Interview with a 10 year marketing analytics and attribution veteran.

Zach Johnson

Dylan Carpenter

John Wallace


John Wallace


CEO and Founder

Apple PodcastsGoogle PodcastsLive on SpotifyLive on Youtube

Entrepreneur and data geek. Founder and CEO of DataSong (sold to Neustar).

Episode Summary


  • Why you should be looking at ecosystem or blended metrics vs platform specific
  • Boosting LTV like a boss
  • Testing different experiments and how to make the most out of them




John (00:00):

As you spend on these campaigns, there's a hierarchy, right? You, you have, uh, the campaign level, the tactical level, the channel level, uh, but on the outcome side, you have hearkens, right? Some of that spend is dedicated for acquisition. So you can split out, uh, new customers versus repeat. Uh, you have some of that spend that ends up being, um, specific to a particular category or a particular line of business that you're advertising. So if you're really advertising for, uh, Bob's hammers, you don't expect that to have a whole lot of effect on the rest of the merchandise hierarchy. It's that intersection now to say, and you have different margin for different products, right? So now you're at the point of saying, if I had another dollar spin, where would I get the most margin? Where would marketing make the biggest contribution

Speaker 2 (00:44):

To profit? [inaudible]

Carson (00:57):

On this episode of the rich dad, poor dad podcast, we have John Wallace, founder and CEO of data song and a 10 year marketing analytics and attribution veteran. You're going to win a listen to how John boosts LTV like a boss and how he kind of dives into why you should be looking at ecosystem or blended metrics versus just looking at platform specific metrics. And lastly, my favorite is how he tests different experiments and how to make the most out of them. So go ahead and sit back, relax and enjoy the show. But before we begin, if you are a media buyer or agency owner, go to to see how their financial tools can help you scale your ads without further ado, here are your hosts, Dylan Carpenter and Zach Johnson. Welcome to another episode

Zach (01:55):

Of the rich dad. Poor dad podcast is your host, Zach Johnson. I'm with Mr. Dylan Carpenter. Dylan, are you ready to geek out on some data? Oh

Dylan (02:04):

Yeah, man. You know, I'm all about that data, especially a billion dollars worth.

Zach (02:09):

I love it. Well, today's guest is the founder co-founder and CEO of lift lab, which measures over a billion dollars in ad spend. Now, if you're a DC brand, uh, or an e-commerce and you've ever wondered what is the attribution analytics stack of some of the brands that spend north of a hundred million, even 200 million a year in media, this show is going to be amazing. So, uh, John, the data scientists, welcome to the show.

John (02:43):

Hey Zach, thanks for having me on, Hey Dylan. Yes.

Zach (02:46):

Yes. So give everybody a high level of, uh, your background and a lift lab.

John (02:51):

Yeah. Uh, you know, I guess I found a passion for data in all places in business school, and maybe that's why I still work in marketing. A lot of the times I've measured all kinds of things in my career, but specialized in marketing measurement, um, probably, um, about 2010, uh, grew a company called data song. We were a multi-touch attribution provider, uh, to a lot of top brands, uh, and, um, fortunate to sell that company. And now we're building a next one. It's called lift.

Zach (03:18):

So how do you, uh, how are you crazy enough to do, uh, an half an analytics and attribution business and then want to do it again? That's crazy.

John (03:27):

Uh, I guess let, uh, happen is a change in the landscape. Multitouch attribution was a lot of fun to work on. It was really, really rich data looking at kind of customer journeys and consumer journeys, uh, you know, through the, uh, through the, uh, acquisition funnel. Um, but we're realizing that that data is all going away. Privacy concerns are making the data that we used to, you know, we used to know that, uh, Dylan, uh, you know, uh, didn't click on an ad, but saw the impression of it and that that data is harder and harder to attribute to Dylan in the first place. And so lift lab is a refresher of the problem, but with a completely new approach.

