How the Prophit Engine Actually Works (Full Walkthrough)

All right, let's go ahead and get rolling and we'll share this. So my goal today for those of you that are joining me and appreciate having you is to give you a sense of how this claim that we're making that I recognize is a bold claim is sort of possible. We're going to give you specific examples of in practice why we think we're living in a world now where one of our people, this role that we're referring to as the profit engineer, has the capability to deliver the work and the outcome efficacy of three people and how that plays into not just is it about the savings that it offers brands in terms of labor costs, software costs, et cetera, but why we actually think it produces better results and why it is a better way of working than having more people. This episode of the e-commerce playbook is brought to you by charge flow. Here's the thing about chargebacks. Most brands either eat the cost or throw hours at fighting them manually. Neither makes sense at scale. Charge flow automates the entire dispute process so you recover revenue without adding team resources or time to clicks to connect a Shopify store and then it runs on its own. Go to charge flow dot IO and use cf 30 that's cf 30 to recover chargebacks free for 30 days. So my goal today is to go through the setup that enables this to occur. And then some of the tooling and efficacy that goes into it and looks going to share a little bit of some practical examples of it coming to life in terms of what we're seeing be possible in this combination of what is really sort of a technology layer that begins with the database side that has on top of it. A set of context related to CDC methodology over many years, combined with what AI is enabling us to be able to do quickly. And then the sort of human operator that sits on top of that. And so I'm going to begin with what I think is the primary enabler of great decision making and progress towards outcome, which relates to clarity. One of the biggest problems I watch organizations with, especially with large teams struggle with is clarity of where we presently stand. And therefore in light of that present state, what is the right inside or action to take now to close the gap or to improve upon the existing outcome? And that sounds like a really simple idea. But there are a million complexities that get introduced into that process. And the more humans that are involved in it, the more complex it becomes. And so what we've spent so much energy around as an organization is this sequence of beginning by building good data structure. So one of the things that we care a lot about is have we absorbed all of this connection between the marketing layer of the data. So think of that as the traditional meta Google TikTok snapchat Pinterest, all the API spend, whether you brand as a brand have out of home podcast, affiliate, like everything that exists in the cost structure of the spend side overlaid with what we would call the customer data. So the order level data out of your platform. So all the order history, customer history, new versus returning customer definitions. If you've migrated once, if you have two different CDPs, all the complexity that gets introduced at that layer. And to the finance portion of it, where we have clear and accurate cost of goods, cost of delivery, to be able to understand and see where we sit on a contribution margin level, how that ladders into a full P&L level expectation of the organization. And then that gets sort of decomposed into the daily expectation based on your marketing calendar and actions. And so there's this giant sphere of information that has to coalesce into one single point that is a endeavor that for most brands, the actual capacity to make AI or any humans effective depends on the quality of that underlying infrastructure. And if I could offer anything, whether it's CTC or anybody else, I would offer you that you don't spend enough time on the organization of the databaseing of your information as an organization in order to enable fast movement and effective team function. And it's like a problem. Ryan, I'm going to mute you just because I'm getting feedback. There we go. This is like an underrated portion of the problem. And everybody wants to move fast anytime someone hires an agency. It's always like, how quickly can you get up and running? But in many ways, it's the wrong question. It's what would enable us to move fast forever? Like that's that's the actual question that we have to go after is what wouldn't allow us to be fast forever? Not how fast can we start today? And so much of that begins with the clarity of the data. So once that exists, we spent a lot of time in the planning process. So planning for us is a process of building a financial forecast that connects to the marketing organization in a way that allows us to understand what we're trying to do every single day. So planning for us begins in the marketing calendar. So I'm giving you this view as an illustration. I'll compact this just so you can see what I'm talking about. This is a brand's entire email send schedule for a month. So every blue line is an email green is SMS yellow lines represent marketing moments that are impacted more broadly. So they show up on the website. They show up in other places. So you can see the beginning of how we're going to generate revenue this month together as a group begins here. And this is why I fundamentally believe that marketing has to control forecasting, at least at the revenue layer. Finance in my mind controls financial forecasting is a release to cash flow. But the revenue generation is the building block of the actions that we're going to take as a group. And so the planning process has to begin here. And when I think about this data infrastructure for brands, we have 200 customers. I have one customer literally out of 200 one for 200 that has a day by day marketing calendar built for a full year, one out of 200. And that sort of illustrates this sort of gap that I think often occurs for why forecasting is so hard is because the financial numbers exist before the actions that we're going to take to create those financial realities. And that's backwards. What actually has to start with is what actions are we going to take as an organization and therefore in light of those actions, one of the financial realities that they will produce. And so that movement is one of the first things that we're trying to introduce is that like we call it units of growth. The way you make e-commerce is not software. There's no recurring contracts. There's no guaranteed money next month. Every month, we have to go out and do things in order to generate revenue. And if we don't do those things, there will be no revenue. You have to send at launch ads. You have to deploy emails. You have to post on a gimmick social. You have to do those PR hits. You have to launch that new product. Whatever the underlying set of actions are, we have to start to be built, to build this organizational idea. Money in the future is a result of actions that we will plan and take. And so the marketing calendar for us is a center point to that. And it's not just for the sake of getting the qualitative expectations down. It's because those moments form the foundation of something we call the event effect model where we can actually now take all of your historical marketing actions. So everything you've done in the past and we can ask ourselves, what did they do? What was the effect of those actions on the future are in the past on your revenue? How much existing customer revenue came out of that sale you