Stop Guessing Your Ecommerce Budget

[00:00:00] Luke Austin: What should my total budget be for this specific point in time? How much creative output and what should the breakdown of that creative output be in order to achieve that, that necessary budget allocation or efficiency goal? And then what is the true revenue contribution of each of my channels and what should the testing, the testing roadmap be to, to sort of reinforce that there?

[00:00:20] Luke Austin: Encapsulates what could represent maybe 50% of the, you know, time spent in conversations and workflow around these things that we've, we've put a lot of time into sort of consolidating what we, what we see working best and create toolings around answering these questions that we don't see work working in other places.

[00:00:36] Luke Austin: And so our, our, our answers to those questions exist in three different models. The spending power model, the created demand plan. Then our our testing roadmap tool.

[00:00:45] 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 Strategy, Mr. Luke Austin, who's gonna walk us through some very exciting offer we have today.

[00:01:00] Richard Gaffin: I'll just be upfront with it. We're gonna kind of break down for you exactly what we want to do for 8-figure ecommerce brands today. But Luke, what's going on, man?

[00:01:10] Luke Austin: Oh, let's see the, this time of year, my, my daughter's a little over two now, and so I'm beginning to understand how especially at this time of year. There are just like sub sicknesses, sub viruses that get created in these groups of two to four year olds. That is you'll have, you'll deal with something for a few days and then get out of it, and then there's like some new sub strain of something that two little two year olds with their combined green snot concocted at the park.

[00:01:40] Luke Austin: So always a joy waking up with something, something new to tackle on that front. But just the, just the time of year, I guess.

[00:01:48] Richard Gaffin: there you go. Like an endless cycle of contagion basically is what's happening 

[00:01:52] Luke Austin: Exactly.

[00:01:53] Richard Gaffin: But, all right, well let's let's jump straight into it then. So, well, first what I wanna frame up here is, is. There's, and maybe I'll, I'll kind of use this framework that you shared with me, Luke, but a lot of the times the three kind of main questions that CMOs have, that owner operators have, the people in the ecommerce space who are in charge of these decisions have, is a, what should my total budget allocation be? And then given that how much creative should I be making and then what's the true contribution of those channels, which really all kind of boils down to one question, which is, where should I be spending my money and is it getting me anything? And for a long time, those questions are really, really difficult to answer in any way.

[00:02:31] Richard Gaffin: That was any more than heuristic. But we've developed a system. To help get really precise about what your budget allocation should be. So Lou's gonna walk through that a little bit. And then I wanna say up front as well that this system that we're about to lay out for you, we are currently offering to do this for you for free if you are 10 million and up ecommerce brand in terms of annual ecommerce revenue.

[00:02:52] Richard Gaffin: So keep that in mind while you're listening that this too could be yours. So I'll turn it over to you, Luke, given that that's kind of the big question on people's minds, talk a little bit about how we. What this system is and kind of the contingent parts of it.

[00:03:06] Luke Austin: Yeah, so. We, the unique position that we have in the world is sitting at the intersection of hundreds of D two c ecommerce brands worth of data. But the subset of the hundreds of brands that we have the pleasure of partnering with is, is we get to see into the workflows, the organizational structures, the toolings of.

[00:03:30] Luke Austin: All these D two C eCom brands between 10, 10 and a hundred million plus. And it's one of the most fascinating parts about what we do because not one is like the other. At, at least at, at some point in time there's so much variance in terms of the leadership founding structure. The operating team structure, structure, the workflow, the meeting cadence, the communication styles, the tooling, the methodologies used to determine the decision making.

[00:03:58] Luke Austin: And it's really fascinating. And so we see it as a, as a big part of our role is to be able to identify in the context of that, what are the things that we see being most effective. And, and to assemble the best pieces of the workflows and the structures and the toolings, and to, and to also identify.

[00:04:16] Luke Austin: Where we're having conversations and spend a lot of time partnering with owners and operators, having these conversations, identify where there's gaps that exist in the space, and then be able to create solutions to solve the problems that we're working to solve on, on a day basis with, with our partners.

[00:04:32] Luke Austin: And so this sort of, this questioning around these three buckets what should my total budget be for this specific point in time? How much creative output and what should the breakdown of that creative output be in order to achieve that, that necessary budget allocation or efficiency goal? And then what is the true revenue contribution of each of my channels and what should the testing, the testing roadmap be to, to sort of reinforce that there?

[00:04:58] Luke Austin: Encapsulates what could represent maybe 50% of the, you know, time spent in conversations and workflow around these things that we've, we've put a lot of time into sort of consolidating what we, what we see working best and create toolings around answering these questions that we don't see work working in other places.

