Raising Funds for Your Tech Startup - Mark Sherman

Speaker A: Welcome to the Tech Leader Talk Podcast with Steve Sponseller. This podcast brings together successful technology company leaders, entrepreneurs and experts from around the tech industry to share successes, challenges, and examples of what's working now to build strong tech companies.

Speaker B: Hi, this is Steve. Welcome back to the Tech Leader Talk Podcast. On this episode of the podcast, I'm talking with Mark Sherman. Mark is a managing partner of Telstra Ventures, which is a venture capital firm in San Francisco with a strong track record. One of the things that Mark and I talk about is the way that Telstra Ventures uses data science and quantitative analysis when analyzing investment opportunities. Mark's also going to talk about what he and his partners look for in a technology startup. And one thing I'll give you a heads up, um, on this one thing they like is to see a passion for the business, uh, in the founders and the leaders of the company. Mark, uh, also talks about other important factors, uh, that they look at, uh, during our call today, um, Mark also shares about how companies can make their pitch stand out to the investors and to be able to make a good impression and hopefully improve their chances of getting funding. Um, Mark's going to share his ideas and his thoughts on what he expects with funding trends in the coming year as we're in a little more difficult situation. And I'm sure you're going to enjoy this conversation with Mark, and I know you'll leave with some insights on how to position your company to improve the chances of getting funding. So let's get to my discussion with Mark. Hi, Mark, thanks for joining me on the podcast today.

Speaker C: Steve, thanks so much for having me.

Speaker B: So I gave the listeners a little intro kind of about you, uh, to get it started, but I'd love to know kind of what's your journey or what was it that the inflection points or whatever that led you to where you are today, uh, working in the venture capital world.

Speaker C: Yeah, sure. So, uh, it's probably a good story of how sometimes things aren't always as you expect, but they become interesting along the way. And I was getting out of Harvard business school in 1992, and the story arc was that the Japanese were going to crush us in virtually every industry but for technology. And even then that was a maybe they had just bought Rockefeller center, um, and were taking over the auto industry and you just sort of looked at every industry and they were winning. And so I was considering where I was going to go, and I just thought, hey, maybe I'll go where the tall buildings are in the land of, uh, Silicon Valley and moved to San Francisco and started working at a technology investment bank called Robertson Stevens. Um and in some respects the winds of change really started to happen in the fall of 2022. And then the energy around technology just started to become you know, being blown by almost gale force winds and uh, it was an incredible ride. I worked there from 92 until 2000 and ended up running the software banking, uh, business where it took Siebel and BEA and many other uh, Quest Software, many other great uh, software companies public. And from there um, I went to Battery uh, Ventures which is one of my clients and was a GP for um, 10 years and uh, led the investment in coupa which I invested in at $20 million pre uh, money valuation and their getting bought uh, right now at 8 billion. So it's worked out well for the team and for everyone there.

Speaker B: That's a good return.

Speaker C: Yep, yeah, exactly. And then from there I ended up going to Telstra Ventures where I run the venture business. Um, and have been doing so for the last uh, 10, it'll be 11 years this February. And we backed 88 companies actually it's now it's up to 90. We just closed two investments pretty recently. So we're up to 90 investments and uh, 17 of those have generated unicorn uh, uh, like uh, milestones and um, it's interesting so the Japanese did not uh, end up crushing us in every industry and uh, things and technology ended up uh, turning out a lot better than I probably expected in uh, July of 1992 which is when I started.

Speaker B: Okay, that's interesting. What sort of the particular types of technology or industries or businesses that you like to invest, either that Telstra uh, ventures invest, invests in or that you kind of specialize in. Yeah.

Speaker C: So um, stage wise we typically invest in series A and Series B companies so typically $1 million to $10 million in revenue. And you know our goal is to help them get to $100 million in revenue and beyond. Um and then geographically it's mostly probably 80% plus in the US and then opportunistically we'll make investments internationally in Australia, uh, in other parts of Asia, Israel, uh, et cetera. And then sector wise um, most of what we do is enterprise driven around our classic themes. I would say things that we've invested in the past would be around cloud, cyber, SaaS, um, some communications and networking oriented uh, businesses. And then we do do a little bit of consumer uh primarily around sports and gaming. Uh, it's been two themes. So those are kind of what I would call the Coke classics of what we've been doing. And then, uh, more recently, venture is always about what's next and where the world is going in the not too distant future. And we've been spending more energy around, um, fintech, logistics tech, uh, some climate tech, um, and then we've been looking at a bunch of, uh, companies in and around the generative AI, uh business, which is, you know, a big theme in the venture market right now.

