He lost all 5 of his first deals—then built the next Looker and raised $250M. | with Colin Zima, Co-Founder of Omni

0:00Like I'd probably done 100 demos and we had five unaffiliated customers that were talking to us that we really thought were going to buy the product. It all like verbally committed to us and we thought we were in paperwork. We were going to sign all of them and we ended up losing all five deals. I think it's hard to reinvent yourself. Like the innovator's dilemma is a real problem. Take a looker, for example. We did a lot of things really well. We did a lot of things not well. Like we were really good at enterprise BI. We were really inflexible. It's really hard to take like your core foundational thesis and compromise it at the margin to do these other things well.

0:31And so it leaves space for other people on the other side. If I'm saying like, I bet that I can either reduce your cost or make you guys smarter and give you a better product and make it really easy for you. Like that's a pretty easy message. Like people like free money and like they like having their job done for them. Be thoughtful about where you're spending your time and what you're doing. Like the company only gets more valuable every day based on what you do. And so if you reflect on a day, like think about whether you made the company more valuable or not. And if you didn't, think about what you should have done to make the company more valuable.

1:06That's product market fit. Product market fit. Product market fit. I called it the product market fit question. Product market fit. Product market fit. Product market fit. Product market fit. I mean, the name of the show is product market fit.

1:18Colin Zima: Do you think the Product Market Fit Show has Product Market Fit? Because if you do, then there's something you just have to do. You have to take out your phone. You have to leave the show five stars. It lets us reach more founders and it lets us get better guests. Thank you. Colin, welcome to the show, man. Yeah, thanks for having me. Excited to have you here, man. I mean, you've built a pretty solid business, raised over$250 million. Last round was just a few months ago. and we're going to hear all about what Omni actually is, what it does, how you made it all happen. But we start not where it all begins, but where this show begins, which is with the product market fit moment.

1:55Colin Zima: So when did you feel like you'd found true product market fit? So, I mean, yeah, the quick backstory on the company is we're about four and a half years old and I've spent a lot of my life in BI. I had spent eight years at a company called Looker that got acquired by Google. And so when we launched the company, we actually had a bunch of really talented people that had spent a long time at Looker. and I honestly thought that we would have traction in like a month. Like we'd tool around a little bit, we'd get people on the product, they'd be like, this is amazing. It probably took us like nine months until we had our first user in the product and it was actually a product manager that I had worked with at Looker and she had started a company and I just convinced her to start using the product every day.

2:31But it was one and two daily active users for like three or four months at that point and like us internally using the product. And about the one year anniversary, we finally like I'd probably done I don't know 100 demos and we had five unaffiliated customers that were talking to us that we really thought were going to buy the product they'd all like verbally committed to us so for people that are doing sort of like normal outbound sales cycle we had the tech win uh and we thought we were in paperwork we were going to sign all of them and we ended up losing all five deals and the feedback we got was like company's too young products not quite there but it's close stuff like that like I showed it to my end user they didn't love it.

3:09Colin Zima: What kind of ACVs were these, by the way, just to know the stakes? So now we're like maybe 45k. At that point, we were just trying to get 15k. And the idea was like as low as possible where we could still justify a sales driven motion. But like we needed to convince you that it was worth some money or it wouldn't be worth anything. And so we went back and we actually just worked on the product for two months. And we said, like, let's get rid of every bug that we could possibly find. So rather than like an error every two minutes, you'd have an error once a day or something like that. And then I actually went on another podcast with Sam Blonde talking about the business, things like that.

3:43We ended up with like three or four trials out of that podcast and all completely unaffiliated. And we won all four of those trials. And that was the moment that we realized that we had something interesting. Like there were people that we didn't know. They paid, you know, full-ish price, 15K, 20K, whatever it was. And they actually liked the product. And it was at that moment, we sort of realized that finally we've built enough and we can sort of create some repeatability. And then like you get your first good customer, like the first person that's using the product and like finding things that you hadn't thought about and just like sort of discovering new ways of using it.

4:20That was when it like the flywheel really started happening. And that was all over the span of maybe four weeks.

4:24Colin Zima: So I have some questions, some follow-ups, but maybe this is a good moment for you to tell us a little bit like, what does Omni do? Cause that's going to help, you know, set the context for this? So, I mean, we were founded February 22. So like a little bit before ChatGPT. But the product's vision was always pretty straightforward. It was, let's build a BI platform that can do everything. Write SQL, use Excel, do pivot tables, and also enterprise BI and embedded all in one platform. And in 2026, it's become, you know, like 90 % AI. But the idea was always a product that could compete with Tableau and Power BI and Looker and your SQL runner and sort of do everything all in one place.

5:00and that was the product that we sort of pitched and were selling from day one. It was like, get rid of all of your other BI, we can do it all. And it came from this place where like I had worked with these very decentralized tools like Excel and these really heavy enterprise tools like Looker. And it was always like, why isn't this one tool? Why can't this actually just work together? And that was the original vision for the product and like kind of what we slowly built over that year.

5:24Colin Zima: BI is a funny space. It gets like reinvented every, well, probably every like 10 years, but now I feel like it's every like two or three years. and then I don't even know what the future is going to be with AI. But my question, as you were telling me the story, was oftentimes when you have the experience that you had, which was a long time of doing demos, trying to sell, nobody really buying, and then five that finally you get all the way to the end, all pulling out, they'll tell you, oh, you're missing this feature, you don't have SOC too, or in your case, you have too many bugs, whatever. And you wonder, is it that or is it just they don't really care?

