Managing a Thin Balance Sheet: 4 Lessons Learned From a 2 Billion Acquisition | Laika Founder + CFO

Thanks, Jesse. Hi, everyone, and thanks for joining us today. Just for a little bit of context, Lycosa is a compliance as a service platform, and we help companies with information security and privacy compliance and ultimately get their certifications and their audits. And as Jesse mentioned, Dickon Chaplin's joining me today, and excited to have him here. He recently joined us as our CFO. And today's topic is how to play offense with a limited balance sheet. And we're going to hear directly from Dickon on his lessons learned from a $2 billion acquisition during his time at Turbonomic, which was acquired by IBM. If we can turn to the next page, I'll just go through high level what we're going to cover today. So why hire a CFO? And I, I'll talk about that. Dickon will go through the path to success, looking back to look forward. We're gonna talk about failing fast in action and what that looks like. And then living and dying by the forecast. So, uh, on the next page, we're going to just talk a little bit about, um, context on Lyca and what led to our decision to hire a CFO this summer. Uh, so Lyca was founded in 2019. We had closed a $50 million Series C this summer with $98 million in total cash raised, and we had a healthy balance sheet. Uh, we had tremendous growth, uh, going from a million, uh, to $10 million, uh, in ARR in just under 7 quarters, and we had aggressive growth plans. We had product market fit, and it was definitely time to scale. We had a small but mighty 3-person finance team, but it was definitely time for some changes. And so moving to the next slide, you know, why, why a CFO? And again, why now? With, for us, with 3 years in business and, and a Series C, we realized that it was time to start looking beyond the upcoming quarter. Looking one quarter out wasn't actually sufficient at this stage. And with the macroeconomic environment changing, we also decided that we really wanted to build out a plan for at some point in the future cash flow breakeven, and that growth at no cost was not an option for us. And so we needed to start with like planning processes, and we wanted to build out a 3-year plan so we knew where we were going and to develop, to develop that roadmap to get there. And part of that was also building out operating budget for the upcoming year. And that was really critical to build out various scenario analysis. If we spent X amount, could it drive, you know, Y in ARR, for example, developing multiple scenarios, if we spent more in marketing and certain channels, is the ROI there. So those were all things that we wanted to consider and to have more discipline around. And there are many different directions that we could go for future growth. And we needed a strategic lens and the data and we'll talk a lot about the data later as well to make these decisions. And so with the planning, then we wanted to think about management, right? Once we had a plan, we needed to ensure that we had the data, we had the reporting, the process, and I would say very importantly, the discipline in place to evaluate progress along the way and to look the around the corner, as I like to say. And what does looking around the corner actually mean? It's being able to look at the data, have the data in place to identify potential pitfalls, areas where we need to fail faster. And we'll talk about there that more a little later, fail fast, make decisions, change our, our approach. And so that was really important. And so with that, we wanted a trusted advisor, someone to be the arbiter of the data, to really help us build out our plans, hold the business accountable, especially as the business was becoming more complex, multiple product lines, geographies, and business opportunities. Finally, before I turn it over to Dikin, the other important thing was really accountability and driving accountability and driving sort of operational excellence across Leica and across all teams. And so, you know, with that, along with my co-founders, obviously we made the decision to move quickly ahead this summer and to hire a CFO. And we hired Dikin and super happy he's here. It's been about a month and I'm going to turn it over to Dikin now.

