Episode 58: Scott Stouffer of scaleMatters on
Using Data to Make Effective Go-To-Market Investments

Episode Transcript  

Shiv: Alright Scott, welcome to the show. How's it going?

Scott: Great, Shiv, how are you doing?

Shiv: Great, I'm doing well, excited to have you on. So why don't we start with an introduction about yourself and scale matters and we'll take it from there.

Scott: Sure, absolutely. So Scott Stouffer is my name, CEO and founder of scaleMatters. And a little background on scaleMatters, we are what I'd call a go-to-market analytics company. And we tend to sell to growth stage B2B companies, companies that are maybe 10 million up to a few hundred million in annual revenue. And our entire goal is to help them be much more efficient, much more effective with their go-to-market investments by better leveraging data as a tool. give you a little history on the, why we started the company at, at our previous company, which was a, about a $15 million provider of a CRM and marketing automation platform for nonprofits. Because of the nature of nonprofits, particularly small nonprofits, it's kind of tough to have a really good lifetime value because they tend to be low pay, high touch. They churn a lot. And so we were forced to get pretty aggressive about how do we get our cost of acquisition really low. And we basically went through this process of deconstructing our whole go to market motion, modeled it out kind of on spreadsheets and in pretty gory detail. And then we reconfigured our tech stack so that we could measure the stuff at the same level of granularity that we had modeled it. And that started producing a lot of really interesting data. I'd sit there every morning, export a bunch of Salesforce reports into Excel, manipulate the data. And what it started showing us is where all this friction was in our go-to-market motions. And, you know, we started systematically knocking off these friction points and literally in about a year's time, we were able to reduce our cost of acquisition by almost 70 percent, shrink our sales cycle in half. And that was really transformative for the business. And it allowed us to have a positive sale to a kind of tier one PE shop. And so when we sold that company, a couple of us said, look, we should be able to productize this approach that we just put in place and make that available to other growth stage companies to really help them, you know, be much more effective with their go to market investments. And if you think about it, and then I'll wrap this little history up, you know, the capital companies raise, I mean, it's very expensive, right? And the bulk of it tends to go to go-to-market, to sales and marketing. And in most of these companies, they're not terribly efficient in deploying that capital. There's a lot of waste, a lot of stuff they spend money on that just isn't productive. So if we can use data to help the leadership teams better understand kind of where that friction is, where the waste is, et cetera, then they're going to get a lot better return on that invested capital and have a higher probability of success. So that's really what we're trying to do.

Shiv: Yeah, it's funny you mentioned that because there's like a stat out there, which is like per dollar of venture capital raised. It's like over 50 cents goes into marketing dollars or sales dollars. And a lot of it goes to paid media. So a lot of money is being raised from capital investors and just being deployed into Google ads and Facebook ads and Capterra and places like that. 

Scott: And salespeople. Yeah.

Shiv: Yeah. And salespeople. Exactly. Exactly. Yeah. And one thing I was as I was looking through and preparing for this episode, one of the interesting things I read about your history here and the journey that led you to starting this business and came across your Data-First Manifesto and talk a little bit about that because this is something that we encounter all the time that companies just don't have a handle on their data and you characterize it as a spewing hand waving and backpedaling, which happens in a lot of companies. So let's dive there a little bit.

Scott: Yeah. So, you know, I mean, I've been doing this kind of stuff for quite a while and, both as an operator, but also, as a board member. And you know, the number of times that functional leaders do not have a sound grasp on what is going on is just, it's, it's astounding really. And not because they don't want to, or they don't care, but you have to instrument, right? I mean, all of these things that we're doing, selling marketing, building products, et cetera, they're processes. And to understand how these processes are working, you have to be able to instrument them so that you can measure stuff properly. And most companies, certainly VC backed companies, are very slow to invest in the necessary instrumentation or infrastructure, you know, instrumentation infrastructure or data infrastructure to give their leadership teams that tool of understanding. And so when you're at board meetings or CEOs having staff meetings, what you see so often is people just kind of winging it, right? That's what I call spewing and backpedaling and… You know, they just say stuff and they think it's right. I mean, nobody's lying or generally people aren't lying. They think it's right, but it's just gut feel and too often or more often than not, gut feel isn't actually right because you're looking at something at a superficial level and the devil's in the details and you don't have those details. Board pushes back. CEO pushes back. And you know, people just start saying stuff that doesn't make any sense. And it's a very frustrating dynamic. mean, most investors experience this on the vast majority of boards that they sit on. And you lose confidence, right? As a CEO or a board member, you lose confidence in the leadership team if it's clear that they don't really have a handle on what's going on at the necessary level of detail. So that's what that was all about.

