172: Predictable Scale: The 6-Step System to Drive Consistent, Sustainable Growth (w/ Pete Caputa)

Speaker A: I think predictability is important so that we can all sleep at night. There are so many things in the world that we don't control, and so what it kind of forces me to do is to focus on what I can control.

Speaker B: Welcome to Move the Needle, the show where we explore how teams turn data into growth. Real stories, proven playbooks. Let's get into it. Hi, welcome to Move the Needle. We have a very special guest today, which is our own Databox CEO Pete Caputa. Many of you know him from his nearly 10 years at HubSpot, where he founded and built the pretty, uh, legendary HubSpot Solutions partner program. Pete has used his 20 years of experience to help thousands of organizations, many marketing agencies to build systems and tools all focused around increasing recurring revenue and profit margins, including his previous, uh, predictable Performance Methodology. Pete has actually been squirreled away for a couple of months now, working on a new and improved methodology, Predictable Scale. It's going to be launching in October, and we thought this would be a great time to have Pete come on and give us a little bit of a peek behind the curtain. So. Welcome Pete.

Speaker A: Thank you, Allie. It was a very good, um, very good intro. Were you nervous entering your boss's boss?

Speaker B: Uh, you know, only a little bit. I took all the snarky stuff out ahead of time, but it could always sneak in in the show notes. So.

Speaker A: Okay, I'll try to watch myself.

Speaker B: I have all the power. All right, so let's start off with why. Why now? Why a new methodology? What's kind of your thinking, um, behind the problem you're hoping to solve with the new. The new methodology.

Speaker C: Yeah.

Speaker A: So the new methodology, as you mentioned, is called Predictable Scale. Um, and the impetus for writing it was a combination of, like, personal frustrations and observations of, uh, from. I've made from conversations with other executives at, ah, you know, our customers, partners, other software companies, et cetera. Uh, and that, like, frustration that I both feeling and hearing is that it feels like we're almost working twice as hard to get half as far these days. And um. And uh, and there's two. There could be two reactions to that. One is like, it's just. It is what it is. We're going to keep working, working as hard as we can. Um, or, uh, at which. Which, uh. Or it can be like, hey, we're going to try to focus in on the things that can have the most impact in our business and frankly ignore the rest. Uh, and. And it. And obviously with the first path, if you just keep working hard and hard as you can, um, and you're living in a state of frustration or anxiety or whatever. Uh, then that leads to burnout. It leads to poor company cultures. And frankly, flitting from one thing to the next, trying to look for that thing that's gonna help you grow your business or improve your margins or whatever. And flitting from one thing to the next usually means you're not spending enough time or focused on something to make it work. And so wanted to build out a methodology for people to follow that does the latter thing, which is about helping them identify the things that will have the most impact on their business. So that's what I tried to do, is what I tried to do. Working, of course, with the team, um, I spent a lot of time talking to, um, strategy consultants, reading a huge amount of books. In fact, uh, my computer right now is sitting on 20 books, 10 books each side, uh, that I poured through to try to figure out how to put this methodology together. Awesome.

Speaker B: Ah, I'd love to hear what are some of your inspirations and your favorite resources and experts that you looked at for building the course?

Speaker A: Yeah, there's a bunch of them. So I think, number one, blue, uh, Ocean strategy. So I wrote a lot on how to develop a strategy for a company. That's the first chapter is Building a Strategy. And what I like about Blue Ocean strategy, uh, is, um, uh, I think they call it the strategy canvas, where the way to develop your strategy is you identify the strengths and or weaknesses of your product relative to other companies in the market for the customer segment that you want to serve. Uh, and what that allows a company to do is really see where their strengths are relative to their competitors and where their weaknesses are. And, um, either address the weaknesses or even turn them into strengths, and then, of course, lean into their strengths. Or if there's a new thing that customers, your customer segment cares about that no one's serving. Like, to lean into that and try to become known as the company with that strength. That's one of them. Um, I like Richard Rumelt's Good Strategy, Bad Strategy book. I like how he stresses that companies should focus in on their challenges when they develop their strategy. A lot of times I think companies ignore areas of weakness relative to their competitors. Uh, it also encourages people to look at, like, you know, what are threats to the business. Um, and so by looking at weaknesses and threats, he helps companies kind of diagnose the problems that their strategy should address, because there's problems that may be preventing the company from achieving, achieving their mission or vision. Um, and by focusing in on those roadblocks and trying to either remove them or at least realize, um, what they inhibit the company from doing, um, it's really important to do that. Those would be two. I could keep going if you want.

Speaker B: No, those are two good ones. Thank you. So, so the common theme that, like I noticed right away between the previous methodology and the new one is this idea of predictability. Can you talk a little bit about, like, what predictability means to you, why that's important now in the context of your new course, your new methodology?