5:57Colin Zima: And the difference between what I'm offering, what's there today is just not worth, the juice is not worth the squeeze or anything. And then you just, you kind of have to move on to something else. You made the decision to double down on the product, just make the product better. It worked. What can you tell us about how you thought through that and why you felt like in this case, no, it really is just make the product better and they're going to buy? Yeah, I mean, it's going to sound a little bit silly, but like we were using the product every single day and we knew it was better. We knew it was buggy, but we also knew it was better.

6:26and it's sort of like, you know, your mom thinks that you're beautiful type thing. Like we could see through hitting a full page error and hitting the refresh button because everything else underneath it was so awesome. But it was really clear that like an unaffiliated user could not. But there were still the bones of really good product. Like it's a space that we understand well and we know what good data analytics looks like. And I at the time was sort of working as the data team for one of our like angel investors. We were like, he was our first customer. He was sort of like, I don't know if I need your product, but I know that you can help me with our business.

7:03Like we set up his ETL, we set up his BI. And I was just like answering questions for him on a daily basis. They would just like ping me questions and I would use the tool and answer questions for them. And in doing that, I understood that we had something special. It was unique, it was interesting, it was doing things really well. It was also admittedly like too rough and not quite good enough. I think when you build product, you need a balance of things that differentiate you and you need to avoid things that differentiate you in a negative way. And what was obviously like not clear to us at the time was that we had too many of those negatives that were overwhelming slightly the positives, but there were positives.

7:41And I think to make product, like you have to do enough good stuff. We had felt convicted that we were doing those things. And like, you gotta be intellectually honest. Like customers will lie to you and they'll say like, hey, this is great, this is wonderful, and then not pay you money. But you should know whether your product is actually differentiated or not. And if a customer is not going to kind of pay you money, they're sort of showing you that it's not differentiated in an interesting enough way.

8:07Colin Zima: This is where, you know, it really does matter where you start in the sense that you started with, like you said, industry, you knew a product that you knew and you used, you knew what like best in class was. And obviously it's going to be biased and all these things. But at the end of the day, your opinion on this is genuinely better than best in class, carries a lot of weight, and can pull you through a lot of, let's say, not positive feedback from the market. But whether it's like no reaction or negative reaction after seeing it, and you know that, and they tell you things, and you can kind of measure what they say and the feedback they give you against what you know reality is and kind of make that all happen.

8:40Colin Zima: And it's, you know, I always think like, as a first-time founder, you're often struggling to figure out what should you build, what idea. And oftentimes what you end up doing is you build for a market you know nothing about and you're just trying to learn as fast as possible. And it can work. Like it genuinely can work. There are examples where it works, but you do face this problem, which is if you don't hit it in the first one or two versions, then you can end up in V10 and you just don't know why you're there, how you got there and whether what you're hearing is genuine or kind of just people trying to be nice to you.

9:09I think that's exactly right. I think the other thing I would add is we were doing a lot of demos the whole time, like a lot, a lot of demos. Literally, I had a spreadsheet with 100 names in it of people that I had demoed. And while they weren't all buying the product, and like, look, a lot of them were volunteering, like, you're too young, I'm not going to buy your product. You can still see when people light up and when they find things interesting. If you have enough of those moments and those things are unique to your product, it can't just be like that screen is beautiful. It needs to be like, wow, that's a workflow I haven't thought about.

9:39That is interesting. You can see those bits and those can be enough to go build on. Like you have to find those moments of differentiation because if you're in a competitive space and you're not significantly differentiated and you're not better at selling or whatever, like differentiation into the market, you're gonna have trouble with pricing power and actually selling your product. Like we couldn't just build a tool that charts. Charting's not that hard. You've got to find these moments where I could do a two-minute demo to a Fortune 500 CIO and they could be like, wow, that thing is actually interesting.

10:12Like that doesn't mean they're going to give me money and it doesn't mean that we've found success yet, but it does mean that they saw like a grain of a problem that we have solved in a unique way that you can then wrap the rest of the company around. And I felt like in using the product, we were starting to see that and experience that.

10:28Colin Zima: So let's go back to the beginning. Now that we found like that moment where, you know, the inflection point started. You're a VP. You're a looker for like almost eight years. You're a VP product there. You touched on it a little bit, but let's go deeper on it. Like how do you make the decision to leave and start Omni? Mine was almost forced in some sense in that like I had started a company before. Actually, with one of my co-founders here, I hated it the first time. I started a company. I sort of jokingly say that our product was like Jamie and Colin start a company, which no one needs. And like we flailed around for a little bit.

10:58that we ended up selling the company. I told people I would never do it again for 10 years. What did you hate so much about it? I just felt like we didn't have an important idea that we needed solving. We wanted to start a company. We didn't want to solve a problem. Sure. And the world doesn't need me to start a company. Like the world needs me to either solve their problem and give me money or they don't need me.

11:16Colin Zima: Most of those stories don't end well. Fortunately, you actually ended up getting acquired. So it worked out. I mean, acquired. Like, yeah, we sold ourselves essentially. But like Looker got acquired by Google and it was a great acquisition. it was like 2.7 billion or whatever it was. I had to stick around for two years as part of that. And I could just feel the company like and the product was not getting cared for, the customer base was not getting cared for. And kind of over those two years by sitting there, I sort of had enough time to build up the courage to say like, hey, I think we have to go do this.

