How to be Strategic

The actual nature of strategic work, and how to become great at it

Many people aspire to do “high level strategy work”. This is the wrong goal, and most of them won’t do much of it.

The problem is not that there isn’t much strategic work to do, it’s that they are focused on the wrong kind.

What they have in mind is making big, consequential decisions - what Jeff Bezos called “one-way doors” because they’re hard to reverse. Launching Amazon Prime was like this. It required an attempt to see multiple steps into the future to develop conviction that it would work. These kinds of decisions happen rarely, and most people don’t spend much time on them, even if they’re on the senior leadership team.

However there is a different kind of strategic work in extreme abundance, and that is what comes after launching Prime. Thousands of smaller decisions that will determine whether or not it actually works. How do we convince people to sign up? Which suppliers should we onboard? How should we set up our fulfillment operation?

Being good at these kinds of decisions is a different thing entirely.

You’re not trying to see many steps in the future. You’re just trying to get clarity on what is actually happening right now, so you can decide what to do next.

And speed matters just as much as accuracy. You want to try to get these decisions right if you can, but you also want to give yourself as much time as possible to keep trying if you’re wrong.

It’s not a linear process, it’s a flywheel: 

This means that the metric you are optimizing for needs to have both a direction and a velocity. It’s something like “good decisions per hour”.

Making good decisions rapidly requires four inputs, and we’ll explore how to get good at each of them.

1. Intuition: short circuiting as much of a problem as possible and driving quickly to a hypothesis

2. Insight generation: a toolkit to rapidly validate or invalidate that hypothesis

3. Solutioning: understanding enough about execution that you can recommend the right things to try 

4. Synthesis: pulling everything you’ve learned into a story that is cohesive, true, and will lodge in people’s brains in just a few minutes

Intuition

Intuition is a machine that takes in questions and outputs good hypotheses as to their answer.

Ben Thompson of Stratechery was asked how he can possibly have an insightful take on what is happening in tech every day, often just a few hours after it happens. His response:

I have a framework - an overall view of the world and how it works. It’s like a machine. When a piece of news happens, I feed it into the machine and out pops a conclusion. If A happened, then B then C then D then E.

How do you develop this?

Intuition is one of those mythical things that it seems some people have and others don’t. But you can absolutely get better at it if you try, especially if you’re intentional about understanding the problem space at three levels of fidelity:

1. The customer. Most fundamental is understanding who the customer is and what they actually care about. You need a mental model of how they decide between alternatives, and what single or small set of variables matter most.

For example, Amazon figured out relatively early that convenience was the thing that mattered most. Specifically ubiquitous, free, fast shipping. Eugene Wei wrote about how clarifying this was: 

“You can't imagine what a relief it is to have a single overarching obstacle to focus on as a product person. It's the same for anyone trying to solve a problem”

2. The industry. The next level of fidelity is the surrounding industry. What is its cost structure? Who are the key competitors and on what variables are they competing? Which of those variables are fluid and which are hard to change?

One big thing changing in online retail is the Temu and Shein business model. They partner deeply with overseas factories and use a tax loophole that allows them to bypass import duties. This makes products so much cheaper that many customers are willing to accept slower shipping. Amazon has been forced to re-think its customer value calculus, and they are launching a Temu competitor.

3. The Idea Maze. Finally, you have to understand what the cutting edge is inside your own company. Your colleagues are all navigating the idea maze with you, and once a company is sufficiently large you can get pretty far just by talking to them, reading their docs, and understanding which goals they are hitting and missing. 

If you were working on the team at Amazon launching the Temu competitor, it would be quite important to understand things like how much other teams have been able to negotiate prices with factories and how much price and shipping times impact conversion rates.

Insight generation

Once you have hypotheses, you have to go actually validate or invalidate them.

An important part of being good at this is simply being clear that this is what you’re doing. Start by stating what the hypotheses are and what conditions must be true to believe them. 

