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

IV (06:32.718) you know, marketing channel they should be going after instead of stepping back and say, what are the problems or challenges in our business and how can we develop initiatives that help us overcome or resolve those? So those would be two. I could keep going if you want. Ali (06:44.328) Yeah, that makes sense. Nice. Also, no, those are two good ones. Thank you. So the common theme that I noticed right away between the previous methodology and the new one is this idea of predictability. Can you talk a little bit about what predictability means to you, why that's important now in the context of your new course, your new methodology? Peter Caputa IV (07:06.404) Yeah. 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, which is really hard. Anyone actually that says they have absolute confidence in what's going happen in the future is a snake oil salesman probably. But I think as an executive running a business, having confidence in what you're doing and that will result in a positive. impact is critical. And data is at the core of that. So our data box, right? Like what we help people do is, as you know, track their performance so that they can, one thing that they can do is better predict the future. And we literally have AI and data science in our product that helps with predicting outcomes. that Ali (07:55.384) Mm-hmm. Peter Caputa IV (08:04.708) 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, 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. Ali (08:11.502) Mm-hmm. Ali (08:23.502) Mm. Peter Caputa IV (08:32.704) I obsess over it, we obsess over it at Databox But the other side of reason I think predictability is important is so that we can all sleep at night. There are so many things in the world that we don't control. And honestly, I felt that there are less as I 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. 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. And And if I'm fully focused on what I can control, that means I should, as best I can, understand how that system works. And so that means building systems that are repeatable so that I can measure them and so that I can then start to predict the future. Ali (09:14.402) Yeah. Ali (09:23.564) Mm-hmm. Peter Caputa IV (09:30.392) 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, I 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. so, it's not really a technology problem as much as it is a Ali (09:47.128) Mm-hmm. Ali (09:52.558) Mm. Peter Caputa IV (09:59.697) a habit and process problem. It's like a management team in a company needs to have a cadence for doing certain things so that they can ultimately have predictability. And so while our software can certainly help with that, 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. Ali (10:02.784) Yeah. Yeah. Ali (10:25.528) That makes sense. I know, I mean, I know just from working with you that you're 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 mind to paper here and for us to all benefit from. let's jump into the, if I can put you on the spot on the Spears methodology within the predictable scale is the acronym for the six key steps in the system. Can you tell me a little bit about those? Peter Caputa IV (10:36.644) Yes. Yeah. Yeah. Peter Caputa IV (10:43.056) Yes. Peter Caputa IV (10:53.392) Yes. Yeah, yeah. You've done your homework. So yes. I don't know if it was an accident that it spells Spears, but it was kind of like, actually there was brainstorming with Heather and Billy, maybe Tori, I think Tori, and 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. Ali (11:18.158) Yeah. Peter Caputa IV (11:21.584) For most of us know what a spear is, it's like there's Norse mythology where I think it was Odin who kind of first figured out spears, obviously way, way, you know, like centuries and centuries ago. And it's like the idea of a spear is that it's about hitting your target. And so it seemed like a really good act, 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. Ali (11:33.069) Okay. Peter Caputa IV (11:51.249) But SPEARS, the acronym stands for strategize, plan, execute, adjust, repeat, and scale. So those are the things, strategize, plan, execute, adjust, repeat, and scale. And so 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. Ali (12:14.796) 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. I know, it's

