Hypothesis-led Investing
How we get to conviction at Northzone
Some say early stage investing is an art. It is inherently gut-driven, intuition-led, and can only be learned through apprenticeship over many decades. I believe that isn’t the entire truth.
The book Thinking Fast and Slow has taught us that the “gut”, our intuition, and the “art” behind complex decision-making all describe an emotional process of decision-making. In reality, emotional decision-making work the same way as rational decision-making - following a system, relying on data. Whereas rational decision-making relies on facts, emotional decision-making relies on feelings. The former happens consciously because we can recall facts, but the latter mostly happens below our level of awareness. That is why it’s so magical, and also very fast. We can pretty quickly decide if we like someone or not without always knowing exactly why, but if you dig deep enough there are some facts underlying the feelings - the person might remind you of your childhood best friend or ex-girlfriend. It takes a lot of introspection to understand when and how we are making an emotional decision.
At Northzone, we rely heavily on our “gut” and each work to refine each of our individual “strike zones” because each investor is very different in our appetite for risk as well as interest areas. This is how we cultivate and harvest our diversity.
We’ve also tried to codify our “gut” into a hypothesis-led approach for fact-gathering so that the “art” of early-stage investing can also be a science. Some benefits of this approach:
It can be learned and taught systematically at scale, vs 1-to-1 over many years
It can live within an organization, vs. just within key individuals
We can have an objective framework for discussing things that are inherently subjective - such as risk - and we can examine each other’s biases
This framework helps us to find the core logical thread that underpins our investment process - what are the biggest risks, how we gather and interpret the data, how do we discuss the deal, and how do we ultimately make the investment decision. More importantly – this helps us to stay true to our own proprietary view of a deal while updating it with real-time market data each time we DD a sector. In the long run that’s the only guarantee of above-market returns. It is one of the ways we harvest our 28 years of experience in early-stage investing across the institution.
Using this approach, we have sourced and invested in companies such as Spotify, Personio, Klarna, Truelayer, Spring Health, and Magic Labs, etc. Furthermore, we continue to build out our perspectives on Healthcare, Consumer, SaaS, Fintech, and Web3, into the next waves of transformation across these sectors.
What is a deal hypothesis?
The deal hypothesis is an inference to a valuable place in the value chain that the company we are evaluating can occupy, based on what we are seeing in the market and the product vision articulated by the founders. For example: Spring health can create a full-stack, risk optimized mental healthcare system with integrated data and aligned incentives.
We then develop a set of base assumptions that answer why now, why this company, and why is there an opportunity? For example: There will be significant market momentum in the b2b market for behavioral health companies in the next two years (in 2019 when we made the investment, this was definitely not a given, but we found out through DD, even before covid happened, that the market was starting to turn through benefit brokers). And Spring Health differentiates at the point of RFP because of its data-driven approach to delivering care and improving outcomes. (we realized through our customer DD calls that this was definitely the case). Our job in the DD process is to prove or disprove these assumptions based on expert calls, desk research, competitive analysis, etc. It is a rapidly iterative process as we find out certain assumptions are more relevant than others. This part of the process aims to be totally objective, down to how we ask the interview questions. Once we get all of the facts down on paper, then we are able to make the subjective judgements about how much risk we are willing to take on each assumption that supports the investment hypothesis.
Another key part of the approach includes a leap of faith on why the company can become a fund returner. For example: Spring can use its position to develop better clinical data tools and capture better economics from the entire mental health system by improving care and reducing risk. The leap of faith is based on our proprietary view on how the company we are evaluating will shift the industry value chain altogether. It is an inference based on the assumptions, but it is not a fact. This is also what gives us a proprietary view on the return potential of the company above and beyond what data exists out in the market, which presumably every investment firm can access. We can DD the elements of the leap of faith to assess the risk level, but unlike an assumption, we cannot prove or disprove it as a fact.
We have been implicitly using this hypothesis-led approach in our deal processing but it wasn’t until recently that we have formalized it within the firm.
It isn’t perfect and has its drawbacks, but it serves us well as we scale our team and our investment judgment across geos, sectors, and levels of experience across the team.
In the case of Spring Health, it’s pretty cool to be able to flip back to the DD notes to see that a lot of the assumptions we had have been correct 4 years later. We were also just extremely lucky that April and Adam chose us to partner with them back then.