00:09:23 · Guillaume Moubeche, Ron Gabrisko
[00:09:23] S1: of that. Okay. So Yeah. That's, like, super impressive, which means that revenue keeps expanding, your churn is extremely low. So how how do you think the the two are are connected or maybe more specifically, like, how do you make sure that you give enough value to people so they actually, like, stay longer? [00:09:43] S0: Yeah. I mean, part of it's just, you know, customer obsession, making sure customers are successful and they love the company, and, you know, I think the entire company, including engineering and sales, are trying to make sure that, you know, they love Databricks. But the bigger part is we didn't just do project. So a lot of open source companies, they build one product, and then they stop. And it's hard to be sticky because then they can probably use that, you know, elsewhere. What we did was we built, you know, Spark, and then we built Delta, and then we built MLflow, and then we combined Delta with Iceberg when we bought Tabular, which was also open source. We bought Mosaic AI, which was open source. Like we continue to do these open source projects and so now it's a fully integrated data and AI platform that's all open source. Right? And so I think that's the key to retention is you can't just have one trick. You gotta, you know, keep layering on these new innovation engines, and that's what we've done really well at Databricks, right, is, you know, created this innovation factory where we're constantly, you know, reinventing ourselves and releasing new technology. So...
00:09:43 · Ron Gabrisko, Guillaume Moubeche
[00:09:43] S0: Yeah. I mean, part of it's just, you know, customer obsession, making sure customers are successful and they love the company, and, you know, I think the entire company, including engineering and sales, are trying to make sure that, you know, they love Databricks. But the bigger part is we didn't just do project. So a lot of open source companies, they build one product, and then they stop. And it's hard to be sticky because then they can probably use that, you know, elsewhere. What we did was we built, you know, Spark, and then we built Delta, and then we built MLflow, and then we combined Delta with Iceberg when we bought Tabular, which was also open source. We bought Mosaic AI, which was open source. Like we continue to do these open source projects and so now it's a fully integrated data and AI platform that's all open source. Right? And so I think that's the key to retention is you can't just have one trick. You gotta, you know, keep layering on these new innovation engines, and that's what we've done really well at Databricks, right, is, you know, created this innovation factory where we're constantly, you know, reinventing ourselves and releasing new technology. So... [00:11:02] S1: And as a CRO, like, how much involved are you in the decisions of launching, like, a new open source project or even acquiring an open source project?
00:11:02 · Guillaume Moubeche, Ron Gabrisko
[00:11:02] S1: And as a CRO, like, how much involved are you in the decisions of launching, like, a new open source project or even acquiring an open source project? [00:11:15] S0: I mean, a lot of that's driven by Ali and the founders, right, like, they have specific cultural things they're looking for in acquisitions, as well as technology fit, and, you know, I would say our seven founders are visionaries for this space. I mean, they teach at, Berkeley and MIT and Stanford, and most of the, you know, other innovations come out of those schools, and they've probably taught most of those students. But, you know, they're... So they have their own vision on where they wanna go with the data and AI platform. My my, role in that, I mean, I do get involved in the acquisitions to understand the sales teams, customers, you know, even try to sell the, you know, the the companies on why Databricks versus going alone. But they're really making the decisions on the technology and the acquisitions. The part GTM solves is like, you know, recently we released Lakebase and Genie and Lake Watch, and so how do I get, you know, all the enablement done? How do I make sure we know how those products add value, how they differentiate with other products in the market, those are massive innovations, for us in the market, and so, you know, we wanna market share and grab customers and get customers using those as fast as possible, and so a lot of the new product introduction materials and enablement and all those things, we go pretty fast on that stuff, so.
00:12:45 · Guillaume Moubeche, Ron Gabrisko
[00:12:45] S1: And what what would you say, like, from your side, like, what is the focus on new businesses versus existing customers and expansion with other product? Like, how do you split kind of the the effort? [00:13:00] S0: Yeah. I mean, spend probably, you know, 30% plus on new business acquisition, meaning in terms of resources, people, because I know if we wanna continue, you know, we've publicly, you know, 65% plus year over year growth, you gotta have new customers, because those new customers turn into million, 10,000,000, $100,000,000 customers. You can't just milk your existing base. I think that's another, you know, mistake that some more mature companies make. I mean, obviously, our existing customers because our net retention's, you know, crazy high, like we're getting lots of revenue in in a lot of our new products, we're selling into those existing customers, but you gotta do both. So, you know, for example, like Genie, like, unlocks such amazing use cases, allows you to talk to your data using AI, does calculations, predictions, it'll do graphs, you know, so CEOs and CFOs are now using Databricks to, you know, be able to get all those answers that they need for, you know, doing predictions and running their business. And so each one of the new products we release, we're telling that story inside that customer, but we're also using those to land new customers. So, you know, you definitely have to do both. So...
00:14:15 · Guillaume Moubeche
[00:14:15] S1: Okay. Interesting. And when it comes to... You you were you were mentioning the fact that you're growing like 65% year on year, or like north of that. Every year, it gets harder to reach, like Yeah. That that revenue milestone. When it's the beginning of the year or, like, before the beginning of the year, like, when you were setting up what's gonna happen for the next year, how exactly... Can you walk us through maybe, like, how do you define where I'm gonna spend money, how I'm gonna do it, who I'm gonna hire, like, what's your reasoning about all of it and how do you plan for it?
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.