Meet the VC


Meet Matt Oguz, Founding Partner at Venture/Science

Oguz started Venture/Science in 2012, with a data-driven "moneyball" approach to venture capital

Innovation series by Steven Loeb
May 7, 2016 | Comments
Short URL: http://vator.tv/n/449e

There has been a big debate over the last few years over whether the Series A crunch is real or not. What everyone can agree on, though, is that there are definitely more seed and early stage funds now than ever before, and more people willing to give money to young companies looking to make it big.

But just who are these funds and venture capitalists that run them? What kinds of investments do they like making, and how do they see themselves in the VC landscape?

We're highlighting key members of the community to find out.

Matt Oguz is Founding Partner at Venture/Science.

His professional background spans ecommerce, analytics, behavioral economics and decision sciences. A Turkish-American businessman, Oguz graduated from Bosphorus University in Istanbul, the first American university outside the US and the country’s equivalent to our Harvard, with a bachelor’s degree in civil engineering. With full scholarship, he then earned an MBA degree in Decision Sciences from Georgia State University Robinson College of Business. Oguz started Venture/Science in 2012, a new generation venture capital fund that pioneered the data-driven "moneyball" approach to venture capital.

Oguz will also be participating in a panel called "Surviving an evolving venture biz, and finding deals as an emerging VC fund" at Vator Splash Spring on May 12.

VatorNews: What is your investment philosophy or methodology?

Matt Oguz: Most people in the venture capital world come from startups, or they come from other companies that they've been in. I think the traditional VC approach is 80 percent social, and 20 percent calculated. Our approach is 80 percent calculated and 20 percent social.

Of course, there's always intuition, and there's always subjectivity when there's a high level of ambiguity and complexity involved, but there are methods and tools out there, that have been used for many years inside the discipline of decision sciences, that you can use to help to you guide you through these complexities, and try to avoid a lot of decision biases that, if you're not careful, you may fall victim to. We’ve built ourselves a toolbox for the venture capital asset class and we just can’t live without it.

We see venture as a capital deployment problem, it's a stochastic finance problem. It's a complex decision problem. My background in is decision science and game theory, so when I started studying venture capital, I immediately saw some of the complexities of this problem and we formulate our strategy around attacking these complexities using the decision theory toolbox we built for this asset class..

In venture capital, deals present themselves in sequential order, and often in bursts and batches. For example, you can buy and sell Google stock any day. It's sort of indefinite. The deals in VC are finite on the other hand. This week you're looking at 20 deals, and next week you're looking at 20 completely different deals. In the meantime, inside of the confines of your fund, you're up against a window of what we call the "investment period.” So you have to deploy your capital in a timely fashion. Constraint number one is how deals become available to you; not all the deals are available at the same time. Constraint number two is you have a certain amount of time before you deploy all of your capital. Not to mention, you have to work on your deal flow every day to get the deals you can pick from.

When we read reports that cover what happened in venture capital last year, or last quarter, we see a retrospective view that pulls all of the deals together. In reality, you never have access to all of those deals and they don’t happen all at the same time, so you can’t make decisions based on this aggregate data without understanding the true nature of how deals happen. We realize that we’re not going to see all of the deals together at the same time. This issue makes the venture capital asset class very different than others.

Another constant area of research for us is the venture return distributions. You hear rules of thumbs such as "One of your investments is going to trump all returns all together," or "Half the time you're going to lose all your money." But what is it really? We're never satisfied by these heuristic claims. In our research, we found out that, in early stages, 48 percent of the time you get what we call a "full loss.” About 25 percent of the time you get what we call a "partial loss," so that's anywhere from 0x to 1x. These are mostly acquihires. You don't get all of your money on that deal but you get some of your money. From there, about 25 percent of the time you get a win and end up somewhere in the longtail. The returns resemble a lognormal curve. Interestingly, about 5 percent of the time you're in a zone of 2x to 5x your capital but in only less than 1 percent of the time you get a a 50x plus return. This sweet spot of 2x to 5x is very enticing to notice and if you can manage to stay in that spot, you're actually going to end of beating 99 percent of all the firms out that are there.

Of course, then the question becomes, if you see an investment opportunity, how much of your overall pool of capital should you deploy? If they're raising $2 million, how much do you invest? $500,000? $300,000? $1.2 million? When we look at venture capital today, we see a lot arbitrary numbers, but we've actually taken a step back and tried to figured out, given the return distributions we see, how can we calculate an optimal level of capital deployment, whereby, in the long run, it's going to maximize the wealth of the fund?

Right now, for instance, AR/VR is very hot, so you're going to see a lot of startups getting high valuations, lots of funding. This has absolutely nothing to go with the premise of the very startup you’re evaluating; it's happening because people are in that particular mindset. There's no guarantee what's going to happen in the AR/VR field. As a professional money manager, I can't just go off of what's popular out there, I have to mathematically justify it.

