#57: Leaders “Are Right, A Lot”
Exploring Amazon’s Leadership Principles
Leaders "Are Right, A Lot" is one of Amazon's Leadership Principles.
How does one go about being right a lot?
What does being right a lot look like?
And how do you balance this statement with another often heard piece of "you have to be willing to fail"?
In an interview, Jeff Bezos breaks down three traits he has seen in leaders that are right a lot.
They listen a lot.
They change their mind a lot.
They seek to disconfirm their most profoundly held convictions.
When was the last time you changed your mind on something?
When did you last seek to disconfirm your most profoundly held convictions?
And what does it look like to seek to disconfirm your most profoundly held convictions?
The third question is particularly important to focus on. What does it look like to seek to disconfirm your most profoundly held convictions?
I don't claim to have the perfect answer, but I've found a few things that have worked for me.
You've got to put your scientist hat on.
Step #1: Look at the data. Figure out what data exists relating to the question you're trying to answer. If you are trying to figure out how to grow sales, dive in deep on whatever sales data you may have. Trying to figure out how to retain customers? Dive into your customer usage data and surveys.
Step 2: Find the gaps in your data. As you dive into the data you have, you'll gain insights that help you better understand the question you are asking yourself. And you will likely find that there is some data that you wish you had that you didn't. Data that, if you had it, would give you direction on what the right path is. Note these gaps.
Step #3: Design and run an experiment. This is how you get the data to fill the gaps and discover insights. And just like a scientist, you need to set a hypothesis while designing the experiment. Define what you expect the outcome to look like. Be as resourceful as you can when designing this experiment while making sure you allocate enough resources to execute it properly. A poorly executed experiment has invalid results, rendering the data useless.
These experiments are where the failure occurs. Some will disprove your hypothesis, and you will need to pivot. These small failures in the short term are what allow you to make better, "right" decisions in the long term.
Look at the data, run the experiment, change your mind when the data proves you wrong, and you'll increase your chances of being "right a lot" in the long term.