11:49Like, I think there's space, we have enough ideas, it's time to go do this. And I think if I wasn't sitting around there for a couple of years, I probably wouldn't have tried to go build something different. Like we had actually tried to build something similar to Omni, Inside Looker, classic kind of innovator's dilemma. It's hard to rebuild your own product type stuff. But it felt like there was enough of an idea that we could have space to go try to do it if we could find a team to do it with.

12:13Colin Zima: It's funny, like these spaces, I think of Zoom coming out of like WebEx and I think of, in FP &A it happens all the time where there's the next gen, every five to 10 years, the next gen. And a lot of times people came from the old thing, that like, this is all that's wrong with it. Let's build the better version. You know, the same BI is a classic example of that. I almost wonder like from the outside, like how does that work? Like why does that work? So, you know, do you think like these guys would figure it out and do it? I think it's hard to reinvent yourself. Like the innovator's dilemma is a real problem.

12:44Take a looker, for example. We did a lot of things really well. We did a lot of things not well. Like we were really good at enterprise BI. We were really inflexible. And it's really hard to take like 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.

13:20It'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, like deleting the whole tech stack, you have to go do that. And then it gives you space to re-challenge those assumptions, build things better. And I think that's what gives it space. Also, just like teams get tired. Like, I think people forget about the people side of this, which is like, if you have a team that does something for 10 years, you kind of do need to reshape it a little bit and inject different energy and things like that.

13:55Colin Zima: Reinventing is a lot of work. Like, not only is it hard and all these things playing against you, but like, 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 go-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. Exactly. And like, if the business is going, like, Looker was still growing 100 % a year when we got acquired, you know? Like, 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.

14:22But 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. 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.

14:58But because we had just been successful, gives people another look. Also, like I probably met with three or 4 ,000 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. Like, you know, I'm good. You know, like you trust me and great. Now I get an opportunity, at least for our product, to go solve their problem.

15:21Colin Zima: Yeah, that makes sense. I mean, obviously, trust, the story overall, trust, credibility, all that stuff matters a lot. The other thing that matters, at least classically in these situations, is the product, usually you can build a product that's better, at least in one dimension, but is it 10x better? Is it way better, at least on some dimension? What did you see that you were able to, what was the piece of what, I'm sure Looker did some things great and some things not so great, as you mentioned. Like, what were the pieces that you said, this is the thing that we're going to be known for? This is the thing that we're going to do so much better than Looker does.

15:51Yeah, the biggest one was data model. Like, 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.

16:28Colin Zima: For people that are not like BI experts, how does that play out? Like, what does that mean in a day-to-day situation? Yeah. So like, 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.

16:54And classically, the choice you'd have to make was you do something throw away and it stays throw away, 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 like for people that live in these tools every day, and AI actually became a huge driver of this, like 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.

17:32Colin Zima: When you leave, do you raise a round and kind of go heads down, just build product? Do you start talking to customers right away? How do you play it? Yeah, so we were incredibly fortunate in that I was close with like a bunch of members of the Looker board. I talked to first round the day after I left Looker. And so we raised money. 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 like I funded it with a loan for a little while to pay salaries because I was like, Like, let's get more founders in here, essentially.

18:03We got sort of comfort that we could fund it very, very early

18:08Colin Zima: so that we could staff it with a lot of people that know how to build. When was your seed and how much was it? Yeah, so we raised effectively like 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. So you had plenty of capital very quickly. 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.

18:43Colin Zima: You also like macro timed it perfectly. Like, I don't know if you would have gone$30 million in like January 23. We got very lucky. Honestly, even in March of 22, it was a little bit more dangerous than February of 22. That's right. But you got it May and August, you said 22? We did. But this is also the nice thing about working with people that you trust. Like these were people that were on the board at Looker for the last 10 years with us that we knew really well that wanted to come take this bet with us. And they were sort of just like, we trust you guys. Go build. so I mean look raising that much money early is obviously awesome and a lot of people will be like jealous of it there are problems there are challenges with it the most common one that I've seen of people that get to raise let's say anything I would say anything above 10 million at like idea stage the challenge is do you spend a lot of time and money on the things that don't matter do you build a lot of polish or do you still somehow find a way to stay focused on the thing that's really going to move the needle because you still got to de-risk them.

19:42Colin Zima: You still don't know. This is somebody gives you 30 million doesn't mean it's going to work. Right. So how did you structure it after you raise that money? I think that just generally we're like on the very stingy side of how we operate. And like we're kind of old guys as startups go. So we obviously spent some money early. Like we had 10 engineers with no customers, but we were very focused on the economics from a very, very early point, like mapping things out. We didn't just spend money. We didn't get an office for a year. Like we tried to be really, really thoughtful about our spend because we do want to minimize this stuff.

20:19At the same time, we knew we were playing the venture game. Like when you go raise money, that is now an expectation that you need to hurdle. And so like you need to understand the game that you're playing. And we wanted to play the venture game. It's a crowded space. We needed to invest in sales and marketing. We needed to invest in products and engineering. We got to go build for a year and a half to go do these things.

