driver tree. And in short, the driver tree is a systematic way of identifying the drivers behind a business problem. What you do is you list your goal and then you reverse engineer from your goal to find the drivers that actually move that goal forward, make it happen. Then once you do that, you can start working on the drivers themselves. You just rinse and repeat to find the things that drive the drivers. So most newbies in any sort of businessto business scenario, you know, they'll land their first client and their first client will say something like, "Hey, our ads aren't working. " And because the newbies are super enthusiastic and they don't really understand how clients work, they'll take those clients at face value and then immediately start pitching solutions and building things out to, you know, improve the stated problem. But this loses the forest for the trees. Now, it will also lose you as a consultant a ton of money. and it will impair your ability to drive real business outcomes because there is a difference between a stated need and then an implicit need, a thing that somebody is not telling you. So instead, you can deliver way more value by just walking through a simple driver tree. Here's how it works. First, you define your goal in the simplest terms. So that might be growing revenue, increasing profit, uh improving retention. And I wouldn't over complicate this part. They're generally a handful of stated business goals that any client out there is looking to achieve. uh sometimes they are implicit which means you must determine them for yourself and then put them in front of them. But yeah really the only key guiding principle here is you have to be specific. You cannot just say I want to grow my business. You instead need to provide a specific metric uh an amount to grow that metric and then a time period within which to do so. So I'll give you an example. Let's say your goal is I want to double my topline revenue in the next year. We have a metric which is the revenue. We have the amount which is double and we have the time period which is the year. Okay. Second, you must identify the minimum essential drivers to achieve that goal. And ideally, you would aim for two to four major drivers. If you do any more, you're probably overthinking it or over complicating it. The way that I see things is I use AAM's Razer here, which is a thinking tool that basically says the simplest answer is probably the right one. Uh so, for example, if your goal is to double your topline revenue in the next year, you have some pretty clear drivers. You can either get more customers or you could increase the lifetime value of existing customers. The third is you take that and you drill down from the driver one layer deeper. So here the exact same principles apply. You keep it simple. For instance, how do you get more customers, right? Well, you can put your offer in front of more people, which is marketing. And you can also increase the conversion rate of the people who have your offer in front of them, which is sales. How about increasing the lifetime value? Well, you can reduce the churn. You can increase the cost of the product. Right? Now, you're seeing we're basically constructing this tree of concepts and drivers. And finally, if necessary, you drill down one layer deeper. Now, how do you do all the things that we just talked about? Well, here's where you're probably going to get hyper specific with, you know, your actual business details and the implementation. So, for instance, in our case, how would I put my offer in front of more people? I would create more content, for instance, or I would improve the engagement and verality of my current content. Now, this is just a thinking tool, but the reason why it works is it basically forces you to structure and direct those thoughts. You're not going to win any awards for this. It's not like you're going to get a Nobel Prize for any insights. uh there's nothing novel here, but what you will do is you will make more money because it turns out that money making more of it is not a Nobel Prize or laurate winning problem. Uh the things that make more money tend to be very simple and simply require focus. So the way that I see driver trees is they're basically a forcing function for clarity. Okay, let me give you a real example from our community. Aar who's one of the members who'd only building automations for a few months was approached by a client who was struggling with their sales team's productivity. Now, the client thought that their problem was that they needed more leads. Aar could have just gone and, you know, built the whole lead genen system for them, which is obviously something that we talk a fair amount about in the program. But instead, he kept things really simple and he reframed the client's goal using a driver tree. So, what he asked was, "What's your actual goal? " And turns out their actual goal was to increase revenue without adding more sales reps. They wanted to keep a pretty lean team. So the main drivers to hitting that goal are lead quantity, lead quality and then conversion efficiency. Basically the conversion rate and how quickly and easily you can convert somebody. So when uh him and the client looked at the data, you know, the company already had a massive database of contacts. So it wasn't the lead quantity that was the issue. What they found was the real bottleneck was lead quality. So every time a rep had to