[00:05:14] Luke Austin: And so our, our, our answers to those questions exist in three different models. The spending power model, the created demand plan. Then our our testing roadmap tool. And so we've built those we built those tools out and the sort of methodology that we see against it, as well as the work that our data science team puts in, into creating these models on behalf of each individual brand.

[00:05:35] Luke Austin: Is is really, really unique in helping us to answer these questions that we spend a lot of time on, and most people I'm sure listening and engaging with this content spend a, a chunk of their brain power on trying to answer those three questions the most effective way possible.

[00:05:50] Richard Gaffin: Yeah. Well, I mean, I think it's a good call out to say that like part of what this does for you is eliminate some of that. Thought work, right? Like it takes, like if you're saying that, that there's that much time allocated to thinking through these things without really knowing exactly what you're doing. A lot of what these models do for you is. Kind of eliminate that, give you sort of a, kind of automatically generate the best practice for your specific brand and then you are able to follow that roadmap. So

[00:06:15] Richard Gaffin: that, that being said, let's kind of get into this specific, so the first piece of this is our spending power model.

[00:06:20] Richard Gaffin: Now if you listen to this podcast, we've talked about this a lot, so we don't necessarily have to dive deep on it. But for the sake of this particular episode, Luke, kind of tease out a little bit what that does for you. Give us a quick overview.

[00:06:32] Luke Austin: Yeah, so the spinning power model is at a high level an ensemble model that looks at a number of different models of historical spend degradation, the seasonality. Impact. We'll look at consumer confidence index that we build and we'll integrate that into the spinning power model. We'll look at categorical and competitive search trends related to brands and ensemble, the model, and to get us to the highest predictability in terms of what your spin degradation curve looks like.

[00:06:59] Luke Austin: So, at what, what, what the degradation of your spend at different intervals looks like in terms of the impact on your revenue and contribution margin outcome. And so for the spinning power model and the rest of the things we're gonna talk about, I think what. What we've spent a lot of time trying to do is to build tools that yes, allow for effective planning and forecasting because I think there's, like, there's so many decisions that we need to make on a day to basis that if we don't have a solid foundation and ground underneath us of the forecast and the plan, then it's really hard to know how much inventory to purchase or hiring and resourcing, et cetera.

[00:07:36] Luke Austin: But the, the more important part and the thing that will. Today, which is like forecasting is is more of an exercise in execution than in planning is, is being able to build tools that allow for action and operation against them, rather than a focus on just the prediction and the output of it. And so the spinning power model, how it, how it ties with that is the spinning power model.

[00:08:01] Luke Austin: Orients around three different optimizations in terms of business objectives that we can sort of, have conversations around to start out. The first is maximizing contribution margin, so maximizing contribution margin in the first month of your new customer cohort. So I want the maximum amount of contribution margin dollars from my new customers in this specific monthly timeframe.

[00:08:21] Luke Austin: We've got a brand example up here for anyone visually following along, but we'll chat through it and we're looking at. As an example, February of 2026 for this business, max cm, the recommendation has spent $142,000 to drive a little over a million dollars in new customer revenue at a 7.0 4:00 AM ER.

[00:08:38] Luke Austin: So that's max cm. The next selection would be sort of on the other end which is maximize new customer revenue. So I want the maximum new customer revenue at break, even contribution margin. And the recommendation for the spend in this scenario is 520 5K at a 1.25 at to drive 1.25 million in new customer revenue at a 2.39 MER a MER.

[00:08:59] Luke Austin: And then the final selection would be max lifetime contribution margin, which is I want the maximum contribution margin, but I'm willing to. Wait a longer against a longer time horizon, 30, 60, 91, 20 days to realize the potential of that which is going to allow us to index more spend, right, than the maxxim scenario, to, to drive that.

[00:09:17] Luke Austin: And so in this case, what I'm, what I wanna pull out most is. The distinction between the max contribution margin and then the max revenue scenario for this, for this business, so the max contribution margin. As a reminder, the recommendation was to spend 140 2K to drive a million dollars. A million dollars in two in in new customer revenue.

[00:09:37] Luke Austin: Maximize revenue would be spending 520 5K. So that is that is $380,000 of additional ad spend to drive $250,000 more in new customer revenue. That's the trade off between these scenarios is to spend 380 k more to drive 250 k more in new customer revenue, that is a bad trade off taken in the context of, of of this lens.