Zach (04:04):

Yeah. Well, pretty timely. Uh, how, how are you responding to, uh, the whole apple, uh, and Facebook fiasco right now?

John (04:13):

We're actually smiling. Uh, most of the premise of lift lab, uh, 18 months ago is if all that data were to go away, uh, what would you do about it? What, how would you still have a reasonable method for measuring? And so, uh, we're actually just being proven right. And in the right place at the right time. Ooh, I love

Zach (04:31):

It. I love it. So give us a, give us kind of a high level of the, I guess, your, your approach in your, your model at, uh, lift lab on the analytics side,

John (04:42):

You're, you're seeing this, uh, happening kind of in mobile gaming right now where they're, they're all of their measurements been turned upside down. And I think what most brands are recognizes that they have to have some embrace of kind of top down attribution. And what we mean by top-down is, um, really being able to look at the signals of when I spend what happens at the cash register, right. Or when I spend, I might able to light up the checkout page. Um, so it is through advanced modeling. It's, um, also underpinned by advanced experiments. Uh, and so if you don't have that user level data, if it, if it, if it's, if it's kind of getting thinner and turning towards note, when you have no data, uh, what were the techniques that you'd put in play and you kind of have to be smarter about it. Um, and that's really the motivation for lift lab, God

Dylan (05:32):

Breath of fresh air, to hear, to be honest with you, with all the tracking issues we're having these days. So like, oh my gosh, I'm geeking out on this because I know one thing we did recently, as, you know, with the whole 28 day click one day view window that Facebook seeing with the new updated seven day click one day view, we've had to create, you know, customized Roaz multipliers, essentially to where we would just go back for six to eight months of data, kind of gauge what the revenue wasn't a one day click day view, seven day click, 28 day click. I'm kind of broke up the numbers to find more of the averages. So, you know, with this iOS 14 fiasco, we can essentially have some tunnel vision into kind of what our average kind of results are based off kind of previous, you know, metrics there essentially so far, it's, it's still pretty early on in it, but it seems to be, you know, helping a little bit and kind of being a little bit more proactive, but she's man, I imagine it's gonna be blown up for y'all even more, you know, once, you know, more of this kind of comes to life.

John (06:24):

Yeah. I mean, uh, just those attribution numbers that you rattle off, uh, we're pretty impervious to it. Uh, we have a kind of a north star, uh, which is, uh, we want to hold them, um, the, the marketing and the media accountable for changes at the cash register or changes in the checkout page. So we're not looking ever at the platform of reported, uh, attribution. We, we know it's wrong and we're not trying to use experiments to fix it. Uh, we're saying what the platform reports reliably is spend and impressions and clicks. And can those be used to explain, again, changes at the cash register that

Dylan (07:02):

Makes complete sense that it's a lot more ecosystem and blended numbers versus platform specific just because they're consistently net never that accurate absolutely.

John (07:10):

On a given day, you're going to have a healthy amount of revenue. I wish you do for your business, right. Without a specific campaign, even without a specific platform. So the question is, as I flex muscles on that platform and I go from no spend up to, you know, whatever your daily is a hundred, uh, K um, what does that, what does that look like? You know, it's not a constant effect. We know that earlier dollars in work better than the later dollars in all marketers are kind of in tune with the fact that there's diminishing returns on these platforms. And really this is a methodology to go tease that out.

Dylan (07:43):

It's interesting. So with these super large brands, you are kind of, you know, analyzing, spending, you know, well over a hundred million a year, do they really, you know, when they're kind of doing the reporting or you're assisting, is it pretty blended? Do they care about platform specific metrics or is it, Hey, we spent, you know, 20 K across every single platform today, we generated 30 K in sales, cooled profit. Is that kind of how I looking at it? Or are they getting really, you know, micro on a kind of, you know, tracking method?