20:38Colin Zima: How many people did you say you were before revenue? Before revenue, we probably got up to 20 people. So like relatively large. Decent amount because at 20, like at five is where you have to do zero management, zero overhead. Once you get to 20, you've got at least some communication, let's say, overhead, if you want to call at least that, even if everybody's a self-starter. I would say a little bit. Like, we're a trio of founders, which helps a ton. And out of those 20 people, maybe 15 of us had worked together at Looker. I see. So a little user, yeah. I want to say it was like 14 Lookers and five people that had worked with Chris at Stitch.

21:11Colin Zima: Okay. So it was 20 people, but it was 20 people that had 10 years of history on average. There you go. And that just increases the bandwidth really significantly. Like, we were just really light touch on management. We just sort of had smart people and let them go build in their areas. so like we just got to sort of operate very exceptionally in terms of how we built like we knew that everyone was going to contribute we knew kind of the types of people that were sitting around we all kind of trusted each other and i think that just let us move quick in terms of building so in a sense i mean you know the the hiring was relatively easy because it was out of network the fundraising was relatively easy because you had the relationships the product idea was relatively easy because it came from kind of the history that you built what did you do on the go-to-market side?

21:55Colin Zima: How did you play that game, especially as you're building and you have a pretty clear idea of what you want to build? I don't think you want to do it, especially with this type of product, like these MVP kind of throwaway products that you're trying to put out, right? So how did you kind of structure that first year of heads down building with go-to-market? Yeah, I just tried to do as many demos as we possibly could. Like we would try to show the product to every single person that would sit down with us for an hour. So I just pinged every single person that I knew that had used Looker in the past, that was a data person, like literally anyone that would take a conversation, I would try to show the product.

22:28And part of it was just that I was excited and kind of wanted to show people the product. I learned how to demo much better. Like sales is a technique that people need to learn. Like you need to ask for things. You need to do discovery. You can't just show them your product and be like, this thing's awesome, isn't it? But we did a lot of demos and it helped both shape what people understood about the product and what they didn't understand. It also just helped us validate our assumptions of like, is this idea good? Do you have this problem? Like, do you want to write SQL and have a data model?

22:57Sounds like a very simple question, but it's another thing to say like, is that enough to delete your other tool and come to this other tool? And we got a lot of like, no, it's not enough yet. It's not enough yet. It's not enough yet until it was enough. And then it was great. Like now we have a product that we have sort of convinced a handful of people is real. Now we just need to sort of keep sizing up those companies and keep making the product better because it's, we also get to continue to improve the product. And when you're in these big established spaces that are really crowded, even though our team was maybe 20 people at the time, our engineering team was moving faster than the incumbents.

23:33And that's an enormous advantage because people want to both buy you for what the product's doing today, but they're buying you for the slope too. Like they can see your progress and they want to buy that progress also because like the beautiful thing about SaaS is we want them to be a customer for the next 20 years. So they're not buying the product today either. They're buying the trust that you will have the best product over the next period of time.

23:57Colin Zima: Especially for a product like BI or really any product where you know you're not switching every year. Like you're trying to build a repo of charts and you know databases and connections and all these sort of things and you don't want to be like every year oh this is the next best thing let's kind of switch out of it. There's some tools where you can do that a lot more easily, you know, be eyes on one of them. So for those sort of things, you're definitely thinking about, you know, we're going to be building, you know, visualizations and data analysis. Like we're going to do that for a long time.

24:23Colin Zima: So who do we partner with? Which is also where selling trust is a big deal. Like, I know it sounds silly to like, for a tech company to talk about sort of selling the human side. But if you are establishing a 10-year relationship with a vendor, in your mind, you want someone that you trust is going to operate well into the future. and you can either buy a brand that is doing that. Like I know if I go buy Coke that like they will operate in a long period of time or you can buy like the young version of that, which is like, this is something that it's developing and they're excited about what they're doing and like I want to be part of that excitement.

24:59And so like I like buying young products also because I know that the person on the other side cares deeply about it. You also just need to convince them that your company is going to be around enough that that's not going to be a painful relationship.

25:11Colin Zima: So give me maybe some timelines. So you start February 22, you raise your seed in May, you're A in August. When do you have a first customer live? When are you doing these demos? Like maybe just walk me through that side of it. Yeah, like demos probably started in May. And like they were terrible demos probably. Like the product didn't do enough stuff. But like you could see the grains of the ideas that we were trying to test. Like first users were probably October, November. and like one to five daily active users probably. First, like what I would call real demos that look like the demos that we do today were probably like March of 23.

25:46So it really took us like a full year to have, I think we landed dashboards in like January. And that was suddenly when we were like a BI tool and not a toy. Like we could actually replace the thing that you have. And that's where you have to understand like what is your market? What is a sufficient replacement? Because the tricky thing is you need to build enough of the existing space to replace it, even if that is none of your differentiation.

26:08Colin Zima: Yes. And so like, it took us a while to get enough of that stuff. I'm really worried because listen, like you've been listening for like what, 10, 20, 30 minutes now. Clearly you like it. And the thing is, the next episode is way better and you're gonna miss it. You're gonna miss it because you're not following the show. So take your phone out and hit that follow button. This is where, again, like this idea of team market fit is so important because first time founders, I find make this mistake often, which is you kind of just, you gravitate to something because you think, you see the idea. Like you might've seen, somebody might've seen like, oh my God, BI is so outdated.