Great, thanks, Eva. And just as way of quick introduction, I spent— before I joined Leica, I spent the last 7 years at Turbonomic, an application resource management startup. And as Eva said, ended up being acquired by IBM in the summer of 2021. I saw the growth phase from— when I joined the company, it was about $20 million in revenue and grew it through $200 million by the time we exited. So I got a good sense for the arc of the growth of a company through multiple different phases of scale. And I'm excited to speak to you today about some of the things I learned and some of the things that I am bringing to Leica that I think are really critical in terms of building out a really robust decision-making engine, if you will, based and rooted within hard data. So the first slide I'm gonna point to is, this is what I think the journey of a company looks like, right? Everybody would love to see a line that looks like up and to the right every quarter, you're growing faster and faster and faster. That rarely, if ever, really happens, right? The pathway to success looks, in my opinion, a lot more like consecutive S-curves. And you see here these S-curves where sometimes you're accelerating, sometimes you're plateauing, sometimes you may, you know, unfortunately be decelerating and going in the wrong direction. But the most important thing about this is, A, you've gotta understand that you're gonna come across, you know, challenges along the way, which I think everybody understands that. But it's really understanding where you are. Like if you were to plot yourself on one of these curves, like understanding where you are, are you at the bottom of one of those inflection points where things are about to accelerate? Are you at the top of an inflection point when things are about to go the other way. And being able to build a system whereby, A, you can measure and understand where you are, but B, and more importantly, anticipate what's coming next. You're making some changes, you're implementing some new strategies, you're seeing the green shoots and you need to be able to really put your foot down on the gas, or perhaps you've made some bets that aren't paying off and you need to pull back. So, This whole concept of playing offense with a limited balance sheet is something that we all in the venture space have to live by every day, right? We, by design and by definition, we live with narrow balance sheets. It makes us more, it makes us inspect the decisions we're making at a much more granular level. And we have a high bar, especially in today's macro environment, for burning cash. And it's gone are the days where, you know, you just buy growth at any cost. Now we have to be frugal, we have to be thoughtful about how we're allocating our spend. So if I click one more time to overlay some of these curves with some of the actions that you may well experience in the journey of your company, these are some things actually that we worked through at Turbonomic and some of the initiatives that I led from the finance organization. And this just tries to give a little bit more context as to what I mean by making these changes in relation to where you are on these curves. So go-to-market evolution, right? Understanding where are you focused right now in terms of your target market? What's your selling motion? And does that support the growth for the business over time? And what we noticed at Turbonomic is we were started off in the low end of the market. Our unit economics didn't support the, the, the, the segment that we were going after. And so we needed to go upmarket. Now, as we went upmarket, we started to sell to bigger and bigger enterprises. And one of the things that we found was our pricing and packaging didn't work anymore, and we had to evolve our pricing and packaging. It was— looking back in hindsight now is, you know, always fun, but, you know, one of the things that I think we realized was our pricing was attached to a static physical unit that wasn't growing with our customers. And one of the things we found in, in our analysis was, huh, we're not growing with our customers. Our customers are growing, their infrastructure is growing, but our, our retention isn't growing sequentially. And so we had to go through an overhaul of our pricing and packaging. And, you know, multiple different initiatives over the course of time come along. So how would you build out this concept of being able to understand where you are and where you're going? So the first thing is, is, you know, defining the key set of metrics. You see a lot of metrics on here. Everybody in this call knows all these metrics inside and out. They're the industry standard, and for good reason. They're important, but they're table stakes. They're the financial fundamentals. And in nature, they are lagging indicators. They don't help you get ahead of where you're going. They may help you understand where you are on those curves, but it doesn't help you understand where you're going and anticipating. And What I often see is a lot of companies will go through their quarterly board meeting exercises 5, 6 weeks after the end of a quarter. They look at these core metrics, they discuss with the board certain things that are working, not working, and they come out with a set of action items. And lo and behold, you're already halfway through the next quarter when you could have been course correcting in the middle of the prior quarter if you're looking at the leading indicators in the right way, right? So what I urge you to really think through, and one of the things that like I, that I built out at Turbonomic was, the concept of what we called our radar function, right? It's that what leading indicators help us identify the business challenges that we are dealing with today? And what are the business challenges that are coming to at us tomorrow, a quarter from now, 3 quarters, 4 quarters from now? And so I'm just gonna walk through, there's a lot on this. I'm not gonna walk through all of them. The idea is, you know, these are some examples of leading indicators, not exhaustive, doesn't apply to every business, but a few examples of how I implemented this and some of the ways that this became a successful tool for us at Turbine Online. So one is the lead volume, the number one metric on this slide at the top. We all set goals of marketing MQLs, like how many leads are we getting in and are they converting through the funnel? But are we inspecting the quality of