John (08:10):

Uh, so the, the tracking and platform, uh, work is still in place. There's people looking at that and, and, and using it for kind of last minute, uh, optimization. So it's not, uh, it doesn't go away. Uh, no, the, the north star here is, um, for any of these tactics, any of these platforms and all the way to the campaign level, uh, how has the last dollar for spin? How was the last dollar performing, right? The last dollar spent and, um, you know, never want to see that number go negative, right? We never want to spend a dollar in and not have the cash register ring enough. Uh, and so that, that that's, that, that becomes a north star and it's done over and over again by, by platform, by tactic and all the way down the by, by campaign.

Dylan (08:55):

That makes complete sense. Well, let's get to the nitty gritty y'all so of course we love to kind of see we're dive into what you see working these days kind of just based off y'all's kind of clientele base. So, I mean, what's, y'all's rich add here. What's working good.

John (09:10):

Uh, you know, I, I, uh, I think that every ad is a good ad or a rich ad. Uh, and the question is just kind of how much, and what separates them. I like to remind all of my, uh, marketing friends, like you have an easy job. Right. And what I mean by that is you only can make revenue go up, uh, and you have to think about it. Marketers have to work pretty darn hard to make revenue go down. Right. And maybe somebody that got in hot water doing that recently was, uh, was Peleton, right. Didn't they make it in the news.

Dylan (09:38):

Oh yeah. Yeah. And that's what we were chatting on before to where a little sexist issue. And I remember I watched, and I was like, what are people freaking out about? But I guess, you know, it's different perspectives, but yeah, that wreak some havoc, but at the end of the day, that got so much awareness off of them to where once it kind of blew over, I feel like it blew them up even more. It's where, you know, whether it's a good at or bad at it, it probably did something good for them. It, you know, in the long run

John (10:00):

Yeah. To say that you have to try hard to make sales go down, uh, you know, you'd have to kind of, so we, we know that at a minimum marketing is flat, but it's usually positive. And so then the question is, w what's what's different between a good ad and a rich ad what's different between a good cohort and a bad cohort. And again, our, our, our, uh, our north star on that is, is look at all of these, look at them simultaneously, and just know at the current spin level that you have, how has the last dollar performing, and that informs whether you put another dollar in there somewhere else where at yields, huh.

Dylan (10:31):

Now with us being on the case for Peloton, what's been one of the more profitable campaigns you've seen that, you know, it's, it's not a kind of revolutionary, you know, tactic, I would imagine most strategies are pretty common across most brands. I feel like none of it's too revolutionary there, but, but what's been one you've probably seen a couple of the brands do that have kind of, you know, per dry browse up a little bit. Like I kind of recognize this. It seems to be working good for them.

John (10:53):

Yeah. Uh, you know, for us, it's okay. It's getting a baseline for any evergreen campaign that's out there. And that's usually eye opening to take the, uh, the performance of anything that's evergreen, uh, and, and start to state it on a last dollar basis. Uh, I think an area that people have been surprised is the, uh, prospecting, uh, campaigns, uh, when they're looking at them often in, uh, in platform, on a column platform dollars, uh, that they're understated, uh, the quality of those campaigns. And so, uh, watching, uh, brands be able to more and more comfortably, uh, fun prospecting campaigns. And, and then where that, where that movie goes is to say, what are the audiences that are, that are better for me? Um, right. So when I, I love watching the, the eyes light up of a marketer that says all the audiences are good, but this is where I'd spend the next dollar.

