Top-line growth is imperative for companies of all sizes because it is the dominant driver of total shareholder return over the long term. Innovation is imperative because it contributes disproportionately to revenue growth in all sectors. Embracing innovation in pursuit of growth requires taking a more intelligent and adaptive approach to managing risk – including making decisions under uncertainty. Silicon Valley has, for many years, been a laboratory for large companies to experiment with and acquire technology. It has also been a superb environment from which to appropriate new management practices around innovation management, adaptive strategy, agile commercialization and risk management.
To get some insight into the perspectives of Venture Capitalists and their approach to investment returns, we turn to our very own Steve McGrath, a Strategos Partner based out of San Francisco. Steve has 15 years of experience as a venture capitalist and private equity investor in Silicon Valley and New York – as CEO, Managing General Partner, and Board Director – financing, launching, positioning, and actively managing highly-disruptive, early-stage technology companies and business model innovation plays. Steve has over a decade of experience as a strategy consultant, was one of the Founding Partners (together with Professor Gary Hamel) of Strategos in 1995 where he sold, managed and led numerous engagements addressing growth strategy, innovation and new venture creation mandates for clients in a wide variety of industries. Steve is also an Active Angel Investor and Venture Coach.
You have chosen to focus on the riskiest end of the spectrum – early-stage investments which are very often pioneering new markets or authoring new categories. Why?
I was trained as a physicist, more on the theoretical side. For a very brief period in the 1980s, I worked in the nuclear industry in the UK doing research into centrifuge technology. It didn’t take me long to realize that doing science wasn’t nearly as interesting as I had envisioned. I was a bit daunted away by the amount of repetition that research entailed. I wanted to see discoveries in action. Essentially I was more interested in innovation than invention or research.
I’ve always been fascinated with the linkage between value creation and value capture – and how you ultimately engineer liquidity and create returns to shareholders. And venture capital is the perfect laboratory to understand that cycle
In 1995, I got the “godfather offer” from Regis McKenna, one of the 100 people who made Silicon Valley what it is today who had founded the McKenna group. His offer to me was to set up a venture fund and work alongside the McKenna Group. Our model was to have the investment arm working alongside the strategic advisory arm, just as Bain/Bain Capital did in its early days. We launched in 2000 and made a number of investments.
After McKenna, I joined a VC firm in Manhattan called Spencer Trask. We invested almost exclusively in really game changing ideas, whether they are technologies or business models, classic whitespace stuff. Our style was to get involved as early as possible, partner with the entrepreneurs as co-founders where necessary, and help build business for the long haul. We essentially acted as business building partners to our entrepreneurs, financing and then helping them launch into and navigate new markets and often authoring new categories.
Could you talk about some memorable investments you’ve made over the years and your thought process behind those investments?
As I’ve said, our model was one of business building as well as investment. One of the earliest companies we built was a pioneer in the biometric authentication space. It went on to be acquired twice, most recently by Apple. It was created to solve the problem of what we called strong authentication in mobile commerce as early as 2000.
As always we were bringing a strong consumer insight component to the investment process. At this time there was a proliferation of mobile devices, increasing number of transactions on devices, increasing value of transactions conducted over these devices, and relatively weak security that was in place to deal with that reality. Our insight was that a strong biometric solution was needed to legitimize eCommerce and that it had to be designed for ease of use.
We cast around and found some technologists in Arizona that had really advanced algorithms for fingerprint scanning. We basically formed this company out of whole cloth. Built the management team, financed the infrastructure, and were the first money in. We weren’t the only people in the space, but were the most advanced. We built an investor syndicate including some of the leading mobile technology players which provided strong endorsement for our solution.
Basically we helped build an ecosystem of partners, customers and investors around an emerging technology and were able to tell a compelling story about a future we wanted to help create that was rooted in deep customer and consumer insight. When your iPhone 6 invites you to authenticate or initiate a transaction by simply allowing the device to digitally match your fingerprint, that’s our technology. This was a classic example of investment in early stage tech that enabled a transactional business infrastructure. We started with the end in mind, how do we essentially build confidence in the business community to enable high volume, high security transactions. And how do we make it painless for the consumer.