fresh on your mind. Yes. And maybe whichever one you just drafted the most recently will be the most fresh on your mind. But compared to maybe the previous methodology, are there any portions of the Spears? Peter Caputa IV (12:15.824) Thanks. we can talk about it. just, as you know, I just finished writing the- I just finished writing the full draft of it, so it's all fresh to my mind. Peter Caputa IV (12:37.679) Hmm Ali (12:40.686) framework that are like really big additions this time around or that you really had to some new thought into. Yeah. Peter Caputa IV (12:44.774) yeah. Yeah, so when we wrote the predictable performance methodology, which was heavily written by Tori, you know, Tori who runs our rev ops team now, Tori Farrell, and then John Benini who ran marketing at the time here at Databox. So it wasn't like a solo venture. The whole point of the predictable formats methodology was to help teams and individuals understand how to kind of manage up in a way, of running their function or their job in a way that they can prove the value of their work. Because when we launched that, most people signing up for Databox were managers and even individual contributors that needed to report up to their bosses. Ali (13:28.568) Mm-hmm. Ali (13:37.774) Good. Ali (13:42.264) Okay. Peter Caputa IV (13:42.381) how they were doing. 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 to, 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 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? Ali (13:49.218) Yeah, yeah. Ali (14:00.238) Mm-hmm. Peter Caputa IV (14:11.568) How do measure that? How do I set goals against that? 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 my impact? So it was very kind of like bottoms up. 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 and predict your performance and then manage. Ali (14:21.326) Mm-hmm. Ali (14:30.488) Okay. Peter Caputa IV (14:41.326) that to that prediction. And so this is very much top down. And what I've found in so many of these conversations with other executives is that most of most management teams skip to the execution. Like they've already decided here's what we're executing. 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 Ali (14:46.145) Cough cough Peter Caputa IV (15:10.352) build our customer support team in this way. And then they're like, all right, now I'm 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. And so what I realized is that most companies are truly skipping two steps. The strategized step, which I talked a little bit about earlier with the Blue Ocean Strategy and Retro Remounts work, and the planning step. So the strategized step is much more about like 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 avoid that competitor set. And then turning that into a vision, a mission, 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 gonna do this year. Ali (15:50.456) Mm-hmm. Peter Caputa IV (16:07.94) They require cross-functional support. And here are the reasonable goals we'll set for those initiatives. And then here's how we'll roll this out to the team who will work on it, how we'll kind of plan out the actual next steps and things like that. So I realized that most companies are skipping that. They say, hey, we're building this product. Ali (16:27.832) Mm-hmm. Ali (16:32.983) Yeah. Peter Caputa IV (16:35.3) we're going to go do this marketing and we're going to go do this sales and instead of really collaborating across, you know, against a handful of objectives, they're just kind of all working in their silos. So that's what this new methodology covers. Ali (16:47.67) Yeah, no, I I can, I love that. can vouch for that working here. And I've shared that on LinkedIn before too. I I've worked at small companies, medium companies, big companies and... 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 goals and initiatives and how they all roll up to the company strategy. It's really clearly mapped out for everybody and very transparent. So I think that's great and I'm excited that you get into that in the methodology. Peter Caputa IV (17:23.824) Cool. That's good to hear from you. How long you been here now? it six months? Five months. Yeah. Cool. No, it's interesting to hear your perspective because I somewhat take it for granted. HubSpot was run pretty well, not as structured as we are, but obviously the company did very well. So they had a lot of things going right. But yeah, over the years we've improved our own. Ali (17:28.014) a little over five months. Yep. Yep. Peter Caputa IV (17:52.209) operational model, and I borrowed a lot of stuff from HubSpot, but also borrowed a lot from other methodologies and came up with our own stuff. 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 Databox. Ali (17:54.711) Mm-hmm. Ali (18:14.582) Yeah. And that was something I really wanted to ask you. we, that 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 kind of already are applying this methodology at Databox? Peter Caputa IV (18:29.648) Yeah, absolutely. as you know, every year we as a management team and we last year we weren't here then, but we included every manager and director in this process where we went through our strengths, our weaknesses, threats that we see or opportunities that we see. And we listed out all of the things that we could do to help us serve our best customers better. We listed out all the things we could do that would take advantage of opportunities with new technology. We listed out ways that we could go to market more effectively, et cetera. And so we did that all together, but what we settled on, one of the things that we settled on, I think we have four or three objectives, main objectives for one, we always have one related. people, but the three that are related to the business objectives. And one of them is to kind of move mid-market, right? We have customers that span all employee sizes, you know, from like literally some solo shops, but the majority of our customers are between 10 and 500 employees. We have some that are bigger. 