Speaker B: Yeah, that's a hot topic right now.

Speaker C: Yeah.

Speaker B: So I was looking into, just reading a little bit about, uh, Telstra Ventures. And you talk about how you use data science and data analysis as part of your evaluation process. Tell us a little bit about that. I mean, not the proprietary stuff, but how, uh, how does that help? And does that kind of set you apart from what a lot of other firms are doing?

Speaker C: Yeah, so I would say that, you know, 90% of venture firms don't really use data science in, as part of their investment process. Significantly, you know, maybe 10% do. And we definitely fall into the camp that it's, it's very constructive, helps to, you know, make better investments, et cetera. So we use it primarily around sourcing new investments and around making investment decisions. On the sourcing side, we evaluate everything that comes into our firm using data science. And in particular, we're looking at, you know, things that you would expect, you know, web traffic, product ratings, team ratings, employment growth, those types of things, as well as some other things that, you know, are probably a little bit more in the secret sauce, uh, angle. And it enables us to score investments. And by scoring investments, it gives us kind of a proxy to say, you know, hey, this is something that we should spend some time on. Or, you know, there's, there's some aspects of this that seem like, um, the company is, is a little bit more of an also ran as opposed, you know, one of the leaders in spaces. And so that's what we do on the sourcing side and then on the, the investment decision side, um, we track the financials of our portfolio companies and many of the companies that we spend time with, uh, deeply on the investing pipeline side. And we've developed a bunch of metrics of about, you know, 200 metrics in about 200 companies that give us a sense of, you know, revenue growth, um, LTV to cac, uh, recurring revenues, so net revenue retention and then operating statistics, you know, how much they spend in sales and marketing relative to revenues and, you know, lots and lots of different metrics. The rule of 40 would be another thing that we, that we look at as well. Um, and then this gives us a way to benchmark the companies relative to many of their peers and gives us just another tool of many to do, um, analytics on the companies. And so regardless of whether we're doing it on the sourcing side or on the benchmarking side, it basically allows us to take lots and lots of data, some of it structured, some of it unstructured, simplify it, and then give us some benchmarks and some tools to make decisions a lot more quickly, a lot more efficiently and a lot more data driven than we would have maybe 10 years ago or so.

Speaker B: Mhm.

Speaker C: Does that make sense?

Speaker B: Yeah, yeah it does. So it helps. I could see if you didn't have the data analysis tools to do a lot of that, to automate a lot of that, that would be a huge human capital investment. Does that maybe filter is not the right word, but it helps you maybe set some thresholds where if certain scoring parameters are up to this level, let's, let's continue the process. But others that just are too far off from what you would want, you can kind of filter them out fairly quickly and easily.

Speaker C: Fairly, exactly. You've totally got it. And you know, we're in an intensely human business and so it helps us to do kind of the numerical, um, structured pieces of our business a lot more efficiently. And so then we can spend time talking to the management teams about, you know, their vision for the future, their product roadmaps, their ability to differentiate relative to competition longer term. And you know, ultimately we're making a judgment as to what we think their ability to execute, um, is. And by doing this, um, data science, it enables us to spend more time on that and basically make better investment decisions.

Speaker B: Yeah. Okay.

Speaker C: The other thing that I would say that we haven't really talked about is, um, it does help us to come up with new ideas for new investment themes. Um, and so about 45% of our investments come inbound, meaning an entrepreneur calls us or a venture friend calls us and says, hey, here's a company, you know, 40% of it's outbound. We found a company in an article or at a conference or something that, you know, we're chasing them. And about 15% of it is, is sourced by the data science team. Meaning that there are interesting metrics or um, aspects of the business that we think are pretty cool. And one of the reasons why we like some of the data science sourced investments is the data science doesn't really care whether it's A cyber company or a cloud, uh, company. It just thinks that the momentum of that company relative to everything else, the hundreds of thousands of other companies that we're tracking, is much better and much more significant. And so it helps our investors to do good things. And what I mean by that is, you know, my partner, Marcus Bartram, who, um, leads our cyber area and his back CrowdStrike and Auth0 and whatnot, knew that cyber insurance was an area that was bubbling up and that he should pay attention to it. And, you know, what happened about four years ago is our data science guys, you know, basically grabbed him by the collars and said, hey, there's a company called Corvus which is bubbling up, um, and we like it, particularly relative to the 28 other companies that were in that space. And, you know, you should spend some time in it. And so it kind of moved from like a manana, manana, manana discussion and Marc his mind to, hey, I should really, you know, spend some time with Phil Edmondson, who's the CEO of Corvus. And, you know, we then did our, you know, after we did our data science work, we then did our kind of, you know, CIA, MI6, um, you know, FBI work, Massadi work in terms of doing our due diligence, and ultimately led to an investment in the company where we led their Series B. Um, a year later or so Insight led their Series C. And the company's been executing, you know, super tremendously in, in the last four years, and we have really high hopes for that.