Are there particular lessons you’ve taken away from specific investments as a Venture Capitalist?
Most funds are managed as a portfolio, though some VCs may work on a deal by deal basis. Portfolios start with a theme, an investment thesis, which usually speaks to a broad opportunity domain. For example, at Spencer Trask we saw a huge opportunity in open innovation (OI), a domain we identified very early on with enormous promise. It had the hallmarks of a classic whitespace opportunity – a well defined problem that was clear and present, lots of potential applications, use cases, and growing appetite for investment.
Early experiments in the area of OI showed evidence of successfully implementing different ways of getting work done, and thereby increasing ROI – and none more so than in pharmaceutical R&D. Pharma had a terrible record of failure in R&D but experimented early with different open innovation models with a big payoff. We saw a lot of early signals of success, and strong incentives to change way work got done (and not just R&D).
But we didn’t necessarily know which play would have the most legs. There could be many different winners, for example deep data mining to look for insights or solutions or collaborative platforms enabling internal employee communities, and finally open marketplaces (which is what our first investment Innocentive has become). We ultimately made investments in all 3 areas.
Within each of those areas, we placed strategic, complementary bets, rooted in a strong point of view. That illustrates one reason why VCs creates portfolios, they don’t know which investment will evolve the fastest but they have a view on the evolution domain being pursued.
Was there a different approach to thinking about the performance of a specific investment versus the overall fund?
Let’s take a look at what performing really means. Some of my investments required more capital and I knew that going in. Others had longer time frames to maturity and liquidity. The CapEx for a company building intensive architectures with a sophisticated back end for big data can be expensive, basically an infrastructure build. This is different from a company like InnoCentive which is building a marketplace, this can be time intensive. Building coalitions of actors – that can take a long time.
So capital and time requirements are distinctly different measurement parameters. Exit valuations are also different. How does the market value these companies? First of all, the market buys growth and they were all high growth stories. But with they attracted different revenue multiples.
We are very happy with the progress InnoCentive – which has chosen to remain private – is making. It’s a business model not a technology play and while it often takes longer to architect a new business model than build a new technology, the returns can be significantly higher. We spun it out of Eli Lilly that was using it exclusively for R&D. We saw it differently – essentially as a horizontal search platform that could be applied to broad range of business problems. Essentially anything from product development, R&D, any kind of question with high business value could avail itself of the marketplace. It was creating a search engine for companies to find solutions to problems, not thinking of it as research but to start thinking about it as discovery.
As VCs think about optimizing their returns, what are their most important considerations?
In no particular order, the starting point has to be about insights at the level of the consumer. There must be the ability to tell a high growth story about the consumer and unmet, unarticulated needs. So, when we think about the focus of the fund we ask ourselves “what’s the domain it’s addressing” and we start with related stories of unmet need.
From the VC perspective then, the art of the game, is to co-opt the customer as quickly as you can into designing the solution. When Eli Lilly built Innocentive, it was because they saw a clear need; we thought others might need this solution as well. Then again, sometimes you’ve got to short circuit the process. With the fingerprint scanning technology, we had to build a coalition around the technology by collaborating with lead customers willing to be beta customers
Next, you’ve got to move fast. The ability to move quickly is a huge competitive advantage. Very fast iterations and pivoting are critical in the marketplace, and has to be ok to fail. Corporations in particular struggle with failure, and in place like the EU, the stigma goes much deeper. Often you are simply not allowed to fail, which leads to sub-optimal solutions.
Don’t be afraid to go first; having no map is high risk but there is a lot of upside and it can be exhilarating. Of course there are tools to help when entering uncharted territory, it’s all about being highly adaptive. The alternative is to jump on a train that has already left the station. Unfortunately we’re seeing too much of that these days, with very little innovation in terms of how organizations are thinking about their investments.
After you’ve decided to zero in on a particular opportunity space, how do you decide which of multiple opportunities to invest in?
There will be times when similar startups or founders will all be telling the same story. Then you have to look at the denominator, which for us means comparing across various categories of risk.
For example, with technology risk, you’re asking tough questions about does this stuff actually work or is it a science project? Will it work at scale? Is it protectable? With the core elements, you’d better make sure it’s real or can be built. As an investor it’s very difficult to mitigate technology risk so we place a lot of emphasis on this.