Shiv: Totally. Yeah. It's funny. Like you guys are solving the problem from the other side and we are solving it from our lens. And it's pretty much a similar take on the problem where we, there's so many board meetings that I’ll sit in or companies that we will do an audit or analysis for. And it is astounding that how many, how few of them actually have their data in order, completely understand the return on investment, on spend, they don't really know which channels are working or not working, which activities that just scale up and scale down. And so you kind of have this, most of these, not most, but a lot of these companies operating on gut feel and kind of just subjective decision-making versus really making a decision that's founded in data, which is really what boards, boards expect. So help me understand, cause on our side, we're doing that through consulting and partnering with private equity firms and all that. Talk about your approach. How do you go about analyzing or auditing and then also fixing a lot of these companies as you come in and partner with them? 

Scott: Sure. So I guess a little bit of history. We started, when we started the company, we said, look, we're going to be a software company. And we built this very nice go to market analytics platform, full funnel analytics, basically software that automated all the work I was doing at the prior company, manually analyzing all this data, right? Went out to market and quickly realized that most of these companies their data wasn't in order, right? So I mean, they weren't measuring the right stuff. They were plagued with all kinds of data hygiene issues. So the integrity of the data wasn't very good. so having a software platform that automates the analysis doesn't add any value if the data stinks, right? And so we backed into probably three or four years ago, we basically said, well, the first thing we have to do is help them get their data in order. And it really encompasses a number of things. One is making sure that the right stuff's being measured. That's what I refer to as instrumenting their environment properly, right? So you’ve got to measure the right stuff to begin with. Then you have to have processes or automations that help to ensure that the hygiene of the data is sufficiently good that it's worth analyzing, right? And it's, you know, we often, people often get frustrated at salespeople, right? For not necessarily working, interacting with the CRM in the way that is most advantageous, at least to get the data. But I think part of the reason is most people don't design the experience like in Salesforce to be very user friendly for the salespeople, right? If you know, I mean, first of all, you should try to figure out a way to get every piece of data you can without relying on human input, but that's, that's, you know, kind of the Holy grail. You'll never get there. So given that you're going to have to have some human input, then you got to make it easy and repeatable and, you know, and that's how you'll get the data and get the confidence in the data that you would need to have. 

So, we put a lot of energy into instrumentation, a lot of energy into kind of design and UI, if you will, UX, if you will, and processes to ensure data hygiene. And then, and then the way we construct our analytics using our software is very focused on action ability. Right. So, I mean, you deal with a lot of the same types of companies we do. And, you know, they got hundreds of reports or dashboards and nobody looks at them. you know, and the reason nobody looks at them is because no answers jump off the page and say, here's what you ought to be doing. Right. And, and so we've come up with this framework, we call it “Data Drives Action.” But basically we start by contemplating the set of actions people could take to improve their performance, right? Double down on a very productive channel and take money away from one that isn't, right? Streamline a certain stage in the pipeline process where you're, you know, chewing up excessive number of days in the sales cycle, right? There's all these different types of actions, people related actions, process related actions, pricing and product actions that you can take. And so we sort of captured this full set of actions that any given company could take. We backed into then, what insights do I need to have that would compel me to take one set of actions versus another? And the way we frame that is what questions do I need to be able to answer in order to inform me to take one set of actions versus another. 