VN: What do you like to invest in? What are your categories of interest?

MO: We see firms shying away from the category focus right now. The category focus idea came about as way for these firm to differentiate themselves, but so many categories are overlapping right now. If you look at Fitbit, is it mobile? Is it social? Is it IoT? It's hardware, software, analytics, health tech all combined in just one company. You can't really pick one category and say, "Our VC firm is going to be in wearables." Well, you're not only in wearables, you're in in software, you're in firmware, you're in hardware, you're in health tech, all of it combined.

Last year we invested in two IoT companies. They were hardware driven, but we didn't really set out to invest in hardware-driven companies. The models that we use, the detailed analysis that we do is often very revealing to us. In the beginning of 2015, all of the sudden we started seeing really, really good hardware-driven companies that are out there, with great teams, great markets, great margins, high autonomy, global applicability. These are some of the risk attributes we consider. On one hand I can look back and say, "You know what? I had the vision. We were so good at seeing the future, and that's why we did it." But, in reality, that's not what we did. In reality, we did such through analysis, all of the sudden these teams started emerging out of the whole pack. And we invested in what we found was favorable. These categories attracted a lot of talent, there's a lot of room to grow here, there are more low hanging fruits.

I often get the question, "Well, you guys are very calculated, very quantitative, but does it ever contradict your intuition?" The answer is that it does not, and it actually improves your intuition because it reveals so many things about the startups that you're evaluating. At the end of that exercise, you end up learning more, you end up knowing more, whereas if you just stick to this one notion or mindset, that's a very narrow-sighted focus.

VN: What would you say are the top investments you have been a part of? What stood out about those investments in particular?

MO: First, we're a very young firm, and, second, we have a concentrated investment philosophy. In other words, we’d much rather invest in fewer companies than a lot. So, we're not going to invest in hundreds of companies that are out there. We're only going to invest in a small number of companies, whether it's this fund or the next fund or the fund after. Anybody in the investment world, any professional investor, will tell you that, if you know what you're doing, you have to have a concentrated investment focus.

We’re fortunate in that our very first investment, Appetas, exited to Google in 18 months. The great thing about this company was their product was complete from day one. Product and technology is one of the main attributes of the companies that we looked at, and to us it's about 14 to 15 percent of the equation. In other words, there was very little product risk there. Some of the other startups that we look at, they're much earlier stages, so they carry a lot more risk in terms of their product and technology.

Their team was phenomenal in this particular case as well. Team is, of course, another risk attribute that we look at, and right underneath team we'll look at their prior track record. Startups with founders that have done prior startups and exits are, at least, 8 to 9 percent more likely to succeed, compared to those that haven't done that. We look at margins and market drivers. We break it down to less than 10B, $10B to $30B, $30B to $50B, or $50B and above. The bigger the market, the lower the risk. Margins are similar. Is it less than 10 percent, is this sort of a broker arbitrage company? Or is more than 50 to 60 percent gross margin? The higher the gross margin the lower the risk. So we try to quantify it all these different ways.

The last two companies were hardware-driven. One of them is in the payments field, Whirl out of Y Combinator, and they make a fingerprint POS payment network. We were actually the lead investor, and they're doing very, very well. They had to sit down and rewrite the entire Bluetooth connectivity stack, because it turns out that the one that comes right out of box for iOS cannot really handle the high volume of transactions between your iPhone and a POS cash register. They stopped doing everything else and took six months to build that. One thing that we look are these companies that are on the cutting edge of the technology. When people say "cutting edge: it's such a cliché, generic term, but what I mean by that is, "Is it some technology that, if you pull it off, you're going to gain a huge competitive edge?"  

In this case, they made it work, and so now they gained this huge competitive edge, where you can go to any restaurant where they have their POS, just walk up to the cash register, as soon as you out your fingerprint down it automatically establishes a connection between your phone and the device. It turns out that's really not that easy to do.

The second hardware company that we invested in is called Peeple (not to confuse with the controversial company), and they're another great company out of a hardware accelerator called Highway1. They make a device that goes inside of your door, looks through the peephole, and captures images and bunch of other useful information outside the door, streams it to your phone, and does a whole bunch of other cool things with the information. Peeple recently won the innovation award at SXSW.

Proximity to tech centers is something that's important to us. The closer you are to a tech center, the lower the risk of failure, based on our understanding and research. We look at in different categories. So if you're in Silicon Valley, if you're in Cambridge, Massachusetts, if you're in Austin or Seattle, these are amazing tech hubs.

VN: What else do you look for in companies that you put money in? What are the most interesting qualities?