In our experience, however, most new ventures fail not because the technology doesn’t work but because they fail to manage market risk well enough. In other words they don’t sufficiently understand (and validate) the fundamental customer/consumer need for the product or service being developed. Of course with very early stage startups, there’s no data on adoption, no data on pricing, no use case, no sense of direction out of the gate. You’re in huge uncharted territory with so many variables, and if any of them are incorrect it could throw off then entire investment.
And even if they do that they often don’t develop a sufficiently differentiated and adaptive strategy to capture the opportunity. We spend a lot of time working with entrepreneurs – both prior to launch as well as deep into the lifecycle of the company – helping to mitigate market risk
The team (its capabilities and experience) is another important risk factor to consider. Do you bet on the team or the opportunity – it’s a perennial VC debate. Historically most VCs have bet on winning teams thinking that this is a leading indicator of future success. We tend to be more flexible because we have to be – many of the investments that we make are in areas that may be new to the world so there’s not necessarily any team anywhere that’s ever done it before. What’s needed therefore is an ability to architect teams as businesses evolve and new capabilities are required. That’s a big part of what we do.
While each of these three classes of risk are important we tend to focus more on market risk than anything else. With this class of risk it’s all about strategy. If you can do the heavy lifting on strategy, you can dramatically increase success and better returns. It could mean the difference between 5 years versus 10 years in terms of returns to shareholders. And in my view, the market risk is the most important to pay attention to, especially at the early stage.
How do VCs utilize portfolio management techniques to manage risk and optimize returns?
There are a number of techniques, the first I mentioned earlier on having a theme or identified domain. Then to place complementary bets within that theme; your portfolio should not be a horse race, but it needs to have a strategic logic around the space that it’s tackling.
Second, what’s the time frame to exit. An entirely new business model may take longer than a game changing technology to come to fruition. From a return perspective you want to have balanced bets and the ability to engineer liquidity if needed.
Third is the degree of capital intensity. In an ideal world, you don’t want all your investments to require $100M. Of course there has been an explosion in startups, partly due to the availability of enabling tools, the costs to start are business very low.
Fourth, what life stage are we at. Most VCs focus on a narrow bandwidth – early, middle, late stage. In practice, we consider investing in things that have different degrees of maturity. This takes a bit more bandwidth as startups have different needs across the spectrum, but ultimately this helps with the goal of diversification.
Fifth, the ability to construct a portfolio that lends itself to co-investment with partners. Most want to invest with other VCs, ultimately because they don’t want to be holding all the financial risk if things go south. If the startup needs a restart, at least you’re all in it together.
What pitfalls do VCs encounter in their investment portfolios?
There are several pitfalls that I can speak to; some of these are unique to the VC context while others apply more broadly. The first is not killing a project early enough. The maxim in the industry is to kill early and kill often. VCs are often caught in a balancing act between their obligations to support their startups and the ROI they’re expected to earn for their investors. VCs expect their startups to constantly be learning. If the data coming back shows the assumptions were wrong, either we adjust the assumptions in our model or we conclude that it’s a deal breaker. If we find that the dog doesn’t hunt, the humane, financially rational action is to discontinue the startup.
The second pitfall is false diversification. With Venture Capital, you don’t have the quality of data to be certain about diversification, relative to public markets. VCs attempt to diversify on two dimension, the first is strategic diversification. There the question is, are we placing bets intelligently within a specific space. In certain cases, VC investments become more opportunistic and geographically based, rather than deliberate strategic bets. The other dimension is financial diversification where VCs don’t want to take on all the financial risk, and so they often collaborate with known partners. The pitfall there is that dueling agendas can often come into play where each of the investors may have different time horizons and expectations for the exit.
The third pitfall can be found in the investment thesis of the fund, its charter so to speak, and the flexibility of that charter. While the focus of the fund is agreed up front, in my experience there are times when there isn’t sufficient strategic rigor in understanding how the domain is evolving. Especially considering the steady tick of the clock – once the fund is initiated, there is a finite horizon in which it must provide returns. You can make course corrections at the outset, but it’s unlikely you’ll be doing that during the second half.