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, the middle market is the best market for us. And so what we decided is to make that an objective as we wanted to move more mid-market. So it meant understanding their needs better, 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. And then that is what dictated a bunch of things that we do. But one of the big things was in product where we realized that our mid-market customers wanted to do deeper analysis of their performance inside Databox. So what we had to do is actually rebuild, re-architect Ali (20:49.038) Mm-hmm. Peter Caputa IV (20:54.296) almost the whole backend platform from the way that 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 manipulate that data, create metrics from that data, and ultimately analyze more deeply in Databox. So that was one big thing. As you know, we launched it in May. 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. Ali (21:10.413) Mm-hmm. Ali (21:18.926) I'm good. Peter Caputa IV (21:22.372) That was key to us serving the mid-market better. Another thing we're doing to serve the mid-market better is we're tightening our partnerships with professional services firms who use us. So historically, we've always had professional services firms, lots of marketing agencies that would use us just to report the results of their work. But what we realized is for some of them, they're trying to be more strategic with their clients 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, helping their, you know, their clients look at their whole performance data and figure out where can we adjust, what should we be repeating, et cetera. Ali (22:10.563) Mm-hmm. Peter Caputa IV (22:10.992) So what we did is launched a true reseller program. So now we have our partners that actually can resell the product for a commission, but more importantly, they can provide a consulting service on top of that, product that enables them to be more strategic with their clients. And 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, some financial consultants, operational consultants. Ali (22:29.164) Thank Peter Caputa IV (22:40.888) 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. Ali (22:48.419) Yeah. Yeah. 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, but that doesn't align to what we said the original strategy was for the year. So we're going to put those on the back burner for now, which is hard to do. Peter Caputa IV (23:19.15) Yeah, one of the best parts of setting objectives as a company on an annual basis is that it gives everyone in the organization a lens in which to evaluate what they should focus on. As an executive or CEO here, it's really freeing for me to say, I've set this, I've finalized, it's not just my brain, it's everybody's ideas, but we finalized these list of things and areas we want to focus and now you guys go figure out. Ali (23:31.822) Mm-hmm. Peter Caputa IV (23:48.337) How do we do that? 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. Ali (24:01.442) Yeah, it's a great blueprint for everybody. That makes sense. 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 in how data plays a role in supporting the methodology? Peter Caputa IV (24:14.319) Yeah. Yeah, I think one of the hardest parts of managing any business is setting realistic goals that also challenge the team. And it's about managing the balance between the ambition of the team or the company with what's realistic given the resources that are available. So there's no like perfect formula for setting a good goal. But data can have, can play a huge role in setting realistic goals. So I walk in the planning step of the methodology, I walk companies through how to use data to set goals. And it's, there's three mathematical functions that are included. One is understanding correlations in your business. I'll go into these a little bit deeper. 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. And so when setting a goal or setting goals, like 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. But for the most part, even most of our customers are pretty poor at setting goals. And I think most people just kind of like give up on it. 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. And so there needs to be a process for goal setting that uses data, not just that initial goal setting stuff, but at different stages. Peter Caputa IV (26:08.612) need to be willing to reset that goal. 