00:14:53 · Ron Gabrisko
[00:14:53] S0: I mean, the good part is, like, we're, know, a data and AI company. We do data science on every single customer, so I can, you know, I can kinda predict plus or minus a certain percentage how fast I think each customer's gonna grow. And so I know just based on our current products, like how much each customer's gonna grow just organically through more data, more queries, all that kind of stuff. Then we'll layer on, okay, these are the new products we're gonna release. Okay. Let's try to predict, okay, how much, you know, you know, should we be able to sell a 100,000,000 plus a Lake Watch or, you know, what are the goals for each one of those other products? And then based on that, you know, we have a model of, like, okay, how many new customers do we wanna add? We know the productivity of the new... The hunter reps versus the core reps, like how many core reps do we need? And obviously, we're putting some assumptions in there and productivity improvements. And so, you know, at this point, it's it's a science, you know, we're doing, you know, like I said, you know, 5,400,000,000 plus publicly is the number, but, you know, it's kind of a science how we do that planning, and again, there's a little bit of predictions on what are the new products gonna drive that year, but once we get kind of a model for that, we'll know how much that's gonna go the next year in in each customer. So it's pretty cool.
00:16:17 · Guillaume Moubeche
[00:16:17] S1: And the... Yeah. It's definitely like really cool, but I'm just wondering like, do you do like outbound on brand new customers that you're gonna try to, like, basically move from a competitor or else to your platform? Because I'm assuming, like, this predictive model works well for existing paying customers because you can tie, obviously, all the data to the usage, and you can look at how fast the usage is growing, and then eventually you can, I guess, draw a line if I simplify it to make the prediction, but do you also think of, okay, in term of new customers, how many new customers do I need? What's kind of my target addressable market? How am I gonna split it? Like, how do you think about all that?
00:17:04 · Ron Gabrisko
[00:17:04] S0: Yeah, exactly. So we'll be like, you know, hey, we've segmented the market, we know 7,000 companies in the world make up about 90% of the TAM, and we've done that using datasets, you know, that we bought, you know, looking at cloud spend, at different product spend, things of that nature. Right? And so we're focused on how do we get all of those companies as customers. So assuming we have, you know, a certain percentage of those, we wanna go get the rest. We know each hunter AE can land, you know, this many new customers, you know, say, like, you know, depending on the segment, you know, if it's like a strategic or named segment, they might be able to land one or two in a year. If it's like a, you know, kind of startup or D and B, they might be able to land 10 or 20 in a year. And so we're measuring those. Now the actual dollar impact of those inside that year is not huge because they're just starting to consume. But we also know by segment. Right? We know for like, hey, a retail customer of this size is gonna grow approximately this much the first year, this much the second year. So again, we're analyzing. We know the lifetime value of each one of those new customers based on the segment, and so we can kinda use... I mean, that's why I'm saying, like, I'm investing ahead of the curve on new business acquisition. We're investing, you know, approximately 30% to make sure we get all those new customers because we know lifetime value of those is in the tens of millions of dollars for us. So...
00:18:39 · Guillaume Moubeche, Ron Gabrisko
[00:18:39] S1: Wow. Yeah. So so you can spend you can spend a bit of dollars for acquisition. [00:18:44] S0: Exactly. Exactly. [00:18:47] S1: And and how exactly is the the, like, the
00:18:52 · Ron Gabrisko
[00:18:52] S0: the revenue team? How is it split exactly between... Do you have, like, marketing, go to market, sales? Like, how how exactly is the the whole structured? Yeah. We have sales. We have field engineering, which is presales. We have marketing. We have SDRs. We have sales ops. Those are kind of the key components, I would say, in part... Partnerships. We also have vertical specialists and product specialists. And so as you get large... You know, larger, I mean, we're probably, you know, 5,000 plus in the field. You know, you need specialists, right, for certain products and certain verticals. So we're verticalized, you know, it's awesome because data and AI, like, literally, you know, will transform these companies, and we can tell a story on how you can use Databricks to, you know, increase revenue, reduce costs, transform these companies. Right? And so, you know, for example, like, we work in, you know, pharma and in the genomes, like, now they're doing new r and d for drugs virtually. They're, you know, doing data and data and AI and data science on different compounds, matching it against medical records, and, you know, Regeneron found a new liver cancer cure. That would have taken them, you know, years and years. It accelerated R and D by 10 x, right? And so we can tell that story, and you wanna be able to tell that story for every pharma company, and if you're in retail, you wanna be able to tell how they're managing, you know, increasing revenue for, you know, how they maximize revenue by where they put products on the shelf, what promotions they're running, you know, I mean, industry has different use cases. Right? So we've mapped those use cases to know, hey, this is how this vertical uses data and AI to make the biggest impact on their company, and so we wanna go sell those outcomes to the CEO, to the CIO, to the CDO of those companies on how we can help accelerate those. And so we're organized vertically. I mean, obviously, we have North America teams, EMEA teams, APJ teams, but it's all organized vertically,
00:21:12 · Guillaume Moubeche, Ron Gabrisko
[00:21:12] S1: so we can tell that that outcome based story for for each vertical. And when you say you're organized vertically, does that mean also that, for example, in the marketing team, you're gonna have, like, specialists on, let's say, this pharma vertical, or do they work across, like, several industries, but the the verticality is more about the product specialist and the pre sales or sales? [00:21:39] S0: No. We have both. So like for retail, we'll have a GM and a tech GM that will understand the entire market, what are all the, you know, call it, you know, different use cases that at say a large retailer would wanna use data and AI, and and we can even calculate what we think for each specific company, what it's gonna be worth to them to implement data and AI. And then on the same... In the same way, we'll have, like, a Genie or Lake based specialist that can go really deep on how does Genie help with their specific datasets, you know, can we configure it to make sure that, you know, our biggest retail customers can take advantage of it, and so we kinda do both. But the vertical specialists sit inside of sales. You know, I compensate them basically based on the, success of that particular vertical, and so, you know, we've aligned everyone on how do you make sure the company continues to grow super super fast. So... Very really interesting. And I I think recently, you've done a partnership with Anthropic to make