ran? How much new customer revenue efficiency was affected by the launch of that new product, whatever it is. And then we can use that to say, okay, as we plan events in the future, how does that affect our revenue? So that's one piece of it. Then when that exists, we're able to create sort of this ongoing relationship between your spend and efficiency as a business over time. So we can look at, sorry, it changed the Delta hair, because this number is crazy. Just going to go out into a future about there we go. Go full screen that. Okay, sorry. Oops, too little. So the next once we have that sort of calendar piece, now we're going to answer two questions about the revenue build on exist on new customer revenue. So we're going to absorb all of your historical ads, spend every one of these dots on the line, represent some spend and efficiency over the history of this business. So you can see spend on the X access efficiency of new customer acquisition on the Y axis. We're going to build a relationship between these lines over time to think about, okay, if we want to spend X, the efficiency will be Y will have a seasonality coefficient for that will have an AOV configuration for that. And then we'll work to determine the monthly budget and then the corresponding new customer revenue. So now we have a calendar. We have a monthly budget. We have an expectation of new customer revenue in light of that. We're then going to build a returning customer model. This is just going to be a cohort specific LTV model. So the performance of your customers over time relative to the expectations of the things that are going to occur. All of that gets broken down and I'm not going to spend a ton of time into the minutiae of all that. I could go hours into the models and everything and how it happens. But for today's call, I just want to illustrate that the goal is to get down to this, which is like every day, an expectation of every dollar in every channel, laddering up to the financial goal of the organization formed that period of time. Yeah, that's, that's what we're after. And the reason this is so important is now I can put together a set of expectations around the month that includes the marketing actions I'm going to take. The financial expectations I expect to produce to get to the financial goal that I have as an organization to create the most important part of modeling and forecasting. Quick word from charge flow. The average charge back takes hours to dispute manually. Evidence collection, portal submissions back and forth of the processors. Most teams just don't bother. Charge flow automates all of it. Evidence submission, recovery, no manual work, no tools to manage. You only pay when disputes are successfully recovered. Head to charge flow dot IOT to get started use code CF 30 that CF 30 for 30 days free. Clarity of where I'm wrong. Because the whole point of all of that exercise. is that on day one, which was looking at the example here, you can see that on March 1st, I had an expectation of sales that was going to be $102,000. I came in at 93, I had an expectation of spend at 26. I came in higher than that. And so I'm off course from day one. And in every model I've ever built for every brand for every day, I've never gotten day one exactly right to the dollar. Because that's not the point. The point is, can you enable the person in charge of delivering this outcome to know where we have a deficit relative to what we thought was going to occur so that I can quickly change my actions. And this is the entire power of the game. Is that it is about the ability to course correct quickly that enables you to get to where you're trying to go. Think of it like your Google Maps. When you set a map, if I turn off course on right, it immediately builds me a new map back to my destination. The destination remains content, the constant, the path changes all the time. So let's use this example here where one of our gross, our profit engineers is managing this business towards the goal. Okay. And you can see in the beginning of the month, there was a promotion or moment that didn't go quite as well. Now 93 against 102, still pretty dang good. That's not a huge deficit. But if we look at contribution margin, you can see these first six or seven days of the month contribution margin was a little light to target. Right. But I can go from all right. Now here I sit on March 11th. And I can see that I'm 3% ahead of contribution margin. And 1% ahead of sales have had to spend a little more to get there. So my MERs behind now, we don't believe that MER should ever be the governor because the ultimate thing I care about is this financial reality because contribution margin, which we would define as net sales minus shipping, minus cogs, minus all fulfillment expenses. This is what covers op-ex. This is ultimately what shows up in your bank account and generates even better. So this is what I'm driving towards. We call this the scoreboard. It's a hierarchy of metrics. The reason this one's in the top and the biggest is because this is the game that we have to win above all else. Now it's not to say we don't also care about revenue. Of course we do, but this supersedes everything. So if I have to spend a little more to get to this contribution margin than so be it. But from here, I have things to do today to resolve still where I want to get in the future. And we're always playing multiple games at the same time, which is that it's not sufficient to say that I'm ahead of contribution margin because I also want to make sure that I'm not we would call this squeezing the sponge. In other words, I'm not getting all of my margin out of the existing customer base. I'm also ensuring the future health of the organization by making sure my new customer revenues on path. So even though I'm winning the overall game for the month, I can very quickly see there is a job to be done and solved. So very quickly we train our people to isolate. We call it the hunt for red October, right? It's like where is red and what problem then in line of that and actions do I need to take today? So new revenue has a deficit 10% or a little bit behind on efficiency of that new customer acquisition. Now that's okay because we have a buffer because returning revenue is coming in. Higher than we anticipated. Now I move down to the layer of the actual efficacy of the media. And now is when I can start to take individualized actions. And there's a couple of really important things here that make this easy and fast to do. So in the quagmire of information, one of the things that slows down decision making the most is trying to make channel allocation decisions relative to real ass numbers that are normalized against one another. Okay, so in our world we work off of Irohs incremental return on ad spend as the normalizing factor to compare the channels. So we do that when you begin with us through a set of benchmarks that are the aggregate of many tests that we've run. But then we start a measurement program for each individual customer to get to the incremental impact of every individual channel. And then we use an I factor to compare the platform reported result against the actual incremental return. So what that means is as you'll see, I'm able to compare everything from Google non brand spend to Google brand spend to Facebook acquisition spend to Facebook retention spend on a normalized scale relative to the incremental impact of each channel and where we're coming in reported against that. So this allows me to go. Okay, as an if I'm going to allocate across these channels, where am I, where is their opportunity or where am I potentially