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 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 a really simple example would be Ali (26:08.814) Mm-hmm. Ali (26:35.566) Mm-hmm. Peter Caputa IV (26:38.288) Hey, we're gonna publish more on LinkedIn, right? Oh, this is a really simple example. And we wanna know what kind of impact that might have on the business. So there's different ways we can measure both our output, the immediate results that those drive, what would it 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, right? We also know that we can publish If we publish on certain topics or in certain ways, we could get more reach or resonance on LinkedIn, more engagement on LinkedIn. So by measuring those two things, and then we can measure impact on, signups or customers even in terms of adoption of something, and we can start to correlate those values and see, 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. Ali (27:11.619) Mm-hmm. Ali (27:33.422) Mm-hmm. Peter Caputa IV (27:33.686) We should write more on those topics. We should publish more, whatever it is. So that's a really simple example, but start to think about all the things your business does and then try to ultimately model your revenue target. Like it gets really complicated. Ali (27:46.637) Yeah. And there's, a time lagging component there, right? I know this has 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? Peter Caputa IV (27:52.079) Yeah. Peter Caputa IV (28:00.945) 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. And they'll say, all well, how much traffic are we 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 the cause and effect. But that lag is so important. And there's almost no way than a human. can think through all the things that you're doing, put it in a spreadsheet, think through all the results, calculate the lag and then predict the results. So that's where correlations done through data science are really important because not only can, 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, you know, 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 Ali (28:55.212) Mm-hmm. Peter Caputa IV (28:57.04) that next month's results is going to impact next quarter's results. And so, no, it's not hard for data scientists or code to calculate that. 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, Ali (29:24.056) Yeah. Peter Caputa IV (29:26.928) Well, this is the ultimate impact you should have 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. doing that through data science and math is so much more valuable than doing that through a spreadsheet or whatever. And then the final thing is kind of like a gut check. It's like, all right, well,

this is what we're doing. This is the result we're getting. How are other companies doing with that? And being able to see benchmarks to see, know, how does that compare? Are we way overperforming? Are we underperforming? What does that mean? I've had companies that are way overperforming a certain metric and they say, that's awesome. We're going to do more. We're going to try to even overperform more because it's working for our business, right? I haven't seen companies that are like, yeah, we know we're underperforming. That's not our focus area. Ali (30:01.4) Mm-hmm. Peter Caputa IV (30:22.776) it's cool, we're going focus on this instead. really, benchmarks don't necessarily tell you what you should fix per se, they just give you a litmus to say like and have an honest assessment of like how your performance makes Ali (30:32.216) Context. Yeah. Nice. It's really fun to listen to you nerd out on this. So as the non-math person in the room, I do my best. This is really interesting. I'd love to know. You can have a little product pitch moment here. Peter Caputa IV (30:39.312) NNNN Peter Caputa IV (30:43.032) But we don't fight. Ali (30:51.992) 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 features either in the product now or coming on the roadmap that tie directly to supporting the stuff that you've laid out in the methodology? Peter Caputa IV (31:10.064) Yeah, so I'll work backwards from the three things that we just talked about. So the benchmarks are in the product already. People can go and 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. But that product leverages 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 X percentage of companies have this value, X percent of this, and it's usually a little curve. And you can say, hey, this is the median, meaning the 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, depending on which way you look at it, positive or negative. So that's in the product. You basically just say you wanted to benchmark say your 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 to the data. We pull the data from LinkedIn's LinkedIn company pages API and then we present those charts to you so you can see how your performance compares. So that's one. Ali (32:05.966) Cough cough Peter Caputa IV (32:26.872) Next is 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. I'm hesitant to give a date. My guess is it'll be live close to the time of the publication of this podcast. 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 whatever. Let's just say you want to optimize for revenue, you want to forecast your revenue so you could sit there and say, 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. 