And let's use the channel example, because it's pertinent to so many companies. Let's say we're doing a paid search, we're doing paid LinkedIn, we do events, we do outbound SDR, cold call prospecting, to name a few. The simple question that everyone should be able to answer is which of these things has the better return on investment? And not what's my lowest cost per lead because that doesn't account for the differences with how those leads may convert through the bottom of the funnel, right? And this is part of the problem is we stress this whole notion of full funnel analytics basically lead-to-deal because that's the only way you can actually understand return. And so, you know, what we do, as I said, as we back into these questions, a question would be, what is my best performing our channel from an ROI perspective? What is my worst performing channel from an ROI perspective? Which, which of these channels is the most scalable, right? That I could throw more, even if it's not the best return, but I can throw more money at it and grow faster. Right. and so once you know, this set of questions. And again, we've sort of built this all out. Then you can reverse engineer to, okay, well, what data points do I need to write? What, what charts do I need to have? How do I need to present these charts in order to make answering those questions very easy. And that's this whole kind of design for action ability, right? You have to think about how do we want to present data that makes the answer to these questions kind of pop off? And if I may, can I share a screen real quick? 

Shiv: Yeah, sure. Go for it.

Scott: So I'll just give you an example, just so it's not all hypothetical. 

Shiv: And for the listeners, we'll definitely have this on the video on Spotify and on YouTube. So yeah, let's pull it up.

Scott: So here, here's an example. Do you see that? Yeah. I mean, you can call this a dashboard. We call it a Data Drives Action Insight Panel. It specifically organizes data to answer this question, ‘What channels have the best ROI?’ And so we're looking at the relative value of opportunities by channel. The relative value of leads. We don't, in this particular customer, we don't have access to their cost data. So we basically answer the question by saying, what's the highest value channel? And then you have to kind of divide it by your cost. But we specifically, again, design charts and the order of the charts and the presentation of the charts to be able to efficiently answer these key business questions that people need to have. So that's kind of the concept of designing for actionability, right? And then of course, once you know, these are the charts we need that says, then you need these data points, right? And once you know the data points, say, then we need to configure the CRM to do this. We have to add a field on the opportunity object. have to have a process, or a flow that does this, right. So, we basically, what, what we do, and we encourage companies, even though they aren't using us to do this themselves, is start with the end outcome, which are these actions, and back yourself into what you need to do in the tech stack and in your processes in order to support being able to inform those actions. 

Shiv: Yeah, I completely agree. And actually, one of the things that you've mentioned that we've encountered on our side is that when we work with a lot of these companies, when we'll come in and try to do the analysis, the data is a mess. And the way we solve for it is by creating almost like a version one to just get to good enough to get those insights first. And then we'll come with like a framework to say, here are the key metrics that we'd like to see as our core marketing dashboard at a channel level, program level, campaign level, so that we can make decisions on an ongoing basis. And then based off of that, like let's create a rev ops roadmap of things to implement so that we can automate that dashboard and have reporting on it. In most cases, I find that companies are doing the opposite. Like you've said, where they just have some basic dashboards up, but they're not enough to actually make business level decisions. 

So one of my questions to you is going to be, is how do you convince a company to take on this infrastructure work? Because with us, the impetus is always like, I want to drive more pipeline and revenue faster. And rev ops infrastructure work does take time to generate an ROI. And so how do you get these companies on board to kind of think about?

Scott: Well, first of all, what we do is we focus on the companies that are, maybe have some external impetus to do that. So PE, right? If you think about the thesis of a PE shop, we're going to buy these companies that, know, have, have gotten to where they are almost in spite of themselves. And our strategy is to professionalize those businesses, right? Invest in getting them the right infrastructure, the right data, et cetera. In some cases, bring in other people or new leadership teams. And we basically have a three to five year time horizon to transform these businesses. So there tends to be a little bit more patience. First of all, there's a lot more commitment when there's PE backing to get the data, right? Because they, you know, they, it's basically in their DNA is to manage all of these businesses on a very data-driven basis. But two, you know, again, the way they create value isn't simply by growing the businesses, but by getting them more efficient, getting them more kind of operationally mature so that when they sell to the next set of PE or to a next strategic buyer, the business really is quite different than when they took it on, right? So it's kind of their mandate to do this stuff. So that's not that hard to get them to say, we need to make this investment. 