Dylan (11:51):

I liked that. Yeah. I mean, it's kind of, Hey, everything's working, but we should really kind of ramp up this area because it's the best opportunity costs there. So you are kind of on the top of her prospecting, I'm curious on your thoughts on retargeting. Do you have any kind of baselines for, you know, if somebody is spending shoot a hundred million a year, what do they kind of allocate to retargeting and how worth it really is it since you've already kind of spent some money on the front end, now you're going to spend some more money on the backend, maybe hit them with the email side. What are your thoughts on the overall retargeting side of things these

John (12:20):

Days we've confirmed multiple times that, that, that, that retargeting works. And again, it becomes a question of am I spent at the appropriate level on it, or have an overspent on it, or, you know, under spin on it. And again, versus all the other things that I have, uh, in the, in the portfolio that I could be spending on, I can give you an example of where retargeting, you know, it didn't work so well for a customer, uh, just back to those cohorts, right. But they're very, very most loyal customers, right? So this was a retail brand and they've got customers that are kind of customer for life. Um, it turns out that, um, they were giving them a lot of incentives to transact. In some cases, these people were even carrying a credit card with the store's name on it. Um, and, uh, you, you can't spend on retargeting there and make a difference, right? Putting an ad in the newsfeed of your best customer. They don't care. Right. It's the fact that you're giving them triple points or other incentives. And so, you know, it's not, it's not to say that customer stopped retargeting, but they found areas where the retargeting just doesn't have an effect.

Dylan (13:29):

Okay. That makes sense. So kind of once you have some data to kind of gauge okay, cool. When somebody bought from us maybe three times, we know they're going to keep coming back, they already have a card on file most likely. So it'd probably be more beneficial for us to kind of hit those first time buyers, maybe middle of fun or usable users to kind of use that money wisely, I guess you can say.

John (13:45):

Correct. Yeah. Um, so if you, what's happening is if you're looking at the averages, you're continuing to spend on these really good customers, they bump up the conversions cause you know, they're going to buy. And so being able to find those patterns and capitalize on is kind of really the motivation,

Dylan (14:01):

Total sense. Now on your deep dives, do you ever kind of look into LTV? That's always been a fun topic too, or, Hey, what is this worth on a one day, seven day, 30 day, 180 day. I'd be curious on with types of brands I'll work with how large rural LTV plays when it comes to, you know, setting up those KPIs to acquire new customers, essentially.

John (14:21):

Uh, yeah, it really depends on the business. Uh, and for us, they, they really, I, the camps that I put them in, are they a transactional business or are they a, a retention kind of a subscription business? And so for transactional marketers, if they are really living for the next transaction, they can't really go much past the next, you know, the next one, uh, whether I need a sweater or not is really going to be why I'm on the, you know, the website shopping for sweaters, um, in, so it's very, um, when you move over to a customer that has an app, uh, or is doing something, um, that's really subscription-based, uh, and that's where you see kind of gaming, that's where you see LTV kind of really come into the equation.

Zach (15:05):

So what's the secret on getting LTV back in to your, your, your ROAS, right. I mean, like, there's, there's been a lot of different analytics tools that have popped up in the last, like three years where, you know, they're, they're, uh, taking their CRM revenue, you know, their Stripe data, Shopify data and, and kind of pushing that in back to the ad IDs or the device IDs. But, um, and even some that are matching, um, based on email address, right. Email opt in to email purchase of those. So what's the, uh, what's, what's the lift lab approach to, um, you know, getting that LTV revenue back into, uh, uh, matched with advertising

John (15:50):

Data. Yeah. We're, I mean, like we said, at the beginning, uh, the best practices and LTV have just kind of gotten torpedoed, um, by issues and tracking, uh, and those best practices. You still also can sometimes question just how strong they were. They were industry practices, but, you know, a lot of the, a lot of the LTV analysis was still tied to last click. Uh, you know, how did I, how did I get this subscriber? And they attach that to a particular, you know, campaign and platform, uh, it's causing people to hit the reset button, uh, you know, our, our, our stance on this is, um, you need to go traffic, you need to go traffic spend experiment. You need to say if I'm, if I'm spending on this channel and I'm acquiring people, uh, how much can I influence LDV? I mean, really that diminishing return it, it plays into LTV as well. We all know that the more that we spend on any given tactic, the crappier, the clicks get, right, it's just that the options are not infinite. Uh, and so, uh, that has a big, um, wheat down on LTV. You're going to start to get people that are kind of tire-kickers and, but never really stay with the brand. Hmm.

