Yeah, so the core solution, and we can talk about the core issue, let's come up with the core innovation and build our way bottoms up. I'm just gonna wait right, actually, to mine.
So the idea behind Narrator was, we wanted to build something that made a difference, not like made you feel like you're making a difference. We wanted to build something... we want to change the world in the way that instead of looking at dashboards, and like, kind of guessing, we wanted to give you a way to answer questions. And we wanted to judge ourselves based on your ability to make the right decision.
So to do that, we have to solve a lot of problems down the chain, right? The first thing is you need to data, what data is available, we needed to make sure that all your data was available in one place. And that's how we built Narrator on top of a warehouse. We need it to be simple. So you have your mental model of how you think about your business, instead of having the data change your mental model to match the data, what if the data matched your mental model. So that's how we build a single table simplistic can be, 11 columns. So it's not like no JSON blobs, no abstractions, 11 columns, all centred around a customer. So we broke it down to like three core entities, customer, time, and action. Last, why customer time and action - because that's how we talk about our business. We're looking to change customer action in time, and therefore that data represented that way. You can start asking questions the way you naturally do.
The third, that's very probably the hardest oneis, is now that you have this really long table. When I asked my questions, I want to relate the havior in nuanced ways, and create why tables that can actually now slice and dice in a plot it? Well, how do I do that? People really consider this activity stream model. If you ask somebody why they won't use it, they'll say it's not credible. Like it can only answer five questions. So how do you get this model to answer any question?
That nuance was probably two years of work, that's like the core of what took the hardest part is getting a single table to be able to assemble any possible table you need. And then you have to build the world around it. We talked about this earlier, too, which is like the idea that you need to build an ecosystem around it right? Because answering your question isn't as simple as getting a table. No, I need to get the table, I need to be able to aggregate and plot and visualise and slice and dice. And I need to be able to look at a single customer and see their entire history of everything they've ever done in our company. So I can be inspired to ask more questions, I need to go with that follow up questions and make changes and edits and add layers to my data and answer that instantly.
And then I need to be able to take that whole thing and use it, whether it's taking a list of people and putting it in a Google Sheet so that me and my team can like fix them, or build a table putting it in materialised view and putting in your dashboard, or even sending it back to the product with a web hook. So we can make like live, we have a five minute recommendation engine, you build the data set quickly moving to a product, or you just tell the story of what your brain is going through to make those decisions and all the questions you're asking in order.
That's why we created narratives is actionable analyses and story like format. So you have to build this whole world around this kind of core innovation of the simplicity and the innovation of being able to assemble any table and the ecosystem around it. And what when you combine this entire experience together, what you get is this freedom from data team, you are in you and your team setting up, you're going to be able to ask questions, you know, you're always using the right data, you know, because always on top of the raw source of truth, you know, there's no way that numbers are going to mismatch, because it's literally one table. So everyone's using the same data, you're free to ask questions that bridge all the systems. So all the hard questions about behaviour you can use now, because of the innovation of bridging sources. You know, this can live on past you, whether it's your analysis, or your dashboard or your product, you can take this data and use it in more places. And you can continue to build on top of it and explore and explore and explore. And that's kind of the thing that narrative has done.
And it shifts what we saw a lot with customers is that because of the way that dashboards are built, people are used to asking questions like hmm, how does gender affect conversion rate? How does like industry affect conversion rate? And we call this a first order question, so you're just like trying to slice it by people. But those industry and gender might be very early indicators, but they're really the kind of worst indicators you can have. Better indicators is like how many times somebody view that how it works page, like that tells me way more than than likely to they're more interested in looking at behaviour, it tells me a lot more about the customer than looking at their like gender. And but looking at behavioural questions is really hard. And then so nobody's really asking those kind of questions, because they take way longer to answer and the data team hates answering those questions.
So a Narrator by building this ad hoc layer, Narrator frees people to ask all the questions they want to ask, answer them so quickly, and get that natural progression. Like we will see us in meetings going, hh, what about this, we just check for the answer. And we go oh, and then it builds up a second follow up question. And a follow up question. And because these follow up questions are happening in minutes, not weeks, you actually are learning a lot more and kind of iterating. That's the experience of having an ad hoc layer. And that's kind of solution that Narrator has created.