I think the more challenging one is if it's not PE, right? And if it's VC, you know, and, I, we're starting to see a change there, but I think what has happened is it's just, it's been this long history of, well, to get things better, we just bring in different people, right? We'll fire a salesperson, fire the marketing person, and bring in another one. And, you know, they've seen this video play out so many times where every 18 months they're just firing people and bringing new people on because nothing's really materially changing. And the reason it doesn't change is you're bringing these people on, not giving them the tools to do their job, right. And the most important tool to do their job is what I'd call understanding, understanding what's working, what's not, and the why behind it. If we all kind of understand the root cause of why things are the way they are, you know, it doesn't require, like, this super level of innovative thinking to figure out what am I going to do about it? But the problem is the people aren't armed with the understanding of why things are the way they are. So I think when we, you know, I've seen this transition over the last two years, right? When growth at all costs was in vogue, nobody cared, right? Particularly VCs didn't care. They just throw money at this stuff. And try to get some exit velocity that puts us in a first mover position, right? But, you know, they do care now. I mean, you know, they're having a hard time getting distributions back to their limiteds, et cetera. I mean, the mantra is changing and it has changed. It's all about efficient, predictable growth. Well, let's talk about predictability for a second. How can you have predictability if you can't actually model something sufficiently well? Right. That's the whole point of modelling, right? That's weather, right? Weather forecasting. It's all based on models, right? Any kind of forecasting that's, you know, legitimate is based on models. So you need to be able to build models, right? You need to understand, if, a paid search lead, a demo request from paid search comes in, you need to understand what's the likelihood that's going to convert to a deal. What's the average deal size? How long will that take? How does that differ from an initial call that gets set up by an SDR and outbound prospecting. And so you have to be able to build these really good models to have predictability. Well, you can't build really good models unless you've got really good historical data to inform them. I mean, you could build sophisticated models, but they're grounded just in a total bucket of assumptions until you have historical data to inform them. And so, you know, I do think with the move towards efficient, predictable growth that we're seeing more and more VC backed companies starting to head in this direction. And an interesting pattern, I think we're recognizing is they're more likely to kind of buy into this stuff and start opening their checkbook to put this stuff in place. If they've got a strategic CFO and I'm sorry, strategic CFO in place, as, opposed to, you know, a CFO is basically a glorified controller because those, those CFOs that are strategic, right? I mean, they view it as their responsibility to be able to drive better efficiency in the company. They need to be able to report on CAC, not just report on CAC and LTV to CAC, but be able to talk intelligently on how they're going to improve that, how the company is going to improve that. And they're not armed to do that unless they have this type of data and analytics that we're talking about.

Shiv: 100%. Actually, it's funny, in our business over the last, I'd say six to 12 months. Normally the deals, the people that are bringing us into engagements are the private equity investor or the CEO, but we've actually had a few CFOs pull us into engagement because strategic CFOs that care about efficient, profitable growth see this as a major lever for their organizations. So I think that's a really interesting part. And then the other thing that you mentioned that I liked is, the PE investor is incentivized. They need to grow their investments. They need to exit this thing in a few years time to be able to generate a return. And so they're super motivated to get a data framework in place, but it's before a company has been sold to PE when either they're VC backed or founder led business, they don't have as much of an incentive in order to get there. But we find that when we work with founders or earlier stage companies, they're some of the companies that can benefit the most from this because they've never implemented any discipline into their go-to market. And then there's a ton of waste. If we come in, you can find 20, 30, even a hundred percent more pipeline or efficient growth that sits inside their existing spend. So can you talk a little bit about that, because founders are often just trying to run fast and land more business that they never really stop and actually evaluate what's working and what's not working. And that can often be to their detriment. So talk about the data models there and how you go about that.