26:42Colin Zima: I can't believe it works like this. It should work better. You don't realize, and this goes for so many different like OS level things, like random one, property management software, right? Like anything kind of at that foundational level of stack may be very outdated, very old school. What you don't realize is you will have to build like 80 % copy just to get your 20 % that's going to be better. And you're going to need money for that and people for that. And if you have the experience you have, you'll get that kind of, hey, you've earned over the last 10, 15 years of work, like you've earned this enough credibility.

27:14Colin Zima: Here's some money and some time to go prove that it's real. You're a first-time founder. You're like 22. It's not happening. So you're never going to see that other side. And you're going to stub your toe so many times building it the first time. Like the amount of corner case handling we built into the first product because we just knew, like we made this decision and this decision and this decision last time we built it like perfect you just jump to like stage three like there were features that took us nine months at Looker that we built in a week at Omni and it's because like you don't have to rediscover how to do all of these things you don't need to handle the quarter cases on the fly and rebuild the bottom like you just you set out and you know how to go build this stuff and like Klon has changed a lot of that like now you know maybe BruteForce solves a lot of these problems maybe but like Like having stubbed your toe a few times so you put the right foundation underneath, it makes a big deal.

28:05Colin Zima: So March 23 or so, you have like the first real demos. When do you have that moment, the primary fit moment that we start talking about? It was like May, June of 23. So it took us a while. And then by the time you come back out and you get those six out of that podcast, that's when? Yeah, that was June. They signed in June, like demo in May. And like I would say before that, in probably September of 22, we had the moment where internally we were using the product. and we're like, wow, this thing feels good. It was just like, we didn't have dashboards at the time. Visualization was sort of young.

28:36It's like, then we were like, okay, we've got some differentiation. Now we have to do all of the debt work that we can actually exist as a BI tool in the existing market.

28:44Colin Zima: And maybe just to bring it home, like in terms of the value, right? You mentioned the analogy, but take me to an example of a real data problem, right? Like where somebody, because at least my kind of idea for this is somebody somewhere in the business is like, hey, can you figure out whether, you know, ACV affects churn? Or can you figure out whether people in this market are more likely to buy this thing if this condition's true? And then that goes to the data team. Like, is that the right construct? It is. It's like, it's everything from like, we're walking into a board deck, we need to get financials to like, how are we doing against different competitors?

29:16And what words, when we mention them, do we perform better with?

29:20Colin Zima: And then flowing through that to, this is how Looker handles it or existing handles it. This is what you were able to do. Yep. So like AI has transformed a ton of that. Like now it's just you put it into a chat box and it just goes and figures out everything for you. Before it would be like, I need to make a dashboard that has a SQL tile over here and a governed metric over here. I know that sounds like the simplest thing ever, shockingly hard to do in every single BI tool that existed. And we could do a demo where we could show you a SQL tile next to a governed financial metric that the finance team approved.

29:52Colin Zima: And from there, we were sort of off to the races. How this is, you brought up AI, so I think it's good to talk about, like, ChatGPT comes out near the end of 22. How has that lived for you guys? How much does that affect the roadmap or not? I mean, I think that early, like, 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, like, kind of in the haiku stage for Claude. When Sonnet came out in October, our engineering team started adopting Claude Code at that point.

30:27And 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. How early did you put these features out? I'm forgetting the years now, so I don't know whether it was just this past year or not, but it was like 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.

30:58And they were like, no, it's actually really good. You should be trying this internally. We did an implementation internally where we're like, okay, let's set things up so it actually works well. And literally like that same week, we stopped using the UI. And it was like 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 like playing catch up and trying to see what the models can do and how you can incorporate them.

31:20Colin Zima: So it's noteworthy in the sense that in 23, and especially, like second half of 23, and all of 24, almost every new company was like, just jumping on AI, jumping on AI, just building with it. So, you know, you're building a new company. By that point, I mean, like you said, I mean, by mid-23, you're just starting to get new customers. So you're right, 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.

31:50Colin Zima: Yep, that's right. It was like about a year later. Maybe tell me a little bit about the go-to-market sense. Like you start off with more this ad hoc, like 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 like get the 1 million, 2 million ARR. What do you use to do that? How do you do it? It's really funny. I've talked to like our head of growth about this and our head of marketing.

32:18and like everyone hopes that there's just some magical channel that they haven't thought of that when they turn it on, like will inflect the business. What we found that is like 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 like our product needs to get implemented. There's a big data network of practitioners that like help people with their data.

32:48some 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 like it's that, but more. On like inbound, it's just like mechanizing the machine, but it's also like we do a lot of outbound programmatic email and spinning up sort of like enrichment techniques and things like that, just like identifying people to go talk to. And then on partnership, like we spent a lot of time with the hyperscalers.

33:20So like Amazon, but also like Snowflake and Databricks. And it was really just building relationships more broadly across the ecosystem. And then like events is probably the last one that we started turning on.

33:30Colin Zima: Did you find partnerships helped early on? I often find they help later. Enormously. They did. Interesting. 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 10 people or whatever. Like, thankfully, they knew us from the Looker days. So like, again, we got to earn a little bit more trust than we would have had. And like, we could go talk to leaders at Snowflake because they knew us and like, would sort of be willing to bet us off the future. But they were not bringing us business.

34:01On the other hand, at the low end, there are these one in 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 analytics 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.

