proper discount to come in one time purchase at like not quite our subscription offering, but like in, in between our one time purchase and our subscription price.
(48:04) So yeah. And then next would obviously be like lapsed customers. The interesting thing about, about supplements and lapsed customers is like very, very low chance of their, we've tried things before in the past, like free gifts and shit. It just like doesn't move the needle. So, um, yeah, we've, we've, we've, we've attempted it, but I would say like, there's probably still more, more room to fix there. I mean, absolute banger.
(48:24) I tried not to talk very much cause I sound terrible, but Brad appreciate you asking good questions. Zach, thank you for sharing. Is there anything else you guys want to go into? No. Yeah. Okay. All set. Poskey Foxwell signing off. Thank you for listening and we'll talk to you guys soon. This episode is brought to you by the Foxwell founders membership that Andrew and his wife, Gracie run.
(48:51) It has been absolutely pivotal for not just the Homestead team, but the easy street brands team. We've had, I don't even know how many members are currently in there that are a part of our ecosystem. But when it comes to anything from learning ads to understanding what's going on to building an agency to knowing retention, it's been absolutely useful for our team.
(49:10) When they get stuck or they need help to just go there and resource all the other experts. So definitely would recommend it for anybody that's looking to, you know, take it a step deeper, try to get a little bit more knowledge on growth marketing and all the world D2C is. Yeah. I think one of the most incredible things about it is you can just like open up the Slack group every single day.
(49:27) You can pin your favorite channels for the topics that you care most about. And like every day there's going to be somebody who just like, because they want to contribute something valuable to the group, you can go learn something every single day. And it's going to be extremely useful. There's some ballers in there that you just get like the benefit of learning from that.
(49:43) But like for the for the costs, like you couldn't pay them that for their time. But through the membership, like you get access to some incredible people and tons of resources. Yeah. I mean, I think the biggest resource to me, too, is like the events that, you know, Foxville Founders does. They've been able to do some even in Wisconsin, even in the boring state of Wisconsin, which is pretty awesome.
(50:01) Getting people together in person and able to have really just like honest conversations of what's going on, what's working for them now, you know, where they're at in their business. Knowing that there's going to be, like Brad said, some real killers in the space in this in this membership that can that can help and are willing to take the time and help.
(50:17) So that's been a huge part of why a lot of our team have really enjoyed it as well. And the applications are now open if you're looking to join. So Foxville Founders. Yeah. Foxvillefounders.com. Go check it out. Go apply. The only way that we grow this podcast is by you sharing it with your friends. Honestly, like reviews kind of don't really mean anything too much anymore.
(50:39) They're really meaningful, but they don't do a lot for the growth of the podcast. And so sharing YouTube links, sharing Spotify links, sharing Apple, whatever we call it under the podcast app now. Anything you can share, the better we're going to be. Guys, anything else you want to say on this? Yeah. Please go check us out on YouTube. Rack up those views for us. We'd love to see it. And then subscribe.
(51:01) Make sure to subscribe on YouTube as well. And I relentlessly refresh the YouTube comments because it dictates my mental health for the day. So please say something nice about all of us. Thank you, everyone. Thanks for listening. Honestly.
