Bangaly Kaba: prediction

Transcript context

…This is really interesting. I imagine many people listening here are like, we put time into understanding, we run experiments, we write strategy docs, we use research. What is it that you think people are, where do you think they're missing? Is it that you dedicate people's actual time, like you're just doing understand work instead of building a new thing. For this next sprint, you're just going to be telling us what the problem here or opportunity is. How do you actually operationalize this versus just what people probably already do, which is user research, data dives? What I found is a couple things. One is when your team does not fully understand a problem space, the balance of work tends to be higher. I think when I started at YouTube we were doing 60% execution, 40% understand work. Right. And over time, as we understand more and more, the mix shifts. Now my teams are probably doing 80% execution, 20 to or 85% and 20 to 15% understand work. And so it's not just about writing a strategy, it's about saying, okay, if we have these themes of stuff that we want to work on, what do we know with confidence because the data tells us and what do we need to understand because we don't have the data, we don't have the research, we don't have the insights? And really being honest with yourselves around what are the things that are low to medium effort but high likelihood of being impactful because you have the data and what are the things that you are interested in perhaps doing, but because you're missing something, you're missing research or feedback from users or data or some insights or strategy. You should actually say, I'm going to go do this before I do something else. Right. An example of this is one of my teams ships paid virtual goods in the live experience on YouTube. And that's a really, really important and hard problem space. And when I joined, we didn't really fully understand the whole live ecosystem, like how we lived in that live ecosystem, how our products worked, where the biggest opportunity was. So the first and most important piece of understand work was instead of shipping iterations to the current product, we needed to actually get the funnel of what was happening. Right. Like what's happening with how many people are watching live every day, how many people are clicking through, how many people are seeing our experience, how many people actually buying it? We didn't really fully have the experience mapped out and to understand where were the gaps and where are the problem space is in, where should we be focusing? And so we had to do that. And that was understand work for multiple people, for multiple teams. We had to do on the engineering side, we needed data science as analysis. We needed the PM to go and dog through the experience to figure out what was broken. All of that was intentional and an affordance on our roadmaps. What's interesting about this is that this is very counterintuitive to how people would probably approach, hey, we need to speed up execution, we need to speed up growth, we need to ship more, execute, go faster, do more. And what you're saying is you find the impact comes from actually slow down how much we're doing and spend more time understanding to execute more intelligently.…

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