Jeff Bezos

And then you may be able to say, you know, something about that feels right. Let's go collect some data on that. Let's try to see if we can actually know whether it's right. But for now, let's not disregard it because it feels right. You can also fight inherent bias. There's an optimism bias.

Like if there are two interpretations of a new set of data and one of them is happy and one of them is unhappy, It's a little dangerous to jump to the conclusion that the happy interpretation is right.

Like if there are two interpretations of a new set of data and one of them is happy and one of them is unhappy, It's a little dangerous to jump to the conclusion that the happy interpretation is right.

Like if there are two interpretations of a new set of data and one of them is happy and one of them is unhappy, It's a little dangerous to jump to the conclusion that the happy interpretation is right.

You may want to sort of compensate for that human bias of looking for, you know, trying to find the silver lining and say, look, that might be good, but I'm going to go with it's bad for now until we're sure.

You may want to sort of compensate for that human bias of looking for, you know, trying to find the silver lining and say, look, that might be good, but I'm going to go with it's bad for now until we're sure.

You may want to sort of compensate for that human bias of looking for, you know, trying to find the silver lining and say, look, that might be good, but I'm going to go with it's bad for now until we're sure.

Yeah, this is very early in the history of Amazon. And we were going over a weekly business review and a set of documents. And I have a saying, which is when the data and the anecdotes disagree, the anecdotes are usually right. And it doesn't mean you just slavishly go follow the anecdotes then. It means you go examine the data. And it's usually not that the data is being miscollected.

Yeah, this is very early in the history of Amazon. And we were going over a weekly business review and a set of documents. And I have a saying, which is when the data and the anecdotes disagree, the anecdotes are usually right. And it doesn't mean you just slavishly go follow the anecdotes then. It means you go examine the data. And it's usually not that the data is being miscollected.

Yeah, this is very early in the history of Amazon. And we were going over a weekly business review and a set of documents. And I have a saying, which is when the data and the anecdotes disagree, the anecdotes are usually right. And it doesn't mean you just slavishly go follow the anecdotes then. It means you go examine the data. And it's usually not that the data is being miscollected.

It's usually that you're not measuring the right thing. And so if you have a bunch of customers complaining about something, And at the same time, your metrics look like they shouldn't be complaining. You should doubt the metrics.

It's usually that you're not measuring the right thing. And so if you have a bunch of customers complaining about something, And at the same time, your metrics look like they shouldn't be complaining. You should doubt the metrics.

It's usually that you're not measuring the right thing. And so if you have a bunch of customers complaining about something, And at the same time, your metrics look like they shouldn't be complaining. You should doubt the metrics.

And an early example of this was we had metrics that showed that our customers were waiting, I think, less than, I don't know, 60 seconds when they called a 1-800 number to get phone customer service. The wait time was supposed to be less than 60 seconds. But we had a lot of complaints that it was longer than that. And anecdotally, it seemed longer than that. I would call customer service myself.

And an early example of this was we had metrics that showed that our customers were waiting, I think, less than, I don't know, 60 seconds when they called a 1-800 number to get phone customer service. The wait time was supposed to be less than 60 seconds. But we had a lot of complaints that it was longer than that. And anecdotally, it seemed longer than that. I would call customer service myself.

And an early example of this was we had metrics that showed that our customers were waiting, I think, less than, I don't know, 60 seconds when they called a 1-800 number to get phone customer service. The wait time was supposed to be less than 60 seconds. But we had a lot of complaints that it was longer than that. And anecdotally, it seemed longer than that. I would call customer service myself.

And so one day, we're in a meeting. We're going through the WBR and the weekly business review. And we get to this metric in the deck. And the guy who leads customer service is to fit in the metric. And I said, okay, let's call. Picked up the phone and I dialed the 1-800 number and called customer service. And we just waited in silence.

And so one day, we're in a meeting. We're going through the WBR and the weekly business review. And we get to this metric in the deck. And the guy who leads customer service is to fit in the metric. And I said, okay, let's call. Picked up the phone and I dialed the 1-800 number and called customer service. And we just waited in silence.

And so one day, we're in a meeting. We're going through the WBR and the weekly business review. And we get to this metric in the deck. And the guy who leads customer service is to fit in the metric. And I said, okay, let's call. Picked up the phone and I dialed the 1-800 number and called customer service. And we just waited in silence.

Oh, it was really long. More than 10 minutes, I think.