Thoughts on Risk, Reward and Optionality in Venture Capital
Around four years ago at a P9 SaaS Meetup in Berlin, I asked a panel of Benchmark Partners whether they had an underlying investment theme given the mix of markets and business models the partnership invests in.
Matt Cohler replied: “Yes, we are looking for Optionality.”
The idea that outliers make up for the lion share of VC returns is not new and you can find many good posts and takes on fund math around the web. Though it might be a trivial point, I have spent many hours thinking about Optionality and how it fits into my style of investing over the past few years. This post is meant to be a forcing function to define and share my current thinking, knowing well that it will likely keep evolving as I learn more.
Note: I know numbers, I have the best numbers, but I am not a mathematician. This post is qualitative and not scientific.
From uncertainty to risk/reward
VCs deal with a lot of uncertainty and in fact it is one of the most exciting parts of the job to me. Consistently uncovering new information — positive and negative — is thrilling and makes every day somewhat different.
A smarter man once told me that the difference between uncertainty and risk is that the former is unknown and the latter can be defined. Following that thinking, I believe that VCs can gain a significant edge by designing the right processes to translate uncertainty into specific risks and rewards when evaluating an investment opportunity. Alas, the nature of unknows is that they are, well… unknown. Therefore you will never be able to get to a complete overview of risks and that is okay. I still found that being brutally honest to oneself about what could go wrong can help not just with making the investment decision, but also with supporting the company post investment. Risks known and monitored is the first step to addressing them and I might go into more details on this in a later post.
Shifting to the other side of the equation, reward is essentially the upside of an investment. This is where we are trying to properly evaluate the potential of any given deal. And unless you`re a fortune teller, this is fuc*ing hard. Here one way to do it that will help to explain my concept of Optionality in the next part, assuming we are looking at a Series A SaaS company with at least 12–18 months of actual customer traction. We would:
- Break down accounts by geography, verticals and employees
- Calculate the respective ACV and past development
- Size up the primary TAM by multiplying overall available customers in the relevant verticals with the respective ACV
This is a simple `rule of three` equation, but in combination with a sensible estimation of market penetration, it`s a great way to quantify the magnitude of the opportunity at hand — even if you are likely off at least 100%…
Yada yada, what about that balloon?
Okay, here it is: The way I think about Optionality today, is as potential beyond the upside we identified with our initial TAM analysis. For a Series A SaaS company for instance, I ask the question: What if we can expand into either adjacent markets, dominate certain verticals/geographies or sell new product lines the same customer base, effectively increasing ACV. This is what the balloon stands for in the above graph, whereas p(i) is the subjective risk/reward ratio a given investor is aiming for (and that is effectively the price s/he is willing to pay). In the scribble above— and mostly to make the 🎈pretty — we are looking at a more risk averse investor who demands over-proportional more upside for increasing risk.
To make it more concrete, here are some examples of Optionality that we have identified and invested in over the past year:
- New products for the same customer: Especially when dominating niche markets, there is big value in the customer relationship and trust established
- More end users within the the same account: Pull from other departments within a company to serve related use-cases
- Expansion into adjacent markets: Adaptation of the initial product to similar use-cases in new markets
- Geo expansion: Initial country (and vertical actually) shrunk from 100% of revenue to 60% and 20% of pipe over the past 12 months
- Data monopoly at scale: Collection of specialized data sets that will create over-proportional value once/if critical mass is reached
- Pricing power at scale: Potential to significantly increase take-rate at scale, while still driving more revenue for supply side (marketplace)
You will notice that for most of the above there were early `balloon-y` data points when we invested, but the latter two are examples of Optionality that will only fully materialize at scale.
Catch `em all!
To sum it up, the balloons I am talking about are investments that have a reasonable probability to grow beyond a base case that already provides sufficient upside in relation to the risks identified. Again, where this falls is very subjective and depends on the individual, stage of investment and overall fund strategy––and some (especially early stage) investors might even choose to focus solely on the Optionality case. For my team though, this means a straight path to a 5x on the initial investment assuming business as usual; and on top of that real potential to land significantly beyond that if things go very right.
That`s the kind of balloons I am chasing. Get in touch if you see one!
Note: Assuming a fund has sufficient follow-on capacity, one of the advantages of this strategy is that you usually invest more $ as you see new data points that confirm that the company is in balloon territory… but that is for another post!