00:12:27 · Molly O'Shea, Joon Sung Park
[00:12:27] S0: And what is in that gap? Is it time? Is it events around you? Is it like, how do you determine that gap that ultimately creates the outcome? [00:12:37] S1: So the part that is actually very consistent in our experimentation is these two functions are actually governed by different function or a different sort of shape of the curve. What people say in survey actually often just follows a normal distribution. So when I say, you know, am I likely to go to the gym this week? I'll likely say with some normal distribution curve, you know, yes, maybe, you know, a seven point scale, I'll likely say five to six every week. Now, do I actually go to the gym that week? The activation energy for me to actually go to the gym is actually way higher than what I would expect. So that looks much more like an exponential curve where it has a much clearer drop off in terms of what kind of activation we need to actually get to that state of, oh, I'm actually going to exercise this week. So those are the kinds of different functions that we need to learn, and that's where often the gap is. [00:13:40] S0: Again, I have so many questions about this. What is the number one or the most common indicator of human behavior? Like what are those traits or things that you've noticed create the reactions or events? What causes people to do things? Why would someone make a decision versus not? Like, what are those? How do you determine if someone will continue a pattern or not?
00:14:07 · Joon Sung Park, Molly O'Shea
[00:14:07] S1: So it turns out, what this actually has to do sometimes actually is the existence of optionality. So let's actually briefly take a look at how psychologists would usually study human behavior in a survey. We have an abstract question, and we have basically a certain set of options that were presented in multiple choice questions or on a likely scale of one to seven. That's a very confined space, but when people behave, they don't usually behave in vacuum or in abstract conditions. They often behave in the real world, right? And what that basically means is even simple questions like, where do we want to go eat? In abstract, if somebody said, hey, let's go eat X, the odds are if I'm really hungry, in fact, I'm very hungry and if there's no food available, I'd almost certainly say, yes, let's go do that. But what often ends up happening is we do not have access to certain options, but we have access to other options. This is where people have this optionality. So now they have to think. They have to think about the opportunity cost of taking that option. So that's actually what really governs people's behavior. That yes, can go exercise this week, but if I don't, I can sleep for one more hour or I can take one more meeting. It's these kind of things. Optionality and the type of optionality often really governs people's behaviors. [00:15:37] S0: Do you pull a lot of data from dating apps? [00:15:41] S1: Not dating apps in particular, no. [00:15:43] S0: Okay, so taking all of this into consideration, how does this then become the business of SIMILE?
00:15:53 · Joon Sung Park
[00:15:53] S1: So SIMILE is a company that is creating foundational order of human behavior that basically can take as input a description of a person or a group of people and then simulate what these people might do in different contexts. So today, simple use cases would actually go into things like surveys, experiments. Many of the insights team have a lot of studies they were meant to run. Now what Simulink can do is actually run them, but in a much faster pace, in a month with much less resources. So that's obvious use cases. So we do concept testing, and in more complex cases, we do conjoint analysis, max dip analysis. The traditional analysis that researchers had to run today that would have taken them sometimes hundreds of thousands of dollars, sometimes millions of dollars or many years, we can run them in simulation basically almost instantly. They are just a query away. So that's the use cases today. But at the same time, the users then come to us and ask, Okay, it's great that we can now do these studies. Can we then do something that our studies couldn't do? What will people actually do? How will they actually react to different stimuli? So this is where simulation really is about also creating model of the environment, not just the people. So imagine that we have customers. Just a dumb example here. Imagine you're trying to sell water. It's actually quite interesting. If this as a behavior question is actually quite interesting, imagine there's only one source of water, then that particular person can likely charge us a million dollar to just have access to water. But the reason why that cannot happen is because of the optionality, because there is a context around the water source where it is everywhere, right? It's about packaging, it's about, of course, the right stream, at the end of the day, this is widely available, that there are other optionalities in this space. So we to model that. We want to model that environment. What are the other options? What are the other alternatives? Given that as the context, can we then expose our agents to this decision making? At what price would you actually buy this water, given all these environmental factors that goes into this? So it really is about modeling people and their behavior, but it's also about modeling the environment and everything that surrounds them.
00:18:24 · Molly O'Shea, Joon Sung Park
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00:20:19 · Molly O'Shea, Joon Sung Park
[00:20:19] S0: Okay. [00:20:20] S1: But one of the customers that we've been working with is, of course, CVS. And they have worked with us to create simulations of both how to better understand their consumers so that they can better serve them both in terms of the store layout, the messaging, and the overall decor. But we also work with financial companies. So think some of the largest financial institutions today, but we also work with international institutions like Banco Itau in Brazil, where they also have similar needs, but they want to understand what kind of policies or what kind of messaging do we need to put out to better communicate our values to our customers. And of course, also work with entertainment companies, CPG companies. So the verticals are pretty broad, but what unites them is their need that they need to better understand their user base, that they need to be and they're trying to be even more customer centric. And today there's a real limitation to be able to talk to every single people every time they need to talk to them. But if we can create simulations of people and be able to ask any questions at any time, that becomes a superpower for these companies. [00:21:32] S0: For each one of these customers, do you create their own digital twins? Like how do you create net new populations within that instead of using the same base for everything where you'd get duplicates?
00:21:32 · Molly O'Shea, Joon Sung Park
[00:21:32] S0: For each one of these customers, do you create their own digital twins? Like how do you create net new populations within that instead of using the same base for everything where you'd get duplicates? [00:21:45] S1: Right. So SIMILI is maintaining vertical specific agent bank across the key verticals. So these are actually simulated panels that's always available on day one. So when we get into a new customer relationship, that's always available. Now then our customers often have further asks. Right, because no one's actually interested in general population. Everyone's interested in their own unique population that they're trying to better understand. So in those contexts, Simile would actually go collect more data to create and train the model to be able to cover those populations. And today, Simile have partnership with over 40,000,000 real people across the panel partnerships that we have. This is across the globe, and at any given moment, we're talking to tens of thousands, if not hundreds of thousands of real people every single week. So that's that's a scale of operation that we are running today. So if there's a new need for new panel members, that can usually be met within twenty four hours.