MO: One of the newer sub-categories we look at is the design focus. A new Kleiner Perkins research shows companies with a strong design focus are a lot more likely to succeed, so we've incorporated that into our selection models..

We look at traction growth and, underneath this category, we ask whether that's organic or if it's purchased. We look at revenue drivers; our background is in revenue management, pricing and revenue optimization.

After that we look at some other risks. Capitalization of the company, whether it's a down round or an up round. We look at legal and regulatory risks. We try to understand if this company carries any inherent legal or regulatory risks.

And then there are the macro economic risks. It's important to know what macro economy means. Inflation, exchange and interest rates are important indicators that you need to look at.

VN: What kind of traction do you look for in your startups? And can you be specific? Are you looking for a number of customers or order volume?

MO: Traction and growth, to us, is about 16 percent of the whole equation, and it's very, very unlikely that all of the other elements are going to be amazing and traction's going to be low. We do look at traction and growth, and it's important to us. Is it a deal breaker if they have absolutely no traction or growth? No, it's not a dealbreaker type of category, but it's very important. Whether or not that's organic is also very important, because you have to be very careful when you listen to pitches at demo days when they talk about their traction and growth. You want to make sure that's all happening organically, and it's not companies buying products from one another, artificially boosting the traction numbers. 

VN: Given that these days a Seed round is yesterday's Series A, meaning today a company raises a $3M Seed and no one blinks. But 10 years ago, $3M was a Series A. So what are the attributes to get that Seed round? Since it's a "Seed" does it imply that a company doesn't have to be that far along?

MO: The naming of it is really not important. I mean, you can it Series A, B, I don't really care. To me, what is the capital requirement of this company and what's going on this particular sub-vertical? We need to understand why companies are at the valuations they received today. We can't blindly assume.

VN: What are the attributes of a company getting a Series A?

MO: I guess some of it is this huge influx of capital and then the other part of it is the sizes of newer funds being so high. It's not that hard to look at the deals, and the companies, and do your diligence and understand what the capitalization level of that company should be and where that money is going and what they're going to do with it. That's the way I would look at it. We see all these companies raising hundreds and hundreds of millions of dollars. It's about return on your capital. When are you going to get the return? When are you going to be able to get your money out?

VN: Given all the money moving into the private sector, I believe there's more money going into late-stage deals in 2015 than there was during the heyday, back in 2000, do you think we're in a bubble?

MO: It's not a bubble. The way it looks, it resembles more of a wave function. The wave starts building, and, at some point, it crashes. A wave behaves a certain way. In the beginning the wave starts early on, the wavelength is not that high, that's the earlier stages, that's the seed stages. More and more, the wave starts getting bigger and bigger, it's really a function of how much capital you have overall available versus how capital started much going to that particular deal. It’s a function of how much overall capital is available in the asset class vs. how much capital is deployed on a per deal basis. The wave breaks when you run out of capital.

VN: If we're in a bubble, how does that affect your investing?

MO: I guess it's kind of like tennis. You're either playing on the baseline or you're on the net. Those billion dollar funds are right up on the net. We're sort of back on the baseline. We just want to make sure we don't get caught in no-man’s land.

VN: Tell me a bit about your background. Where did you go to school? What led you to the venture capital world?

MO: I'm an immigrant. I'm a Turkish-American, and I've been in this country 23 years. My background has always been in science. My father was a surgeon, and my mother is a pharmacist. I went to science-heavy academies. College entrance is very competitive there. Over 2 highschool graduates take the placement exams every year. I scored in the top 100 kids and became a national science scholar. What I've done has always been science-driven.

When I finally came to the United States in 1996, I attended business school in Atlanta, Georgia State, and there I focused on decision sciences. They've got a phenomenal decision science program there. I had no idea at that point how this would apply to my career 20 years down the road but it did.

First I started investing as an individual, not just in the venture capital asset class but also real estate, equities and other asset classes. I noticed that the venture capital world was highly unstructured and that the timing was right for a more structured way to attack this problem based on all of the teachings of decision theory and game theory. We're continuously researching and studying the nature of venture capital, and how companies are formed, how they grow, how they exit, and all these complexities that I talked about. So that was very enticing to me.

I was lucky in that my very first investment exited to Google. I don’t believe in learning by full failure. Appetas was a company we picked using an earlier version of this very selection model that I talked about. From there, we've set out to raise a fund. As an emerging manager, as a first time funding manager, it is difficult. It's always difficult to raise a fund no matter who you talk to.

VN: What do you like best about being a VC? What makes you excited?

MO: I think its two-fold. Number one, I haven't invested in hundreds of companies, but the companies I've invested in I really admire. It's not really a cliche, but when I talks to founders at Whirl, and they're telling me about a complexity that they face for this Bluetooth stack, and how they had to sit down and rewrite it, I go, "Wow, I had no idea." It's amazing; I'm learning so much from these people and they also let me be part their growth, their story, and that's very humbling.