34:33Like 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 like, hey, the cool data practitioners have seen Omni. They use Omni. They think Omni is very good. And again, it's like another person that can validate whether the product actually does something effectively. Like they would give us feedback. They would have customers. They would say like, hey, I work with this e-com brand that I used to work at and I'm their data team and they listen to me for tool recommendation.

35:07I'm going to recommend Omni if you take care of me in terms of like building a good tool. like I will help make these guys successful.

35:14Colin Zima: 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 out 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, you know, 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?

35:42So I think that the thing is, like, 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, like 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 10 times. and it becomes very, very easy for them. So there's still a hurdle to get over.

36:15Colin Zima: 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's materially better. Incentives help a little bit too. Like in the same way that Google doesn't care about us because we're too small, Google probably doesn't care about them because they're too small. And so if I can find a one person partnership and we found like a handful of them to start that are trying to build a brand about being a really good service, they can be a little bit more objective about what the best product is and help you build out a new stack. And as long as we can work with them well as we develop and like as a business, they're happy to go pull us in with the right customer.

36:49Like they just need to know that their customer is gonna get taken care of. Because the worst thing that can happen to them is they engage with someone, they build it, they bring in a product and the product stinks. Like, and that could stink because the product's bad or because the company is bad. But like, that's their reputation. Inversely, if they can bring us in and we can say, I will guarantee your customer will be successful. Like they'll get great support. We'll build things that they want. They'll be excited to bring you in. And like even better if we can give them some services because they can implement for us.

37:18So we just found that to be like a really effective flywheel for us.

37:21Colin Zima: You know, but it's funny about your earlier point. Like there's no silver bullet. I was talking to another founder earlier this week or last week and it was that same sort of thing. Like, hey, we're growing, we're growing slowly. Like, what have you seen work? And I'm like, dude, like I talked to founders who through probably Mark Fitch show, like are doing tens of millions in revenue. they're well beyond product fit almost none of them figured something out they just they have a solid product the product provides value right place right time and then they do the obvious stuff they do events they do partnerships they do outbound they do inbound they do inbound through the channels like you would probably do inbound and maybe they execute marginally better than you but like i've heard only like one or two things here and there sporadically where i'm like wow that's really creative that's awesome like everyone wants the consumer viral hit like you just what You're like, why can't I just be viral?

38:08It's like, I don't know. I wish I could. Here's the secret to get 10 million views on X, you know?

38:12Colin Zima: Exactly. Cool. Well, one technique that you did use that we talked about earlier and we can go deep on now is using LinkedIn to the fullest, right? Social selling on LinkedIn. So maybe tell me a little bit about what it is, how you use LinkedIn, what you did, and we'll go as deep as possible again, trying to create a bit of a playbook, at least something that somebody could take pieces of and start replicating themselves. Yeah, the big one is like we sort of mentioned that we were staying in a space that we knew well and we had big networks. So I probably when I started Omni had, I don't know, seven or 8000 connections on LinkedIn, maybe maybe 6000 or something like that.

38:42And a lot of these were people that had purchased BI from Looker at some point in the past that I talked to in person.

38:48Colin Zima: A lot of them I just pinged and said like, hey, can you take a look at our product and give me some feedback. And if you build up a social network over time and you're trusted in the space, like people will respond to that. And like, they don't necessarily need to be buying product today. Again, like when we talk about SaaS, I'm trying to sell you a product over 10 years, not over a year. And so like getting to show them the product and then show them progress over time can be really valuable. And so like you probably have a bigger social network than you think of former employees and customers.

39:16I pinged, I think, almost every single person that used to work at Looker. We had a thousand employees and we got acquired. I probably sent a LinkedIn message to 900 of them. And it's not because like we're trying to exploit them. It's because like I think that I can give them a great experience. I do love seeing a lot of them. And so like you can kind of mix being social and getting feedback on your product. This may be a dumb question, but why?

39:38Colin Zima: Like they're not like somebody who works at Looker is not potentially a customer. Former Lookers. Like a lot of them left and went other places. And they're doing data at these other places? Or like what's the... A lot of them. Yeah. Or they have a data team at those other places. And you say like, hey, can you connect me to the data team? And also like take a look at the product. Because a lot of people that work at Looker might be marketing or sales or engineering. or like they're not necessarily like, okay. Yeah, but like they hopefully like us and like we had a large group of Looker Continuants.

40:05So like there was some, there was like some love and they like, they want their business to run better too. And like the thing that people forget about selling is that good selling is just connecting someone with a problem to someone that can solve their problem. And if we can just be trusted to help solve a problem for you, like someone will take 30 minutes out of their day to see if their problem can get solved. If I'm just asking for money, like that's not a helpful situation. If I'm saying like, I bet that I can either reduce your cost or make you guys smarter and give you a better product and make it really easy for you.

40:38Like that's a pretty easy message. Like people like free money and like they like having their job done for them. So we just found a lot of people were like willing to connect us with other people in their org. And then the other thing I would say is when founders of startups, especially ones that like know their space well, ping other people, very often your ICP customer early does want to talk to you. And the reason is because people like discovering new products and they like seeing new things and they like being part of the build-out process. Not everyone, but like if you ping 100 people, maybe 30 of them are curious about what you're building and want to see it.