It’s then about having a toolkit that allows you to rapidly assess those conditions. There are many different types of businesses, roles, and problems, but if you’re at a tech company, 90% of the way you generate raw insights comes down to just two things: learn SQL, and learn to talk to customers.

The way to get to the data you want inside most tech companies is SQL. Sometimes people who have only spent time in fields like consulting think they can get by with spreadsheets because someone else will give them a nice neat dataset to work with, but they are wrong. Just invest in getting great at this right away so you can unblock yourself on working with data.

Talking to customers is an important counterbalance, because it can give you something data can’t: anecdotes. Until you’ve heard at least one customer talk about the dynamic you’re seeing in the data, you should be highly suspicious that that dynamic actually exists. Bezos has a line about this:

The thing I have noticed is when the anecdotes and the data disagree, the anecdotes are usually right. There's something wrong with the way you are measuring it.

To get value from customer conversations you must avoid leading the witness. You have a hypothesis, and if you’re not careful you can probably get the customer to agree with you even if they don’t. To counteract this, start your interviews with general topics and let the customer take you to more specific ones.

You’ll often have to vacillate between data and customer feedback as you zero in on an answer. At BCG I was taught a principle for how to do this: “qual → quant → qual.”

In other words, start with a few customer calls while you’re still in hypothesis generation mode to make sure you’re smoking out all of the possible issues. Then use data to size and validate the things you’re hearing. Then return to customers at the end to make sure what you found in the data is real. 

Solutioning

Now you must drive to recommendations. Coming up with good ideas is usually not the hard part. It’s knowing which of them are worth trying and in what order.

That doesn’t mean trying to predict exactly what will work, which is usually too difficult. It’s the more fundamental task of knowing which ones are feasible and how much effort they will be to implement, so you can assess whether it’s worth taking the shot at all.

This requires knowing how long things take, what is already on roadmaps, where there are dependencies on other things that must be built first, and a bunch of other things you can’t really know until you understand the teams that are doing the building, like engineering, operations and marketing. 

That is why strategic work can’t be decoupled from execution. The people working on strategy should either be the same ones that execute it, or embedded with them and working closely from the start of the process.

Synthesis

The most important skill of all is writing. That is because it is the best proxy for the thing that really matters: synthesis. Synthesis is taking all of the data and anecdotes and recommendations and telling a story that is cohesive and true.

Slides or bullet points are not as good at forcing synthesis, because you can list things that sound smart without actually figuring out if they’re important. Writing in prose forces structure and exposes gaps. Cultures that write (like Amazon) are not just making a stylistic choice; they actually learn and execute faster as a result.

Getting good at writing seems to be mostly about doing it a lot. There are certainly things that help: for example Barbara Minto created an excellent structure for good writing, and Paul Graham has published a lot of useful things on writing, including the appropriately named How to Write Usefully. But mostly you just have to write (and read good writing) a lot.

We’ve listed synthesis last, but it shouldn’t actually happen at the end. Start writing a skeleton of what you think the ultimate output will be at the very beginning. This has a way of exposing where there are holes in your logic.

The ultimate output should be something that lodges an understanding of the situation and the path forward in your reader’s brains in a few minutes. Not in thirty minutes, not after a follow up discussion. If you can make it understandable with just a few minutes of reading, your work can spread through your company and have much greater impact.

Are small and large decisions so different?

The sheer magnitude of smaller strategic decisions that require this process - going from hypothesis to insights to recommendations to synthesis - means that you can get a lot of reps at it. As you do, you’ll probably notice that you’re developing stronger and stronger opinions about the bigger strategic decisions as well.

That is because almost all decision making in business follows a pattern: one big decision (launching Prime) followed by an endless series of smaller decisions that will determine whether or not it actually works.

The only way to develop the instincts to be good at those big decisions is to understand deeply what it would take to make them work. So start there and the rest will follow.

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Credits

Thank you to Max and Lenny for their feedback on a draft of this essay.

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