overspending. And you guys who probably don't even like you're no work in our system could probably look at this and go like, oh well, it looks like we're might be overspending on Facebook retention spend and underspending on Google Irohs non brand here relative to the results. Facebook, we should probably go tighten up and see where that opportunity is because we're spending to the total target, but we're behind. So like the next set of actions just reveal themselves very quickly as we go. And then we can break this down like email. I have an expectation of email revenue as we go so I can see every piece of this. And then I can even go further down into the individual day layer of the individual campaign. Now I'm not going to click into what we call our Facebook trackers right now for this brand because it would give away the data. So I want to be a little bit protective here, but I'm going to show you in a second how I can go all the way from the daily expectation of the channel level down to the daily expectation of every individual campaign in the account. So the key here is one person looking at that can get to a set of decisions very quickly about what we're trying to accomplish. In addition, the tooling that they have at their disposal, you may see this YouTube one here, which is like, hey, that one feels like the further stuff. But what I know is that we're actually running this is a launch of a new channel that we're running an incrementality test against YouTube for them for this period of time. And that's all at their disposal if you go to the studies section here, what you'll see is that an individual profit engineer can set up launch, execute, measure and implement geo holdout studies entirely themselves. So this is something that, you know, if you go and try and hire a house or a measure you're paying $10,000 a month exclusively just for the measurement process of setting up geo holdout studies. That all happens directly within our system where the test recommendations are designed for the regions that are held out. The people can implement the holdouts in the individual channel, the validation and results come back to them to give them and then they can immediately deploy them as the benchmark settings in their accounts. So all of this is how like one person can set up a test design the test execute the test implement the results see how it affects the immediacy of the outcome and often and they're often running. So this is the sort of foundational idea is that clarity breeds insight and action fast. Okay, and every one of these calendar moments every email that's planned every moment every one of these emails and I'm going to show you an email tracker a little later has an expectation of revenue every campaign and every channel has an expectation of revenue so that this person can very quickly see where they're at. So the other piece that becomes really important about to a different brand here because again I'm trying to protect a little bit of the data side is that there's a creative function so if we think about these three roles that we say that this person can encapsulate we would say it's your head of growth sort of the person that does the financial plan media allocation management of day to day decisions there's sort of the or case drug conductor across all of your online revenue. And then the second is the creative strategy now in our world when I think about creative strategy the core job that I think is missing from what people define is really what I would call creative supply chain. Is that every month we need to answer a question of what how much do we need to produce focusing on what and why and where's it going to come from and when it's going to go live there's an entire orchestra conduction around that workflow that often involves many different producers. For most brands the best brands we see are actually using three or four different sources for creative whether that's a creator program influencer program an agency in a house team customers sometimes there's this like network of creative that's coming back into the account. Well what we've built is something we call the creative demand plan and what this does is it takes your financial plan every month. So once you build that forecast it's then going to look at your historical media performance we're going to talk about the metrics that it uses to do that to define how many ads do you need to make this month in order to accomplish that financial goal. And then it takes that number of ads and it breaks it down into how many videos do you need to make how many images do you need to make what products should you focus on and what marketing moments should you connect it to. And then depending on what producers you have in your system so who are you sourcing them from from how many are going to come from the client. How many are going to come from CTC's you do see if there was a third party agency we can add another one let's call this like the creative shop right like whoever's contributing to the ecosystem we can divide up the responsibility of the specific expectations by moment by campaign and this builds an entire plan where for the month I can see. For ever green I can see what how many forecast it adds who's making it whether they've been delivered or not where we're at this month relative to the expectation and it sets up everybody from the start to know exactly what the job is to deliver and I as the creative shop is a profit engineer know exactly who I'm supposed to be getting ads for when and whether they're there or not and what products that they should be focused on. And the way that this works is it analyzes these five metrics to determine how many ads you need to create in order to get where you're trying to go so you can see that it's connected to this scenario plan so when we build the financial plan for the organization how much money I'm trying to spend on meta it generates this output based on these five things one it the first metric is what we call zero revenue rate. So this is the percentage of active ads that never converted during the measurement period high values indicate many ads aren't effective so what happens a lot of times is brands have lots of ads running the account and what we can see is how many of them get zero spent right so this is sort of this classic thing that we all know occurs is that eventually media spend consolidates into the best performing assets that's a good thing we want that to happen. But the signal of this tells us how many ads if we launched a hundred of them how many could we expect that the spend would concentrate around and you can see that this percentile shows where this brand this is Nike strength. have a really good creative score because a lot of their ads do get spent. And you can see that only 38% of ads don't get spent. That's actually like really high. Oftentimes it's more than that. Then there's the next metric, which is add concentration. This is also a metric that Meta uses a lot in their own creative reporting, which is the percentage of total spend concentrated in your top five ads. So when we think about how many ads we need to create in the future, part of that is portfolio risk. How much concentration of ad spend do you have into a small subset of ads? Tells me how much risk there could be that if you lost one of those ads, you would lose a large chunk of the spend. And so you can see that only 22% of the spend for this customer is concentrated on their top five ads. That's really low concentration. That's pretty dang good. We want a diverse spread. Like the best case scenario is you have lots of ads generating lots of