deal with a new lead, they spent hours researching every account, pulling, you know, tiny little scraps of data from LinkedIn websites and Google. and they tended to do so in a pretty undirected way. So Aar built an end-to-end enrichment system, meaning now with just a company website, the system immediately enriches all 50 fields automatically, which standardizes the data, pushes all that into Salesforce, instead of the team having to waste a bunch of time digging around for data and not really knowing fully what the context is before they get into the call, reps now get complete profiles and AI processed insights basically immediately. So all of this despite not necessarily being what the client thought that they wanted in the short term ended up solving their problem. Okay. So that is the driver tree. The next thing is even if you identify the right problem you obviously still need to understand what metrics are important how to actually drive business value. So that's what I want to talk about and
what this really is business acumen. So as people offering a service like AI by default we care mostly about the implementation details of that service. And if that sounds like French to you, what I mean by this is how many API calls? Uh what models should we use? What platforms should we integrate? How should we drag and drop the nodes? Well, got a hell of a Eureka moment for you. Customers, they don't actually care mostly at all about the fancy technology that you're going to apply to their business outside of some superficial or high level understanding. What they care about are the outcomes that you are driving. Now this isn't rocket science to anybody but in order to frame things in their perspective what consultants do is they pitch everything in the context of three major business outcomes. Okay? And I call this your business acumen. Uh you knowing this is basically the 8020 of consulting and no you don't have to go get an MBA uh like the big four guys that I'm working with. Uh you can just spend 5 minutes running through some of this and you should have more or less everything you need to know. So what is business acumen? It is the ability to understand how businesses actually create, lose, and then measure value. You can bin this into three major outcomes. As I mentioned, first and obvious one is profit equals revenue minus costs. It's a very classic framework that I think we're probably all familiar with. Every business decision ultimately impacts some measure of this equation. And obviously, we can go really deep. We could start figuring out uh gross margin and operational margin and so on and so forth, but honestly, for the most part, literally, this is sufficient. The second big framework is growth equals acquisition plus retention plus expansion. Now this might be a little bit more complicated. So what the heck does this mean? Well, you either bring in new customers in a business which is acquisition or you keep existing ones which is retention or you sell more to the ones you already have which is expansion. So let's say you have a monthly retainer for a consulting firm and it has a 50% monthly churn rate. Okay, that means for every 2 months that somebody stays with you, somebody leaves. I. e. the average uh relationship length is around 2 months. Well, you could focus your time, effort, and money on getting new customers for the service. And of course, that would probably improve your topline revenue to some degree. But the much more effective thing to do would be to reduce your churn, aka improve your retention. So, using this formula, you would be foregoing acquisition to focus on the retention. And while it's not always the best play, uh, in this situation, retention is just so much cheaper per unit work to move than acquisition. So, it's, you know, what I'd recommend doing. Okay, so that's the first two. The third is something you guys probably haven't thought about, which is that value equals cash flow divided by risk. Now, this is a valuation mindset instead of a usual accounting one. Probably is not going to mean a lot to people here. So, um, you know, we all come from a services background. Let me run you through this. The higher the cash flow and the lower the risk, the more money a business is worth, aka the higher the valuation. So, a concrete example is if you had two SAS companies, both are in similar industries, and they both make $100,000 a month in recurring revenue. Let's say company A has a bunch of very predictable enterprise clients, and they have longer contract lengths, you know, maybe two years or so, whereas company B tends to work with small to mid-size businesses, uh, and then they're all on a month-to-month contract instead. If you think about it, company A working with a two-year, you know, enterprise cycles, uh, which are typically more stable and less likely to change vendors. Company A is going to be worth significantly more than company B simply by virtue of the fact that it has lower risk. And if you look at our equation, because the risk is lower, the total valuation of the business is going to be higher. Now, when you're pitching systems, something to keep in mind, specifically with automation that not a lot of people talk about is that risk. Sometimes AI systems increase risk. So even if they do increase cash flow, if the increase in risk offsets the increase in business value and revenue, sometimes this isn't actually worth it. Now obviously