Speaker A: Um, yeah. So I think most people know what predictability means. It means that you can have confidence in what the result will be in the future. Uh, which is really, um, hard. Uh, anyone actually that says they have absolute confidence in what they have in the future is a snake oil salesman probably. Um, but I think as an executive running a business, having confidence in what you're doing and that will result in a positive impact, um, is critical. Uh, and data is at the core of that. So for Databox, what we help people do is, as, you know, track their performance, um, so that they can. One thing that they can do is better, um, predict the future. Uh, and we literally have AI and data science in our product that helps with predicting outcomes. So, um, that predictability is one reason why you should be really data driven. You should be really good at building processes, having repeatability with those processes, measuring the effort required in those processes, the results of or outcomes from those processes, modeling that to understand the correlation, uh, playing with forecast models to figure out, like, if I do this, what will happen? And so data is core to predictability. So that's why I obsess over it. We obsess over it at, uh, Databox. But the other side of reason I think predictability is important, is so that we can all sleep at night. Um, there are so many things in the world that we don't control. And honestly I felt that, that there are less. Maybe it's just because I'm getting old, but less and less things that I feel like I have control over. There's more and more things where uncertainty, I have uncertainty. Um, but most of those things are outside of my control. I can't necessarily control them. And so what it kind of forces me to do, and I think this is the right thing to do for people, is to focus on what I can control. Um, and so, and if I am fully focused on what I can control, that means I should, as best I can, understand how that system works. That means building systems that are repeatable so that I can measure them and so that I can then start to predict the future. Um, and I think a lot of companies are still, even though there's so much technology out there, so many different tools that you can use around a process, AI or agents even that can do processes for you, there's still very few companies that really have a global view of how everything's performing in their business so that they can have that predictability. Uh, and so part of it's not really a technology problem as much as it is a, um, ah, habit and process problem. It's like a management team in a company needs to have a cadence for doing certain things, uh, so that they can ultimately have predictability. And so while our software can certainly help with that, uh, the more challenging thing for most orgs is to adopt a sequence of activities on an annual, quarterly, monthly, weekly, even daily basis so that they can ultimately have that predictability.

Speaker B: That makes sense. And I know, I mean, I know just from working with you that you're, um, obsessed and have a very strong brain for like, systems thinking and structure and processes around the strategy. So this sounds like a really great thing for you to put your, put your mind to paper here and for us to all benefit from. So let's, let's jump into the. If I can put you on the spot on the spears methodology, within the predictable scale, um, is the acronym for the six key steps in the system. Can you tell me a little bit about those?

Speaker A: Yeah, yeah. Um, you've done your homework.

Speaker C: So.

Speaker A: Yes. Um, I don't know if it was an accident that it spells spears, um, but it was kind of like there was brainstorming with Heather and Billy, maybe Tori. I think, Tori, this was maybe a year ago now. And I said, I think these are the steps that a company needs to follow. But I don't know if these are the right words and they don't spell anything. And I'd really love to have an acronym. So we ended up coming up with spears, um, for most of us know what a spear is, but it's like there's Norse, uh, mythology where, uh, I think it was Odin who kind of first figured out spears, obviously, way, way, you know, like centuries and centuries ago. Um, and it's like the idea of a spear is that it's about hitting. And so, uh, it seemed like a really good, not just an acronym, but almost an analogy for what we're trying to do is help people to more precisely hit their future targets. Uh, but spears, uh, the acronym Stands for strategize, plan, execute, adjust, repeat, and scale. Uh, so those are the things. Strategize, plan, execute, uh, adjust, repeat, and scale. Uh, the methodology or the course that we're building has a chapter for each one of those and walks through basically how to do those, because five.

Speaker B: Awesome. And I won't make you go through all of them because that's what people need to sign up for the course for.

Speaker A: Oh, I just. As you know, I just finished writing the. I just finished writing.

Speaker B: It's fresh on your mind.

Speaker A: M of it. So it's all fresh in my mind, so.

Speaker B: And maybe whichever one you just drafted, uh, the most recently will be the most fresh on your mind. But compared to maybe the previous methodology, what. Are there any portions of the Spears framework that are, like, really big additions this time around or that you really had new thought into?