spend so that at any one moment if you lost that ad, you wouldn't lose the account. Right? It's a really important thing to be aware of because just like your own portfolio of investing, if you have all your money in one stock and that stock collapses, the whole thing's going to collapse. Right? The second, the next metric is what we call row-ass degradation, which this illustrates the change in row-ass after an initial week of launch. So when you launch an ad, do you usually see that it increases in performance over time or decreases in performance over time? This brand, one of the best, sees a 58% increase in spend after the initial week of launch. Okay? Well, why is that important? If we're going to model the efficacy of every creative that we launch to think about how that campaign is going to perform in the future, well, if we know that every time you launch an ad, it actually starts out good. And then it collapses, then that needs to be baked into the modeling of the performance of the channel. If in this case, we see that as you actually spend optimizes over time, it tends to improve in performance that also gives us something to model. And then the last metric is what we call evergreen share. This is the percentage of ads that have been running consistently for 30 plus days. Right? And Nike is the best in our entire portfolio at this. So 61% of the ads live in the account have been live and spending for more than 30 days. That's the single best performance of any brand. And so what does that illustrate? Well, the higher that you can get this number, the less job there is of replacement for ads the next month. So the creative supply chain needs become less, right? What is actually true for most brands is that very few ads run for longer than 30 days. And so imagine this metric is at 15%, 15% of ads have actually had duration durability. What that signals is you have to do with ton of ads. And so the creative needs of every organization relative to their own ambitions are different. And people, this is like a concept that most brands are wholly unaware of. Most brands come to us with some pattern that is like every Friday we launch 10 ads. It's like, well, why 10? It's like, well, that's what our system is currently just designed to do. Is that sufficient for where you want to go? I don't know. And the problem with that becomes that a media buyer stuck inside of that system only has one lever to solve their problems. It's budget up, budget down, and they have no choice but to scale on the assets that they have available to them. And it tends to leave to overperformance on bad ads. So this, this is where this idea that Nike only needs 58 new ads this month to get to their spend goal comes from is that they have really good evergreen share. They have a great base of existing assets. So 58 ads, I don't know if that sounds like a lot like raise your head if that sounds like it a lot. 58 ads to produce it a month. Okay, Hannah. Okay, what I'll tell you is that's like one of the lowest numbers that we see. Like most of the time in order for people to get to where they want to go, it's north of 100 plus ads a month is the actual expectation based on what the data if they wanted to ensure that they had a chance of achieving their objective. So that is like 58 is like pretty, pretty light work for what we would expect for most of the brands to deliver. But this is all set up. Now, again, I'm sitting at the center of this. I'm a profit engineer. This is all there for me. I can share this with the customer. I know who's deploying everything. And now the next thing we had to figure out was like, okay, well, but how does that same person actually manage the add account? And when we built, when we started looking at like the biggest hindrances to people doing work. We found that over 50% of a media buyer's time in our ecosystem was spent building it. Like the literal trafficking of an asset to uploading the account to setting up the campaign structure that whole workflow. One like asset trafficking is like this hidden nightmare in our industry where every one of you run a different creative process in terms of where does an asset go once it gets created. It goes into a separate process. It's named differently. It exists here. There are folders that are sometimes this way. Sometimes that way like it's all over the place. So what we created is what we call push to build. So what this enables is if I'm one person, I don't have time to go build all of these individual ads into an add account. There's no way that that would be sustainable. It would completely break. And it's actually the lowest value task that you could get anybody to do. And so what we created is we for every customer that comes in, this is now a different brand looking at Sunday red here. So they come in and when their team has an ad completed, they just drop the folder link here. They come in whether it's a Google drive, a drop box link, they tell us the go live date that they want. If it's connected to a marketing moment, any specific text that they want and they just hit submit and they can upload 50 ads at once through one drop box folder if they want. All of those then get created instantaneously for me to the profit engineer based on the outline of all those sub headers. And I can just simply click deploy ad. This gives me a little opportunity to make sure that I'm confident in doing that. I click to deploy the ad and instantaneously the ad will be live in the add account. So I'll show you so you can see. So there's the ad right now. They're all pushed at paused. So we're getting more and more confident in that sort of the right capabilities to feel like we could just publish and go live and track that. But this ad right here is the one I deployed. It gets published and shows up in the ad account in the right spot and now I can just turn that on. So again, this whole idea that like what makes all person be able to do the work is this idea that there's a set of tooling, whether it's deploying incrementality test, whether it's launching a Facebook ad, whether it's building a financial forecast. That is predicated on a series of long standing methodology for how we do things, the technology to make it possible. And then the ability to go from I as the individual know what the gap is. What we saw in these cases was that the general workflow would be if there's multiple individuals is I as the head of the growth, recognize the deficit. I go, okay, there's a gap here to some result. And I go off and I send a message, hey, media buyer, we're behind on meta by 14%. Can you take a look and let me know what you think. And then because that's a human that isn't just sitting waiting for your Slack message, 38 minutes later, they respond and go, hey, I see that this campaign is behind. I'm waiting on two ads. Can you check when creative will have it? And they send a message to the creative strategist and they say, hey, when is those next set of ads going to be ready? They respond, I think by Thursday we'll have them and you can watch this you can like go in your own Slack channels and just look at the gap between message. Go look at the email deficits and watch it play out like and this isn't because your team's bad or broken. Because this is how human communication works. It's not it's instantaneous. It's this slow translation of information from the person who first found on the insight to the actual person who has the capacity to execute it. And this is what like we think teams are. It's like someone ascends to this role of manager or leader and then they get the responsibility of determining what we're all going to do