Our background in is pricing and revenue optimization, so we help our portfolio companies formulate their pricing strategies mathematically, and I think, today, the startups are really looking for some solid value add. I mean, everybody knows everybody in Silicon Valley. When I hear truisms, like encouragement, supporting iconic founders of tomorrow, no founder buys into that anymore. You have to do something real for these companies. For us, we sit down, we analyze their products, and we come up with methods to measure the elasticities, the appropriate pricing strategy that will work for them, and they really value that. They learn from us, we learn from them.

I really enjoy being in this emerging asset class. Venture capital is new and it has been around 25, 30, 40 years, but the modern venture capital, as an asset class, I can say that it's still new. If you look at the United States, what are the value add things we make here? We don't make gadgets anymore. We make high-value add items. We make pharmaceuticals, we make technology and we do a phenomenal job in entertainment. These are the things we make, so to be a financier in one of the top three categories of a major economic driver of the U.S., of which I'm a proud citizen, I think that's phenomenal.

I'm 42, and I'm still early in my career, but there's nothing that I'd much rather do. I personally see this as my career from now on. Hopefully we'll live up the expectations of our LPs, and support our companies, and continue to do the hard work for it. I get e-mails at 4 a.m. in the morning from our investors in Istanbul which I reply immediately and that doesn't make me upset at all. That's really how I feel.

VN: What is the size of your current fund?

MO: The current fund that we're raising, we're targeting $20 million. Our first fund was $10 million.

VN: What is the investment range?

MO: We would much rather write larger checks, so we're looking to write checks that are several hundred thousand dollars. The idea is to invest in fewer companies rather than a lot. In that regard, we're at the opposite spectrum of a 500 Startups strategy.

VN: Is there a typical percent that you want of a round? For instance, do you need to get 20% or 30% of a round?

MO: We know how much we want to deploy on a per deal basis. We can deviate from that by a certain factor of safety but not much. It’s more of a function of the size of the fund. So on a $10M fund, we’d invest in 10 deals at about $1M each. This is a deviation from the “40-deal rule of thumb” heuristic. So if $1M is outside the 20% to 30% range, we’re ok with it as long as the deal justifies is. What percent of the deal we receive is not a factor for us.

VN: Where is the firm currently in the investing cycle of its current fund?

MO: We're at the tail end of it.

VN: What percentage of your fund is set aside for follow-on capital?

MO: Great question. This is called “dry powder” in VC lingo. If you have a $20 million fund, how many companies do you to invest in? How many of those companies do you want to do follow-ons? How many of those companies do you think will fail? How many companies that you haven't invested in before, but maybe you want to in their later rounds? These are actually very complex questions, and you have to do a lot of math. You can't just haphazardly say, "I'm going to take this $20 million and I'm going to put aside $4 million for follow-on." Why $4 million? Where did you come up with that number? It's actually very, very complex. We constantly attack that problem, and try to solve it. We do have a good handle on it, and a good direction of where we're going to go. Much of it is going to depend on the size of the fund. 

VN: What series do you typically invest in? Are they typically Seed or Post Seed or Series A?

MO: Pre-seed to seed. We're in earlier stages; we're moving up to Series A with the new fund.

VN: In a typical year how many startups do you invest in?

MO: Want to keep it really small, so three or four startups.

VN: Is there anything else you think I should know about you or the firm?

MO: We could take a whole team of entrepreneurs out to dinner, and give them pats on the back and make sure they're encouraged, but I think majority of value is that we're going to sit down with them and help with a lot of calculated decisions. That's what we do. I'm not saying the other way is bad, we just stay away from truisms. Whether it's investing, whether it's selecting which companies to invest in, and how much capital to put in, we do the math. We're going to expand on these. Our goal is more an expansion of our capacity.

Thank you very much for having me. My contact information is matt@venture-science.com and if anyone wants to reach out, my door is always open.

Editor's Note: Our annual Vator Splash Spring 2016 conference is around the corner on May 12, 2016 at the historic Scottish Rite Center in Oakland. Speakers include Tom Griffiths (CPO & Co-founder, FanDuel), Andy Dunn (Founder & CEO, Bonobos), Nirav Tolia (Founder & CEO, NextDoor), Mitch Kapor (Founder, Kapor Center for Social Impact); Founders of Handy, TubeMogul, VSCO, Vinted; Investors from Khosla Ventures, Javelin Venture Partners, Kapor Capital, Greylock, DFJ, IDG, IVP and more. Join us! REGISTER HERE.

Related companies, investors and entrepreneurs

Venture Science
Angel group/VC
Description: Venture/Science is a venture capital firm located in San Francisco. The firm deploys decision theory, quantitative and stochastic models ...
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