A Product Market Fit Show | Startup Podcast for Founders
00:12:40 · Colin Zima
[00:12:40] S0: I think it's hard to reinvent yourself. The innovator's dilemma is a real problem. Take Looker for example. We did a lot of things really well. We did a lot of things not well. We were really good at enterprise BI. We were really inflexible and it's really hard to take your core foundational thesis. We are great enterprise BI and data modeling, and compromise it at the margin to do these other things well. And so it leaves space for other people on the other side. And that's why you get this whipsaw back and forth in these different markets. Like Zoom Webex is a great example. They probably needed to delete their whole tech stack and start over again. Because the platforms had changed, because the market had changed in a significant enough way. It's really hard to do that, because you can't just wipe your customer base out. They all have these little features at the edge that they're using and it's really hard to be like, no, give it a little time and this whole thing will be better. And in some sense, deleting the whole tech stack, you have to go do that. And then it gives you space to rechallenge those assumptions, build things better. And I think that's what gives it space. Also, just teams get tired. I think people forget about the people side of this, which is like if you have a team that does something for ten years. You kind of do need to reshape it a little bit and inject different energy, and things like that.
00:13:55 · Pablo Srugo, Colin Zima
[00:13:55] S2: Reinventing is a lot of work. Not only is it hard and all these things playing against you, but you have to really want to go through that. All the pain, all the inevitable backlash, when your sales go down because you're not focused on going to market. If you're a hired CEO, it's like, why would you do that? You got to be a real killer to want to do that stuff. [00:14:10] S0: Exactly and if the business is going, like Looker was still growing a hundred percent a year when we got acquired. The business wasn't going poorly. If it wanted to do that five years from now, maybe it probably needed to do some different things or things like that. But in some sense just being told you have to restart gives you a sense of like, okay, I need to delete everything and start over. What's actually important, let's go do this again. But the context on the market matters so enormously. Like, we were able to get carried early by being the Looker guys that started the next BI tool and even if we hadn't built something good yet. People were curious and would go take a look at us. [00:14:45] S2: Right.
00:14:45 · Pablo Srugo, Colin Zima
[00:14:45] S2: Right. [00:14:46] S0: We'd have an opportunity to have a conversation with them and you've got to find these windows of opportunity. Because we could have built the best product in the whole world as two or three completely unaffiliated founders, and maybe no one would have cared. But because we had just been successful gives people another look, also, I probably met with three or four thousand Looker customers and they trusted me as an individual. And that means that they can now have a conversation where I can say like, hey, come look at my thing. You know, I'm good. You know, you trust me, and great. Now I get an opportunity, at least for our product to go solve their problem. [00:15:21] S2: Yeah, that makes sense. I mean, obviously trust. The story overall, trust, credibility, all that stuff matters a lot. The other thing that matters, you know, at least classically in these situations is, is the product. Usually can build a product that's better at least in one dimension, but is it like 10x better, right? Is it way better, at least on some dimension? What did you see that you were able to, like what was the piece of what, I'm sure Looker did some things great and some things not so great as you mentioned. What were the pieces that you said, this is the thing that we're gonna be known for. This is the thing that we're gonna do so much better than Looker does?
00:15:21 · Pablo Srugo, Colin Zima
[00:15:21] S2: Yeah, that makes sense. I mean, obviously trust. The story overall, trust, credibility, all that stuff matters a lot. The other thing that matters, you know, at least classically in these situations is, is the product. Usually can build a product that's better at least in one dimension, but is it like 10x better, right? Is it way better, at least on some dimension? What did you see that you were able to, like what was the piece of what, I'm sure Looker did some things great and some things not so great as you mentioned. What were the pieces that you said, this is the thing that we're gonna be known for. This is the thing that we're gonna do so much better than Looker does? [00:15:51] S0: Yeah, the biggest one was data model. Looker was built on the data model. So this idea of build a semantic layer, it does everything. It was extremely rigid and so the idea was, can we let you do all of the normal things that you do in a BI environment, like write SQL and do things on the fly. But then turn it into semantic layer later? And so it was just an inversion of how you go do it. Like, do you have to model and then ask a question or can you ask a question and then model? And that's small, but for a data person that really matters. Because it lets you move more quickly and so it was just that sort of flip that unlocked our ability to do things. [00:16:28] S2: For people that are not like BI experts, how does that play out? What does that mean in a day to day situation?
00:16:28 · Pablo Srugo, Colin Zima
[00:16:28] S2: For people that are not like BI experts, how does that play out? What does that mean in a day to day situation? [00:16:33] S0: Yeah, so the way to think about it is sort of like do you build the foundation of the house before you build the house or do you just build the house and then figure out the foundation after? The big thing that we did was let you build the house and put the foundation in after. So you could walk into a tool, you could go do something that's a complete mess that you would throw away, and then after you do that thing. You could decide, I want to keep this thing for later and actually take good care of it. And classically, the choice you'd have to make was, you do something throwaway and it stays throwaway, or you do it right the first time, and it's really solid. And then you have a good foundation. We wanted you to build throwaway things that could turn into a solid foundation and it's subtle, but for people that live in these tools every day, and AI actually became a huge driver of this. It really matters, because AI needs to do new things when it answers questions. So you can't constrain it, but as it does those new things you want to be able to make sense of them and you want to put foundation underneath it that's solid. So that metrics are consistent, so that permissions are consistent, so that people understand what's happening. [00:17:32] S2: When you leave do you raise a round and kind of hope, go heads down, just build product? Do you start talking to customers right away? How do you play it?
00:17:32 · Pablo Srugo, Colin Zima
[00:17:32] S2: When you leave do you raise a round and kind of hope, go heads down, just build product? Do you start talking to customers right away? How do you play it? [00:17:39] S0: Yeah, so we were incredibly fortunate in that I was close with a bunch of members of the Looker board. I talked to First Round the day after I left Looker, and so we raised money, or we didn't even raise the money. We had a handshake agreement to raise the money like four days after we left the company and that just gave us the sort of cushion. So I funded it with a loan for a little while to pay salaries. Because I was like let's get more founders in here essentially. We got sort of comfort that we could fund it very, very early. So that we could staff it with a lot of people that know how to build. [00:18:20] S2: When was your seed and how much was it? [00:18:22] S0: Yeah, so we raised effectively a seed A over like six months apart but very, very close together. The seed I think was in May of '22, and it was $9 million and then the A was in August of '22, or something like that. And it was another $17 million I think. [00:18:38] S2: So you had plenty of capital very quickly. [00:18:40] S0: Yeah, we got to found the company essentially with $30 million, which like a lot of people don't get to do. But it let us invest really nicely in product and engineering, and we were kind of off to the races immediately. [00:18:44] S2: You also like macro timed it perfectly. I don't know if you would have gone $30 million like January '23.