Dylan (16:56):

That makes total sense, especially when they're expecting deals and went on incentives every single week or something, man. Okay. That's interesting. So rich had knocked out. I want to hear some horror stories. So, I mean, what have been some of the cases you've seen that have resulted in very poor ads or does flop strategies or things that kind of, it's been like, Ooh, we probably could have avoided that pretty hardcore.

John (17:22):

Yeah. I mean, uh, you know, there's those, there's those edge cases where people have spent money and made sales go down, but unfortunately that's just really, uh, not that common. Uh, well, we are always looking for is, uh, an OnGuard against our, where are the cases where you're spending more than it's producing, right. If you, if you're, if you're last dollars in you, you know, your whatever, your last $10,000 in you don't move the needle enough in terms of revenue, you really have passed diminishing returns and you have no business doing that. Right. It's just hard to argue, especially if, uh, you, uh, had another channel where you actually were still leaving money on the table. So that, that for us is kind of the, do not go zone. Uh, it's always a bit sobering when those are found. It's fortunately not often the case.

John (18:13):

Uh, so often there's still lift in these campaigns and it really becomes a question about where I want to put the dollar, but those chances, uh, where you've actually overspent your past saturation, uh, that's what we're on guard against. Um, and think about D I dunno if it's happened to you, Dylan, or, you know, but if you're the person who has found a case where they've overspent, think about when you're an unwinding that you kind of have to hold your breath, you have to cut, spend, we know revenue's going to go down. Remember revenue only goes up except for our edge cases. So you're going to have to watch revenue go down, but it goes down to less than what you get you pocketed and savings, right. Uh, that just taking, uh, marketing teams through that exercise is always, uh, you know, it's always, eye-opening

Dylan (19:01):

Now, what are some of the cases you've seen where, Hey, we're going to ramp up, sales are going down, you know, sales should only be going up, you know, regardless of kind of what's going on based on the growth side, but would have been a couple cases where it's kind of flip-flopped.

John (19:14):

Uh, no, we, yeah. So the only case, uh, the only, uh, uh, scenario where, uh, we would cut, spin and watch sales go down is that the spend savings outweighs the decrease in revenue. Right? So, yeah, so that's the whole, the breath moment and saying, okay, I just pocketed 10 grand in sales, went down by whatever, pick your number three, three grand. That's a smart decision. Uh, and now you get a double whammy because you're going to go put those 10 grand that you saved into another, uh, campaign, uh, where it's has positive yield.

Dylan (19:46):

That makes definite sense. Yeah. It's interesting. The amount of people I even come across, Hey, we're spending, you know, 30 K a month, you want to spend a hundred K a month to do the exact same stats. It never goes that way. Would you be able to explain why, you know, when you kind of ramp up and start doubling budgets of why you're a CPA or your acquisition costs may differ, did you have any kind of insights on that side of things to where, you know, no Metro is going to stay the same, that kind of, you know, higher spend threshold, essentially.

John (20:13):

Yeah. I mean, it's, it's diminishing returns. Uh, you know, you're relying on these platforms to find, uh, the best targeting they're using machine learning. They've got tons of data. They've tracked consumers, you know, eight ways to Sunday. Uh, the whole point of using those models and taking advantage of them is that the cream is at the front, right? Your, your, your earlier dollars on are performing better, uh, than your later dollars in. So, uh, it, that principle hasn't gone away. It, it, uh, it, it holds for every platform, as long as there's targeting in places. There's, if you're spending money, money randomly, or if, if there's no targeting, then yeah, it doesn't hold. But once you start to try to cherry pick, and then that's when you know that the quality's not infinite, you front, you've stacked the front of the deck with the best people in it. At some point, the quality goes down and really it's your job as a marketer to kind of reverse engineer and figure out where that point is.