Speaker A: Yeah, yeah. So when we wrote, um, the Predictable performance methodology, which was heavily written by Tory, you know, Tory, who, uh, runs our Revops team now. Uh, Tory, uh, Ferrell, and then John, uh, Benigni, who ran marketing at the time here at Databox, so it wasn't like a solo venture. Um, the whole point of the Predictable formats methodology was to help, um, teams and individuals understand how to kind of manage, um, up in a way of running their function or their job in a way that, uh, they can prove the value of their work. Uh, because when we launched that, uh, most people signing up for Databox were managers, uh, and even individual contributors that needed to report up to their bosses, um, how they were doing. It was their boss coming to them and saying, hey, you've been doing this for a while. How's it going? What kind of results are you giving? You know, delivering to the business? And so that's the way that is framed. So it's very much where, like, your boss kind of gives you a. A scope of work or an objective or a function to lead. And then you have to go and, like, figure out, how do I measure my outputs, how do I measure my outcomes, how do I make sure the quality of my work is high? Uh, how do I measure that? Um, how do I set goals against that? If, you know, if the boss gave me this goal, but I don't have control over that goal, I have to do these things to get that goal. How do I set goals that are appropriate for me and for them to measure, measure my impact? So it was very kind of like, bottoms up. M. This methodology is the opposite. Now when people sign up for Databox, it tends to be the executive team who are looking for better ways to predict, uh, and predict your performance and then manage, uh, to that prediction. Um, and so this is very much top down. Um, and what I've found in so many of these conversations with other executives is that most management teams skip to the execution like they've already decided. Here's what we're executing. Uh, we're going to do ads, we're going to do outbound prospecting, we're going to have an SDR team, we're going to um, build our customer support team in this way. And then they're like, all right, now I got to measure everything. And usually what happens is everybody ends up just going to work on their own thing and they really don't make any progress. Uh, and so what I realize is that most companies are truly skipping two steps. The strategize step, which I talked a little bit about earlier with uh, the Blue ocean strategy and retro remote work, um, and the planning step. So the strategized step is much more about understanding the customer segment. You're focused on understanding who competes with you in that customer segment and then designing initiatives or plays that allow you to serve that customer better, beat or uh, avoid that, uh, competitor set. Um, and then turning that into a vision, a mission, uh, ambition, and then turning all that strategy stuff into a plan where you say these are the five most important things or initiatives we're going to do this year. They require cross functional support. And uh, here are the reasonable goals we'll set for those initiatives. Um, and then um, here's how we'll um, roll this out to the team who will work on it, how we'll um, plan out the actual next steps and things like that. So um, I realized that most companies are skipping that. They, you know, they say, hey, we're building this product and we're gonna go do this marketing and we're gonna go do this sales. And instead of really collaborating across, uh, you know, against a handful of objectives, they're just kind of all working in their silos.

Speaker B: Yeah, technology, commerce, I love that. I mean I can vouch for that working here. And you know, I've shared that on LinkedIn before too. I mean I've worked at small companies, medium companies, big companies. And uh, I've never really worked anywhere where I've come in and it's been so immediately clear like what the company strategy is, how everybody's department rolls up to that, what everybody's individual goals and priorities are. The OGI system that we use around setting our um, Goals and initiatives and how they all roll up to the company strategy. It's really clearly mapped out for everybody and very trans. So, um, I think that's great and I'm excited that you get into that in the methodology.

Speaker A: How long you been here now?

Speaker B: Is it a, uh, little over five months?

Speaker A: Five months, okay. Yeah.

Speaker B: Yep.

Speaker A: Yeah. Cool. Well, it's interesting to hear your perspective because I somewhat take it for granted. Um, you know, HubSpot was run pretty well, not as structured as we are, um, but obviously the company did very well, so they had a lot of things going. Right. Um, but yeah, over the years, we've improved our own, um, operational model, uh, and borrowed a lot of stuff from HubSpot, but also borrow a lot from other methodologies and, you know, came up with our own stuff. Um, and so this methodology is not just like something I sat down to write, you know, research and write. This methodology comes from the eight years we've been operating Data Box.

Speaker B: Uh, yeah, that was something I really wanted to ask you. Obviously, the strategy piece. I've seen how that comes to life at Databox. Are there any other examples you could give of how we already are applying this methodology at Databox?

Speaker A: Yeah, absolutely. Um, as you know, every year we, as a management team. Last year, you weren't here then, but we included every manager and director in this process where we went through our strengths, our weaknesses, um, threats that we see or opportunities that we see. Uh, and, and we listed out all of the things that we could do to help us serve our best customers better. Um, we listed out all the things we could do that would take advantage of opportunities with new technology. Um, we listed out ways that we could go to market more effectively, uh, et cetera. And so we did that all together. But what we settled on, one of the things that we settled on, we have, I think we have four or three objectives. Three main objectives. Um, for one, we always have one related to people, but the three that are related to the business objective. Business objectives. Um, and one of them, um, is to, um, kind of move mid market. Right. Um, we have customers that span all employee sizes, um, from literally some solo shops. But the majority of our customers are between 10 and 500 employees. We have some that are bigger. Um, but when we look at our customers and we identify who we serve best, who pays us most, who sticks around longest, who uses the product most intensely, um, the middle market is the best market for us. Um, and so what we decided is to make that an objective is we wanted to move more mid market so it meant understanding their needs better, uh, understanding what they want the product to do that it doesn't. Understanding things that we could do that they're not asking us for but could serve them better. Uh, and then that is what dictated a bunch of things that we do not. But one of the big things was uh, in product where we realized that our mid market customers wanted to do deeper analysis of their performance inside Databox. Uh, so what we had to do is actually rebuild re architect, um, almost the whole back end platform from the way that uh, the types of APIs we can pull data from, how we pull that data, how we store that data and then how we enable our customers to uh, manipulate that data, create metrics from that data, uh, and ultimately analyze more deeply in data box. So that was one big thing. As you know we launched it in May. Um, we've had hundreds of our existing customers using it, signing up customers now at a good clip every month that are using that functionality. So that was key to us serving the mid market better. Um, another thing we're doing to serve the mid market better um, is we're uh, tightening our partnerships with uh, with professional services firms who use us. So historically we've always had um, professional services firms, lots of marketing agencies that would use us just to report the results of their work. But um, we realize is for some of them they're trying to be more strategic with their clients, um, and actually help with the type of stuff that's in the methodology like you know, helping their clients truly develop a better strategy, helping their clients build a better company wide plan for executing against that strategy, uh, helping their um, um, you know, uh, their clients look at their whole performance data and figure out where can we adjust, what should we be repeating, et cetera. Um, so what we did is um, launched a true reseller program. So now we have our partners that actually can resell the product for commission. But more importantly they can provide a consulting service on top of that our product that enables them to be more strategic with their clients. And uh, we already have a bunch of partners doing that. They're all different types of professional services and marketing agencies of course, but also rev ops firms, business consultants, technology implementation consultants, uh, some financial consultants, operational consultants. So there's a wide range of consultants that are seeing value in reselling our product and then helping their client leverage data better to improve performance and have better predictability.