41:14And like, you got to be careful about wasting your time. Like plenty of people will kick tires and things like that. But if you know what your customer looks like and you can get other people to engage that look like your customer, then you've now created this match. So like the qualification is on you to figure out who a good person to talk to is. Because again, there'll be lots of time wasters. But a lot of people want to discover the next product. And they want to see it early.

41:39Colin Zima: And they want to touch and feel it. I think this speaks total sense. Like, let's get into the specifics. Like, how do you set this up? Are you every day you're doing 10 outreach? Every week you're doing 100. You have quotas. How do you? Because LinkedIn is not, it's like got so much data, but it's not really set up to help you run a workflow. And like, you know what I mean? It's just not that clean. Yeah, I mean, we just tried to ideate on things. So like we literally ran a campaign where I sat down with a BDR and we looked through every single person that used to look at Looker, work at Looker.

42:05We decided whether we thought that they would like find value in our product and we sent them a note.

42:10Colin Zima: Manually, boom, boom, boom. Yeah, yeah, yeah. Like it doesn't take that long. Like two hours, you can get through 200 people. I also printed out every single LinkedIn connection I had and was like, this person could be interesting. This person could be interesting. This person could be interesting. Like, no, no, no, no. Yes, yes, yes. And like, sometimes it's your real friends and you hadn't even thought about it. And you send them a message and you're like, hey, will you look at this thing with me? And that's the other question is like, the message is what? Like, hey, I build this. Do you want it?

42:36Colin Zima: Like, what's the craft in that message? We tested every message you could possibly imagine. Like some of them were about the product. Some of them were like, I'm a founder and I'm building something new. Would you like to look? Like, that's what I'd say. You aren't going to know what messaging works. You need to play around. What worked for you? Did something work better for you or would you find it was all kind of similar? Shorter is better. More direct is better. Less flowery language is better. So we just found like honesty and simplicity to be the easiest approach. So that's where you need to be a little bit careful.

43:06Like if you ask for feedback, you will get closer to a tire kicker than anything else, which can be good depending on the stage that you're at. But like the ask could be clear. Like if I think I built you something good, then like, hey, I think this would work for you guys. I would love your thoughts if that's true. Like people are afraid to say what they actually mean. And you can just go on. And if you can describe your product crisply and it solves the problem for that person, you can say like, hey, I bet you have this problem. We solve that problem. Like, would you like to see? The worst is that someone's not going to respond.

43:39Like, again, if you're doing this inside a social network, very often people will respond. And you get an opportunity to say like, hey, did what I say come true? How was that?

43:49Colin Zima: Walk me through the expectations because I think this is another big one. Like if you were, and I don't, I know that you have the numbers right in front of you, but just high level, you sent a hundred of these. How many should you expect respond? How many meetings do you should expect booked and whatever? Yeah, yeah. So like when our BDR team was doing this, they were getting like a 3 % response rate. So they'd get three responses out of a hundred LinkedIn connects. I might get a 50 % connect rate. And on a message, depending on the group, like to the ex-looker group, I probably got a 90 % response rate.

44:20Wow. Because these are people that I knew. Like, to real LinkedIn connections, I probably had a 90 % response rate. And I had 5 ,000, 6 ,000 LinkedIn connections. So, like, again, not all of them are data people. But if we find 1 ,000 good people in there, there's 900 responses. And look, like, there's probably 200 people that are a reasonable demo there. That's enough funnel to get started. Because, like, all we're trying to do is start the flywheel at that point. But, like, you need to be able to describe your customer really well. And you need to give LinkedIn a little bit of money. Like those are the two things that you have to do.

44:52Which money on LinkedIn, is that just for the sales nav account or were you running ads on it? No, just for sales nav. Like we didn't start running ads for a couple of years, but it's like a sales nav account. And then you just need to ping people with a message that actually targets a problem that they have. And like you need to create your own credibility. So like, hey, I'm Colin. Like we tried a lot of the I worked at Looker for eight years thing. Like that works really well to a Looker account. it doesn't work great to a non-Looker account. So like you want to tune up your message a little bit based on what you're saying.

45:22But if you've been using Looker for the last five years and it's like, hey, I'm Colin. I led product at Looker for eight years. Like I built something new. It's awesome. Do you want to see it? You get a lot of yeses. It's really not as complex as it sounds. If you're not getting response rates, I think it's probably a good indication that like there's a product market fit issue at some level. But like you got to be able to describe what you're doing also. And we've found like different success over different times with different features and things like that also. Plenty of people ignore you too.

45:50Colin Zima: The other thing that you did that I'm a big fan of, it's a weird one where it's like, I think anybody could do this. You were better suited to do it, but anybody could do it. And it's done so little, which is hiring from your own industry. Tell me about what you did there and how you did it. Why the thinking behind it all? Yeah, I mean, part of this is because Looker sold to Google and people were there post-acquisition and either loved the big company atmosphere or didn't. but when we set out to go build a competitor to our last company, we worked with a lot of great people and people would ping us and say like, Hey, you're doing interesting stuff.

46:26Can I come work there? And we like, yeah, I know you do great work. Like come over and do it here. And like everyone has jobs and you know who the good people are that you worked with. If you're starting a company, those are the people that you should go hire. Like you don't have to think about it that hard. If you're doing something interesting, it's like the product market fit problem. Like, can you pitch them something interesting about your business? And again, like we were very fortunate to have fundraised really nicely sort of like give people a safe place to go work as well. But we could sort of give a convincing argument that it's a good place to work.