this is a you know nuanced thing. You are happy to increase cash flow in certain situations. It does mean you increase risk and I do it all the time. But in other instances um you know it does the exact opposite. So sometimes you will automate a process that used to be up to a bunch of people and the people didn't do a very good job and thus the performance is pretty variable in output and then because you're automating you'll actually decrease the risk. And so those are the situations that you want to go for as somebody that is selling AI services or automating business processes. Right? If you can find a golden goose scenario where you both increase cash flow but also decrease risk, what you're doing is you're basically multiplying that equation out many more times and improving the total value of the decision significantly. Another real example from maker school was a guy called Nick. Very handsome fellow, I'm sure. He was working with a real estate coaching client who wanted an AI chatbot. uh the stated need there was you know they wanted to build out some sort of intelligent visibility dashboard and he was convinced that the problem was that he didn't have enough advanced AI in his business that the AI needed to be smarter which is obviously a relatively unsophisticated belief but Nick drilled down further when he thought about in the context of business acumen so the things that we just talked about profit equals revenue minus costs growth equals acquisition plus retention plus expansion Nick found that the real issue was not the quality of the AI at all it was the client's churn essentially the client's turn was just way too high, which made all of this unjustifiable to begin with. So instead of building an AI system or spending a bunch of time chasing money and marginal returns, Nick built a churn prevention system, essentially AI personalized onboarding sequences, a proactive 7-day and 30-day success check-in, and then a few triggers that would flag when clients said things like, "Hey, this feels overwhelming. " before they canceled. And these simple things, despite not being what the client asked for, significantly improved the bottom line and ultimately amount to the project being a success. So what the client thought they needed was AI. What the client really needed was less churn. The way that you figure this out is through driver trees and strong business acumen to build your models with. So before you pitch any AI solution, identify which of the three major formulas you are actually moving. Are you increasing revenue? Are you decreasing costs? Are you improving acquisition or retention or expansion? Are you decreasing or increasing risk? Right? And you can do multiple of these at the same time. In fact, most usually do. But you'll find that there is one major thing that a system will do uh over others. And if you can't answer any of those questions clearly, in reality, your client probably won't be able to either, which means you probably shouldn't do the thing to begin with. Okay. So, now we understand how to diagnose problems that are high leverage using the principles of both driver trees and then also business acumen. But there's still two more frameworks that I want to talk about that these big four consultants uh showed me. And one of them was very interesting to me and one that I never really sat down and thought about. Uh, and it'll sound very simple
but it was basically just the principle of communication. So, you can have the best ideas in your head, but if you can't get those ideas across to a customer, doesn't matter, right? You and the customer are going to lose. So, consultants don't just spend all their time actually just building their ideas and making themselves better thinkers. They also work on becoming better communicators, which if you guys are in maker school, you guys have seen my content, is very, very important to your bottom line. So, I want to start with what I would call probably the most important communication principle I learned in consulting, which I was already doing, but I didn't fully realize, and it's called the pyramid principle. Uh, in the pyramid principle, it's very simple. You just state the conclusion first. Then you supply any supporting arguments after. Basically, you're not like burying the lead. Most people will do this backwards. They will start with a bunch of context. They'll build up a compelling story and narrative, and then finally, they will at the very bottom of the thing get to the point. uh by the time they reach the conclusion, the client has probably already come up to their own conclusion or just stopped paying attention because everybody skims nowadays. So, as a quick example of the pyramid principle, let's say my goal is to grow the revenue of maker school to 200% in the next 12 months, right? That is a driver tree style goal. If I was communicating this to myself, I would first use driver trees to determine the factors of my 200% revenue increase goal, which in this case is going to be increasing acquisition, customer transaction volume, and then lifetime value. Right? I would then write this goal. I would put it first and then I would explain how after. Uh I'm just going to pull something out of my ass here, but to grow the revenue of Maker School by 200% in 12 months. Uh we can expand who pays, how much they pay, how long they keep paying. Uh we can increase customer acquisition. We can do this through more