Speaker B: And those focus areas really wouldn't have become clear if we hadn't to your point started with the strategy which was who are our best fit customers. We're going to focus on mid market. Those initiatives came out of that. And I have even seen how in conversations we've evaluated opportunities that look really attractive and then kind of brought it back again to okay, but that doesn't align to what we said the original strategy was for the year. So we're going to like put those on the back burner for now.

Speaker A: Which is one of the best parts of setting objectives as a company on, um, an annual basis, is that it, it gives everyone in the organization a lens in which to evaluate what they should focus on. Uh, uh, it's really as an executive or CEO here, it's really freeing for me to say, you know, I've set this, you know, we've finalized. It's not just my brain, it's everybody's ideas. But I've, we finalized these list of things and areas we want to focus. And now you guys go figure out how do we, how do we do that? Right? Uh, and of course, as you know, I'm fairly hands on in certain areas of the business, so there's certain areas where I get into the weeds. But for other areas, I can just trust that they know the direction and they can make those decisions autonomously.

Speaker B: It's a great blueprint for everybody that makes sense. Um, you mentioned data and how that has a really important role to play. Can you talk a little bit more about any other specifics there we didn't touch on, and how data plays a role in supporting the methodology?

Speaker A: Yeah, I think one of the hardest parts of managing any business is setting realistic goals that also challenge the team. Uh, and it's about managing the balance between the ambition of the team or the company, um, with what's realistic, given the resources that are available. There's no perfect formula for setting a good goal. Um, but data can have, can play a huge role in setting realistic goals. Uh, so I walk in the planning step of the methodology. I walk companies through how to use data to set goals. Um, and there's three, um, mathematical functions that are included. Uh, one is understanding correlations in your business. I'll go into these a little bit deeper. Um, two is modeling the impact of changes that you can control or can make. And three is understanding how your performance compares to other companies when setting a goal or setting goals. Using those three mathematical functions can make goal setting a lot more scientific. I haven't seen many companies that do this well. We did this really well at HubSpot, we do this well at Databox. Um, but, uh, for the most part, uh, even most of our customers are pretty poor at setting goals. And I think most people just kind of like, give up on it. Um, or they might set goals at the beginning of the year and then realize, all right, that one's not going to work. And they kind of just say, all right, we'll set goals next year again, we'll try again. Um, and so there needs to be a process for goal setting that uses data. Not just that in the initial goal setting stuff, but at different stages, need to be willing to reset that goal. Um, of course, but the first step is really, as I mentioned, is correlations. Understanding the correlation between the work you're doing and the result you're achieving. Sounds simple, stupid, but very complex, uh, in practicality or practice. And of course, really complex in a business when you start to factor in all of the things that different people or different teams are doing. But, um, a really simple example would be, hey, um, we're going to publish more on LinkedIn, right? A really simple example. Um, and we want to know what kind of impact that might have on the business. So there's different ways we can measure both our output, um, the immediate results that those drive, um, what I call leading indicators, and then try to understand the impact on lagging indicators. So, for example, we know that we could publish more on LinkedIn. Um, right. We also know that we can publish, uh, if we publish on certain topics or in certain ways, we could get more reach or resonance on LinkedIn, um, more engagement on LinkedIn. So, um, by measuring those two things, and then we can measure impact on, say, signups or customers, even, uh, in terms of adoption of something, and we can start to correlate those values and see. All right, well, if we do these things, this is what, when we did these things, this is what happens. So we should maybe do more of those things. We should write more on those topics, we should publish more, whatever it is. Um, so that's a really simple example, but start to think about all the things your business does and then try to ultimately model again your revenue target. Like, it gets really complicated and there's

Speaker B: a time lagging component there. Right. I know this has, uh, come up in some of my other podcast conversations with, like, correlation can't be. I did this thing on LinkedIn today and tomorrow our revenue went up. Right?