those MQLs? Are they in the target markets that we've identified is our sweet spot that we're going after? Do— are the ASPs of those leads converting at a high enough rate that we're going to be able to get to scale? Can we hit the next year's plan of 100% growth with these small, small customers? Or do we need to figure out how we— how do we take that marketing engine and point towards the market that we're going after? So we at Turbonomic, we went from a big broad-based marketing play where it was a volume game, and we got a lot of great leads from it, to an account-based marketing approach where we had to, where we started to really go after the target customers. And we did a lot of work around the target demographics and the ideal profile of the customer and start marketing directly towards them. It may seem obvious, but it's very easy, and we all know how expensive marketing programs can be. It's very easy for that to run away thinking that you're okay because your MQL volume is high, but not taking it through the next couple of steps of the funnel. Now, if you wait for it to show up in your LTV to CAC ratios, it's gonna show up, but it's gonna show up later, right? This probably shows up 1, 2, 3 quarters later. So, so that's one example. The second example, something I used religiously at Turbonomic was this pipeline coverage of the plan. Constantly looking at not just this quarter, next quarter, 3 quarters for out, 4 quarters out of how much pipeline coverage we have and then back solve from that to understand what do we need to build in terms of being able to hit our plan. It has implications for everything from hiring plans to incentive structures to being able to forecast accurately. And that was something that, you know, I found was incredibly valuable. Again, seems like 101, but it's something you need to do from the beginning. And then 3, this one was a really important insight for us as we changed that segmentation model going from an inside sales model to an enterprise, and focusing on the commercial space, going to the enterprise space, going upmarket. We made this change, we put our plan into motion, and what we noticed in the first 2 quarters of making the change was our sales rep attainment of their quota just was nowhere near where it needed to be. Now, that brings up a whole host of questions. But one of the most important insights that we had was we have a whole host of people who are about to walk out the door because if sellers aren't making money, they're not going to stick around. So we were doing something wrong as a management team in terms of our strategy. And there were also other implications around product needs, etc. But it also helped us identify the right profile for the sellers. It helped identify where we needed to make performance changes, but also that we had perhaps set our incentive structures wrong. And so we managed to proactively change that and make sure we retain our talent. So those are a few examples of how to implement this radar function. It's important to hire the right team, get the right skill set to manage this, and build the right systems, because you can't do this without the right systems and the right data that you need to collect. So that's the whole thesis and theory around this radar function, is anticipating where you're going to be next. I also wanted to make a note, something that I think we all, as leaders in venture startups, have a responsibility to both ourselves and to the investors, is we have to make bold bets. We have to— although we have limited resources, we've got to, you know, take a deep breath and we have to say, this is going to be a big strategic initiative for us, we're going to do it, right? And we're going to spend. It's going to change the trajectory of the business. But we don't wanna do that by bleeding out the balance sheet. So what do we need to do? We need to build out a very detailed operating model that lists out the assumptions. Early on, you don't have a lot of data to be able to test out whether the assumptions are right or wrong in your model. The important thing is not whether you've got the assumptions right, it's more that you are measuring against those assumptions and trying to figure out what is holding up and what is not. So you've gotta define that model, you've got to define the leading indicators, you've got to really, and this next point is really important, You got to define what success looks like on a monthly and quarterly basis. Are you tracking on plan? So you're not waiting to say, how do we do in the first half of this year? We look, how did you do this month? Do we see the green shoots? You've got to constantly measure against it and then finally be very decisive. If something is measuring well against your plan, great, pour more water in it. If it's not and you don't see the green shoots, that's when you've got to pull back. And you've gotta be very decisive, otherwise you bleed out the balance sheet. And then finally, this point about living and dying by the forecast. This is something that is absolutely imperative and is often something I think is, as simple as it sounds, is overlooked in the early stages of building a company. You're so focused on building product market fit and establishing that, which is absolutely important, but you've got to start building the forecasting capability on the sales side. So holding the sellers and the sales management accountable attributable to the forecast, but also to triangulate that sales forecast with your own statistical forecasts. With small datasets, hard to do. But the important thing is you're going to continuously refine that methodology so that when you get to week 4, week 5, week 6, and a quarter, you can call the number very, very accurately. But then also looking at 2, 3, 4 quarters out and staying ahead of it. And then if you build that level of intensity around the inspection of the forecast, good things will happen. So just a few takeaways before we turn over to questions. So one is the concept of building a robust model. Build it well, build it right. Second is hire the right team, the sort of the radar team, as I like to describe it, that are going to help with the analytics to understand where you are on the S-curves, where you're going next. Three, make— bold bets, but measure it closely and be decisive. And lastly, live and die by the forecast. So Eva, I'm going to pass it back to you before we jump into questions. You're on mute, Eva.