traffic. ads. We can do this through more partnerships, uh more content, and then we can also increase the conversion rate on that traffic. Uh second, we can increase the average transaction value of the converted traffic, which we can do by introducing some sort of upsell, uh adding some premium tier to Maker School, adding some private one-on-one coaching, offering cross cells, uh templates, playbooks, and automation bundles. And finally, we can increase the uh customer lifetime value itself, which we can do by increasing retention through better community ties, more touch points, structured progress tracking, reducing some form of churn with, I don't know, accountability, all stuff that I am myself actually doing. But anyway, the point that I'm making is we started with the thing that we wanted to do, right? The goal and then after we stated the goal and the client bought into the goal, then we supported it with a bunch of stated actions that we are saying are likely to actually improve the goal. Okay, this is how you get buy in. Another big principle is when you are pitching a consultant always start with the problem first. So, uh, practically what that means is if you're creating a proposal for some service, like don't start with the executive abstract. You know, don't start with company whatever is a $5 million per year business in the XYZ industry that does blank. Uh, cuz nobody really cares about that. You're just wasting character space. Instead, start with the problem the customer is suffering from, right? Leftclick is currently spending $45,000 on customer acquisition and seeing next to no returns. This is crippling growth because you don't really have that much cash flow to begin with. That makes it an existential risk, right? If you put that problem in front of people and if they resonate with that problem and then you support it with conclusions, people are going to be a lot more likely to like listen to you because you're not bearing the lead you're putting in front of them, but also actually like take your advice seriously and do something about it. Okay, so in a nutshell, once you are done identifying and quantifying the problem, okay, once you start with the end and then you frontload what the need is, then and only then do you actually
focus on the solution. All right, the last major consulting framework is fast and it is a model not just for consulting but for better decision-making too. So, I learned this from watching those big four guys work. It is essentially a four-step process you can apply to any problem, whether it's a business problem or a personal problem to guide your thinking. Effective consulting is effective thinking. So, if you don't structure your thinking in some way, you are usually leaving some sort of outcome on the table. This will help stop that. And the way it works is the F stands for first principles. This is where you strip a problem into its fundamentals. You rebuild that from the ground up. You do not accept assumptions. uh practical example instead of you know we need to automate our invoicing process which is a solution that you are pre-imposing on the problem you know start with no prior solution just ask yourself what the hell's the problem what's the straightest line path to fixing the problem right so instead of hey we need to automate our invoicing process ask what do I want well I want to get paid faster and then you use that driver tree idea to figure out where to apply your time and your effort A stands for actionoriented so focus on what can be implemented and action in the next 24 hours not on some perfect solution which will take months or years to generate uh basically you just want to bias yourself and your organization towards doing something rather than sitting around in your ass and planning all day. So instead of you know let's research uh all the CRM options for 3 months and then make a decision you should know that like the probability of you making a good decision with no actual real world market feedback is quite low. So say something like hey why don't we pick the top two CRM today? Why don't we just like both uh run them in parallel for a week trial and then at the end of that week trial we'll decide and just move forward. It's like yeah will that be the perfect decision? probably not. But if it's like 90% of the way to a perfect decision and you do it in like onetenth of the time, that's massive leverage. S stands for second order thinking. So we got the first principles down and now we have the action down. What we need to do is basically go one step further and assuming what we're thinking of implementing works out, we need to consider the outcome beyond the immediate. So what happens if this thing works, you know, or what happens if it doesn't work? Instead of saying, "Hey, this chatbot will handle 80% of support tickets," uh, you should say, "If the chatbot handles 80% of support tickets, what the hell happens next? Are we going to be able to deal with that? And if not, how do we implement another solution to deal with the inevitable bottleneck that we're going to be creating? " And then finally, T stands for triangulation. This is really important. Once you've gone through this process of thinking through first principles, then making your stuff actionable and then considering the second order consequences of your idea and then laying it all out in front of you, then and only then do you triangulate this information