Speaker A: Yeah. I think most financial analysts will look like it's almost impossible for most financial analysts to model that because they'll, they'll model things by month because that's the way finances get modeled, uh, and they'll say, all right, well, how much traffic going to get this month? And then how many sales are we going to get this month? Right? And like that is, does not take into account that lag or even just the complexity of, of the cause and effect. But that lag is so important. And there's almost no way that a human can think through all of the things that you're doing, put it in a spreadsheet, ah, think through all the results, calculate the lag, and then predict the results. So that's where correlations done through data science, really important because not only can, um, you know, you can run a statistical function or data science where you can say, this is definitely correlated to this at this level, but this value. But we can also see that that lag is 30 days or 60 days or 90 days. So the things you're doing now are not going to impact that next month's results, is going to impact next quarter's results. Uh, and so, no, it's not hard for data scientists or code to calculate that. Um, and then that's where the next step comes in, which is that forecast modeling. So now you can start to say, now that you understand those correlations, you can start to say, well, what if I do more of this? Or what if I improve the effectiveness of this thing that I'm doing by this amount? What it can then do, what data science can then do is say, hey, well, this is the ultimate impact you should have at least, uh, over time, right? And literally chart out like, this is our low estimate, our realistic estimate and our high estimate. Even with data science, you can't accurately, perfectly pinpoint one value, but you can get a range of results that you might expect. Um, and doing that through data science and math is so much more valuable than doing that through a spreadsheet or whatever. Um, and then the final thing is kind of like a gut check. It's like, all right, well, this is what we're doing and this is the result we're getting. How are other companies doing with that and being able to, um, see benchmarks, to see, you know, how does that compare? Are we way overperforming? We underperforming? What does that mean? I've had companies that are way over performing a certain metric and they say, that's awesome. We're going to do more, uh, we're going to try to even over perform more because it's working for our business. Right? I've seen companies that are like, yeah, we know we're underperforming that's not our focus area. It's cool that we're going to focus on this instead. So it really. Benchmarks don't necessarily tell you what you should fix per se. They just give you a litmus to say like, uh, and have an honest assessment of like how your performance might compare to others.

Speaker B: Nice. It's really fun to listen to nerd out on this as the non math person in the room. Um, I do my best. Um, this is really interesting. I'd love to know, ah, you can have a little product pitch moment here. So how does this tie to Databox as a product? As you said, this stuff is not stuff that's easy to just like sit down and do on pen and paper for most people. Are there um, features either in the product now or coming on the roadmap that tie directly to um, supporting the stuff that you've laid out in the methodology?

Speaker A: Yeah, so I'll work backwards from the three things that we just talked about. So benchmarks are in the product already. People can go, um, they can actually go to benchmarks.databox.com, sign up for free and get access to a lot of benchmarks. But we also have a more advanced benchmarks capability in the product that's available in our growth plan. Um, but that product leverages um, the data that we have access to that we have of course anonymized and allows people to compare their performance against the aggregate of other companies. So what they'll see is a chart that shows, you know, x percentage of companies have this value, x percent have this, and it's usually a little curve and you can say, hey, this is the median, meaning the, you know, 50% of the companies perform below this mark and 50% perform above and you're at 32, which basically means that you're outperforming 32% or you're underperforming 68% regardless, you know, depending on which way you want to look at it, positive or negative. Um, so that's in the product. Um, you basically just say you wanted to benchmark, say your um, LinkedIn company page. You can go in there, just connect your LinkedIn company page with your oauth just by logging in and giving us access um, to the data. We pull the data from uh, LinkedIn company pages, API and then we present those charts to you so you can see how your performance compares. So that's one. Um, next is, um, we have some forecasting capabilities built in the product, but we don't have forecast Modeling built in the product that's actually coming very soon. Uh, I'm hesitant to give a date. My guess is it'll be live, um, close to the time of the publication of this podcast. Uh, we expect that to go live this quarter. And again, what that does is allow people to connect different data sources. So you can connect your Google Ads, your Facebook ads, your LinkedIn ads, your Google Analytics, your HubSpot, your um, you know, your whatever. Uh, and then let's just say you want to optimize for revenue. You uh, want to forecast your revenue. So you could sit there and say, well, what if we increased our Google Ad budget by this amount and what if we increased our LinkedIn ad budget by this amount and what if we grew our website traffic by this amount through some other means or whatever. What it can do is start to model out your future revenue that you might be measuring, say within your HubSpot deals, for example. Uh, so as long as you're measuring those things, the forecast modeling will allow you to kind of play with those variables and see what's possible in the future. Um, and then uh, the correlations, we have correlations capabilities behind the scenes. We haven't built like a front end yet. Um, we have a mock up, we have a um, spec. We're still kind of evaluating exactly how to build it from a user perspective. Um, but we're already have functionality, um, behind the scenes that can go and basically correlate values between uh, two different metrics and understand the relationship. Um, when you look at two metrics, there's a way of measuring the strength of the correlation of negative one to one. And so if something is correlated one to one, um, that means it's perfectly correlated. In reality, there's never anything that's perfectly correlated. So, so the best you're going to get maybe is 0.9 or something like that correlation. So it could be something like, um, if I get you know, uh, uh, 10 clicks on this ad, I know that I'm going to get one new customer that could, you know that even though it's, you know, only 10% like that could be a 0.9% correlation if that, that 10% conversion rate sustains uh, itself. Uh, so that means that that's strongly correlated. We get 10 clicks, we're going to get one customer. Um, there could be things opposite correlation, right? You could say that. All right, well if we get certain number of customers complaining about uh, or uh, abandoning the use of this feature, um, then that might correlate uh, inversely to uh, retention of those customers. Right. I'm making stuff up so I have to think through good examples. But that might be a um, negative 0.5 or something like that correlation. Uh, so that's built in behind the scenes in Databox we use that again. We're using that again in the forecast modeling um, tool. Uh, but uh, WIM will most likely build something that will allow people to kind of create a map of all the different metrics in their business so they can then ultimately just kind of browse through and see the strength of the correlation. I think a lot of times two things happen. One is people assume that something correlates well. Right. You might say, hey, we've been publishing um, to YouTube consistently and um, growing our subscribers. Uh, you know, by this and people are telling us that they heard about us on YouTube. Um, but um, if that's really erratic, like it's not based, you might be assuming that it's based on the public volume of publication but really it could be based on like one or two videos or something like that that you published. Um, and so um, understanding uh, that correlation consistency is, is um, is something we'll reveal in the product at some point or we'll have a front end for that. People can kind of navigate the correlation.

