Welcome back to the game podcast. We talk about making fat stacks of money or just helping people and doing good stuff. Anyways, today, this is the third in our little mini series of audio first podcasts. Please, again, let me know if you like this by tagging me and sharing it, because I'm looking at the metrics if these podcasts do better than I will do more of them. But today I want to talk about data, which sounds really boring, but it's really important and it will make you a lot of money. So maybe I'll just frame it that way. But I had a few recent, very frustrating instances that occurred in my business that may have occurred in your business that I didn't recognize as data problems. And then once I did, was able to resolve them. And so I want to show you at least another one of these patterns so that you can recognize it faster and solve it to grow. That being said, I want to talk about a meeting that I had this last week or two or three. It was incredibly frustrating. And when something's frustrating, a lot of times for me, it's because I don't know why it's frustrating. 'Cause if you know what the problem is, then it's just like, "Great, let's just go solve the problem." There's not really a lot of frustration there. It's the unknowns that are the ones that drive you mad or at least drive me mad. And so I'll describe the scenario, because hopefully if you have the same scenario, you can recognize the pattern faster than I did. And so I'll tell you probably two or three stories here to make this real. So first stories from years ago. One of our portfolio companies came to me, he was super stressed out, and he was like pulling his hair and I was like, "What's going on, dude?" And he said, "Dude, there's so much money coming in." And I was like, "Well, that's a wonderful thing." And he was like, "Yeah, but there's so much going on." And I was like, "Oh, well, that's not as wonderful of a thing." Okay, but what's the issue? He said, "I just don't know where everything's going." And this was a business that we were scaling rapidly and it was a business that had a reinvestment. Basically, we had to get returns on capital. So some businesses have low cap X. So if you have an accounting firm, you just hire more accountants and you can continue to expand. If you have a manufacturing business, after you get to a thousand widgets a day, you need to buy another machine, right? You have to reinvest capital to expand the business. And so this was a capital heavy business. He basically said, "I don't know the rate at which I can expand "based on the cash that we have." And I'd still like to taste distributions and I obviously am a big fan of distributions as well. I said, "Freeze this moment," 'cause he was really stressed out. I was like, "This moment, this feeling you have," I was like, "Is a lack of a finance function "or an underdeveloped finance function?" And he just kind of looked at me. I was like, "Right now you have a bookkeeper "and we've scaled far beyond that. "We need an in-house controller, "not just somebody who's going to manage credits and debits." That person could then give us kind of operationalize the financial information to give us cash flow projections so that we can see how many locations or how many reinvestments we can do and still maintain cash flow as we meet these projections. We got that installed in the quarter later. He was like, "Dude, oh my God, all my stress is gone "because I know exactly how many we can open "or reinvest every single month "and still get distributions." And I was like, "Right, wasn't that great?" And so the constraint of the business at that time was a lack of data, specifically finance data. And so this got me thinking about data as a constraint overall within businesses. If you think about departments of a business, so you've got marketing, you've got sales, you've got product, you've got customer success, I would consider those core value creation. And then you've got supporting value creation, which would be like HR recruiting if you pull that out of HR, which we do. IT, so information technology, you've got finance and legal maybe to some degree. You've got these other departments, right? Interestingly for me, IT, what it literally stands for is information technology, it's data tech and finance. Both of those are data departments. Their entire departments, what their output is, is information. So think about it, like when you have a good finance department, they send you data. That's actually what they do. They literally send you a sheet of data. Which sounds weird for me to say this 'cause it's like, well, we'll do. But I haven't thought about it like this, and maybe you have and so if you have, then you're smarter than I am. But thinking about that way, it means that the output of finance is data and the second part is in order to make decisions. And so if the data that you get from finance doesn't change how you operate, you don't need the department. Now it would make the argument that you do, and that if you aren't using any of the data, that they're giving you the wrong stuff. And the same can be said for IT, which is that IT isn't actually a real department, it's just that, you know, 100 years ago, IT was just ledgers. You know, somebody who kept contacts, like we should put their name and address somewhere. It was how we organized the information of the business. Both of those, IT and finance kind of spread across the entire organization. Because if you change your offer, for example, then finance needs to know because they're gonna have to change the contracts and they're gonna have to change the invoicing and they're gonna have to change the billing. And maybe if the, the processor know that you're gonna have to increase your limits or you're gonna go to a recurring model, whatever. Like it's gonna affect things. If you change anything about your offer, for example, you're gonna have, or your sales process, IT has to know. The pipeline's gonna change. The stages are gonna change. Our funnels are gonna change. How we tag people inside the CRM is gonna change. But fundamentally, the only reason we have either of these departments is to have data. You're like, okay, I get it Alex, why are you bringing this up? So fast forward to the meeting that I had a recurring meeting with my marketing team recently in-house. I haven't been this frustrated in a while on a meeting, on a recurring basis. And I even brought up, I was like, guys, why is this so painful? Like, this is horrible. This feels like pulling teeth. Like, what is happening here? And so basically, what I had never had before is a big brand, right? We were like, well, Alex, you've been doing this well. It's like, yeah, but I haven't really like done anything very much with my brand besides, you know, we feel inbound inquiries that come in for portfolio companies and we don't do a tremendous amount of deal volume in terms of deals per year. It was novel for me because we basically get traffic from all over in many different places. To improve things, we still didn't have adequate tracking. Like if you just have ad tracking, which is what I was accustomed to, that tracks all your ad traffic, for the most part. And in my history, that was the only traffic I had. But with organic, you have word of mouth, you have all of the different sources of organic traffic. And if you start emailing, you have email traffic. Now, of course, there's attribution for email. There's some levels of attribution that you can do on organic. But all of this was just honestly a mess. And so it was like the third meeting in a row where I would end the meeting and I was like, what