Dylan (21:07):

That makes sense. Now, you kind of brought the algorithm and the machine learning side of things. How strong do you think those are on platforms these days, like fricking, you know, Google, Facebook, all these different, you know, acquisition tools, how well do you think the algorithm is performing? Cause you mentioned kind of audience targeting. And as if, you know, if your targeting is good, it's usually a little bit nicer there, but have you even noticed the algorithms smarter than most media buyers? So whenever I try and get really specific with, you know, engage shoppers luxury goods, it usually kind of has the opposite effect. I usually feel like to where the more broad we go that typically better just because of the data we have, the machine learning is just so good for us.

John (21:44):

Yeah. I mean that machine, I mean, look, look at the amount of sophistication that the, the teams that have been put together and look at the size of the, uh, of the platforms and the growth that they have, none of it's an accident it's because they're providing value, right? It's, it's, it's, it's, uh, they're able to, uh, provide lift and their, their marketing, uh, juice is, uh, often working better, uh, than what we were doing, you know, whatever five years ago. Um, the same principle holds though, uh, at some point those options get tired, right? There's fatigue. Uh, you're, you're, you're going too deep into the auction. And for lack of better terms, you start to get crappier.

Dylan (22:23):

Oh yeah, no, I run into that all the time. So

John (22:29):

Yeah. I like the idea of putting, uh, I mean, uh, putting more and more, uh, of the heavy lifting on the algorithms and it, it, it, as a marketer, you know, you have those cases where, um, you know, maybe, uh, the cost of that really this does boil down to cost, right? The cost of targeting luxury people, uh, like your example is, is prohibitive and you're better off buying, um, call them the best prospects out of a machine, learning, driven a broad audience and those questions that are what we answer day in and day out. Um, what's the economics of, of scenario a what's economics, a scenario, scenario B. What I really like about this is that it's not often the answer is not winner-take-all. And so often, um, we've we frame these in, in marketing is as winner take all, should I find audience a or audience B, right? Which one has a higher response rate? Okay. I'm going to run with that. Uh, I love, uh, rethinking that and saying, what if the answer was spend $2,000 on audience a and $5,000 on audience B, right. Let's get the cream out of both audiences and just know at what point we want to stop, where, where do we hold them a lot better than just saying all in, on, on what audience that makes

Dylan (23:42):

Sense. Hey, it's probably saving us problems, eggs in one with, you know, creative fatigue, audience, fatigue, audience saturation. There are so many things out there that are just variables involved to kind of gauge performance. It's, it's only going to get messier too. And I was even reading something on how they're probably gonna come up with some more restrictions on, you know, making Facebook and Google dive more into what their algorithm actually looks at. So I'm kind of curious where the future holds, uh, you know, understand more of how the algorithm works, how many data points are truly kind of tracking there. Cause that stuff's just bananas to me and I love it.

John (24:15):

Yeah. I mean, their algorithms are black boxes there, you know, it's kind of tighter than the, than the, you know, the recipe for Coca-Cola I think, um, you know, when those algorithms have big, uh, changes that happen, like, like the decrease in tracking for iOS 14, you're still gonna rely on, they're probably the best game in town, but that same principle that I'm talking about, you know, did that, did that low yield curve come down because Facebook's not working as well? That's all the industry wants to know, uh, right now is, you know, w w when I was spending optimally last week and it had all of its mojo and some of it's taken away, should I change my spin? Right. And part of the change in tracking there and moving it to seven days, just, it, it, it also makes it a lot harder to compare. It's making everybody, you know, it's obvious gazing quite a bit. You know, how much of this is due to a change in attribution rules and how much is it due to a change? And they don't have the same mojo.

Dylan (25:13):

Exactly. Especially with the Q4 elections, you've got Valentine's day coming up, people gifting suddenly without all the things going on. Is it a tracking issue? Is that the iOS 14 updated aspire perception or consumer perception, it was just so wild these days. And how many variables are involved in performance that, you know, it's never really a one size fits all.