Yes, I was on mute. Thank you, Dickon. That was great. And I definitely see some questions in Q&A. I think before we move to that, the only recommendation that I would make here to the team is, and to everyone who's listening, is, you know, once you find product market fit, you know, time to scale, I definitely would, you know, and then it's time to scale. And so I would say at that point, at early stages of finding product market fit, I would say invest at that time early on a strong like FP&A function and on some of the tools before you even get to the point where you need to hire a CFO. Building sort of this capability, building out the KPIs, all of the metrics, and that discipline that, that Dickon talked about is something I would definitely recommend all do earlier rather than later at earlier stages of product market fit. So I think that's the only other thing I would add before we go to the Q&A. Oh, okay. So at what stage should a company hire a CFO? I guess that's the first question. Um, so like we— I think we talked, uh, a bit about this. So, um, when you're getting to the point where you really have found strong product-market fit, um, and you look— you're looking to scale, that is the time to invest. That is the time that we made that decision to scale. Um, earlier on in the stages of a company, uh, may not make sense to bring in somebody to— as you're still experimenting and trying to find that product-market fit. So I think, um, our stage was actually the right time. I would recommend— I would definitely recommend that. I don't know if there's any other, um, insights you would have on that in your experience, Dickon.

No, I think that's right. I, I think, you know, once you go to figuring out how we scale, it often comes part and parcel with the amount of cash you've raised. And the ability to make bold bets, right? And when you have the ability to make bold bets because you put, you know, a significant amount of cash on the balance sheet, I think that is the natural time to really bring someone in.

Okay, next question. Blind spots. What type of blind spots should they be looking out for? I think there are numerous blind spots, and there are definitely areas, you know, as you're For founders, you're building the company, you're experimenting, you're identifying some of these bold bets, but being able to have the data, have the metrics— are we spending too much in a particular area? Are we actually getting the ROI that we intend? Are we spending not enough on a potential area where there is traction? Do we have the activity metrics to evaluate how how is productivity amongst the teams? Like, those are some of the blind spots because we're so busy, right, as founders. And we're running, and we're building, and we're making decisions fairly quickly. And so those could end up being blind spots. And so we want that arbiter of the data, is how I say it, and having the right data and the metrics and activity metrics to evaluate all of those things. Those are some of the blind spots.