[00:10:03] Luke Austin: Now, there's all sorts of things in terms of halo effect on Amazon and retail impact, et cetera. But if we're looking@dotcom.com primary business, and this is the, this is the lens which we're focusing around. That trade off is is not one worth worth making. It's a, it's very negative a MER for that, for that additional $380,000 in, in spend.

[00:10:24] Luke Austin: And so what this does for us is not primarily to illustrate. Let's spend less money and just reap the additional contribution margin in this time period. That, that may be the case for many brands. But the problem is you, we need to push for growth and particularly we need to make sure our new customer revenue is at least comping year over year, if not growing year over year, or else we're gonna, that's gonna show up in our returning customer cohort at some point in time.

[00:10:47] Luke Austin: Especially for this business that has about a little over 80% LTV in one year. It's really substantial, especially for its category. And so the, the primary objective here is to understand what the trade off and the efficiency curve is so that we can know how much of the investment dollars we should take from our sort of core paid media investment and reinvest into new channel growth areas.

[00:11:10] Luke Austin: So if, if I'm looking at this as the owner operator decision maker against this business, then. I am likely going to orient around the max lifetime contribution margin scenario rather than the maximize revenue scenario. Right? Like I'm, I'm, I'm, I'm gonna spend $200,000 in ad spend instead of 400 or $500,000, maybe trade off a hundred or $200,000 in new customer revenue, but make a lot more contribution margin, right?

[00:11:39] Luke Austin: And then what I'm gonna do is challenge myself and my team to say, okay, instead of pushing for break even. On a new customer revenue, a MER, like we have been, what we're going to do is we're gonna take that $200,000 of ad spend we would've spent otherwise, and reinvest it into areas to produce growth or to increase our, our improve our efficiency degradation.

[00:12:00] Luke Austin: Increase our spending power is how we, is, how we think about it. And that's what makes this model actionable is we are able to do this exercise. Okay, we're gonna. Take the $200,000, we're gonna reinvest it into creator content, UGC street interview style videos, reinvest it into bringing on a, an, an agency or a partner to help us stand up on TikTok shops and do creator distribution.

[00:12:24] Luke Austin: Create moments out of that. You can go on down the line, right, of like, what are the units of growth we're gonna invest this in? That is going to be in, in the context of net new channels. So channel expansion. Creative and email, like those are the sort of the three buckets, right? So reinvest those dollars into creative ways in those areas with the goal of improving your spending power or improving your percentage over model.

[00:12:46] Luke Austin: And so every month we're able to see the, the spend name, your model, what the, what the plan was heading into the month. And then we actualize it at the end of the month to see how much over or under the model's expectation did we land. So all those things we did, the, the new TikTok shops launch the additional creative volume.

[00:13:03] Luke Austin: These new creative formats, did that improve our percentage over model? By 5%, 10%, 15%? Because what that does is it allows us then in March to be able to expect to perform 10, 15% over model again and continue to push that forward. So now we have an anchoring of. This is the amount of spend we need to get to sort of that baseline.

[00:13:24] Luke Austin: This is the amount that we can reinvest into these new growth initiatives with the goal of improving our spin p spinning power, or moving us further along this efficiency degradation curve. And that's how it, it helps us to yes, be predictable in the month around the objective, but to operationalize the activity that we're going to do in the context of the model and try to beat and improve against the model through that activity.

[00:13:49] Richard Gaffin: Yeah, that makes sense. And I think one way to summarize this that I've maybe used before when we've talked about this, is like at the most simple level, what this tells you is when you're wasting money, basically it tells you at what point you're spending too much on the current set of ad platforms that you're spending on. So buy one of, like if you're optimizing for. Let's say contribution margin. This will tell you at what point the next sort of tranche of spend, like the addit additional 10,000 bucks or 5,000 bucks or whatever that you're

[00:14:15] Richard Gaffin: spending in platform, starts to degrade that rather than contribute. And then same with new customer revenue.

[00:14:20] Richard Gaffin: At which point does it drop MER before below zero? And so on and so forth. Same with Max Life. Contribution margin. So the idea there being like, very quickly, this will tell you when you shouldn't be spending any more money on your current set of ad platforms and when that money can be used elsewhere.

[00:14:35] Richard Gaffin: So

[00:14:35] Richard Gaffin: let's let's jump to our, our kind of the next piece of the puzzle here, which I believe is the creative demand model.

[00:14:40] Luke Austin: Yes. Created man model. So how one of these exercises could look to sort of like, pull the line through again, as we say. Okay. Rather than pushing that additional $50,000 in spend that we might have done previously just to like drive the revenue up and it was really low. A MER, we're gonna take that $50,000 to spend and one of the areas we're gonna invest it in is.

