I’ve been thinking a lot about the outcome distributions in different circumstances. The dimensions that I think about are:
1. What’s the cost of attempting something?
2. What’s the probability that an individual attempt is successful?
3. Whats the probability that the aggregate outcome of various attempts is successful overall?
In venture capital, the cost of attempting something is meaningful, but not that high. For most funds, an initial investment into a company is something like 1 – 3% of total capital (for example, a $300M venture fund may make a $5M first investment into a company, representing 1.6% of total capital). The probability that an individual investment is successful is pretty low, but a venture capitalist can be very successful if they invest in that one or two companies that pay back for all the other losses and generates a big return for a fund overall.
I find this to be somewhat similar to the outcomes of job hunting, except that the cost of an “attempt” is much lower. I remember when I was first looking for jobs out of undergrad, I interviewed at many many companies, and I had informal discussions and contacts with many more. Ultimately, I got a few interesting offers and chose one. I got many more rejections along the way, although I consider the process overall to be positive.
When I talk to students about jobs, I usually give this advice: Try to narrow the world down based on a couple dimension, say industry and geography. Then make a list of every single company that meets these broad constraints, and that list should be in the neighborhood of 50 or more. Then, systematically work your way through the list, meeting people at the companies, learning about them, getting interviews, etc. Through that process, you learn a lot about the different opportunities (and your own preferences) and I’m highly confident that a good job offer will result.
Most of the time when I give this advice, I see fear and apprehension in the student’s eyes. Doing something like this that involve a lot of “shots on goal” and enduring many “misses” is really uncomfortable. But I think in situations where the cost of experimentation is low, and the value of success is very high, an approach like this tends to work.
I find that most people are not tuned to this sort of outcome distribution. Especially those who have had a lot of academic success. Our schooling system typically rewards us for consistent successful performance. And we tend to be tuned to situations where the probability of success can be relatively high at each attempt if you are talented and put in the effort. Same thing for many sports. In tennis or basketball for example, teams and individuals that are successful win at least 50% of the time, maybe much more. If you win less than 50% of the time, it’s pretty bad. I notice a lot of people are tuned to this kind of outcome distribution, and that colors the way that they go about solving problems.
But I find that in many avenues in life and work, the cost of attempting something is low, the probability that a given attempt fails is high, but the potential value of success is very high. Given that kind of distribution, a different approach and strategy can work that involves a lot of smart, coordinated iteration. You just need the persistence and thick skin to pursue it.
In venture capital, an example of this sort of strategy can be seen in the growth equity realm. Typically, a lot of venture capital deal flow is driven by proprietary relationships. A lot of VCs historically milked relationships and backed a small cadre of entrepreneurs over and over.
But then some firms took an entirely different approach. In particular, a firm called Summit partners formalized an outbound cold-calling model where analysts and associates would scour the ends of the earth for companies that might potentially be interesting, and then work like crazy to cultivate these leads over time. Each analyst at Summit spends years tenaciously calling and tracking hundreds of companies. 99% of these efforts fail, but every couple years, an analyst sources a deal that is successful. Each individual call is likely to fail, but as an entire effort, the process works, and Summit has been very successful, and attracted a bunch of copycats. I’ve found that many former analysts at Summit have a very different approach to solving problems which is distinctly different from the way I am hard-wired.
Some people think of this as a brute force approach, and in some sense it is. But in another sense, it’s just a rational way to attack a problem where the cost of experimentation is very low, and the value of a successful outcome is very high. The goal then is to figure out a way to systematically take shots on goal in a way that may still lead lots of failure, but has a high probability of yielding a successful campaign overall. I think this applies in way more places than we think, but we tend to miss them because our brains are tuned very differently.
Rob Go is a Co-Founder and Partner of a seed investment firm called NextView Ventures. You can find this post, as well as additional content on his blog called robgo.org. You can also follow Rob (@robgo) on Twitter by clicking here.