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. Ali (33:20.088) Nice. You good? Peter Caputa IV (33:26.02) future. And then the correlations, we have correlations capabilities behind the scenes. We haven't built like a front end yet. We have a mock-up, we have a spec, we're still kind of evaluating exactly how to build it from a user perspective, but we're already have functionality behind the scenes that can go and basically correlate values between two different metrics and understand the relationship. When you look at two Ali (33:51.566) Hmm. Peter Caputa IV (33:54.959) 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 that means it's perfectly correlated. In reality there's never anything that's perfectly correlated. So the best you're going to get maybe is .9 or something like that correlation. So it could be something like if I get you know 10 clicks on this ad I know that I'm going to get one new customer that could you know that Ali (34:05.912) Mm-hmm. Peter Caputa IV (34:23.906) even though it's only 10%, like that could be a 0.9 % correlation if that 10 % conversion rate sustains itself. So that means that that's strongly correlated. If we get 10 clicks, we're gonna get one customer. There could be things opposite correlation, right? You could say that, all right, well, if we get certain number of customers complaining about or abandoning the use of this feature, Ali (34:35.533) Yep. Yep. Peter Caputa IV (34:53.104) then that might correlate inversely to retention of those customers, right? I'm making stuff up so I have to think through good examples, but that might be a negative 0.5 or something like that correlation. that's built in behind the scenes in Databox. We're using that again in the forecast modeling tool, but we will most likely build something that will allow people to kind of create a map. Ali (35:09.646) That makes sense. Okay. Peter

Caputa IV (35:22.19) 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 to YouTube consistently and growing our subscribers by this and people are telling us that they heard about us on YouTube. Ali (35:28.002) Mmm, that's cool. Peter Caputa IV (35:50.481) 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. And so understanding that correlation consistency 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. Ali (36:04.642) Mm-hmm. Ali (36:17.76) Awesome. That sounds good. Peter Caputa IV (36:19.316) And then you can see strength of correlation too, right? We can see like, like the more we do on YouTube and making all this up, the more we do on YouTube, the stronger our sales are. But the more we do with cold prospecting, it's not as correlated, right? It's less correlated. So you can start to see the relative correlation between different activities. Ali (36:35.242) Mm-hmm. Yeah. That's fascinating and super powerful. 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 that's one of the themes that keeps coming up is like we have to lean more heavily now into correlations versus like direct linear paths of click throughs to conversions. But then actually putting that into practice is still pretty hard. So this is, this sounds really cool. Peter Caputa IV (37:14.564) Yes. Yes. Yeah, yeah, exactly. Yeah, part of this stuff is born out of our own challenges. Ali (37:25.216) Yay, good. sign me up. I'll be a guinea pig for the first beta. 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? Peter Caputa IV (37:48.389) Yeah, every business is so different. think it's hard to sit here and say he's a key leading indicators. I think most business owners or executives know what financial metrics are important to track and what sales metrics are important to track. I think a lot of companies kind of stop there, which is a mistake. think it's important to track customers, customer happiness, customer success type. metrics important of course to track

marketing and advertising metrics. So it's I don't have like one metric to say like you should pay attention to this. You know at the end of the day it's the financials are most important to stay on top of but but I think. Ali (38:27.928) Mm-hmm. Ali (38:33.934) Yeah, that makes sense. It's more of a way of thinking and the process and structuring everything that goes around it. Peter Caputa IV (38:39.982) 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. And 10 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. Ali (38:43.726) Okay. Peter Caputa IV (39:08.378) But nowadays, and 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 the team thinks that way. And maybe they don't have like influence or even necessarily visibility into everything that's happening, but they think about, you know, their Ali (39:25.751) here. Peter Caputa IV (39:36.901) function role as a system and they think about what are they doing with the impact that has, how can they do that better with higher efficiency, higher effectiveness, and then how can they have maximum impact on the area that they're supposed to have. 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. 