[00:14:58] Luke Austin: Increase creative volume through net new creative formats and types against additional marketing moments and products, right? So increased creative output and additional creative formats and types against those, those, those items. So if, if that's the exercise, then what we're, what we're wanting to.

[00:15:17] Luke Austin: Make a plan against is what is the baseline of creative output needed and then what is the additional investment we're making above that to see what the impact is against our efficiency curve. So the creative demand model. Is what we what we have built to help us get a better sense of what is the, what is the necessary amount of creative output to hit that baseline expectation of our, of our spend goal.

[00:15:42] Luke Austin: And the creative demand model is built around five key metrics that we look at and build this model for each brand specifically. So the the first metric that we look at is zero revenue rate. So this, this is the percentage of active ads that never converted during the measurement period.

[00:15:59] Luke Austin: High values indicate many ads are not effective. And so for and then each of these, each of these metrics, what we're doing is we're benchmarking them against our data set of hundreds of D two c ecommerce brands. So for this business in particular, their zero revenue rate is in the 91st percentile.

[00:16:15] Luke Austin: Which is which is really great. It's a really strong outcome. This is like one of the metrics that's that's a, a strong point. The second metric is ad concentration. So this is the percentage of total ads been concentrated in the top five performing ads. You don't wanna be too overreliant right on, on, on a few ads.

[00:16:31] Luke Austin: So 15% of the. Spend in this account is concentrated against those fi those ads. When it starts to become problematic is when a third of your spend or half of your spend is concentrating just a few ads. So this brand is in the 87th percentile of ad concentration. That's a, that's a strong point. The next three metrics are where this business struggles relative to the data set.

[00:16:51] Luke Austin: And the third metric is ROAS degradation. So this is the change in ROAS after the initial launch week. The fourth metric is spend degradation the change in daily ad spend after the initial launch week. And then the fifth metric is evergreen. Share the percentage of ads that have been running consistently for 30 plus days.

[00:17:08] Luke Austin: These are your evergreen ads. They provide a stable baseline for the account. And so this business, zero revenue rate and ad concentration are in the upper percentile. The three metrics that are more challenging, ROS spend, ROS degradation, spend degradation and evergreen share. Are, are the challenge.

[00:17:26] Luke Austin: And all of these metrics then culminate in a combined creative score. And this creative score exists on on a benchmark between where 50 is the midpoint. So if you're, if you have a creative score of 50, what that's going to indicate is that. You need the same amount of creative volume output to achieve the sp the same spin objective as you would've historically had to hit.

[00:17:49] Luke Austin: So said another way. In February of 2026, if your business has a creative score of 50 and you're planning to spend the same amount in February, 2026, as you did in February, 2025, you're gonna need about the same amount of creative volume and output if your creative score is above 50. That indicates you're gonna need less creative volume and output than you did historically to achieve the same spin target.

[00:18:10] Luke Austin: And below 50 you're going to need more. Your, your creative is getting less impactful. And so what this allows us to do is identify where our creative score is relative to that baseline and try to improve that creative score. Over time, right? So what we're, what we're wanting to see is that if we're starting with a creative score of 45 in this brand's case, that we are going to increase the creative volume through new creative formats against new products, new marketing moments, different split of video imagery, et cetera.

[00:18:42] Luke Austin: And the whole objective is that the creative score when a, at the close of February when we rerun the model, that it shows up as a 50 or 55, that we've improved it against the 45 creative score and how that's going to show up most likely. Is one of the three metrics that are below benchmark for this business.

[00:18:59] Luke Austin: So roas, degradation, spend, degradation, evergreen, share one of those one or more of those metrics is going to increase, which is gonna lead to that creative score getting. Getting above 50. And in that way we have, like, now we have a game setup that we can go play as it relates to creative volume and creative output that like, we'll, we'll acknowledge there's a lot of there's a lot of wiggle room as it relates to the understanding of creative volume and what's diverse versus not.

[00:19:28] Luke Austin: But if we constrain it around this idea of. Improve the creative score and here's the five metrics that we're looking at that improve the creative score, then that's the objective We're after the the folks that are responsible for creative output and the performance of the creative for the business, then have an objective that thereafter and improving that score.

[00:19:47] Luke Austin: And how we get there. There's gonna be all sorts of ways we get there, right? Additional creator content, additional imagery against these evergreen products that we haven't supported in the past. There's a bunch of levers that we can pull to get there, but now we have something that we can measure.

[00:19:59] Luke Austin: Against it. Identify how much that activity moved the creative score over time. And then try new things to continue to try to to continue to try to improve the creative score.