Scott: Yeah, well, I think it depends on how much activity and data there could be, right? I mean… If you’re founder led, let's say founder led growth, right? And the founder is basically bringing in almost all of the deals and then maybe it's six or 10 deals a year. It may not be that hard for the founder to keep a pretty good tab on that stuff. So on the other hand, if it's a high velocity business and you're selling SMBs, you know, and you got to bring in 10, 20 deals a month. I mean, it starts to get to where it's not practical that any one person can have their finger on the pulse of that thing. Right. And so that's when you really would benefit from having better data and analytics because otherwise, as you said before, you're just going on feel, you know, and, I don't want to be dismissive of feel because it feel is basically intuition. Right. And for people that are very experienced, their intuition may be really good more times than not, right. But the issue with a lot of these earlier stage companies is their founders and their leadership teams are not that experienced. So they haven't, they haven't done this stuff enough time to have developed a high confidence intuition, which is why you'd say it's even riskier for them to not have data, you know, to depend on and to lean on because they don't have, you know, 30 years of experience and having seen these patterns over and over again to really have built up a well-grounded intuition at that point.

Shiv: What would you say, regardless of PE-backed, VC-backed, founder-led, what would you say are five of the most important dashboards or metrics that operators need to be looking at and where are the biggest opportunities? like, I think we've talked about the data infrastructure and stuff and working backwards to that, but just what are the insights that really help make some of the most pivotal decisions and what dashboards should all these operators ensure that they actually have access to?

Scott: Yeah. And I, I mean, I don't know that there's a single dash. I mean, we think about analytics or data and analytics as really serving three purposes. One is to report on performance. And so, and that's kind of the easiest one, right? So you think about what, are the key performance indicators for, for a SaaS business? Well, it's, contracted ARR it's a year over year growth and contracted ARR. It's LTV to CAC, which is basically a proxy for customer unity economics. And then some kind of efficiency, you know, metric, et cetera. I think people should have a dashboard that has those key performance indicators on it. And what that answers is, how are we doing? 

The second use case of data and analytics is to help you improve how you're doing. And that's where you have to have, like I showed earlier in the session, data designed in such a way to answer specific questions. So if, if you're the marketing person, really, if you're any of the good market leaders, but certainly the marketing person needs to understand which of the marketing channels have better return on investment and worse return on investment, right? So they can kind of allocate the resources to the ones that are more productive. So, you need a dashboard or, or a presentation of data specifically tailored at that. Maybe you have four different segments, right? And let's say call it, you sell to SMB, you sell to mid-market and you sell to enterprise, three segments. It's important to understand kind of which of these segments have better return, right? So you need to design a presentation of data to answer that. So, I think, you know, and if you get to any given functional leader, maybe the sales leader, what are the, what, one of the key things they have to understand is how do they improve the performance, the relative performance of their people, right. So, they should have a set of data presented in a way that answers the question. What is the relative performance differences or what are the relative performance differences between my salespeople and why are those performance differences the way they are? Right. Is it because the person that's most productive is also the person that puts in the most activity, right? They hit the most dials or whatever the measurement of activity you choose to use, or is it because they are much more effective at stage one in the pipeline of moving their prospects through that than any of the other people, right? You need, you need to be able to, as a sales leader, you don't just need to understand who's got the biggest pipeline and who's doing best on generating deals, but you need to be able to drill down and understand where those differences are because that's what's going to guide you to coach the people that aren't doing as well, right. You want to figure out what are the great people doing at which stage of the process and let's mimic that throughout. so, and the marketing leader doesn't care about what they care because they want everybody to approve, but they're not going to look at that, right. So, there's different things for each leader. But again, I want to stress the importance is to design the presentation of data so that it answers the questions they need to be able to answer.

Shiv: And how do you do that? How do you design it in a way that the most important decisions or actions are obvious? And one of things I noticed is you guys talk about this idea of there's say seven ish or core actions is changing people, processes, changing channels, changing investment levels, pricing and packaging markets and ICP products and services. So how do you, how do you design the insights in a way that you can focus on those seven areas and kind of talk about those seven areas in terms of the opportunities inside each.