Speaker B: So it's interesting so the, the data science can go out and help you find. Is it looking for individual opportunities or is it looking for larger markets or like, larger categories or both?

Speaker C: Yes. Uh, or C. All of the above.

Speaker B: All the above.

Speaker C: It's. It's looking for. It's looking for all of the above. You know, basically what it's looking for at the most simple levels is momentum. Then it's looking for momentum of those companies relative to other companies that are in their categories and then relative to other generic companies overall. And so what it highlights is companies that we should focus on. So we affectionately call these companies Simbi Companies, um, which is a complicated acronym that stands for companies you might be interested in. Um, and so we use it as a tool internally to say to investors, hey, here are some companies that have been bubbling up, um, in the C level or the Series A level that we should pay attention to so that we can think about investing in their Series A, in Series B as they're bubbling up and continuing their evolution.

Speaker B: Okay, all right, that's interesting. So as you, whether you do some initial kind of filtering or as you start taking uh, a look yourself, maybe, or maybe analyzing the data, what are some of the key things you're looking for? If there's a listener here who's at a, at a tech startup and they're looking at whatever their next funding, uh, event is, what, what should they be doing that they would, I guess, attract your attention or attract other VCs attention?

Speaker C: Yeah, I think the two key things are, um, be somebody who's authentically passionate around the area that you're going after. Um, and then the second is to build a great product in and around those areas. And I think with most of our companies you just can't. Those are two things that you just have to have. Um, and if you don't have them, they're basically uninvestable. Um, and you know, we do a lot of other work around, you know, competitive differentiation, market size, financials, the valuation of the company, the prospects for long term liquidity and those types of things. But I would say that those other elements, uh, beyond people and products are downstream and are, you know, secondary versus, you know, the key elements. And you know, the, the reason why I would say that is on the people side, we kind of don't know. We don't care where you come from. You know, it's interesting because we've just been looking at, uh, you know, we invest in a fair number of companies that have, you know, grade A institutions behind them, meaning, you know, Harvard and Penn and Stanford and MIT graduates are in a fair number of our, of our companies. But there are lots of other companies in our portfolio that, you know, the graduates came from, you know, lesser well known, uh, institutions. And the same is true. You know, we have a fair number of people from the Facebooks, the Amazons, the Googles, the Microsofts of the world, but we also have lots of people that come from, you know, institutions that are less well regarded. And in the end, um, somebody who's passionate about innovation, building, creating that ability or capabilities you can test for and see over time in their background. And then ultimately looking at products in terms of how delighted customers are when they use these products, when they get viewed, how well reviewed they are, you can tell something that is the best expression of a human is a super high quality, interesting, innovative product. And that's something that we can, you know, really test for and see.

Speaker B: Okay, um, so, so passion is a, I don't know if data science can measure it? Maybe it can. How do you, what do you look for? To see if, or to confirm that somebody's really passionate about their business, their product, their industry.