Speaker B: Awesome. That sounds.

Speaker A: And then you can see strength of correlation too. Right? We can see like, oh, like uh, you know, the more we do on YouTube and making all this up, more we do on YouTube, the stronger our sales are. But um, but the, you know, the more we do uh, with um, you know, with cold prospecting, it's not as correlated. Right. It's less correlated. So you can start to see the relative correlation between different.

Speaker B: That's fascinating and super powerful. I mean as you know we're having all these conversations ourselves and on the podcast and with customers and the market at large around the difficulty in sales and marketing right now, go to market teams trying to track the impact of dark social and clicks and traffic when all of that has shifted so quickly with AI. And um, that's one of the themes that keeps coming up is like we have to lean more heavily now into correlations versus direct linear paths of click throughs to conversions. Um, but then actually putting that into practice is still pretty hard. So this sounds really cool.

Speaker A: Yeah, yeah, exactly. Yeah. Part of this stuff is born out of our own challenges. But yes, good.

Speaker B: Well I'll be, sign me up. I'll be a guinea pig for the first beta. Um, okay, so talk to me a little bit more about are there Any particular metrics that you highlight in the methodology that are like key baseline foundational signals that leaders should be watching to know that they're scaling predictably? Or do you kind of leave that up to the discretion of each organization?

Speaker A: Yeah, every business is so different. I think it's, it's hard to sit here and say these are key leading indicators. Um, you know, I think most business owners or executives know, you know, what financial metrics are important to track, uh, and what sales metrics are important to track. I think a lot of companies kind of stop there, um, which is a mistake. I think it's important to track customers, you know, customer happiness, customer success type metrics, um, important of course, to track marketing and advertising, um, metrics. So it's, I don't have like one metric, um, to say, like, you should pay attention to this. Uh, you know, at the end of the day, it's the financials are most important to stay on top of.

Speaker B: But, but I think, yeah, that makes sense. It's more of a way of thinking and the process and structuring, um, everything that goes around it.

Speaker A: Yeah, I think it's more about the system. Like you said earlier, my brain is systems thinker and so I want to understand how everything works and what kind of impact it has. Um, and ten years ago I feel like I could do that like a few hours a month and I'd have a handle on how everything's working and of course I'd pay attention to it more frequently, but nowadays and, uh, I can't possibly pay attention to all the things happening in the business and the impact of those. So that's why I think it's so important that there's a culture where, um, the team thinks that way and maybe they don't have influence or even necessarily visibility into everything that's happening, but they think about their function, role as a system, and they think about what are they doing, what's the impact that has, how can they do that better with higher efficiency, higher effectiveness, and then how can they have maximum impact, uh, on the area that they're supposed to have? Uh, so I think the more important way to look at it is there's not one or two or even 10 metrics. It's about having a system and understanding the system. Um, and if you can build an organization that way, someone like me who does think that way, or chief operating officer or whatever can have a global view and I can let you know, I can let the people do their work, I can let the AI do its work. I can let the data uh, tell me how things are performing and have much more ability to kind of come in and help um, remove roadblocks, help uh, people with brainstorming, help people with um, coming up with better processes, whatever it is, um, because I have that global visibility and ideally everybody in the company has that global visibility so that they can actually independently um, help others and yeah.

Speaker B: Would you say that's the biggest mindset shift that leaders would need to embrace for the predictable scale methodology to be the most effective?