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. I can let the people do their work. I can let the AI do its work. I can let the data tell me how things are performing and have much more ability to kind of come in and help, know, help remove roadblocks, get, you know, help people with brainstorming, help people with coming up with better processes, whatever it is, because I have that. global visibility and ideally everybody in the company has that global visibility so they can actually independently help others as well. Ali (40:45.27) Scalable. 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? Peter Caputa IV (40:56.814) Yeah, think, yes, I think that's the most important. A lot of owners, executives 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. We have our own objectives, have goals, we share exactly how we're doing against the goals. how we're doing versus year over year. Everybody at the company has access. I'm not sure they understand everything, but they have access to all that data, frankly, in real time. But I think most companies are resistant to that. In studies we've done in the past, I think we've found that more than 50 % of companies don't share performance of the company beyond the manager level. So they only managers and above have access to that. I think that's a big miss. Ali (41:53.87) Mm-hmm. Peter Caputa IV (41:54.749) What it does is it disempowers the rest of the organization from having ideas, from helping each other, 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. And I think what I'm hearing from others is that other executives that kind of are on the other side who are embracing transparency Ali (42:03.778) Yeah. Yeah. Ali (42:11.534) you Peter Caputa IV (42:21.2) is that they felt they had to do it. It's not because they wanted to create this new age culture. It was out of necessity because business is so complex now. There are so many functions, tools, data, so many different ways to grow a business. It's mind boggling at this point. then as we build, as we start to leverage AI, build agents, et cetera, there's... Ali (42:24.074) Yes. Ali (42:42.893) Yeah. Peter Caputa IV (42:50.06) Absolutely no way, even a management team can have visibility or understand all the systems that are at play. So it's so critical to empower everyone in New York 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. Ali (43:11.768) Mm-hmm. Ali (43:18.848) And I would think important to set that foundation early because the bigger the company gets, the harder it gets to go back and like build in that transparency and alignment because everything becomes so fragmented and siloed the bigger. Peter Caputa IV (43:31.714) Yeah, 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. And so I think it's kind of easier to embrace risk 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 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. Not only do you have to spend time educating the entire company on complex things about how the business runs, et cetera, but you also have to be able to deliver news that could be upsetting to people, could be concerning to people. If a company is burning capital or not hitting targets, Ali (44:22.242) No. Peter Caputa IV (44:30.916) You have to be willing to talk about those things and say, hey, here's what we're doing about

it. And I think that's hard for a lot of leaders. A lot of leaders want to present confidence and they got everything under control, everything's gonna be awesome and your job is secure and all that and not have anyone doubt on that. And I think that's increasingly difficult given the complexity of businesses. Ali (44:30.926) Mm-hmm. Ali (44:41.42) Yeah, good news. Yeah. Ali (44:47.928) Yeah. Ali (44:53.868) Nah. Claire's kind. Peter Caputa IV (44:59.085) huh. Ali (45:01.142) One more, maybe we'll take it up a level and have a more reflective question here. So looking back on your own career building, scaling companies, helping other companies scale, what's one thing maybe you wished that you had learned sooner about predictable scale? Peter Caputa IV (45:18.416) One thing that's related to the methodology? Okay, that narrows it down. So what's one thing that I wish I had learned earlier in my career that's related to the methodology? I think it's so hard because everything I've written is things I've lived through and learned, almost any part of it I could pick. Ali (45:24.622) Yes. Ali (45:45.71) I hear it. Peter Caputa IV (45:48.017) I think the biggest thing that I wish we had spent more time on at Databox was the strategy piece. Early on, after I joined Databox, we found product market fit for a decent part of the market. in a good way, obsessed over that, you know, that part of the market and really making sure that we had a good product for them. But I don't think we we looked like beyond that well enough to say like, what is it that we could build that would really serve a different part of that market or a different market? And and like we weren't bold enough to say like, Yeah, we got that part 80 % figured out. We'll dedicate X amount of X percentage of resources to continuing to refine that, but we need to swing bigger for a bigger piece of the market. And so we'll need to dedicate resources to that. And I think we kind of waited too long at Databox to do that. I think we were, I won't say we were content