Scott: Yeah. So with each of those, we call them action buckets, right? And each of those like change processes, there could be a whole bunch of different process related actions you might take. And what we would normally do is for any given customer, we'd say, okay, well, given the stage you're at and the dynamics, you have a single go to market motion or whatever. The most likely thing levers you're going to push are going to be around people, process, and channels, right? We sort of start with a priority, with a gut level prioritization. And then we, then we say, okay, if it's people, processes and channels, what are the questions you need to be able to answer? and, and, and, and if you say, I mean, you just say to yourself, what is the relative ROI of my channels? Well, you say, okay, well, right, I mean, that's your question. Well, the answer would be something that shows the relative ROI of your channels, which means you need to understand the cost. So it's top-of-funnel costs. So if it's a marketing channel, a digital media channel, it's the media spend. It might be spend on an agency that does the work for you. Then there, let's say that the ads result in people coming to your website and clicking a demo request. Then there is the investment of the SDR or salesperson or whoever is following up on that demo request to try to actually get the person into a meeting. So you could just walk through what are the various pieces of data we need that, when they come together, they are the elements of ROI on a channel basis.

Shiv: Right. And how much of the work here… So one of the things that we find is we'll come into companies and analyze channel performance or campaign performance and uncover insights and how things can be adjusted and make recommendations. And that definitely drives action. But one of the gaps that I often find is that even if a company has a dashboard or there are insights in there, there's a disconnect between the conversations happening at the board level versus what's happening at the functional level, right? And you kind of have this, you sometimes have a top down forecast that will say that a certain amount of growth is possible or a certain amount needs to be invested in an area. And then you also have the bottom up realities within a particular function or channel or activity, and you kind of hit some natural limits on let's say paid media spend, you can't spend beyond that certain amount. So how do you ensure that the data model accounts for that? And also that the insights are connected to expectations and finances and cash flow and revenue projections and everything at that level.

Scott: Yeah, and this goes to what I'd say is the third use case of data and analytics, which is modeling. And we work very closely with our customers to build pretty granular, like new bookings models. For example, again, let me just share one real quick. It's just Excel or Google Sheet, but it'll help make the point. What we'll basically do is for each of their channels. So we call them funnels, but the paid search, right? What's the investment that's been made? How many website sessions do we get? How many did that convert into low quality leads, what we call L1s, high quality leads L2? How many of those convert into meetings that get set up? How long does that take? Right? So this is a, this is a model for bookings for, for a particular customer. And when you do this modeling and then you capture all these assumptions, right? It makes it very easy. If you just basically build your model out based on historicals, which is what this template has done to say without doing anything substantially different. Here's what we're going to end up at. And if you then say, geez, that's 30% below the board's number, right? Then you use a model, this model or something like that to start scenario modeling, right? What if we were able to increase this conversion rate by 5% over the course of the year, right? So these are basically buttons you can push in this model on any one of the levers. Maybe we're going to increase the investment level, right? But by having a proper model, it allows you to basically figure out the set of things you could do or that you would need to do in order to achieve that board level goal. then the conversation is a very mature one, which is, boss, my CEO or my board, here's what kind of steady state looks like. You're asking us to do 35% more than that. And here's what it would take to get that. Right. And that conversation is grounded in the modeling. And so you don't end up with. You know, you don't end up with people just saying, well, you got to just do 30% better because it's not realistic. Right. Instead you have both the top level and the functional leaders using this model as the tool to have that conversation. And that's how we've seen companies reconcile it. And of course it goes back to, a little bit to the conversation we had earlier about, the emerging involvement of strategic CFOs, right? I mean, these are people who have an FP&A background. They, they, they think of models, right? And so they're, they're able to kind of lend that expertise into the go to market arena because you know, this, this isn't that derogatory at all, but most people when go to market do not have that same analytical model driven FP&A type mindset. And so that's one of the reasons why go to market plans are often so flimsy, right? But, if you really, if you really want to, you know, get to the brass tacks on this stuff, you have to have a really good model. And more often than not, we're to see a CFO having their fingerprints into that thing.