John (25:31):

Yeah. If there's one thing for sure is that yesterday, uh, is different than the day. Um, and the list that you started is, is really is, is long. Uh, as long as our, um, you know, on the auction side, there's, you know, you have really control over some things, right? What you spend, uh, what, you know, what your ad quality is, where you choose to do placements, you know, creatives, you're targeting there's things that you don't control like your competitors spend, uh, and changes through the algorithm. Right? And then the other side of this, besides the option is as you acquire that traffic, how does it convert? Right? You get that traffic to the site, but, uh, your main thing on, on site, once you get that, you've you made the decision to buy that traffic is really whether you're on promotion or not, right. Or you're making an offer, but so many things are out of your, out of your control, right? Um, uh, holidays, uh, seasonality day a week, if a competitor has an offer, uh, your site performance, you don't necessarily control and marketing, uh, inventory levels of your buying traffic, and you don't have product. That's not going to work so well, right? The list gets long. And, um, all of that is taken into account when you're making your plan.

Speaker 6 (26:42):

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Dylan (27:47):

Into the final piece of the pie. You know, what the name of the podcast we'd love to kind of find the crossroads of the marketing and the financial side of things, some kind of curious what kind of financial tips or principles you can kind of share with the audience based off your expertise.

John (27:59):

Yeah, I mean, we, um, we really do, uh, take it back to first principles and say, if we're going to make, spend decisions, uh, we can't look at platform Roaz, we can't look at last-click ROAS. We need to know what is my marginal CAC, or what's my marginal Roaz for every one of these tactics. Right. And so that, that, that becomes the north star. Uh, and, you know, we gave examples where maybe that number went negative. Uh, we had never want that to happen. Um, if you think about it, it does kind of simplify it to say, I want the marginal CAC to be equal for all of my tactics. Right. I shouldn't be overspending on one and making the cat go down, marginal, caca down. Right. I should kind of have them trend towards similar performance that makes

Dylan (28:45):

It w when it comes to kind of testing different angular taxes or strategies, how do you kind of, you know, separate any kind of, cause it kind of stems from our earlier convo to where, Hey, we could have just one campaign, a at 10 K let's feed the beast, or we can have campaign a or B at 5k piece. What's your sweet spot on contest and these kind of tactics

John (29:02):

Here. Yeah, well, we, we S we've made a new style of experiment. That's a spin level experiment. And so we'll take a campaign or a group of campaigns, uh, and, uh, the software clones them, uh, in platform, it applies, uh, geo-targeting, that's mutually exclusive. And again, at the geo level, we don't have exposure to, you know, challenges and, and audience and tracking individuals. Uh, and we're saying as we pour more money into the system and this random randomized third of the country, what did we observe? Right. We know that as you spend more money, the CPC is going to go up, right. Or as you spend more money, the CPMs are going to go up, but at what rate, and as spend more, and, and the unit cost goes up, um, what's the quality of conversion, right? And we know the response rates are going to go down and it's those two dynamics over and over again, that we're, uh, kind of teasing out. And in this example, we're doing it independently for what audience doing it the same time for another audience. Uh, and that's what we're using to decode the economics. And then our, our, our, our B our decoded economics is for that audience, what was, and when I was spending $2,000, what was the next dollar yielding? And if our example from before the second audience has been $5,000, you want those numbers to be equal. You wouldn't overspend, uh, keep spending on one audience when it's marginal performance is gone, you know, lower than an alternative.