or happy because we were never really content happy with where we are, but. Ali (46:48.942) Thank Peter Caputa IV (47:12.016) But I think we were maybe complacent and we were optimizing maybe for short term results as opposed to taking a big swing that might impact 12 or 24 months later. I think we've gotten a lot better at doing that more recently, a lot better, but I think we waited too long. and what I should have learned that at HubSpot and I did like I knew that one thing that Brian Halligan, CEO. Ali (47:17.432) Mm-hmm. Ali (47:24.43) Yeah. Peter Caputa IV (47:40.049) former CEO, founding CEO of HubSpot did really well as he looked two years out and he said, hey, this business we're in now has a chance of flattening, the growth has a chance of flattening. I don't, he didn't know for sure, but what he did know is that if we were to start building something in an adjacent market or a complimentary product for the

same market, that we would be able to ride another growth curve. And he did that. They did that. It helps a lot brilliantly from going from like, um, top of the funnel marketing software that helps with content, you know, publishing and search optimization, then to middle of the funnel marketing, where it was more marketing animation, and then taking a leap into customer relationship management and sales software, and then into services software and like so on. And they have, you know, three or four more hubs they call them or products now that, um, that Ali (48:10.84) Mm. Ali (48:18.445) Mm-hmm. Ali (48:37.624) Mm-hmm. Peter Caputa IV (48:39.854) they launched which all, they're not all billion dollar businesses yet, but they all have strong potential to be multi-billion dollar software businesses. And he kind of stacked them in a way where it's like, all right, marketing is going to top out at some point. So let's get sales started here and sales will take over as marketing tops out. Now what happened in those spots, actually marketing didn't top out. And so they have marketing continuing to grow while the sales software is growing. And if you look at their growth rate in like 2019, 2020, Ali (49:00.791) and Peter Caputa IV (49:09.752) It went from like this, this to boom, because two or three of their products kind of took off and started having really meaningful, meaningful impact on their revenue. so, and again, I should have known to do that. I don't think we did that well at Databox. Part of the reason is we bootstrapped Databox since I joined. So, you know, we've been cashflow break even for these last eight or nine years. So, you know, we didn't take some of those swings, but I regret not figuring out how to take some bigger swings. Ali (49:12.152) Mm. Ali (49:32.334) you Ali (49:40.162) Live and learn, Peter Caputa IV (49:41.55) Yeah, Never too late. Ali (49:42.83) Awesome. Anything else I didn't ask? Anything else on your mind around the course or without any questions? Peter Caputa IV (49:48.753) 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 want to tease it a little because I think 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, Ali (49:59.468) Yeah. Ali (50:10.702) you Peter Caputa IV (50:17.528) repeating, your billing system so you can repeat and scaling is the reward, right? It's that it's when you can start to grow, you know, grow your revenue at a pace that outpaces the growth of your costs, right? And it starts to help you grow your margins. And so that's the reward. But I also in the chapter talk about things that people can do in their strategy to to start scaling like kind of on day one as long as they think about it. And 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, 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. Ali (50:51.362) Hmm. Ali (51:04.674) Yeah, go for it. Peter Caputa IV (51:17.072) 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 to outsource and number five is to delegate. And so when I look at the first one, self-serve, 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 for it. 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 free variant or trial, et cetera. And that's enabled software companies, certain software companies to scale really fast. HubSpot actually implemented that right around the time they had that growth curve as well. So it might've been a factor in their growth. It was after I left. Ali (52:11.49) Yeah. Peter Caputa IV (52:13.648) Slack is a great example. was looking at data the other day on Microsoft Teams growth and Microsoft Teams growth actually makes 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 assets that they can leverage to grow fast. But self-serve is really key in growing things fast and I don't think most companies beyond software think about it. Ali (52:26.19) Hmm. Peter Caputa IV (52:43.118) 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 and really enable that self-service? Number two, I have as partnerships as you might not be surprised, but 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. Ali (52:46.411) you Ali (52:52.664) Mm-hmm. Mm-hmm. Peter Caputa IV (53:12.88) If you're a smaller business, if