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The lesson is that you have to account for re-gelling costs after periods of change, not that you should never change them.
Finally, the one thing that I’ve found at companies with very few interruptions and have observed almost nowhere else: really great, consistently available documentation.
What I’ve found most successful is to identify a few areas to improve, ensure you’re making progress on those, and give yourself permission to do the rest poorly.
You do need to delegate some risks, but generally I think it’s best to only delegate solvable risk.
Take a look at your calendar and write down your role in meetings.
Take a second pass on your calendar for non-meeting stuff, like interviewing and closing candidates.
Look back over the past six months for recurring processes,
For each of the individuals you support, in which areas are your skills and actions most complementary to theirs? How do you help them? What do they rely on you for?
Audit inbound chats and emails for requests and questions coming your way.
If you keep a to-do list, look at the categories of the work you’ve completed over the past six months, as well as the stuff you’ve been wanting to do but keep putting off.
Think through the external relationships that have been important for you...
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Product management is an iterative elimination tournament, with each round consisting of problem discovery, problem selection, and solution validation. Problem discovery is uncovering possible problems to work on, problem selection is filtering those problems down to a viable subset, and solution validation is ensuring that your approach to solving those problems works as cheaply as possible.
The first phase of a planning cycle is exploring the different problems that you could pick to solve. It’s surprisingly common to skip this phase, but that, unsurprisingly, leads to inertia-driven local optimization. Taking the time to evaluate which problem to solve is one of the best predictors I’ve found of a team’s long-term performance.
The themes that I’ve found useful for populating the problem space are: Users’ pain. What are the problems that your users experience?
Users’ purpose. What motivates your users to engage with your systems?
Benchmark. Look at how your company compares to competitors in the same and similar industries. Are there areas in which you are quite weak?
Cohorts. What is hiding behind your clean distributions? Exploring your data for the cohorts hidden behind top-level analysis is an effective way to discover new kinds of users with surprising needs.
Competitive advantages. By understanding the areas you’re exceptionally strong in, you can identify opportunities that you’re better positioned to fill than other companies.
Competitive moats. Moats are a more extreme version of a competitive advantage. Moats represent a sustaining competitive advantage, which makes it possible for you t...
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Compounding leverage. What are the composable blocks you could start building today that would compound into major product or technical leverage10 over time?
Once you’ve identified enough possible problems, the next challenge is to narrow down to a specific problem portfolio. Some of the aspects that I’ve found useful to consider during this phase are: Surviving the round. Thinking back to the iterative elimination tournament, what do you need to do to survive the current round?
Surviving the next round. Where do you need to be when the next round in order to avoid getting eliminated then?
Winning rounds. It’s important to survive every round, but it’s also important to eventually win a round! What work would ensure that you’re trending toward winning a round? Consider different time frames. When folks disagree about which problems to work on, I find that the conflict is most frequently rooted in different assumptions about the correct time frame to optimize for. What would you do if your company was going to run out of money in six months? What if there were no external factors forcing you to show results until two years out? Five years out?