Zach (30:23):

Okay. I want to know what are you just absolutely most excited about and geeking out about right now, um, of what you guys are working on at lift lab? It could be incredibly technical. It could be incredibly, uh, specific, but what are you, uh, what are you geeking out on

John (30:41):

Right now? Uh, well, we are geeking out quite a bit on, uh, and I let let's geek right out on, on hierarchical modeling. Uh, and what we mean by that is, uh, as you spend on these campaigns, there's a hierarchy, right? You, you have, uh, the campaign level, the tactical level, the channel level, uh, but on the outcome side, you have hierarchies, right? Some of that spend is dedicated for acquisition, so you can split out, uh, new customers versus repeat. Uh, you have some of that spend that ends up being, um, specific to a particular category or a particular line of business that you're advertising. So if you're really advertising for, uh, you know, uh, Bob's hammers, you don't expect that to have a whole lot effect on the rest of the merchandise hierarchy. Um, and it's that intersection now to say, and you have different margin for different products, right?

John (31:34):

So now you're at the point of saying, if I had another dollar spin, where would I get the most margin? Where would marketing make the biggest contribution of profit? Uh, it's, it's intense, it's fun stuff. And it's kind of the next level, uh, you know, uh, contribution to profit is, is a, is a graduation from, uh, from Roaz, uh, it takes into account margin, and those are numbers. You're not really going to try to start passing to the ad platforms, right. That that's, that's kind of your business once we get to that level of detail. Oh my gosh,

Zach (32:05):

I can't wait. Uh, tell just everybody stopped using the word Roaz and starts using contribution margin, hierarchal, attribution. Uh, that'll be a fun day. Uh, well, this has been an amazing episode. I'm super geeky and, and dry. I think there's a certain cohort of, of, uh, rich and poor listeners that are gonna really appreciate this. Um, well, good stuff. Well, tell us how we can support you and, uh, how can folks get in touch?

John (32:34):

Uh, yeah, well, uh, we're always, uh, you know, partnering and bringing new marketing teams onto the platform. Um, you can find Uh, don't hesitate to reach out.

Zach (32:46):

Cool. Cool. Thank you so much. Thanks, sir.

Speaker 6 (32:55):

Thanks so much for listening to another episode of the rich ad poor at podcast. If you're like me and listen to podcasts on the go, go ahead and subscribe on apple podcasts, Spotify, YouTube, and rich dad, poor And if you absolutely love the show, go ahead and leave a review and a comment share with a friend. If you do take a copy screenshot of it, email me Show me you left a review. I'll give you a free copy of the rich add or ed book to learn more about the book. Go to rich ed for to leave a review that a rich ed or Thanks again.

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About The Podcast

Jason Hornung is the founder and Creative Director at JH Media LLC, the world’s #1 direct response advertising agency focusing exclusively on the Facebook ads platform. Jason’s proprietary methods for ad creation, audience selection and scaling are responsible for producing $20 million + of profitable sales for his clients EVERY YEAR

Zach Johnson

Zach Johnson is Founder of FunnelDash, the Agency Growth and Finance Company, with their legendary Clients Like Clockwork solutions. Under Zach’s leadership, FunnelDash has grown to over 5,000+ agency customers managing over $1 Billion in ad spend across 41,000 ad accounts on. Zach’s private clients have included influencers such as Dr. Axe, Marie Forleo, Dan Kennedy, Dean Graziozi to name a few. Zach is also a noted keynote speaker and industry leader who’s now on a mission to partner with agencies to fund $1 Billion in ad spend over the next 5 years.

Dylan Carpenter

Dylan Carpenter

Dylan Carpenter will be diving into what he and his team are seeing in 200+ accounts on Google and Facebook when it comes to trends, new offerings, and new opportunities. With over $10 million in Facebook/Instagram ad spend, Dylan Carpenter had the pleasure to work with Fortune 500 companies, high investment start-ups, non-profits, and local businesses advertising everything from local services to physical and digital products. Having worked at Facebook as an Account Manager and now with 5+ years of additional Facebook Advertising under my belt, I’ve worked alongside 60+ agencies and over 500+ businesses. I work with a team of Facebook, Google, and LinkedIn experts to continue to help companies and small businesses leverage the power of digital marketing.

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