and then passing away the responsibility to the execution to then be reported back to to then make more decisions to then pass back the sets of actions to then receive back the information. And I'll just tell you that world is gone. And a lot of people think that the way to think about this is that like, oh, the task doers get faster. But what I'll tell you we actually believe is that I want the smartest person the highest level person in your organization to actually be man with the capacity for execution. I don't want to try and bring strategy down to new people. I want to bring execution up to smart people. And so the the question is how do you get the highest leverage person in your organization? This is why you see the Toby Lucas of the world are coding now is because the barrier before was it would take going to build in the ad account would be a waste of my time as a head of growth. That would be a bad use if it took me four hours to build in the ad account. But if it takes me four seconds, then there's no reason I shouldn't go from instantaneously recognizing a problem to instantaneously executing that idea. Because any lag between that is just wasted loss. So that's like an example of every day what are people are doing. So they're just hunting opportunities and we have other views which which like show them their portfolio mix where every brand they're working on is behind her head. What this and then AI recommended actions for exactly what they should do in light of that. All of those different things exist. And then there's this whole other layer which I'm going to hand the Luke now to talk about which is that inside of our stateless interface is just one example of the workflow. But the real power of what we're starting to see in this moment with AI is that the underlying database where all this information lives. So let's say we we've absorbed all of your marketing data financial data order history into a single database instance. Then we do some transformations. So now there's all this forecast data that includes contacts of expectation of every individual metric. And then we've done a bunch of incrementality tests and had a bunch of calls and Slack messages. The organization of all of that data into a single database is actually an asset that enables something totally new which is in this quad open claw world that we all now live in. We only as an organization designed and built an HTML so like we have eliminated decks like we don't build Google slide decks anymore we just build live things in HTML. So if a customer says to Luke hey Luke I'm curious about the volume of ad creative that we're producing whether it's enough or not and how to think about how many we should produce. Well, if we don't have a pre-built dashboard for that, the old process of trying to answer that question was probably 30 hours of multiple people's work in order to produce the analysis and the display of the information in a way that can answer. But I'm gonna let Luke show how the underlying database connected to ClawD and OpenClaw now allows us to create instantaneous dashboards and analysis connected to our methodology instantaneously, saving tens and tens of hours across the organization. So look, I'll hand it to you. - Yeah, so I'll bring something up and we'll keep walking through a visual with a bunch of data like we've been doing. But before I get there, sort of like take a step back, everything that Taylor's walked through, the profit engine, the profit engineer and what that enables, we will do this thing where we started analyzing our Slack conversations and calls across the organization and assessing how much time it's spent discussing different topics with an organization and your organization 80% plus of your time is being spent on diagnosing what the problem is or what the problems are to solve rather than net inside generation. Like on average, you're gonna be 80% plus and that's what we see across our data. So it's like the conversations and the amount of time that we all spend in. So what's going on, what's the performance like? Is this the right data? Should we look at this report as well? Let's look at this different attribution model and try to uncover what the action is to be taken. It accounts for so much of our time. And so everything that the profit engine and profit engineers compressing the time from inside to action, one capable operator to handle all of those roles, which enables this workflow, this output, the efficiency and then the thing for us to think about is like how do we expand on this? Like how do we build on this to produce the net new things that are actually gonna continue to add incremental value? Now that we're not spending 80% of the time trying to figure out what's going on and what to do, right? We're spending 20% of the time, right? And can spend a lot more time on this sort of set of actions. And so like Taylor alluded to, what we see as our unique position and providing, operating leverage to you all as brand owners, as operators marketers, is at the intersection of the data, the database, the methodology and then the capable operator enabled with AI. And so the sort of three layers. And the data piece Taylor's talked about, all the data is aggregated for your business, an order level finance marketing cost level, the data lives in the context of targets in your business forecast and historical performance. It was not just like data in a sheet, right? It lives in the context of your actual business expectations. And then it's further informed by our context of a larger data set of hundreds of direct to zero e-commerce brands. So that has to exist. The data within that structure within that context. The second piece is the methodology, which is our unique place in the world is today at the intersection of the data of billions of dollars at GMV, grow outside of your brands. And what we're able to do is that we're able to benefit from the pattern recognition across those that data set. So we're able to see what's working well, what's not working well, we're able to see into the individual streams and workflows of all the different data seed brands, how they run meetings, how they run their organizations, the tools that they're using, how they're media-bind methodology, their creative vendors and resources. And then we build our methodology around what we're seeing working well and not working all across the broader data set. And then the final piece is a capable operator enabled by AI, who has what we like to call clarity accountability capacity, setting at the intersection of all the tools and everything Taylor talked about. And that allows us to focus our time on the highest leverage impact insight generation and so data methodology and an capable operator enabled further by AI are like the three pieces that help to enable this vision altogether. And so an example of this is like, OK, we have the data, we have the methodology, and we have the system built out. And so we're able to uncover these insights. What does that actually look like? And so the example Taylor gave is a good one. I'll pull my screen here and share an HTML, not a deck, around analysis that we did for this topic around creative volume needs and creative performance. And this is likely one that question that you all have circled around in different spheres at a different time. So so probably beneficial in terms of framework. But the starting set of this is, OK, what is the necessary creative volume for this brand? And what is the what is the right allocation of asset type? Video image, creator content, low fibers is branded. How should we how should we think about