you're a professional services business, I would recommend the former. Think about, they should be thinking about what partner programs they can join. If you're a scaling business already, that's when you start to think about how do I build something that other companies can partner with me with low effort on my side, but they get a lot of value. And that's what I did at HubSpot. That's what we're doing at Databox, what many software companies have done. Ali (53:17.422) Mm-hmm. Peter Caputa IV (53:37.039) and thinking about how partnerships can start to take work that you might have to do internally and they would do it for free because it benefits them in some way. So the obvious example is reselling software. So if you have partners out there that are doing marketing and doing sales and talking to customers and helping them get onboarded, you can do that all at no upfront cost to you as a software company. Ali (53:46.968) Mm-hmm. Mm-hmm. Peter Caputa IV (54:04.944) 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. We have agentic AI built into our product. We use products, other products that enable agentic AI to automate processes in our businesses. I think it's so shiny right now that everybody's thinking, how do I automate stuff? And that's funny I talk to these such 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 but 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 that helps automate a process or enable a process and Ali (54:33.382) huh. Ali (54:40.779) Thank Ali (54:45.624) Yeah. Peter Caputa IV (55:02.288) And so that's really powerful and I think it will be ever, you know, ever more powerful. 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 prompt is amazing. Like the stuff that's coming is amazing. the more you should, should be, companies should be thinking about how do they automate. Next up is outsource, right? I think. Ali (55:27.758) Mm-hmm. Peter Caputa IV (55:31.448) Outsourcing something generally you get a more skilled person But you also don't have overhead costs Right and you can you can leverage their expertise and their efficiency that they've built up Over time of doing something so outsourcing is always a good option I guess important though one caveat is that like you don't outsource it and leave it like it's important to outsource it and make sure you're building processes and repeatability Etc if you do eventually want to bring it in-house Ali (55:44.451) Mm-hmm. Peter Caputa IV (56:00.619) And then delegate, right? 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, you know, more junior employee unless you've already thought through the processes and given them, you know, documented at least the first draft of the processes that they can follow. But those are the five things that I think are kind of, again, things you can start to... Ali (56:09.59) Yeah. Peter Caputa IV (56:29.828) things that you can start to do that really help you scale your business. But again, 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 they want it. So it's important to think through the rest of the steps and the methodology before you jump there. But I do think most companies are missing the opportunity to do those five things well. Ali (56:37.742) Mm-hmm. Ali (56:55.884) Yeah, those are great. Awesome. I'm still working on the delegation one myself. I'm getting there. Awesome. There's a lot of meat there. I'm really excited for everybody to get access to the full course. Peter Caputa IV (56:59.824) You're pretty good at it. You're pretty good at it. Peter Caputa IV (57:11.203) Yeah, come soon. Ali (57:11.68) when it becomes available very soon. So the waitlist is already up, so everybody knows. That's at academy.databox.com slash predictable dash scale. We'll make that all available here so people can already sign up now for the waitlist, get early access, and that will send out information about the full course when it's live, a live cohort that I think will be happening, and then other resources as they become available in early October. So that's all available. Yep. Peter Caputa IV (57:38.042) Sweet. Ali (57:40.435) And of course, LinkedIn, I think is the best place for people to follow you. Peter Caputa IV (57:45.34) Yeah, you know I'm very active on LinkedIn but I would encourage people to follow our podcast. You're doing a great job with it and subscribe to our newsletter if you prefer email and our YouTube channel. You and your team have been doing a great job of getting more more content up on our YouTube and I think I was looking at our self-reported attribution. 9 % of our signups are coming from our YouTube channel according to self-report attribution. Ali (57:55.502) Mm-hmm. Ali (58:09.855) Woohoo! Nice. Peter Caputa IV (58:13.646) Yeah, so it's good stuff, so it must be working, Ali. It's correlated. Correlated. Ali (58:13.878) Awesome, I'm trying to stay consistent. Yes, it was me. 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. was a great ton of great information and thanks for coming on. Peter Caputa IV (58:22.713) Ha Peter Caputa IV (58:33.616) Thank you. Ali (58:34.721) Alrighty, bye bye.