A Product Market Fit Show | Startup Podcast for Founders
00:29:55 · Pablo Srugo, Colin Zima
[00:29:55] S2: You brought up AI, so I think it's good to talk about it. ChatGPT comes out near the end of '22, how is that lived for you guys? How much does that affect the roadmap or not? [00:30:05] S0: I mean, I think that early, just talking personally. I used early ChatGPT for data and I was sort of like, this is not that great. It's not very interesting. I'm not going to use this and so I'd say for six months, I was just completely unconvinced. Kind of in the haiku stage for Claude. When Sonnet came out in October, our engineering team started adopting Claude code at that point and it was so obvious that the world had sort of changed in an instant. In terms of the way that people were writing code, that it became obvious that it was going to start happening for data too and even though we didn't believe in it. We started putting these AI features in the product, and our customers started using it. [00:30:42] S2: How early did you put these features out?
00:30:42 · Pablo Srugo, Colin Zima
[00:30:42] S2: How early did you put these features out? [00:30:44] S0: I'm forgetting the years now. So I don't know whether it was just this past year or not, but it was October. Whenever Sonnet came out, we had started landing some of that stuff. So I think it was October of '25. But we had a handful of customers that had started using the AI and they were like, no, it's actually really good. You should be trying this internally. We did an implementation internally where we're like, OK, let's set things up so it actually works well and literally that same week, we stopped using the UI. And it was one of those moments where we had to test ourselves. And it was so good that it was just really obvious the world was changing. And then, you just sort of playing catch up and trying to see what the models can do, and how you can incorporate them. [00:31:21] S2: It's noteworthy in the sense that in '23, and especially the second half of '23 and all of '24. Almost every new company was just jumping on AI, jumping on AI, just building with it. So, you're building a new company. By that point, like you said, by mid-23, you're just starting getting new customers. So you're really at the stage of these companies and you kind of, I don't know, I'm going to say ignore it but you kind of just kept going. You were basically on a path. You didn't really go off the path or change a roadmap dramatically until considerably later on. [00:31:50] S0: Yep, that's right. It was about a year later.
00:31:50 · Colin Zima, Pablo Srugo
[00:31:50] S0: Yep, that's right. It was about a year later. [00:31:53] S2: Maybe tell me a little bit about the go to market, since. You start off with more this ad hoc, go through and just find people that use Looker that you could pitch this to, et cetera. That gets you to that product market fit moment. Now, once you've got something that people use and onboard and seem to like, you need to scale that, scale in a small way, but get the $1 million, $2 million ARR. What do you use to do that? How do you do it?
00:32:15 · Colin Zima, Pablo Srugo
[00:32:15] S0: It's really funny, I've talked to our head of growth about this and our head of marketing. And everyone hopes that there's just some magical channel that they haven't thought of, that when they turn it on will inflect the business. What we found, that is the least satisfying but sort of most obvious conclusion that you could possibly imagine, is you just need to do more of literally everything. So early we had some organic, we had a little bit of outbound that was mostly the founders through LinkedIn, and we had a little bit of partnership. Which is our product needs to get implemented. There's a big data network of practitioners that help people with their data. Some of them would sort of recommend Omni and bring us in. And as we grew we've really just sort of leaned into each completely independently. So the outbound, we've scaled a big BDR org and they're on the phones sending email, scaling up founder LinkedIn. And so it's that but more. On inbound it's just mechanizing the machine but it's also we do a lot of outbound programmatic email and spinning up sort of enrichment techniques and things like that. Just identifying people to go talk to and then on partnership, we spent a lot of time with the hyperscalers. So Amazon, but also Snowflake and Databricks. And it was really just building relationships more broadly across the ecosystem. And then events is probably the last one that we started turning on. [00:33:30] S2: Did you find partnerships helped early on? I often find they help later. [00:33:34] S0: Enormously. [00:33:35] S2: They did, interesting.
00:33:36 · Colin Zima
[00:33:36] S0: Partnerships come in two very different flavors, at least for us. There are gigantic tech partners like Snowflake and Databricks, and Google, and Amazon. They don't care about you when you're two people or ten people or whatever. Thankfully, they knew us from the Looker days. So, again, we got to earn a little bit more trust than we would have had and we could go talk to leaders at Snowflake. Because they knew us and would sort of be willing to bet on us for the future. But they were not bringing us business. On the other hand, at the low end, there are these one and two person data practitioners that were essentially former people that I used to work with or people that were customers at Looker that went and started outsourced analytic shops where they're the data team for five people. Those people bring in tools and recommend tools, and if they understand our philosophy, and how we build, and can implement our tool really well. They'll pick up our tool off the shelf and recommend it to someone, and make sure it's implemented well, and take really good care of them. So we push out all of our services externally, like we would go pay a partner $5k to implement one of our customers and in turn, they learned about our product and would recommend it. And so even though the numbers were tiny, they were enormously valuable to get our brand out and say, hey, the cool data practitioners have seen Omni, they use Omni, they think Omni is very good. And again, it's another person that can validate whether the product actually does something effectively. They would give us feedback, they would have customers, they would say, hey, I work with this e-comm brand that I used to work at and I'm their data team. And they listen to me for tool recommendation. I'm going to recommend Omni if you take care of me in terms of building a good tool, I will help make these guys successful.
00:35:17 · Pablo Srugo, Colin Zima
[00:35:17] S2: What did you find made the difference, because obviously. So this is a great approach, right? And a lot of times when people talk about partnerships, they do think more about the big vendor. Whoever that is in the space that's going to cross sell my product and it's like, no, it's not going to happen because they got their own things to sell, right? For the most part, but the people doing services, they're smaller. The question is, how do you get them from, they obviously are already recommending something. They're doing Looker, they're using whatever they're using. What have you found works best to get them to move to recommending you? [00:35:42] S0: So I think that the thing is, in a weird way they can actually be a little bit faster moving than the customer base. Because they get more at bats. So for any given one of our customers, moving from Looker to Omni might be risky. They don't know what it looks like. It might be hard. If you can convince a partner to do it once and they can see how easy it is or how good it is, or what the advantage is, they now want to go mechanize that ten times. And it becomes very, very easy for them. So there's still a hurdle to get over. [00:36:15] S2: But your point is it's product. It's not like it's the incentives you give them or whatever. It really is like if your product is materially better.