that? So step one, the database exists, right? We have all the data, the stateless MCP is something that is coming where we have all the data connected into the models that were able to leverage to quad, et cetera. And so that exists. Then we have the methodology of how we think about different ways to look at creative velocity and volume needs. And we'll look at that across four different dimensions here. The first is spending power. The second is the outlier engine. Third is creative rotation. And then fourth is activation economics, right? So these are four different sort of like, when you think about methodology, when they look across the data set, these are four critical ways that we see our creative volume and velocity needs are being informed. So to start with the first spending power, this is something connects to the Spending VR, the Spending Power Model at Taylor Walkthrough as well, which is at the highest level, if we are making adjustments to our media mix and producing additional creative volume and diversity of our creative content, all the things that we're doing creatively, what we should see is that it increases our spending power over time for the highest level. It allows us to spend more dollars and press against the degradation efficiency curve that exists for us, right? So for each one of our business, the Spending Power Model at highlights that as we spend more, the degradation curve, we start to hit that ceiling. And so improving our spending power is all about how can I spend more money and press against the ceiling of that curve, make the curve flatter of what my spending power looks like. So that my CAQ and AR efficiency doesn't degrade as fast as it would have historically, right? So for this business, their CAQ over this time period was stable, basically flat year rear and spent 34% more in total adds bit, right? So like up what you want to see at the happening at the highest level, we're able to spend 34% more, drive much higher to newer volume and our CAQ remains consistent to really good signal as it relates to spending power and sort of this first bucket of the creative methodology that we want to see happening. That said, there's all sorts of factors that pull into this, right? Which is like the new product launches, the competitive space, et cetera. So we want to start here, but then we need to get to a level that's much more specific to the actual ads with inner ad account and the behavior of those ads, which brings us to the next section here, which is the outlier engine. We have a series, the data did called outliers that sort of builds around what we've seen over, across our data set over the years in terms of the behavior of ads within our ad account. And what that is is that across every ad account, the tiny percentage of ads drive the vast majority of results. This is what apparelized, this is how meta's algorithm works. So we define high performing ads within this ecosystem as ads that spend greater than or equal to one standard deviation above the mean. So in your ad account, if you took all the active ads over a time period and then all the aggregate spend in your ad account against those ads and divided it as you have the average spend per ad, right? So on average is so much spend we get graded. There's a wide distribution of where that's coming from, right? It's an even distribution of, okay, on average, this is how much spend is going to come through each ad. And so what we do is we take what the mean is, what the average is, and then we increase one standard deviation above that to signal ads that are getting one standard deviation above the mean in terms of their spend distribution. Are signal to us as high performing ads or your outliers, right? So what this does is it helps us to understand what percentage of our media spend is being driven by a smaller subset of ads. Ads, you know, to your question, ads in this case is ad ID, so unique ad IDs within the week. So, so as it relates to the outliers, so what we can see here is for this brand, three and a half percent of all ads are outliers. So 194 of the 5,595 ads, three and a half percent are outliers of the total ads in the ad. The those three and a half percent of ads account for 66% of the total ads spend in your account. Three and a half percent are outliers. Those three and a half percent of your ads account for 66% of the total spend. And you can see that this is all scrolled on here real quick. Like we have some bish for other brand, you can see this is really normal, right? This couple of the brands here, 5.7% outlier percentage, 4.4%, 3.7, 1.8%. This is typically what we see is single digit outlier percentage, somewhere in the range of two to six to seven percent, right? On the upper threshold, this is how it works. And you'll have some experience of this, right? Where like the three ads in your ad account that have been running for the past two years, still account for 70% of your total spend, right? And so we can see how that plays out here, where in this chart, here's all the ads that were launched in any single month over time. And then this dotted line is the outlier threshold. So the main one standard deviation. And you can see all the ads launched over time. And the ones that are above that threshold will sort of signal the distribution that are becoming, that are becoming outliers within that, within that time period. And there's in terms of discovering outliers, there's a pretty wide distribution in terms of that's trying necessary to discover the thing. It's not like launch it at and then within two weeks, we know for sure it's also not like we launch ads and then it takes six months to find out liars right but the distribution the fastest quartile like the fastest 25% of ads that you launch will become outliers within 18 days that's the fastest you can expect it to happen on average it'll take 39 days so a little over a month it'll take to find outliers and then it takes up to 81 days so the last sort of quartile in terms of the distribution of these ads it's gonna take 81 days to find out so what does that mean we can't call it early two week two week timeframe is too slow and then volume ultimately is the core thing that's necessary in order to be able to discover these outliers and be able to see them play out over time as well okay so spinning power outlier engine and then third creative rotation right so there's this other piece which is how how quickly are your creatives rotating out of your top 10 in your car you're seeing the same as in your top 10 in quarter after quarter after month for month but what does that look like so for this brand 70% average quarterly turnover so 70% of the ads in the top 10 are replaced every quarter for this brand so seven out of 10 of the ads are gonna be replaced for sure to signals to us what the necessity is to replace those over time right and so we can sort of see how that sits on a monthly basis 60% turnover 70% 100% turnover in this quarter and and sort of hivers around there so what this frames up for us is this last piece of how we think about creative vault and and the necessity to replenish around activation economics so ads don't last forever right so we see this happen where ads die off and stop producing in different time frames and so there's a replenishment need in your account so in addition to like discovering the outliers and identifying what those ads are we need to replenish the sort of baseline of our account because there's ads that are dying all the time and so in this case for this brand 79% of ads don't activate and this in this case how we're defining activation is they reach 1k and spend or