George Bonaci

I think it's a reasonable framework. I think you have to have some threshold that you agree on as a business. Like this is what we're willing to spend and this is what we think a customer is worth. But the reality is it's like false precision. Like you're not going to know what your LTV is if you've been in business for a year or six months or whatever it might be.

I think it's a reasonable framework. I think you have to have some threshold that you agree on as a business. Like this is what we're willing to spend and this is what we think a customer is worth. But the reality is it's like false precision. Like you're not going to know what your LTV is if you've been in business for a year or six months or whatever it might be.

So I wouldn't over index on that false precision. I would instead just acknowledge that there's some threshold we're going to be okay with in terms of spending to acquire a customer and what that customer is worth. And that should change over time as you learn things and run experiments.

So I wouldn't over index on that false precision. I would instead just acknowledge that there's some threshold we're going to be okay with in terms of spending to acquire a customer and what that customer is worth. And that should change over time as you learn things and run experiments.

So I wouldn't over index on that false precision. I would instead just acknowledge that there's some threshold we're going to be okay with in terms of spending to acquire a customer and what that customer is worth. And that should change over time as you learn things and run experiments.

No one should be that attached to the experiment that they're running. I think that is a cultural problem. I think that they should acknowledge that the vast majority of what they work on is going to fail. And that if they're not failing, honestly, they're probably not doing their job well. You need to be running a bunch of stuff. It needs to be unique. It needs to be creative.

No one should be that attached to the experiment that they're running. I think that is a cultural problem. I think that they should acknowledge that the vast majority of what they work on is going to fail. And that if they're not failing, honestly, they're probably not doing their job well. You need to be running a bunch of stuff. It needs to be unique. It needs to be creative.

No one should be that attached to the experiment that they're running. I think that is a cultural problem. I think that they should acknowledge that the vast majority of what they work on is going to fail. And that if they're not failing, honestly, they're probably not doing their job well. You need to be running a bunch of stuff. It needs to be unique. It needs to be creative.

And that means that most is not going to work. If you're that attached to something that's working, you should be very, very high on the confidence aspect of how we prioritized it. And in that case, like maybe we were wrong, we should have that conversation, but you definitely, no one should be that attached to any experiment.

And that means that most is not going to work. If you're that attached to something that's working, you should be very, very high on the confidence aspect of how we prioritized it. And in that case, like maybe we were wrong, we should have that conversation, but you definitely, no one should be that attached to any experiment.

And that means that most is not going to work. If you're that attached to something that's working, you should be very, very high on the confidence aspect of how we prioritized it. And in that case, like maybe we were wrong, we should have that conversation, but you definitely, no one should be that attached to any experiment.

I would say like you should be doing premortems and postmortems. I think that's part of like good experimental design, like understanding what are the different failure modes for this and acknowledging like what the probability of those failure modes are.

I would say like you should be doing premortems and postmortems. I think that's part of like good experimental design, like understanding what are the different failure modes for this and acknowledging like what the probability of those failure modes are.

I would say like you should be doing premortems and postmortems. I think that's part of like good experimental design, like understanding what are the different failure modes for this and acknowledging like what the probability of those failure modes are.

That's one way to do it. I would actually get more specific than that, when you're planning the experiments, like why would this fail? It's like, oh, we're not going to have a large enough sample size as we predicted. Or like there's a million things. You should probably write out those million things.

That's one way to do it. I would actually get more specific than that, when you're planning the experiments, like why would this fail? It's like, oh, we're not going to have a large enough sample size as we predicted. Or like there's a million things. You should probably write out those million things.

That's one way to do it. I would actually get more specific than that, when you're planning the experiments, like why would this fail? It's like, oh, we're not going to have a large enough sample size as we predicted. Or like there's a million things. You should probably write out those million things.

And then I think what's more interesting is when you do a postmortem, if the experiment failed for something that you didn't actually anticipate, something that you didn't like factor into your experimental design, that I think is an interesting conversation.

And then I think what's more interesting is when you do a postmortem, if the experiment failed for something that you didn't actually anticipate, something that you didn't like factor into your experimental design, that I think is an interesting conversation.

And then I think what's more interesting is when you do a postmortem, if the experiment failed for something that you didn't actually anticipate, something that you didn't like factor into your experimental design, that I think is an interesting conversation.