Thesis-driven early-stage Investing

This is not written from years of experience. It is a collection of notes from discussions and thoughts over the past months and part of my effort to make sense of the topic. I would like to thank @justvideesha, @drosskamp, @simonschmincke and @benediktherles for the discussions, and especially @alexruppervc for their feedback.

This article specifically looks at early stage investing. As you go later stage in venture investing and towards the realms of private equity and public equity investing, historical data grows in importance as the basis for analysis. The number of late stage private equity or public equity investors writing about their investment thesis rises — see for example the classic “Intelligent Investor” by Ben Graham.

There is some confusion as to what a “thesis-driven approach” even describes in venture investing. While Wikipedia helps us in defining the term as “a concept or idea that can be falsified by various (scientific) methods“, the application of this process may take two forms in venture. The produced position paper about a sector or deal will also shape the terms the investor is ready to propose or accept in deal negotiations later on.

Thesis-driven Definition of Investment Focus

“Thesis driven investing involves drawing a picture of where your particular area of focus is going”, says Fred Wilson of Union Square Ventures. As David Rosskamp puts it: “Deductive thesis investing starts with a vision of the future. The investor will formulate a narrative of a target industry’s development and map out the individual elements that will be needed to spur that narrative.”

The process normally starts with intense learning about the sector, from own experience, industry experts, science, and potentially drawing parallels from similar fields. This method can then be applied to different “dimensions” of a thesis.

• Vertical: How will an industry develop in terms of economics, impact by various technological and regulatory trends, etc.?
• Model: An opinion about the outlook of an operating/business model, f.e. the 1041 economy driven by the Uber-model or the various sharing economy models
• Use Case: e. g. “What will happen to concierge services? What interfaces will Amy, GoButler, Alfred, etc. use? Who will use them?”
• Technology: How will R&D break throughs user adoption and regulatory changes rive the (non-)financial success of a specific tech?
• Geography: What regions are the most under-rated in terms of timing (pricing, potential, regulations, adoption)

The outcome of theses can look like this:

• Hardware Investment Thesis (Rodrigo at Point Nine Capital)
• 16 Mobile Theses (Ben Evans of A16Z)
• Earlybird Investment Strategy

An alternative approach I have seen in other funds is what I would call “competence-based” investing. Implicitly, most investors do this: There is only a limited number of markets or business models any given VC understands based on own experience. Investments are made either in the combination of promising markets vs. known business models (Point Nine Capital) or known market vs. promising business models (Berliner Volksbank Ventures). Some investors add a third dimension around “value-add”: They only invest in companies where they know they can either be a customer (Santander InnoVentures), help with public relations or marketing (DvH, Project A) or can support operationally with experienced investors from their investment team (SpeedInvest). This has a lot to do with the branding of venture capital firms (see Florian Heinemann’s / Joel Kaczmarek’s Podcast for an exercise of the German VC landscape) and is sometimes used as an argument in competitive deals.

Thesis-driven Company Analysis

A lot of former strategy consultants and equity analysts are joining the ranks of private equity and venture capital every year, bringing with them a strictly regimented methodology of hypothesis-led analysis. While researching a business, initial theses about the market, team, KPIs and/or business model will be rejected or validated — informed by the data produced from fundamental analysis or supplied by the venture to the investor.

We use this approach ourselves as it not only helps structure the analysis but also tracks the train of thoughts and uncovers biases in the initial deal assessment.

Voices from the Industry

While I am still structuring my thoughts, other venture investors have spoken or written about the process of building an investment thesis.

Scott Belsky of Benchmark: Scott is summarizing his method as “invest your energy and money in the overlap of what excites you (the opportunity), and who you respect (the team).”

Brian Laung Aoaeh of KEC Ventures: Bryan describes the process of investment analysis in the early stage ventures as a debate between art and science.

Taylor Davidson, formerly of kbs+ Ventures: Taylor “traditionally thinks about startup and investment opportunities through broad frames, sectors and trends.” He then goes on defining his thesis in eleven statements and provides reasoning for every statement.

Union Square Ventures in 2012: USV defines their thesis as “a macro level view about the world — the Internet world — that we then (attempt) to structure investment activities around.”

Thinking Around The Next Corner — A Reality Check

Diversification and conflicts of interest: Venture returns follow a power law, the chances are slim that one bet on a thesis will result in a home run. The venture industry has not shown to be very good at picking winners. In order to diversify risk, this would mean building a balanced portfolio of companies based on a thesis. (It is kind of funny and speaks volumes about the intricacies of venture investing when you need to use the terms “power law” and “balanced portfolio” in one paragraph.)

Investing multiple times in companies that fulfill all the checkmarks of a more specific/narrow investment thesis will create a conflict of interest for the investor: If portfolio companies consider expanding into adjacent offerings, they may start competing with other portfolio companies. How can we address this problem?

1. Widening the focus on an entire industry avoids this problem: The investor builds a thesis on a value chain or follows a stack approach as described by Roger McNamee and Mike Maples, Jr..
2. Building a thesis portfolio: An investor may really believe in a narrow subset of the market. Still, he/she is not sure which of the companies will be category winner. Is it possible to build a portfolio of companies around a narrow thesis? A way around the conflict of interest may be limiting information and control rights. It would be like investing in a narrow index of an early stage market; selling individual titles would not be possible. This could be facilitated through the kind of funds David Siegel has proposed in 2015.

The reality of investing shows: Very few companies in any given market niche are both great businesses (team, traction, product, market) as well as a great investments (pricing, cap table, deal structure). It takes intensive networking and deep research to identify the investment opportunities we are looking for at Redstone.

I will start publishing our thesis on some of the (sub-)sectors where are looking at over the next couple of months. Looking forward to the debate!

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