Fourth is the finite life of the fund lifecycle. The fact that VC funds have a finite life places undue influences on the timing and allocation of investments throughout its life. Let’s say you’ve got a 10 year fund, what if the bet takes longer to come to fruition? This is particularly true for business model plays that may require the delivery of educational benefits and behavior changes to their market. If you’ve invested late in the fund’s life, there isn’t a long horizon. And of course, VCs don’t want to prop up investments with new money from the next fund.
Fifth is related to syndicate agendas, and governance here is key. The minute you bring multiple partners into an investment, each of them have different expectations for optimizing the return. If your investment partner is at the tail end of their fund, they are highly motivated to generate liquidity from their investment. If the fund is doing well, they will have a lot more patience with the remaining investments in the fund. You’re immediately faced with multiple agendas from an exit perspective.
And, the sixth pitfall is around engineering liquidity. Cash on cash is everything and is the only measure that represents the success of the fund. While there is a healthy secondary market, the investor is not in control of those dynamics. Obviously the desirable exit is through public offering or an M&A. The danger here is that VC networks are not broad or deep enough to provide substantial connections with corporate partners that are seeking an acquisition.
What differences in approach to whitespace innovation do you see from corporations versus VCs
Large corporations have a unique set of issues they are wrestling with versus VCs. For most large corporations, in order to move the needle, the idea needs to be really, really big. They don’t do small bets. However, this doesn’t preclude them from validating the market and running rapid experiments, it just makes it all the more important.
Another thing, corporations are not as patient. A venture fund is a legal entity with a 10 year life, money is returned to investors within 10 years, VCs are usually finished allocating within 5 to 7 years. The structure of a VC fund is a managed time horizon, which is quite long. They are paid by investors to take a long term view. This means they can approach white space, by definition, with more ambiguity and more time taken to learn way into the future.
There’s also a human resource difference. People in corporations move around, have shifting allegiances, and fear of failure. Volatile people dynamics can kill a venture real quick. Unfortunately, corporations tend to believe that failure can be not just expensive, but also catastrophic.
Finally, there is a difference in the metrics of innovation in the short term versus the long term. There is a struggle to measure progress vs success and I believe that the KPIs for innovation success haven’t quite kept up with the proliferation of innovation spend. This tends to undermine confidence in the organization – is our spend intelligent or appropriate? Are we measuring the wrong things?
What are some lessons that corporations might take away from the VC mindset?
Sure, there are several important lessons that corporations can take away from the VC context, ranging from the big picture to how to capitalize on individual investment opportunities.
- The first is to think strategically about the investment thesis. This means making deliberate choices about the spaces to pursue, but having the ability to experiment and evolve within that thesis.
- Separate but related is having a portfolio logic, in other words, to place multiple bets. This could mean plays that test different elements of the business model. Or it could mean having adjacent plays, testing different biz models.
- Next, business model plays are winning plays. Most corporations need to be building unicorns in order to move the needle, and business model plays are where scale of returns exist.
- Kill projects early, do not fall in love with your investment. Don’t do anything without a learning agenda and make sure that learning is institutionalized.
- Import the lessons of agile. Fast test, learn, and pivot cycles are the key to getting to successful investments.
- Consider full lifecycle funding – what is this really going to cost. The worst thing is to have too much or too little money. Have the financial discipline to calculate the fully loaded cost to build the business.
- Timing and product lifecycle management. When to stop, spin out, or take it public. Sometimes in order for it to grow, it’s got to leave the mothership. A lot of corporations want to own and control every aspect.
- Companies are actually better placed to do this, but often underleverage it. No sole entity is best qualified to do everything. Corporations need to take advantage of the much richer access to resource and expertise to grow the opportunity.
Conclusion
Ultimately, VCs operate under a unique set of constraints, but apply a set of principles to identify, accelerate, and exit a set of investments. While some of these principles may be specific to the Venture Capital space, many can have a direct and dramatic impact to the approach corporations take to their product development portfolios. In our experience, the companies best able to leverage these lessons have taken a systems approach to managing their activities. They have the ability to orchestrate then accelerate the opportunities they find successful, employing a strategic approach to whitespace identification then a proper diversification rationale to develop a true portfolio of opportunities.