more so 79% of all ads launched never spend a thousand dollars so don't activate 21% reach greater than 1k and lifetime spend and so actually out activate and then three and a half percent again is the act is the outlier rate of of if when we launch ads what percentage of them are going to become outliers and this is sort of a visual of that happening where the great and the stacked bar chart the great section here is all ads were carrying over into the future so they're sticking around with us and we're building our foundation of creative the blue section is the new ads within within that specific time period and then the red the red line is the ads that are dying off each month and so what we want to see is something like this where we're stacking net new ads on top each other and we're creating this foundation where ads that were care over in the future the future stick with us and we're replenishing enough ads in the account to take the place to the one that die off we're also creating enough volume to where the outlier outlier rate becomes more of a math problem than a gambling problem and that's and that's sort of what we see as a release to create it right with like as we zoom out spending power outliery activation economics ultimately there becomes a level that's sort of a simulator for like at different levels of spend what it looks like ultimately becomes their level the amount that we're spending or the amount of creative that we're launching where because of how the math works the the outlier person activation economics it's going we're we're playing a game that is much more similar to gambling only creative than it is to like working a math problem and making the probabilities work because the volume's just too low to get it there right so like for the brand when we're launching 50 ads a month we're we're getting we're gonna be very lucky if we find one outlier break out within that subset right just based on how the probability and that outlier percentage looks like whereas when we increase the volume we can start to see okay if I launch 200 ads this long 46 are going to activate and are gonna be expected outliers and that's the game we're playing right which is not let's launch 100 ads 200 ads 300 ads and they're all gonna produce at some level there's gonna be 80% of them that don't activate at all so 20% of them are actually going to activate and then of those a few percentage points of those ads are actually going to become outliers that become the outsized growth engine for your account over time and that really to become the framing for I would think about great volume and then necessity to be able to drive the outcome that we're all after and so this again taking a step back this is sort of how we think about okay we have the data infrastructure in place we have the methodology in the view what we see across our data set and then the operator enabled by an AI were able to spend more time creating net new insight and and creating action off of creating action off those insights and born by the data set and the method of the as well so that's sort of framing up this piece we move it all the way to the right just to see it can only want to see what what happens when you want to spend infinity dollars there you go five outliers yeah and that's what it take right like this is this is what people just don't often struggle to fundamentally grasp about media creative is that we all want the idea that we all know how to make hits every time which is not how it works like this is not the reality of making ad creative but look let me ask you question do you know how to code no how to code so you don't know how to code anything how are you as a designer designer I've plugged around in Canva here or there yeah yeah like you can screenshot things I can mix mix and pixels around so like I I could show you some Luke's deck from back in the caveman days like six weeks ago when we had to just make them in Google slides with screenshots a lifetime ago and but he built that by himself chatting with an interface on top of our database he built an interactive HTML dashboard to make a point built on top of methodology and like that's the world today it's just like so different and faster in terms of getting to the inside that that to create that to actually end up with that end asset like a front end interactive UI on top of that data now this is would have taken like five people a hundred hours it's just like it's so so so this idea of what the biggest struggle I have right now is like what are my people supposed to be like capable of like what's the actual boundary of what we should expect from people to be able to produce because it's there there are people that feel like they're playing limitless the movie you know like in side of the organization in terms of what they're able to do but I want to show you like why I'm gonna make a this is this is a bit of my like self-esteem pitch of like why I think okay so cool today I'm gonna go do this myself but I want to show you something and this is so this is my I run open claw it's like my my AI instance of how I interact with the world but I want to show you how the sequence of interaction often works without this context layer you're gonna hear this phrase a lot as you realize the AI context layer so while we're sitting here I took this screenshot okay from stateless and I gave it to my clone I said hey building interactive HTML analysis of the right actions to ensure we had our contribution margin goals for the month how should I prioritize the actions okay and it starts to go through and what it first tells me is that the problem is almost entirely Google spend is up 51% revenue down 45% row is cratered from 265 to 218 that's roughly took blah blah blah blah blah but why am I go and look like Google is actually way ahead of its efficiency target so it's just like fucked it up it just like hallucinated like that this is the problem right and then it tells me I was like Google is ahead on spending efficiency it's not the problem at all green is good read is bad it's like oh you're right I missed read the color coding right this is like this constant thing that you deal with but I want to show you what I did so I have this video I it's called the hierarchy of metrics okay and it's what our dashboard was built around it's built around this idea that the way you should sequence through information is contribution margin is the most important thing and then there's layers of things that work below it then I said okay hey dumb dumb bot watch this video and apply the knowledge to your analysis of the screenshot okay so this is methodology on top of data on top of context right and so now it goes good and I watched it your permanent success framework changes the entire analysis let me redo this properly top down and I'll copy and paste this in the chat so you guys can like read through the distinction between okay what was the analysis about got the first time absent a contextual understanding of methodology for analyzing e-commerce data and what did it get when I gave it that context and how different is it right it's like to 75,000 characters too long I can't even paste it in the chat but you get the idea of what I'm what I'm getting to and then so I asked it like all right now rebuild that in light of so let's see let's see what it did while we were sitting here okay so while we're sitting here it was like all right in light of that here's an action plan off the back of where we're at so pyramid success four layers is the only thing that matters so it created sort of this pyramid view of it created a deep dive broke the expectation