Speaker C: Yeah. So on, on the passion side, it is a little bit trickier to, to test on, on the data science side there. There's kind of some simple things you can do in terms of, you know, team ratings and company ratings, um, but it's usually a little bit more of kind of an interview slash interpersonal interactions over a bunch of weeks or a bunch of months where you just kind of get a feeling for the person in terms of how good they are at type of building things. We also look for them to be at interesting companies, um, over time and be a real contributor where you know, I'm sure Steve, you've been involved with projects where they're like, you know, Steve has had his hand fingerprints all over this and you know, is kind of given credit by many for really driving something. You know, we can background check people pretty easily through lots of our, you know, networks just to see, you know, did Steve do what he said he did. Was he really sort of the key product manager in this product area or were there really, you know, it was kind of a federation of a bunch of people and he was a contributor, but maybe not, you know, the, you know, the quote unquote, the contributor. And so a lot of interviewing and a lot of back channel diligence, um, ultimately you know, deals with that and then on the product. It depends a little bit on the product itself, um, in terms of where it fits in the ecosystem. But you know, for more enterprise products we can, you know, talk to existing, um, customers. But then, you know, we oftentimes like, um, to introduce the company to new prospects and just to be able to see, um, you know, how well the company does, um, you know, relative to the other priorities that are, um, competing for the dollars and the attention, you know, that are out there. And you know, many people can get a product sold to a couple friends and family from, you know, days of Christmas past. But you know, coming to the de novo conversations cold, you know, really gives you kind of a flavor for, you know, how the company will perform, you know, longer term as it's going from 10 customers to 100 and 100 to a thousand. You get kind of a feeling in terms of um, its probability to execute on those types of plans.

Speaker B: Okay, so that's the human element of it that maybe data science at least isn't there yet. It's crunching the numbers, but then that's where you come in and the rest of your team to help kind of

Speaker C: look at the human component. And in both of those cases we can use, you know, kind of quick tools, you know, like Glassdoor, you can use G2 Crowd or Gartner, or um, you know, um, the Apple Store and the Play Store for rating systems. And that gives you kind of a quick cut. But you know, ultimately what we're trying to really, you know, quote unquote bet on is the trajectory of these companies. And it's a little bit more nuanced than that. And oftentimes talking to experts in the industry, talking to other product managers who know the space well, can give you a sense of, you know, is this a company company that's gonna, you know, leave San Francisco and you know, end up in Sacramento? Or do you think it's going to end up, you know, on a flight trajectory that's going to circle the globe type of thing?

Speaker B: Okay. Mhm. That's interesting. What's, what do you love most about the work that you're doing today?

Speaker C: You know, what I love most about the work that I do is working um, with the entrepreneurs and helping them to solve problems. And um, the reason why I like it is, you know, the people that we work with, it's a privilege. They're you know, builders, creators, innovators, you know, technologists, people that can kind of oftentimes see around corners in terms of how, you know, the world is going to be. And you know, they want to meet new customers, which is something that we spend a lot of energy on and try to help them, uh, generate revenue. They need to find new people and finding, you know, new VP of sales to move from an individual contributor person to somebody who's a little bit more of a, you know, has built out a team and an infrastructure, uh, is another thing. And then, you know, the third thing would be, you know, helping them find, you know, follow on layers of financing that you know, are helping to fuel their further growth. Their you know, uh, product roadmaps they're building out of their go to market strategy, et cetera. And so just working with these people I just really enjoy because you know, they're passionate, uh, they're enthusiastic, they're interesting, they're thoughtful, smart, creative. And just being around that energy, I find, you know, infectious.

Speaker B: Yeah, I feel the same thing. I work with a lot of tech companies and especially some of the smaller startups where I almost become part of the team. M. It's, it's exciting to be in that environment and, and help them and watch them grow. So that is fun. What. So as this particular episode of the podcast is being published in early 2023, it's been a crazy year or so with, uh, the economy and tech and, uh, financing. What do you think we're going to see, uh, in the coming year or so?