[00:20:09] Richard Gaffin: Gotcha. So again, to, to do then kind of like a, a layman's quick summary of this. The idea here is if the first kind of report is, or the rather, the spending power model is meant to show you when you're starting to waste money on platform. What this is showing you is how many ads you need to make to, to maintain the status quo or rather to hit the spend target that you have set. If, for instance, your creative score is low, that means that you're going to have to do a little bit more than usual. If it's high, you're gonna have to do a little bit less.

[00:20:38] Richard Gaffin: So talk to, I mean, if, if this is relevant, I think it's, it'd be interesting to dig into the, the moments ad planning piece, because this gets into the sort of element that each month is going to have to look somewhat different anyway because of marketing moments.

[00:20:51] Richard Gaffin: But maybe speak to that a little bit.

[00:20:52] Luke Austin: Yeah. So the, the creative demand model gives us at high level, here's our creative score, here's the total amount of output needed. So for this business in February, 2026, we need 217 total ads. So far in February, a few days in, we've created six 16 have been created. Right? So that's, that's where we're at relative to to the objective.

[00:21:13] Luke Austin: And that's the gap we need to bridge 217 total ads needed for this month. Then the question becomes, okay, yeah. What is the decomposition? What is the breakdown of that creative volume? And where is the agreed upon plan for how and when and by whom that's going to be delivered? And so the creative plan, we break down into two main subsections, moments, added planning.

[00:21:34] Luke Austin: And evergreen ads planning. So moments ad planning is going to be the creative that you're delivering that is connected to a marketing moment on your calendar. These are gonna be unique things that are happening this month. The new product that you're launching, the the, the promotion that you're running, the influencer drop, the VIP giveaway, whatever it is, right?

[00:21:54] Luke Austin: Like these are the marketing moments that need creative output. Over the course of the month, evergreen Ads Planning is going to be all oriented around products or categories. And this, these are gonna be ads that are going to be able to be run over a longer period of time and not tied to a marketing moment or, or a marketing calendar.

[00:22:09] Luke Austin: So moments ad planning for each of these the, those buckets. Then we identify every single marketing moment over the course of the month. We integrate it. This ties directly with the marketing calendar that's integrated in stat and identify for the marketing moments we have planned. What products are gonna be focused on for those marketing moments.

[00:22:25] Luke Austin: So if it's just like sitewide, all products, great, we have that. If it's specific categories or if it's specific subsets of products, those get identified. Then from there we identify who is producing that. So we have different selections for if. The brand or the client is producing that versus if we are producing that and if we're producing that in a branded ad format or a creator content UGC format, so we have clarity of who's, who's producing and delivering that.

[00:22:50] Luke Austin: And, and then we break that down into video versus image. So, our model runs and looks at your. Video and image historical performance. And then creates a recommended split of, of, of the total assets, which should the split be between video and imagery based on prior performance. And then we track the delivery of those things.

[00:23:07] Luke Austin: So we create the plan. Here's each of the marketing moments and each of the products that we're gonna. Be creating ads for here is the, here are the products or categories that they're gonna be focused on. Here's the producers who are responsible for delivery, and then here's the amount of videos and images for each one of those things.

[00:23:23] Luke Austin: And then once they're delivered they get, they get tracked. And so we can see against the 217 total ads for this month. Here's the specific plan of. What they're being created for, by whom in what timeframe? Video versus imagery et cetera. And then track. As those ads get launched in platform we can track the delivery against that goal.

[00:23:41] Luke Austin: Okay.

[00:23:41] Richard Gaffin: Right. So in other words, it takes it, ob obviously like it, it's simplistic to say that this just shows you what, how many new ads you need. It also shows you or allows you to kind of execute on exactly which ads are gonna be for what. Whether they're evergreen, whether they're for specific moments. So it in all, in all, it's sort of a completely actionable report and not just merely a model.

[00:24:03] Richard Gaffin: So

[00:24:03] Richard Gaffin: speaking of actionable things, I think let's jump then into the incrementality roadmap because this. I believe is going to be relatively new. So we've talked on this podcast about these first two models a few different times, so apologies to those who've heard it before. We've talked about incrementality a lot, but we haven't specifically dug into the way that we are modeling and then kind of building an actionable plan around that.

[00:24:25] Richard Gaffin: So Luke, why don't you walk us through this.

[00:24:28] Luke Austin: Yeah, so just like the contact, the conversations around creative, which is like how many ads do we need and against what moments and products and what formats. And there's a lot of ambiguity and lack of clarity related to what's informing that decision making and then the plan against it. So just like the creative demand model and plan is helping to solve.