happened? Because I like to have very clear marching orders for everyone if we end the meeting. It's like, what are we going to change our own behavior? If we don't change our behavior from this meeting, we shouldn't have had a meeting, right? Like, what are we going to do? Three in a row, we had nothing to do. And so I was like, this has to change. And so the big realization was that I was in the exact same shoes as that founder with the missing finance function, except I was lacking marketing data. I was like, oh, it's one of those, right? And I try to get into pattern recognition as fast as I can so that I can make faster, better decisions within businesses, which is why every business that I've started has made more money than the one before it. Because hopefully you recognize all the patterns to get to this level and then you have to learn new patterns. And so for me, this was a new pattern, whereas the finance pattern was one that I had encountered plenty of times before. And so it was just that this pattern wasn't just 101 at attribution, which duh, you should do that. But it was omni-channel tracking and attribution. And then funnel conversion across different types of traffic. And so you can't even run all types of traffic to one funnel because they're going to convert differently. Again, what do I do with this? And so I'll tell you what we did to solve it. And so I think there's two kind of takeaways that I change about my behavior that may be helpful for you. So number one is, and you'll see it on our sites, I made a change for acquisition.com, the URL, within all of the stuff we send out. And so emails, it's like acquisition.com/one or two, right? And my Instagram will be another number and YouTube will be another number. And so the very first version of this, by TV sent back, it was like blah, blah, blah, forward slash, you know, IG, for Instagram, I was like, don't be that obvious. But I am telling you guys the secret behind the scenes, which was I would just put a number next to it because I also like to be short, right? I wanna be able to say acquisition.com/four if it's, you know, the podcast one. That was the first thing that we did, was that attribution for tracking is really tough. And so at the most basic level, I just put different links for everything. Now you can say UTM parameters, whatever, it's up to you. But that's what we did. The second thing is that I have like a meta lesson, which I'm taking away from this, which is that if I'm ever confused about what to do, it's because I don't have data. And data itself is the constraint, rather than what I need to do about the data. And so I kept trying to come up with actions to take to change in the marketing, but it felt frivolous because every time I would come up with, you know, a potential solution, the team was like, well, here's this other thing that could be conflating that decision that you had. And so it felt like I just kept my ideas deflated by the team, not because they were trying to be malicious or something, but because they rightfully were like, we don't know anything. And I should have just been like, well, shoot, we don't know anything's the problem. And so if you find yourself kind of in these meetings again and again and again, not being able to make a clear directive, pick a clear directive of what to do next, then you might need to chunk up a level and say, I lack data and once I have the data, making the decision tends to be the easiest part. That was a meeting that recently happened. So that became the priority and they fixed that stuff. And so here we are. But I thought I would share that very frustrating experience with you. And I'll give you one more caveat. The team might say, well, okay, cool, we need to track data and they come to you and bring you 100 pieces of data, right? Well, that's not useful either. And so there's a little, I think there's an Irish tale of a kid who catches a leprechaun to get the pot of gold, right, the kid says, he catches him at night. He says, okay, tomorrow morning when there's light out, I will, like I'll give him a wish. I don't remember how leprechauns work, but basically he was going to tie a ribbon around a four leaf clover. And if you picked it, then he'd be able to get the wish, whatever. So the next morning the kid wakes up and he looks at the field to find the ribbon and to his dismay, there are millions of ribbons because the leprechaun put a ribbon around every single clover. And so it became impossible to find the four leaf clover. And so having too much data is a lot like that, which is you don't know which piece of data is the right piece of data. And so I want to give you a filter that I use for data in general, which is, if this datum, singular for data, changes, does it change something about what we do? If you say, okay, you're reporting this number, if this goes up, what do we do? If the answer is nothing, delete it. You don't need to track it. If the answer is, well, if that, 'cause whenever we're like, we want to keep tabs on that, I'd be like, why? Well, it's good to keep tabs on why. Why? I had a vendor for one of my platforms years ago who told me we want to make, you should make shorter podcasts. And I was like, why? He said, so we have longer completion times. And I was like, so, why does that matter? And basically, I was trying to get to the point where, if you can say, this will grow the podcast or this will get more people to listen, then I would say, sure, that makes sense if growing the podcast is the objective. But the three questions that we ask are, number one, what does that mean? So you have this piece of data. What does this mean? What does it measure? Number two, how do you know? So when someone says, well, this is gonna grow the podcast, I would say, great, how do you know that? Well, short form is gonna grow, is gonna get more people to watch long form. That sounds a good, good narrative. How do you know that? And if you don't know that, then you're just making it up, in which case, fine, but don't make decisions off that. And then the third question is, so what? Logic, evidence, utility. Logic, what does it mean? Evidence, how do you know? Utility, why is that useful? And so you can think of those as the filters through which you analyze the few pieces of data that you want displayed to you, so that you can make the decisions to move the business. I would encourage you to try and eliminate as much of the data, which sounds counterintuitive, but because I don't think the human brain can handle that much data. And so you can usually make most decisions in the business if you have the right data with two or three key points that you need to track for a department or for a role. And it doesn't usually take much more than that. The rest of the stuff, just because you can measure it, doesn't mean it's important. And I think that a lot of employees conflate that, and because it takes them time to put the data together, and they hear that data is important, they then put lots of data into slide decks, into reports, because they want to do a good job. And there's nothing wrong with them wanting to do a good job. But I think translating you doing a good job means that you help inform decisions, and decisions create changes in behavior. And so two massive departments in every company, depending on your size, finance and IT, both are data departments. And if they are not clear about how they make the business more money by informing decisions that change behavior, then it's very likely that they will be wasting your time and your money. Again, if this was cool, let me know. And if it sucked, you can let me know too.
