Co-Written by Adrienne* and Alexis
Strategic thinking is powerful -- it can often enable you to play chess when everyone else is playing checkers. Take Tesla’s Master Plan for example, “The strategy of Tesla is to enter at the high end of the market, where customers are prepared to pay a premium, and then drive down market as fast as possible to higher unit volume and lower prices with each successive model.” Tesla’s strategy enabled widespread adoption for the electric vehicle, where other OEMs with much more resources had failed.
Strategic thinking might seem like something that is hard to get better at, because it’s not as concrete and straightforward as creating wireframes to learn design or reading documentation to learn a new programming language. But at its core, strategic thinking is about thinking far and deep about alternate paths, being creative about them, and then tying the paths back to your current reality.
I’ve come up with a few exercises I’ve done while PMing at Tesla and Google to get better at strategic thinking. Here they are:
Exercise 1: Pick a technology and identify the 1st and 2nd order consequences of the invention.
For example, you could take self-driving cars and identify the 1st and 2nd order consequences to brainstorm interesting business ideas.
1st order consequence: With widespread adoption of autonomous vehicles, there are less accidents on the road because autonomous vehicles can drive more safely than accident-prone humans.
2nd order consequence: The auto insurance market shrinks.
3rd order consequence: Cars will need to talk to each other to prevent accidents, so the data networking market will grow.
This exercise can be useful in helping you decide what to build next in your roadmap. For example, at a bio startup I worked at several years ago, we played out the implications of computer vision algorithms getting significantly better and identified an opportunity to leverage the algorithms to automate parts of drug development.
Exercise 2: Take a lofty vision or statement about the future (i.e. a hypothesis) and identify the conditions that need to be true for the hypothesis to be true.
For example, if you work at a logistics or d2c startup and want to understand how autonomous drones will impact your business’s delivery costs. A hypothesis you might have is: “Autonomous drones will be commercially adopted by X timeline.”
The conditions for that hypothesis to be true are three things:
Condition 1: Government regulation / Federal Aviation Authority (FAA) passes legislation allowing companies to operate their drones in the airspace.
Condition 2: Technology is sufficient.
Sub-condition 2a: Drone batteries get lighter. Currently, batteries are very heavy and are not very efficient. Widespread drone adoption requires a high energy density to kilogram ratio.
Condition 3: Drones can deliver packages in a precise location so they don’t get stolen.
Whenever you have a future scenario that seems hard to evaluate, identify the conditions necessary for that scenario to be true in order to find clarity in your problem.
This way you can work towards making sure the assumptions become reality.
For example, when I was at Google working on Chrome, our org-wide mission was to increase browser market share, which is an ambitious hypothesis. I did an exercise to figure out all the conditions that needed to be true (involving variables around customer preferences in a browser, device pre-installation, browser switching costs) for our organization to achieve that mission. As a result, I was able to achieve a lot of impact by focusing on building out the features that would make the conditions a reality sooner than before.
The Unreasonable Effectiveness of Data
When making a decision, most people think they need some brilliant way of analyzing the situation. The reality is, they’re often not missing insight -- they’re missing data. The exercises in this memo can help you get at what important pieces of data you might need in order to make a solid, strategic decision.
I was recently talking to a friend who has worked with Elon Musk, Mark Zuckerberg, and Evan Spiegel. He mentioned that these great CEOs are not smarter than your average person -- rather they have a huge data advantage that enables them to be better at decision making. While some decisions in the world mirror a chess game, where all the information you need is on the board, and insight is key -- most decisions that went badly in the world weren’t due to a lack of insight, but rather because the decision maker was lacking some piece of information.
If you can set up a process so that you are systematically consuming lots of data and analyzing it from non-obvious perspectives, you can design around this trap. And when you combine the data with the above two exercises, you’ll be well-positioned to take any problem that might seem fuzzy at first -- like a go-to-market strategy for electric vehicles -- and driving it to success.