Speaker A: Yeah, I think, um, yes, I think that's the most important. A lot of owners, executives, um, do not share performance of their companies transparently within the company. So they don't tell people what's going on. As you know, every quarter we literally share everything, including our bank balance with the whole company. Um, you know, we have our own objectives, we have goals, we share exactly how we're doing against the goals, how we're doing versus year over year. Like we, everybody at the company has access. I'm not sure they understand everything but they have access to all that data, uh, frankly in real time. Right. Um, but I think most companies are resistant to that. In studies we've done in the past, um, I think we've found that more than 50% of companies don't share, um, performance of the company beyond the manager level. So only managers and above have access to that. I think that's a big miss. What it does is it disempowers the rest of the organization from having ideas, from helping each other, um, from even caring about their own performance because they don't necessarily see how it impacts the rest of the organization. So I think that's a really important shift. Um, and I think what I'm hearing, what I'm hearing from others is that other executives that kind of are on the other side who are embracing transparency is that they felt they had to do it. It's not because like they wanted to create this new age, new age culture. It was out of necessity. Because business is so complex now. There are so many um, functions, tools, data. It's so many different ways to grow a business. It's mind boggling at this point. And then as we build, as we start to leverage AI, build agents, et cetera, um, there's absolutely no way even a full management team can have visibility or understand all the systems um, that are at play. So it's so critical to empower everyone in the org, um, with that visibility so that they can more autonomously at least propose ways of improving things Even if they don't have the autonomy just to act, but at least they can be involved in coming up with solutions to problems or ideas that can be exploited, et cetera.

Speaker B: And I would think important to set that foundation early because the bigger the company gets, the harder it gets to go back and build in that transparency and alignment. Um, because everything becomes so fragmented.

Speaker C: Yeah.

Speaker A: I think earlier in the company everybody's a little more risk tolerant. Right. If you join a startup with a handful of people, you know, it's a risk. Um, and so, um, I think it's kind of easier to embrace risk, uh, for smaller businesses because everybody knows that like, we need to close deals, we need to keep that customer happy. Like, everybody knows the importance of that stuff. Um, and everybody sees when that doesn't happen. So I think it's really easy to be transparent when you're smaller. As you get bigger, it becomes a lot harder. Um, not only do you have to spend time educating, uh, the entire company on complex things about how the business runs, etc. But you also have to be, be able to deliver news that could be upsetting to people, it could be concerning to people. Right. Like, you know, if a company is, um, burning capital, um, right. Or not hitting targets, you have to be willing to talk about those things and say, hey, here's what we're doing about it. Um, and I think that's hard for a lot of leaders. Most, a lot of leaders want to present like, confidence and um, we got everything under control, everything's gonna be awesome and your job is secure and like all that and not have anyone doubt on that and that. I think that's like increasingly difficult given the complexity of businesses.

Speaker B: Anything else? I didn't ask. Anything else on your mind around the course or.