Yeah, and I think one of the ways in the design of the radar function The part of where I see it fitting in in the organization by design is in the finance organization, rolling into the CFO so that the information becomes black and white. The blind spots come when there's an inter— level of interpretation within a functional group. This isn't to say that there's not trust among the groups. It's to say that there may be what— someone interprets the data some other way and then communicates that interpretation and that insight back to the CEO or back to the founding team. Or whoever the decision makers are. And at that point, there's been a step, a level of interpretation that goes along instead of seeing that black and white data. So what we've done at Leica is we've actually centralized all the analytics and strategy and operations teams to be under the umbrella of the CFO, solely for the purpose of making sure we have a consistent way that we're analyzing the data and the information, which I think is a really important way to avoid the blind spots. I see a question on here saying, do we have any advice on how to build a radar? Tools or external partners seems really critical, but also hard to do. It's hard to do, but it's a matter of building the right— you have to have the right team. You need to start thinking through what the profile of those people look like. I have had— I've hired a lot of people into these roles over the last decade. Where I've had a lot of success is, you know, consultants, management consultants, you know, investors, the young investors that are looking to try and get some operational experience, people that have really, really, really strong analytical capabilities, but at the same time have the business context, the business sense to be able to make sense of that information, not just a pure math person. So one is you've gotta hire the right team and the right profile. And two, when you're thinking about the tools, I mean, the biggest tool for all of us to begin with is like your CRM. It's Salesforce if you use Salesforce or HubSpot, etc. And then as you build out the rest of your stack, it's how do you collect the information from product? How do you collect the information from marketing and having, having all those tools implemented in the right place and then being able to figure out how to make sense of that information through through robust analytics and business intelligence. I'll answer this next one. What does the org structure look like under the CFO and how big is the team? So, so for Leica, I have my corporate controller, I have my finance person who think of that as your FP&A, person. I then have revenue operations that are going to be all your systems, tools, metrics management on the, on the go-to-market side, and then strategy and analytics. So it's really 4 key direct reports and then the teams, teams underneath them.

How about the next one, building a forecast at what stage?

Yeah, I think it's a good practice to start immediately. It's hard to do sometimes. It's like if you're pre-revenue, you're like, how am I supposed to forecast? And, you know, it's challenging. But I think once you pick a methodology that you want to start using, you're going to see how accurate it is and maybe you have to change methodologies. But the fact that you can play with that when the stakes are lower earlier on and you'll start to understand really what moves the needle. That's where I think it's so important and valuable to build it from the beginning. Not only that, but also to start requiring the sales reps to start giving you a forecast that you hold them accountable to. You may give them a little bit more leeway earlier on as we're building the capability out, but you get to the point where that's part of their job is to make sure they're committing a number to you as a team.

Yeah, I think, I think earlier the better as well for the reason of just creating that muscle in the whole company, right? It's not just for the seller or NDR, it's for maintaining your churn. It's like, it really is just creating that muscle as an organization. So the earlier you start, the better. And it could be a limited number of things that you're going to forecast out. It doesn't have to be a full, you know, P&L, but it can be some very key targets. And, um, yeah, my, my recommendation is earlier the better.

Yeah, that's right. The first thing I did actually on day one when I joined Leica a month ago, I asked everybody in my organization to provide a forecast for our quarter. And a lot of people would say, well, I don't know how to do that. Now I don't care how you do it. It's a matter of building that culturally that everybody is thinking about the quarter. Everybody is thinking about the forecast because collectively if we've got a lot of smart people thinking about the same problems and, but maybe tackling it from different ways, and different perspective, we're going to be much better for it. So I highly urge you to do that.

And then the last one is on bold bets. I think we talked about that specifically around success criteria. I think we had that slide that you went through, Dick. And I don't know if there's anything—

Yeah. Yeah. So this, yeah, this I think is really— and we talked about it, but to reiterate is, is building what success looks like and really, you know, defining what the milestones are through that process. You may want to say, we need these leading indicators by 4 weeks, by 8 weeks, by 12 weeks, or whatever the appropriate timeframe looks like. So you've got something to measure against. If you don't have something to measure against, it's impossible to know whether something's working or not. And the number of times I've seen people make decisions saying, we're gonna go off and do this. And then in 9 months they say, well, I don't know if it's working yet. So we're gonna give a bit more time to, to percolate through and see if it's gonna be a success. At that point, you may have already wasted 3/4 of, of, uh, of cash, uh, or perhaps you were underinvesting in it because it's working really well, but you just don't know. And that's when you need to say, I need to double down on it. So I think having that measuring stick, whatever that is, and defining those, those metrics, and measuring constantly against them will set you up for success. Mm-hmm.