[00:24:46] Luke Austin: For that the same sorts of conversations exist related to measurement roadmaps and testing roadmaps on the media channels. And what it in our experience, what happens when there's ambiguity related to either these things, they get the whole creative workflow or the measurement testing workflow.

[00:25:02] Luke Austin: Is that what it leads to in more cases than not, is not actually like an abundance of. Activity and chaos is, it leads to inaction in most cases, where everyone's just like sort of at a standstill because there's more questions than answer and there's not agreed upon framework for how we determine these things.

[00:25:17] Luke Austin: So there's like seven channels we want to test and all sorts of interesting questions we have. But then for months, we just end up talking about. These things and not actually doing any one of them. And same with the creative workflow where it's like, oh yeah, there's, there's probably new formats we should explore, but like, who's responsible and how does it like, and so it's like, well, let's just keep creating the 50 ads that we have for each month, and like using the same designers we have and not looking at other, you know, partners or ways to get there.

[00:25:43] Luke Austin: And so, as it relates to the testing roadmap, what we've. Built, and I have here up on the screen and I'll talk through is is our, is our incrementality roadmap and what, what we do. The core thing that we're trying to get at is which channels and tactics should you test first and then the sequence of your remaining channels and tactics in your media mix based on the potential.

[00:26:08] Luke Austin: Revenue impact in the worst and best case scenarios for each. So what I mean by this is we have a database of incrementality tests that from from our customer subset where we can see. The upper and lower bound of incrementality results for each channel and tactic. So for meta acquisition, for example, let's say as an example that the, that the, on the lower end, we have seen tests as low as a 0.3.

[00:26:36] Luke Austin: Incrementality factor for meta acquisition should be. Really low. Right? So meta seven day click acquisition 0.3 on the, on the worst case. And then best case, we have seen tests come back as high as a 2.8 or 2.9 incre effect. Right? Just as an example. So it's a really wide range of the incrementality that brands see and.

[00:26:56] Luke Austin: And so we can take an average of all of these tests at a starting point. So that's, that's great. Like, that's a, that's probably the best starting point if you're gonna use anything for the measurement, is use like an average benchmark, but it doesn't account for the wide, how wide the distribution could be.

[00:27:09] Luke Austin: And if you're closer to the worst case that then the best case, like there's millions of dollars in between those upper and lower bounds that the channel could be impacting for you. So for this business. Specifically meta acquisition is top priority channel for them. 'cause right now the platform is reporting 2.5 million in on platform reported seven day click revenue for their meta acquisition campaigns.

[00:27:31] Luke Austin: In the worst case that could, that 2.5 million platform revenue could actually be 1.5 million in incremental. Against a little over $700,000 in spend. So at that point, we're getting really close and on some categories potentially already at the level of negative contribution, right? So worst case, 1.5 million in revenue against that 711 K in spend.

[00:27:55] Luke Austin: Best case scenario is, is the revenue that med acquisition is driving could be closer to $4.6 million if they're at the upper, sort towards the upper bounds of the incrementality based on our dataset. So 1.5 million to 4.6 million. So that's a $3.1 million swing between the upper and lower bounds of this is unaccounted for undefined revenue right now as.

[00:28:19] Luke Austin: The decision maker against the indexing, the resources against this business and where I'm gonna put budget. Like if there's a $3.1 million swing between what this channel could be contributing that is one of the biggest problems to go solve, to get clarity against that, to know what the actual incremental impact this channel is.

[00:28:37] Luke Austin: And then go on down the line. The second channel in this list is TikTok. On the low, low end, it's 590 k. Of revenue on the upper limits, it's 1.77, so that's a $1.2 million swing. So that's why it's second on the list, right? Instead of $3.1 million swing, it's 1.2. Still a lot of money to go and get clear, clear on but less than meta.

[00:28:56] Luke Austin: And then on down the line. And that's how we use the incrementality testing roadmap to sequence the prioritization of tests between each of the channels based on the upper and lower bound incrementality factors that exist in our data set.

[00:29:09] Richard Gaffin: Right. Okay. Yeah, so I was gonna say like, what, to be clear, what this is prioritizing is how urgent it is for you to, to test this essentially. And so is the idea here. So obviously that there's just this enormous range that Facebook acquisition. Could be in terms

[00:29:23] Richard Gaffin: of its incrementality. So the idea is then we'll test that and then would this report kind of narrow down the bound as you get closer and closer to what you're looking for?

[00:29:32] Richard Gaffin: And then at that point it would drop it down the priority list as you get more and more clarity on incrementality. Yeah.