Shiv: Yeah, and I'm really glad you shared your screen on that because that's something that I completely agree with. And I think a lot of companies should have a model like this in place, but a lot of them don't. They may have one half of that or some of the metrics being tracked, but not all of it. But one of the things that jumped out to me as you were sharing that is you can have a steady state reality inside of a business. Let's say you're a $10 million company per year. You're able to close, I don't know, 1.2 million in bookings. And now you're being asked to close $2.5 million in bookings. And no matter how much you tweak the model, you can't necessarily generate more pipeline or revenue for that business, right? Because you'd have to create some really unrealistic numbers. And so at some point, the conversation needs to graduate from, these are all the roadmap items we're working on. Here's how we're going to maximize what we can get out of the spend. And here's based on the data, we're also potentially reallocating or stopping some activities that aren't working and definitely those things need to happen. And you could probably see a 20% lift there, but to get like a hundred percent increase, let's say in new bookings, you definitely need more budget and people because MQLs aren't free, pipeline isn't free. Everything has a customer acquisition cost. And so how do you connect that idea into this process? Because I think that's like a missing element, as much as you want to have ambitious targets or grow faster, you definitely need the investment in order to be able to get there.

Scott: Well, and that's, I mean, I think that again is the whole point of models, right? Because the point of a model is to show what a dollar in at the front end turns into on the backend. Right. And so again, you can talk about, well, you know, we're going to, we're going to train our salespeople a little bit better so that we get slightly better win rates and stuff like that. But if it forces you to have a 50% lift on win rate, and you've been 20%, 25% win rate for five years, and now you're expected to suddenly be 37 to 40%, I mean, nobody's going to believe that. And so you would then say, well, we're just going to need more volume then, which means I got to invest more at the top of funnel. You can't just hire more sales heads. I mean, unless it's a kind of big elephant hunting business where it really are the salespeople that generate the top of funnel, right? But in the vast majority of SaaS businesses, it's not that way. Salespeople are the bottom of funnel resource necessary to close this stuff. So you're gonna have to invest more in the top of funnel. You may need to invest more in SDRs if they're the people that do the initial follow-up, right? So you just have to be very thoughtful about that. in order to achieve the type of growth you want. You know, yeah, sometimes the models are going to say you can't do it unless you invest more money. Most of the time they will say that. And the problem we've had Shiv, is that since so few companies do this level of modeling, those functional leaders aren't armed with the tool to have that conversation. Right. And so they sort of, they sort of just get beat back and have to accept these unrealistic goals. Hence by June, when everyone knows it's clear, nobody's going to meet that people start looking for jobs. People start looking for replacements. Right. I mean, that's part of the reason why the tenure of these people is so short in our industry is because they're not set up to succeed.

Shiv: 100%. I think all of it is a symptom of the same problem or underlying causes is that if you don't have your data in order and that it starts with your data hygiene, then you're not going to have the right data models to give you insights on what's working and what's not working, which then leads to bad modeling and bad forecasting, which then leads to bad performance or missing expectations and which leads to people being fired or companies missing targets and all of that. So I think that's a great insight and I think a lot of companies could benefit from that. We're coming up on time, so just to make sure, we have a ton of PE firms that listen to this podcast and a bunch of founders and operators as well. If they have data as a core issue that they're looking to address, how do they learn more about you guys and leverage your services?

Scott: Well, they can reach out directly to me at scott at scalematters dot com or info at scalematters dot com and it'll go into our, to our CRO. So either of those are legitimate ways and you know, we're very responsive.

Shiv: Awesome, yeah, and we'll be sure to link your website and a couple of the resources that you pulled up into the show notes as well so they have access to that. Again, for the listeners or the visuals that Scott was bringing up, we'll definitely include those on the video platforms as well. So with that said, Scott, thanks a lot for doing this and coming on and sharing your wisdom. I think data is one of those areas that is often not talked about, but it's a huge value creation driver. So appreciate you doing this.

Scott: Shiv, thanks for having me. I enjoyed it.