created a diagnosis made a recommendation maintain spend close the app through AOV and retention protects future while hitting scoreboard here are some prioritized sets of actions blah blah blah blah and like again this was three seconds while I was sitting here balook was talking and so so now could I go through and probably find some some issue with some of these but again like interactive changes to if I can affect these things by this much off we go this is just sort of the world that we live in now where suddenly our people can take everything that CTC has intended for them to understand how to deploy we can provide them with this really well structured database that we've taken from you guys and organized into way that makes it actionable eliminate all the API risk associated with trying to interact with all these things gave them the tools to move fast and now it feels like there's like super humans everywhere and I think the thing that's really hard is that this public messaging around like you know many making real one role combined into three or like, I don't know how many of you guys are playing for Triple Oil and House and all the different tools all together at once. It's a big leap to go like, oh wait, one person, these three people, like the three people on my team work hard, they're smart, they're capable. And it's like, yeah, you're absolutely right, they are. This is not about someone becoming sort of suddenly more intelligent than others. But it is to say that like for 12 years, we spent as an organization like 10,000 reps at trying to build a system for this for forecasting effectively, for building methodology, for thinking about how these things might occur, for building a creative demand plan that would help a system to engage to organizing Dropbox links that make people able to move fast. So the delta between how an internal thing might function around what you guys do, which is one brand, one instance of data, to all the things that go into it, it's just a byproduct of messing it up a lot, learning a lot along the way, but the organizing and compounding of the information. And the other thing that affects it is like, okay, so Luke did this analysis. Well, what now is that in the old world, like that deck sits on G drive, and like every organization has the same, we're like, we record every meeting and they're available if you want to read them. And it's like, nobody has ever watched back a meeting in their life. Like that's what a nightmare to watch a meeting I wasn't even in, let alone read meeting notes or whatever exists, or read the deck that Luke created for some client meeting. Like, but what instead happens is the template for that analysis can now instantaneously become a report and stateless and be published through everybody. So the way that you can compound the knowledge set is that every time one person has a good idea, it can affect everybody and the person's, right, in the old days, it was like, hey, I have this idea for a report, create a product ticket, put it in Gira, the dev team will prioritize it never, and maybe eventually it'll get built, right? So when all of a sudden everybody can build the analysis that's in their head for smart people, and then that can compound across an organization, it all just moves really fast. So that's a little bit about what we're trying to create and give to you all to help you do what you're doing. And really, what we want to give you back is like the dream state of the best relationships we have is that there's a lot of things that we're never going to do for you, and we're not going to build your product development roadmap. Not the same way we do. We're not going to help you organize your supply chain. We're not going to manage your cash flow. We're not going to define the big marketing moments in the future for your business. The ones that are going to break the model that are going to tell stories and do that big influencer partnership and all those things. But the best partners we have work on these counter cycles, where, okay, CDC, your job is to, we're going to agree on the expectation. We're going to have the marketing calendar. We're going to trust you to execute tomorrow and the next day and the next day and deliver to this outcome and hold you accountable to that outcome. Meanwhile, we're going to go build the transformative plans for distribution expansion and product development in big marketing moments, and we're going to work off the counter cycle. And that, like, because the reality is that's what growth primarily comes from in our industry, is that it's not this idea that you're going to make the next great ad. Like that happens sometimes, and it's awesome when it does. But primarily the best brands that are growing are developing new products, expanding channels, finding new stories to tell that unhinged the data from the model, they break the model, they reach a new tier of performance, and we aren't set up to accomplish that for you. And so this is what we think we can be, is like the best execution engine of your realized business. So of the products that exist, of the stories that are capable of telling, of the creative that it's been produced, how do we ensure that every day the allocation of those efforts produce the most consistent, pliable or possible outcome? That's us. That's what we're going to do for you. And it's why we're able to forecast so accurately, 3% to target across $3 billion, is because forecasting isn't extra as an execution. It's working to make it right. It's not guessing. It's how quickly am I, of course, and can course correct. So if you want to build that level of confidence into your own business, to give you the freedom to step back, to go do the bigger things, that's what we think we can create a great partnership around. So that's our a little bit of what we're up to. We're hoping to add more and more things to this right now. Google still happens as an individual buyer. It's not built into the system yet. There's some complexities around the deployment that we're working through email and SMS. We have a service offering that's still just people designing emails and deploying it. But all of that is coming. Like the reality is, is that it is just way too capable and way too fast and way too good for us to stop, that are to continue to hand humans, whose brains aren't designed for perpetual data analysis. Like the reality is, like, that's just not what we, our brains are designed to do, is the monitor data 24/7 and make decisions. It's just not the thing. Well, we would love to do a demo for you guys of this, specifically, the easiest entry point we have is what we call our profit system, which allows us to basically build all the infrastructure out for you, hand you the analysis of what we do, and then either give it to your team to run or we can do on doing service from there. The other thing we're really confident in is all of our pricing will include some sort of outcome based obligation that sort of ties us to doing what we said we're gonna do because we believe it has the capacity to do it. So that's what we're up to with the profit engine. That's what we mean. It's a combination of tooling and engineer together. We like to use the Ironman metaphor. It's a bunch of Tony Stark's combined with the Ironman suit that make it possible. We have awesome, hungry, intelligent people that are AI native that are excited to use these things and then we give them the 12 years of history and all the tech to let them play with and set them free on your behalf. So, appreciate you being here. 12 o'clock on the dot. Have an awesome afternoon. (dramatic music)