Speaker C: Yeah, I think the plan is, uh, unpredictability, volatility and chaos. And so I, um, don't think that it's going to be an easy year to predict exactly where the economy is going to go. I think it will also be a little bit tricky, uh, to predict exactly which sectors are going to be doing better or worse. And so I think one of the things that we're just looking for as we back companies is that the people are nimble, thoughtful, creative, um, and can pivot, you know, pretty quickly. Because we have a lot of question marks today, you know, around geopolitics, around the economy, around recessions, around interest rates, around inflation, uh, energy prices. You know, you can kind of go on down the list. And so with all that unpredictability, um, you know, I think it's going to be, you know, very hard. Um, having said that, I do think, you know, there are some sectors that were pretty excited about and we think will do well, you know, in lands of unpredictability, chaos and volatility. Um, you know, cyber is an area where we've made about, you know, 12, 14 investments. Um, and I've mentioned some of the ones before. Um, you know, cloud, Knox and Imperium, uh, in the least, uh, privileged security space and in the mobile security space have done very nicely for us. And, you know, it's almost like the Energizer Bunny of spaces. There's, you know, uh, lots of challenges as it's talked about in the press very frequently around cyber. It's a tricky area. Uh, domain is super important, and we just continue to see neat companies there. Um, you know, we just made our first, um, formal climate tech investment. Um, and, you know, we think climate is going to continue to be just an enormous, um, challenge. A buddy of mine, Bruce Usher, um, who's a business school professor at Columbia Business School, just wrote a book called, uh, Investing in the Era of Climate Change. And I think it underscores lots of the big trends in terms of where it's going. And what we like is where the software industry and the AI and machine learning industries intersect with climate is just a rich, rich, fertile ground, uh, for companies bubbling up. And we think that'll be A second area. And then, um, if I was to pick a third, you know, artificial intelligence and machine learning are redoing all types of businesses and you know, we're seeing it kind of everywhere. So just, you know, where the, where those technologies are interacting with all sorts of subsectors of software we think are interesting. You know, cybers. You know, pretty much all of our cyber companies do some sort of AI or machine learning. Um, we have a Spectru company named Cohere, um, which essentially maps a user's radio signal similar to the way Google maps, you know, maps the world. And that enables you to, um, increase the amount of spectrum that mobile operators can use, uh, through their, uh, radio networks, which is, you know, very exciting. Um, you know, marketing Analytics Singular is a mobile, uh, user acquisition company that we have that's using tons of AI and ML. And you know, we, I could kind of go on and on and on, but I, uh, I think, you know, cyber climate tech and where artificial intelligence and machine learning, you know, touch different subsectors of the software industry, we think are going to be the big, big themes for the next year.

Speaker B: Okay, what's interesting, yeah, they certainly are a lot of press and there seem to be a lot of breaks, breakthroughs, and just continued growth and improvement in those areas. AI seems to be growing at an exponential rate. It's at least its ability to do even more. I was reading one of your recent articles talking about what's coming in 2023, and you mentioned that there's $300 billion in dry powder available to VCs. Can you tell us, first define what dry powder is and how do you think that may help with, uh, getting things back to whatever our next normal is going to be in this world?

Speaker C: Yeah, so dry powder is large. Foundations, endowments, pension plans. So like, um, Harvard or the Rockefeller foundation or General Motors pension plan gives, uh, venture firms, um, like Telstra ventures money. And then we deploy it and invest it in, um, other companies, such as the ones that I've mentioned earlier. The amount between we just raised a $350 million fund and we've invested, let's just say a little over $100 million, uh, of that fund thus far. So 350, less 100 is 250. And that would be the amount of dry powder that we have to invest. So it's the amount of capital where we have available to invest. And if you take that for the venture industry overall and add up all the different funds, how much capital they've gotten, they've received from the endowments and foundations and how much they've deployed, um, the amount that's available is $300 billion, which is a pretty big number. And one of the reasons why I think that, um, there will be some interesting venture activity in the next year is that once people feel comfortable that we're not going to have a monstrous recession next year, they're going to want to make investments in companies that they think are interesting. And the reason why they do is because in the long term, all the trends of software change in the world. Digital technologies changing the world, artificial intelligence and machine learning technologies changing the world. Those are all very fundamental and will create trillions of dollars of opportunity over the next five or 10 years. And so people are going to want to invest those $300 billion of dry powder in emerging companies that they think are interesting so that they can earn great returns. And so while the Ukraine and energy prices and inflation interest rates keep them up at night, in the morning, the reason why they want to, um, deploy some of that dry powder is because they're meeting entrepreneurs like Cyber GRX in our portfolio, or Cohere in our portfolio, or Singular in our portfolio, all of which have tremendous opportunities in cyber and in, you know, spectral efficiency and marketing analytics.

Speaker B: Okay. Okay, that'll be interesting. I didn't, until I read that article, I didn't realize there was that much, um. Yeah, kind of capital sitting.

Speaker C: Yeah, it's been, it's been growing significantly. And it's, it's in part because a lot of, you know, capital has been raised by the venture industry, you know, particularly in the last couple of years. And, you know, when, um, the market started to head towards recession in 2022, you know, people became a lot more, um, cautious in terms of their deployment rates because they saw the multiples that public investors were paying for software companies was compressing. And so, um, the multiples that they wanted to pay for software companies, the private markets were compressing. And, you know, in the end, a lot of the companies, um, that could wait to raise money did, um, because of two reasons. One is they had raised a lot of money in 2021, so they had some resources to basically wait for a while. And then secondly, they didn't really like the prices that they were seeing in the marketplace. And it just takes a little bit of time for the market to readjust and kind of, you know, get a sense of what the revaluations are looking like. So many of the companies were looking to kind of grow and put up some more milestones Then maybe re approach the market last. That's re approach the market next year, um, and where they can, you know, continue to make progress and you know, raising at a, you know, same valuation or a higher valuation that they had raised before.

