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. We believe that isn’t the entire truth. We believe the gut can be broken down into two parts.
Early-stage investing relies on an emotional process of decision-making as well as a rational process. Whereas rational decision-making relies on factual data, emotional decision-making relies on feeling data. 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. Maybe they remind you of your childhood best friend because of how they respond to your laughter or an ex-coworker you had a hard time getting along with because they squint a lot when they’re thinking. All these small interactions generate emotional data, and it takes a lot of introspection to understand when we are making decisions based on that data, let alone discuss it with the broader team.
At Northzone, we make investment decisions based on emotional and factual data, our “gut”. However as an institution, given we are so diverse as a team and our individual emotional patterns are so different, we needed to find a way to thoroughly discuss the most complex nuances of a deal as a team in order to build the most well-rounded perspective from our institutionalised “gut”.
Over the years, we’ve codified our “gut” into a hypothesis-led approach for fact-gathering so that the “art” of early-stage investing can also be a science. Some other benefits of this approach:
This framework helps us to find the core logical thread that underpins our investment process. For example: How is the market shifting? Does this company stand out amongst its competitors? Does the go-to-market motion position them for the most valuable position in the value chain? How do we assess the founding team’s dynamic? The framework also informs what data we gather and which areas we discuss, ultimately leading to how we 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 due diligence a sector. It allows us to account for hype from our peers as one piece of data rather than being totally driven by it. We believe that in the long run, that’s the only guarantee of above-market returns.
Using this approach, we have sourced and invested in companies such as Spotify, Personio, Klarna, Truelayer, Spring Health, and Magic Labs, amongst others. Furthermore, we continue building our perspectives on Vertical AI, Healthcare, the Future of Work, SaaS, Consumer, Fintech, Web3, and into the next waves of transformation across all these sectors.
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 see in the market and the product vision articulated by the founders.
Spring Health can create a full-stack, risk-optimised 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 there is an opportunity. For example:
Through the due diligence process, we carefully want to challenge these assumptions based on expert calls, desk research, competitive analysis, and advice from our Operator Network. We must quickly assess which assumptions are more relevant for the investment thesis and double down on them rapidly. 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 subjective judgments about how much risk we are willing to take on each assumption that supports the investment hypothesis.
We then take these assumptions and our core findings to our Investment Committee where the team can challenge our thinking and help highlight our biases. We focus our discussions on the level of risk of each of these assumptions based on the team’s perception based on their own context and level of pattern recognition combined with the key facts. The investment hypothesis serves as a very helpful narrative to guide the discussion.
Another key part of our approach includes a leap of faith on why the company can become a fund returner – especially as VC funds operate under power law effects. This is a leap that separates us from how our peers might look at the company and also the potential driver of exponential returns.
In 2019, we had to believe that Spring Health could 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.
This leap of faith was partly based on our proprietary view of how the company we are evaluating will shift the industry value chain altogether, and on the founding team’s ability to deliver against that vision. It is an inference based on assumptions we developed and DD’ed as a part of the hypothesis. In hindsight, looking back at our investment memo for this piece, it’s of course rewarding to see assumptions crystallising four years later. However, we are definitely not discounting the fact that we were also extremely lucky that April and Adam chose us to partner with them back then.
We have been implicitly using this hypothesis-led approach in our deal processing, but it wasn’t until recently that we formalised it within the firm. It isn’t perfect, but it serves us well as we continue to scale across geographies, sectors, and levels of experience across the team.