with some other data source. Why? Because in this way you are forced to think through a problem yourself first and your conclusions will not be constrained or colored or biased by somebody else's work. I see it really similarly to like doing your homework yourself first. uh you know to like learn the concepts and so on and so forth before checking the answer book after. You would never check the answer book before if you really wanted to learn something right like because then that's just memorization. So instead you focus on applying first principles to a problem. Focus on your own human intellect and applying that to a system then after you are done and you have some stated hypothesis with some stated solution. Then check to see what other people did. That's how you end up with novel creative solutions that win and then drive disproportionate outcomes. Okay. Okay, so here's a real example from our community. Maxi Maximillian, he's one of our members who was consulting for a client in the healthcare industry. Uh he was approached with what looked like a very simple request which was hey can you build me an AI transcription model for doctors and this is very stereotypical fast. So thinking about it through fast first principles instead of accepting the problem as just stated he just asked hey do we actually need to build a new AI model for this? And what he quickly realized was no you don't need to build a new AI model for this. All you need to do is find a way to record audio and then just get it transcribed accurately, right? Like that's really the constraints of the problem if you just approach it from a first principles perspective. So the fundamental requirement was literally just a microphone in the room and then he needed to be able to send it off to some sort of transcription thing. Building a model was just a suggestion that the client had sent him because they did not fully understand what the technology behind that meant. Building a model is a very involved process, right? You need AI engineers. You need uh you know tons of data. You need like a massive data pipeline and just way more in the way of hardware, servers, and work than most people would ever be able to accomplish on their own. But using a pre-existing transcription model, well, that's an entirely different story. So next, actionoriented uh rather than building like some full stack app with a custom AI model, all he did was he identified a very simple test. Okay, it was basically like a pilot. Hey, can I do a voice recording? Send it to uh some pre-existing model and then get that summarized. And it was a very tiny little MVP that you could literally implement with an hour to let the client know yes, this is possible or no, this isn't. Okay, so after that second order thinking, which is where you ask yourself, hey, if this works, what the hell happened? So assuming that he solved this problem, which he was obviously going to do. It's not a very complicated system. What happens next? Well, the company would obviously want to implement it, right? And if that occurred, what gives? Well, as we know in the medical industry, we have a lot of compliance issues. And so HIPPA compliance for instance was going to matter a lot to medical records for that business. So he went and he checked to see what were the penalties if I broke this compliance. And then he quickly realized that he needed a solution to avoid breaking the compliance which was either local models or some sort of secure infrastructure. And then finally triangulation instead of reinventing the whole wheel now he just looked to see if there were any existing tools and there ended up being some commir and caragon that were available that would already do what he was asking. So I use fast every week with my own clients at leftclick. When a client comes to me with some sort of very complex automation request, I do not immediately start mapping out their workflow. I will run it through fast, which in reality is a 5 to 10 minute process. It is not a very long or difficult thing to do. And if it makes sense, and only if it makes sense do I continue. If it doesn't, I will discuss it with the client. Now, I've used frameworks like this over the course of the last couple years to make hundreds of thousands of dollars in agency work. It is very, very straightforward to do, and I'd highly recommend thinking about every project that you take from here on out in that way. All right, so to wrap things up, here are how these four frameworks work together. First, you start with driver trees to identify what actually matters in a business. Uh business acumen then ensures that you are moving the actual right levers and that you know what it is that you are trying to do. Uh communication then gets clients to say yes to your solutions. And then fast makes everything systematic, repeatable. Then it's also a thinking tool that extends outside of just business. So I very much transitioned from just an automation builder a few years ago to a full-fledged like systems consultant. Instead of just selling AI tools, I actually help companies think through problems systematically and that is the bulk of what I get paid for. AI is just one of the many levers that I could pull. Uh and the technical implementation, while fun and enjoyable and also pretty interesting, uh is not the thing that ends up making me most the money, it is the way that I approach business problem solving. So, if you