[00:29:38] Luke Austin: Yep, that's right. Yeah. So as we can see sort of in the right, we have in the right hand column here, the tests that have been run for each of these channels. So for this business, we haven't, we don't have any tests run but for others we do. And so as we. As we go through, we'll say, okay, med acquisition is at the top of the list.

[00:29:51] Luke Austin: We're gonna stand up a geo holdout, inverse holdout incrementality test. Run through that. Once that test is finalized and at stat, at stat sig p value of, of of 0.1 or better, then we get the clear incrementality read. We get a check mark against this channel. It's been validated. It drops to the bottom of the list.

[00:30:08] Luke Austin: Right? And then TikTok TikTok acquisition is now, then it's Google Ads brand, then it's Google Ads non-brand in that, in that order.

[00:30:14] Richard Gaffin: Right. So it's primarily like what the, the action. That is actionable from, this is testing priority, but presumably there's also other decisions that can be made off of this. For instance, we can see from this particular report that branded search on Google is almost definitely overreporting by a

[00:30:33] Richard Gaffin: lot. So does that then indicate we should be pulling back there or we should be pushing it?

[00:30:39] Richard Gaffin: Or like what exactly, what kind of like second tier actions can you take based on this report?

[00:30:45] Luke Austin: Yeah, yeah, exactly. Yeah. So go Google Ads brand. There is a range for Google Ads brand that's illustrated here. But what Google Ads brand for this business is currently reporting is way outside the range. Right? Which is, which is what you're referencing. So, what, what we do as a starting point is that any given point in time you want to use.

[00:31:02] Luke Austin: You want to use the best representation of truth that you have at that point, at that point in time, right? Like you we're gonna use the best test result that we have available and, and action against it until we have another data source that indicates differently. And so for us, the starting point is using.

[00:31:22] Luke Austin: Are benchmark incrementality factors which are against each of these channels. It's looking at a benchmark factor that's that's that more represents the average test result for these channels. So for med acquisition, the benchmark incrementality factor is a 1.2. For Google Ads brand, it's a, it's a 0.3, so we're gonna use those starting point factors immediately.

[00:31:40] Luke Austin: To your point, like Google Ads brand is, yeah, most definitely Overreporting relative to even that range. What we're gonna do is for every single channel, use the Ben, the benchmark incrementality factors, that's gonna be represent more of a midpoint. And we're gonna action against that. So for Google Ads brand, that would look like adjusting the ROAS target in platform to be in align with that factor.

[00:31:59] Luke Austin: And most likely that's going to result in that channel having to run more efficiently and likely at lower spend volume and then reallocate those dollars elsewhere. But then one, when we actually run an incrementality test for this. Brand for Google Ads brand as a channel and tactic. Whatever that factor is over overwrites, the incrementality factor that we're using as the baseline, and then that's what we're using as the best representation of truth moving forward to action on.

[00:32:25] Richard Gaffin: Right. I think that's, that's a

[00:32:26] Richard Gaffin: really great point too, like that with this particular example of, of the way that you're probably overspending on Google ads, we, you don't actually know that, so that's worth acknowledging to say that like. It's totally possible, or it's within the realm of possibility maybe that your Google brands really is returning $3 million or 3.2 mil rather than 1.6, which is the upper bound of this particular of our kind of average incrementality factor here for

[00:32:53] Richard Gaffin: Google or incremental revenue. But. So it is possible that it would be a mistake to pull back. However, this is the best information that you have right now. So yeah, you are rolling the dice to some extent, but you are making the right choice based on probability that like the Google, Google branded is most likely overspending here. So once you can narrow that down, you'll get a little bit clearer.

[00:33:15] Richard Gaffin: But at least for decision making purposes now, that's the thing that you should go off. Yeah.

[00:33:20] Luke Austin: That's right. And I, and I think what's maybe to take this a little more high level as well which is part of the challenge here is that. I think it's more comfortable to not make a decision than to put a stake in the ground and say, this is what I believe and this is what I'm gonna action against.

[00:33:40] Luke Austin: And so, but what I, what I wanna say is, in, in not making a decision, you are making a decision, right? Against these things. Like if we were to say Google Ads brand, no, I, I, I don't trust the incrementality. Baseline factor. I'm not gonna, I'm not gonna trust an incrementality reading for Google Ads brand until we run a geo hold that test for this, our channel and our business specifically.

[00:34:00] Luke Austin: Right. So that, that could be, that could be someone's initial approach, in which case what you're saying is you are gonna operate off of. Some other indication of measurement and belief rather than the baseline factor. In the meantime, maybe it's platform reported revenue. Like I know, I actually do believe the platform reported revenue is the 3.3 million that's that's happening here.

