 person, a particular person, and say, we know who this person is, we know their contact information, you know, we also know the things they're interested in, the way they want to consume that content, and where they are in our process. So on that bottom right, you'll see, it might be a little hard to read, recommended next engagement is educated for this user. That's really valuable. So if you can start to say we have these individuals that we really care about, but we have these populations within them that we're trying to service, and we know the kind of content they need, and the cadence they need that content to be delivered on them, now you're starting to build a content operations model that is both backed by data and formed by data, and helps explain to people what you're actually doing with the content operations and the data that you're getting back. Like, you know, in terms of affinity here, this is sort of a little stretch goal, it's harder to do affinity right at the beginning. But you can see that this person is less interested in global security, and more interested in renewable energy. And so that can even help you decide, are you going to turn somebody off with a called action, or are you going to, you know, have something be positive? And so this is really valuable for sharing between development teams and communications teams, and other groups like that, because now you kind of have a shared understanding of what you're trying to do. And, you know, this is a little simplified for a lot of organizations, these would be more specific to like your major events and things like that. But you can kind of get a sense from this, just looking at data in this way and formatting the data in this way, suddenly, something people want, and it sounds really valuable. First, when, you know, people pull up Google Analytics reports or whatever and says like, this thing got 55 hits in the last 90 days, and then people all look at each other and they're like, great, you know, this actually tells you what's that worth doing, you know, isn't it useful, it should be changed when we're up there. And if we go forward, one of the things this also does is it creates your roadmap for potential improvement. So once you're sort of saying, what do we want to know about people? How do we want to identify them? Now you start to think about each of those engagement touch points you have with somebody, and what you want to collect and what data is really important out of all that data that's available starts to really work laid off really easily. So for an event, someone who spoke at the event is something that's really valuable. Those often are your biggest like sort of brand boosters who aren't necessarily your direct constituents, but that's often not tracked as tightly as the people who attended an event. And you know, so those are the kinds of things that you can start to think about, like, maybe we need to include that in our profiles about people, maybe that data is more valuable than we thought. And then, you know, like CRM and email data, you know, what is your last touch point? Like if somebody is starting to not engage with you, that's a really good population to look at. You know, that group tells you a lot about, you know, are you collecting enough information to note that you're targeting that person right, or you're not creating the right cadence of data, or sort of your canaries and the coal mines. And so thinking about why people are becoming less engaged with you, is actually as interesting sometimes as why they're engaging with you. And that's a little, you know, counterintuitive with people. And I think that's another useful thing these profiles can help you do. You start thinking about the data of your touch points differently. It's less about volume, it's less about scale, and sometimes even less about reach, it's more about quality of interaction. And so that's like a key part of having, like, this data framework to model.