Speaker B: Okay, all right, that makes sense. So if you were giving a tip to a leader of a tech startup today, in, in our current environment, what's, what will be your, your top tip that something that's actionable, they could implement relatively quickly.

Speaker C: Yeah, I think in the end the market shifted from growth to capital efficiency. And those are just fancy terms for, you know, ultimately revenue growth. You know, what you were looking at this year versus last year or next year versus this year, that's pretty straightforward. Um, capital efficiency is basically just seeing how you, uh, invest the dollars, um, that you are, um, using and how much can you really generate, uh, for that. And one metric that people use in capital efficiency is the rule of 40. And basically the rule of 40 takes revenue growth and then adds to it, um, the EBITDA margin, uh, of a company. And most of our companies are typically losing money. And so, um, a company that might be growing at 100%, um, may have an EBITDA margin of minus, uh, minus 60%. And so when you add those two together, you get to 40. And um, people think that there's sort of a magical element around the level of 40, such that if the number you get to is a greater than 40, then it's a reasonably capital efficient company. If the number that you get to is less than 40, so 10 or 20 or even negative, then, you know, people are not deploying the capital very efficiently. And so, um, the most important concept is that, you know, last year in 2021, people really cared mostly about revenue growth and it was kind of growth at all costs. And people didn't really care that much about how efficient you were in terms of, um, generating that growth. Whereas in 2022, and you know, likely in 23 and beyond, you know, people will care very much about, you know, how much are you losing relative to the revenue growth that you're generating. So that would be the kind of key tip that I would give you.

Speaker B: Okay, all right, that's good. That's, that's valuable.

Speaker C: Yeah.

Speaker B: So what's ahead for, for you and, and the rest of the team at Telstra, uh, Ventures over the coming year?

Speaker C: Yeah, So I think in the end, you know, we're looking to do another eight or ten investments in 2023. Um, we're very excited about some of the themes that We've talked about, um, you know, I think we've been working with a lot of our portfolio companies to make sure that they have, um, you know, the capital that they need to continue to grow. And so, you know, we're, we're thrilled that, you know, well over 50% of our companies have more than, you know, two years of capital. Um, uh, there's a, basically a third that has, um, you know, between one and two years. And then, um, there's a smaller amount, about a sixth that have, um, you know, less than, less than, uh, 12 months of capital. And so the ones that are in this sixth category, um, you know, we're spending a lot of energy on just to make sure that they've got, you know, processes to either, um, raise money on the debt side or raise money through, um, equity raises. And I think most of our companies are doing pretty well on that factor. So I think, you know, that's, that's something that we've been spending energy on. And, you know, it's, it's, I think, continue to pay, you know, nice rewards because our companies continue to grow. And, you know, ultimately we'll get back to a time where there's mergers and acquisitions and there will be IPOs again and whatnot. And, you know, so ultimately the, the cycle of life for, uh, our companies will continue to, to chug along quite nicely.

Speaker B: Okay, well, that's great. And obviously it's in your best interest to, to help them and, and, and share your, your team's experience with those companies to, to get through this until things start getting a little rosier.

Speaker C: Yeah, exactly.

Speaker B: So. Well, you've provided a lot of great information, uh, to the, to me and to the listeners, and we appreciate that. And a lot of useful things that the people in the tech world can, can take advantage of now and help them get through this cycle, uh, that we're in. If someone wants to reach out to you or learn more about you or Telstra, uh, Ventures, what's the best way to connect?

Speaker C: Yeah, either through LinkedIn or, uh, you know, my email is Mark Elstra Ventures. Uh, and it's just like it sounds.

Speaker B: Okay. I will get links to, um, your email and LinkedIn as well as the company website in the show notes, so any of the listeners can easily find those links there. So I want to thank you for your time today, Mark. This was, uh, very educational and I loved hearing the great stories that you, that you shared. And thanks for your time coming on the podcast today.

Speaker C: Great, thanks. Thanks so much for having me. Steve. Have a good rest of your day.

Speaker B: Thank you.

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