Speaker A: Uh, the chapter that I'm most excited about is the scale chapter. And it's the last chapter so people can't skip to it and just start doing it. But I do, I do want to tease it a little because I think, um, it's the reward. Almost. Scaling, of course, is the reward of lots of hard work. Right. Strategy, good plan executed well, being willing to adjust when things aren't working, repeating building systems so you can repeat. Um, and scaling is the reward. It's when you can start to grow, um, you know, grow your revenue at a pace that out, outpaces the growth of your costs. Right. And it starts to help you grow your margins. Um, and so that's the reward. But I also, in the chapter talk about things that people can do in Their strategy to um, to start scaling like kind of on day one, as long as they think about it. And so, so I have five ways for companies that they can scale and I put it in order of how I think people should think about it because they provide the most leverage. So I'd love to walk through those a little bit. Yeah, yeah, go for it. So first I'll give you the five and then we'll come back. So number one is enabling self service for your customers. Number two is partnering with other organizations in a way where you both grow. Number three is automating. Come back to why that's number three. Because I think most people these days will put that as number one. Number four is or outsource. And number five is to delegate. And so when I um, look at the first one, self serve, um, that's a really common thing that software companies have done over say the last 10 years. Right. There's this whole thing called product led growth which I think is an awful name um, for it. But um, but really the concept behind product led growth is to enable self service for your customers, allow them to sign up for your product, allow them make it easy for them to step by step set up your product and start using it and getting value from it without even paying. Right. Either in a free version or a trial, et cetera. And that's enabled software companies, certain software companies to scale really fast. Um, HubSpot actually implemented that right around the time they had that growth curve as well. So it might have been a factor in their growth. It was after I left. Um, Slack is a great example. Um, but I was looking at data the other day on Microsoft Teams growth and Microsoft Teams growth was actually make Slack's growth look like child's play. They came in later in the market but then they shot past Slack and of course Microsoft has so many distribution um, assets that they can leverage to grow fast. Um, but self serve is really key in growing things fast. And I don't think most companies beyond software think about it and I think it's really a mess is like how can you enable your prospects and customers to kind of get value from you without a human involved? Um, uh, and really enable that self service. Number two I have is partnerships as you might not be surprised. Um, but uh, uh, that's often an overlooked one. I think there's two ways for companies to think about this. Number one is you can join a company's partner program and number two you can build a partner program of your own. If you're a Smaller business. If you're professional services business, I would recommend the former. Think about, they should be thinking about what partner programs they can join. If you're, you know, a scaling business already, that's when you start thinking about how do I build something that other companies can partner with me with low effort on my side, um, but they get a lot of value. Um, and that's what I did at HubSpot, that's what we're doing at Databox, what many software companies have done. Um, and thinking about how partnerships can start to take work that you might have to do internally and they would do it for free, um, because it benefits them in some way. Right. Uh, so the obvious example is reselling, reselling software. Right? Um, so if you have partners out there that are doing marketing and doing sales and talking to customers and helping them get on boarded, like you can do that all at no upfront cost to you as a software company. Uh, and at the end of the day you pay them a commission on basically their performance. So it's kind of an amazing way to scale a business. Number three is automation. This one is hot right now, of course, because of AI. Not just LLMs, but also agentic AI. Uh, you know, we have agentic AI built into our product. We use products, um, other products, um, that enable agentic AI, uh, to automate processes in our businesses. Um, I think, uh, it's so shiny right now that everybody's thinking how do I automate stuff? And it's funny, I talk to these small businesses saying we're going to automate everything. And I look at what they're automating and it's all shit that our software has been doing for us for about 15 years. So I think, I don't think the hype is quite caught up to, or the hype is beyond the reality of it at this point in some cases. Um, but uh, you know, there are ways to automate things fully now and I don't think you need to do AI necessarily. It could just be software, uh, that helps automate a process or enable a process. Uh, and uh, so that's really powerful and I think it will be ever, you know, ever more powerful. Uh, you know, there's again, we already have AI functionality built into our product for our customers to use. But some of the stuff we're working on now where you know, companies will be able to just like use our product through a, through a, through a prompt, um, is amazing. Like the stuff that that's coming is amazing. So the more you should you should be. Companies should be thinking about how do they automate. Next up is outsource. Right. I think, um, outsourcing something generally you get a more ah, skilled person, um, but you also don't have overhead costs. Um, Right. And you can leverage their expertise, uh, and their efficiency that they've built up, um, over time of doing something. So outsourcing is always a good option. I think it's important though, one caveat is that you don't outsource it and leave it. It's important to outsource it and make sure you're building processes and repeatability, et cetera. If you do eventually want to bring it in house, um, and then delegate. Right. Um, I think this one's been around forever. But a lot of companies I think miss the opportunity here because they don't do the previous chapters very well. They don't think about how do they execute well, they don't think about how do they repeat something. You can't delegate something to a more junior employee unless you've already thought through the processes and given them documented at least the first draft of the processes they can follow. But those are the five things that I think are kind of again, things you can start to, things that you can start to do that really help you scale your business. But again they can't, you can't skip to most of them. You have to really think through your strategy first and all that. Otherwise you're just either automating stuff that's not important or you're trying to make things self service even before you've proven whether a customer cares about it, whether or not. So it's important to think through um, you know, the rest of the steps and the methodology before you jump there. But I do think um, most companies are missing the opportunity to do those, those five things.

Speaker B: Well, yeah, those are great. Awesome. I'm still working on that. Still working on the delegation one. Awesome. There's a lot of meat there. I'm really excited for, you know, everybody to get access to the full course when it becomes available very soon. So the wait list is already up. So everybody knows that's at academy.databox.com predictable-scale we'll make that all available here so people can already sign up now for the wait list, get early access and that will send out information about the full course, um, when it's live, a live cohort, um, that I think will be happening and then other resources as they become available in early October. So that's all available. Yep. And of course LinkedIn, um, I think is the best place for people to follow you. Yep.

Speaker A: Yeah, you know, I'm very active on LinkedIn, but, uh, I would encourage people to follow our podcast. You're doing a great job with it. And subscribe to our newsletter, uh, if you prefer, email, um, and our YouTube channel. Uh, you and your team have been doing a great job of getting more and more content up on our YouTube. And I think I was looking at our self reported attribution. Um, 9% of our signups are coming from our YouTube channel to self report attribution. So yeah, so it's good stuff. So it must be working.

Speaker B: Ally.

Speaker A: Uh, it's correlated with me.

Speaker B: Five, five months. Well, yeah, that's totally what the correlation would say. I love it. Good stuff. Slow and steady. Love it. Awesome. Well, thank you. I've learned a lot. It's a great ton, uh, of great information and thanks, uh, for coming on.

Speaker A: Thank you, Ali.

Speaker B: Thanks for listening. If you enjoyed this episode of Move the Needle, make sure to subscribe and check out our newsletter. Every other week we send out episode recaps with key takeaways, plus practical playbooks, free templates and more. Sign up@databox.com Newsletter

Speaker C: thanks so much for listening. If you found this episode valuable, check out our other episodes or subscribe to get new ones. If you want to support the show, we'd love for you to leave a review or share it with someone. And if you want a tool to help you track and improve your business performance, try databox free@databox.com.