[00:34:19] Luke Austin: You are making a decision at that point that you are using the platform reported revenue based on the attribution model that Google uses as your. Indication of what is actually happening rather than the incrementality baseline. And it, it may, this may sound very like, yeah, yeah. Like of course that's, that's the case.

[00:34:38] Luke Austin: But the, this, this, we, we run into these conversations and have to work through this so many different times with the partners that we work with, with, with folks on our team as well, where that is. Choosing to not use the incrementality baseline factor is making a choice to use a different measurement source.

[00:34:55] Luke Austin: And it may, it may seem, I think it seems safer to people because that's sort of just like what we've been doing. And so like if something goes wrong. There's not gonna be implication or blame associated with it because that's what we've been doing historically. So let's just like keep it there. But by making a decision to not move to the incrementality baseline factor and to keep using that thing, you are making a choice that that is the better signal.

[00:35:17] Luke Austin: And so, and, and this like what, what we're after is a clear decision making framework against these things so that we can all agree upon it and make decisions in line. And we're pushing our. Are people as well equipped with these tools to be courageous in making this decision? Because it's, it's a, it's a, it is a courageous decision for a business that has been, let's say, looking at on platform reported or whatever attribution model reported factor for Google Ads brand for someone to say.

[00:35:45] Luke Austin: Incrementality benchmark is gonna be our, our best source of truth, and we know it's going to be wrong actually. We actually know it's going to be wrong once we run an incrementality test for this business in the future. But it's going to be more right than the current thing. That's, that can be an uncomfortable.

[00:36:01] Luke Austin: Decision and, and conversation. And it's, and it's courageous to sort of sit in that middle gray area of like, we know this isn't the fully right thing and we won't get the fully right thing and for two months from now until we can run that test, but this is the better, the better signal and we're willing to use that and put our stake in the ground at this point.

[00:36:19] Luke Austin: And that would be, that's our encouragement to our people in these conversations is to move towards the. Better indication of the better signal, the better indication of what the, what the actual contribution is of these things. Even with the creative demand model. Like, do I really need 217 ads?

[00:36:35] Luke Austin: Like, like that? That seems like a lot. We've been making 50 like that over a hundred ads, right? It's like. That's not the point. The point is make more ads in more new formats for new mar more marketing moments and more evergreen products, right? So if it's 150 instead of 50 instead of 217 instead of 50, that's fine.

[00:36:55] Luke Austin: At least make a, at least make a movement in that direction towards the thing that has the higher likelihood of being impactful for the business.

[00:37:04] Richard Gaffin: Yeah, that's right. No, I think, I think that that's a good point that we may well be wrong, that there's a 0.3 incrementality factor on this brand's branded search. However. Every single, basically every single test that we've run indicates that Google branded search is always wrong and it's always over reporting more or less.

[00:37:22] Richard Gaffin: So in this case, that means it seems like a better gamble, but may maybe a way to

[00:37:26] Richard Gaffin: kind of tie it all up is what this is giving you is a way to make leaps of faith require a little less faith, or to have like a little more clarity that you can make the jump. And so any, anything else that you want to kind of hit on these three models here?

[00:37:40] Luke Austin: No, I don't, I don't think so. I think if there's, if there's ambiguity that exists in your organization or if you're feeling a lot of time. Spent wrestling with this or, or varied perspective as it, as it relates to any of these questions or even just the exercise of like, if someone were to come in and ask you versus two other people who are your peers or on your team?

[00:38:01] Luke Austin: If. What should my total budget allocation be? How much creative do we need for this month to be able to get there? And then what channels are we testing next? And why? If anyone would answer those questions differently between a small group and your team, which in most cases I would, I would bet is the case.

[00:38:17] Luke Austin: I'd encourage you help let us build these models for you and give, give you some, some clarity at least be helpful for you in the midst of those conversations.

[00:38:24] Richard Gaffin: Yeah. And, and as Luke is alluding to, let us build these models for you, we're, we're doing it for free. If you are a 10 million and up brand, you're in the eight figure range. We will do this for you for no cost, which is, in my opinion, a crazy deal, but we're doing it right now. So for for the the next little bit here, we will be doing that.

[00:38:43] Richard Gaffin: Please reach out to us common thread code.com, smash that higher up button. Let us know that you're interested in us building these three models for you to provide that clarity that you may be lacking right now. And we'll gladly do it for you. Alright, that's gonna do it for us for this week for both of us.

[00:38:57] Richard Gaffin: Take care of everybody and we will see you next time. Goodbye.