46:58And then the talent like accrues over itself. Every good person that you hire that you used to work with makes the place more attractive to the people that you used to work with. So if you worked at a big company with a thousand people and you hire the 10 best people, you're going to hire the next 10 best people also.

47:13Colin Zima: This is obviously in your case, like you came from there, you had some outbound, inbound, I'm assuming maybe you did outbound, we'll talk about that. But would you also recommend this to someone who's building, let's say these days, an AI native product in whatever category, where there are some like SaaS incumbents, 10, 15 year old companies, 2000 employees, 5000 employees? Would you go poach, you know, you probably don't want to poach like the series A or B startup in your space, because then it's just that's, that's gonna come back at you in there. But like, if there are 2000 5000 employee company, like they probably don't even notice they don't really care?

47:43Colin Zima: Like, would you do that outbound on purpose sort of thing? I think the thing that you have to be careful about is like different scale companies work differently. Like an effective engineer at Google is not going to be an effective engineer at a startup necessarily. An effective engineer at Looker when we were private is going to be an effective engineer here. So how big was Looker when you're when just before the acquisition employee was? We were a thousand when we got acquired a thousand employees. But like, I think the average tenure of our first 10 employees was like eight years. So it was a lot of people that were there on the first, you know, 30 people.

48:16Got you. I think you do need to be a little bit careful if you're going for like incumbent because you want to know that the person is not like has a meaningful understanding of the work that they're doing, that they're not over specialized. But like context on your space matters a lot. So I'd say expertise is good, but you need to be able to vet the quality of the person really, really clearly. Like, do they align with the type of work that you do? is the culture fit good? Like I would probably recommend way harder back channel on that because they'll appear more credible in your space. So on paper, they will look much better, but you got to make sure that the work product that they want to produce and like the way that they work is aligned.

48:54Like we were really comfortable being like, there's no framework for anything here. Like no performance management. You will not get mentored. You'll come here and do stuff. And we want to ship code every day. If you like that, then like you've now self-selected into like a good setup.

49:09Colin Zima: Did you run an outbound process for hiring as well? Was it mainly inbound people that just said, hey, you're building, like, can I come work? It was mostly inbound, like, loose social network stuff. And mostly just because we had to be careful about, like, solicitation and stuff like that. And what about on the go-to-market side? Like, obviously on the product side, I can see that being helpful. Was it also helpful on the selling side, where it's like, yeah, I've sold Looker into all these vendors. I'm going to call, you know, the people I used to sell to. A hundred percent. Our first handful of sellers were all Lookers.

49:35Like, our third hire, who now runs our Europe, was in Dublin. and he was just a great seller, a looker. And so we were like, hey, we got no one over here. I don't know how this is going to go, but we're going to put a team in Dublin and I trust you. And I know if you can do it, then it works. And if you can't do it, then we need to go work harder as a product. But like, good luck.

49:53Colin Zima: This is where to me, this playbook, like you mentioned the risk, which I think is a genuine risk. But I'm like, if you're selling large mid market or enterprise, why wouldn't you go and try to hire? I mean, there were times where it was just like not doable because they're making so much money. you can't take them, but you've got the SaaS apocalypse playing for you still now. Like, you've got these stocks that are flatlining, are going down. They're certainly not going up fast. You can go to these people and be like, hey, you've sold your old product to, you know, X number of people. Come sell my new hot product to those same people.

50:24Colin Zima: It's a no-brainer. A hundred percent. And again, like, if they're a great seller, they have a Rolodex of people that they just sold to in your space. Exactly. And that product is going to turn over over time. So like a great enterprise seller is almost like a corporate advisor to their accounts. Yes. Like you want them to come with a bag of business that you can go address. You have to make sure that you're ready to go address their business. And like it's taken us time to, you know, like scale up to enterprise. But like that has so much value for them to actually bring a person that trusts them to have good software.

50:57And so like that was a huge vehicle for us in GoToMarket.

51:00Colin Zima: Plus one of the, I find, positions that has the most turnover or is around go-to-markets, maybe like head of marketing, head of growth, or head of sales. And if you hire somebody from your industry who's sold whatever, and let's say you can back channel and know that they actually sold that, and they can't sell your product, now you've got a real something to look into and start thinking about why. It's true. Perfect. Well, let's stop it there. I'll ask the one question that we always end on. If you had maybe one top piece of advice, something you find yourself saying to early stage founders pretty often, what might that be?

51:32I think the main one is just like, be thoughtful about where you're spending your time and what you're doing. Like the company only gets more valuable every day based on what you do. And so if you reflect on a day, like think about whether you made the company more valuable or not. And if you didn't, think about what you should have done to make the company more valuable. I think people fall into doing work. It's like going to meetings. If you're at a big company, you go to a lot of meetings. When you're a startup, you're keeping the company afloat yourself. Like if you have a meeting, you didn't make the company money that day.

52:07Like maybe you made the company more aligned. Like think about where you're spending your time and make sure it's good.

52:13Colin Zima: Love it. Perfect. Well, Colin, thanks so much for spending the time, and it's been great. Of course. Thanks for having me. Wow, what an episode. You're probably in awe. You're in absolute shock. You're like, that helped me so much. So guess what? Now it's your turn to help someone else. Share the episode in the WhatsApp group you have with founders. Share it on that Slack channel. Send it to your founder friends and help them out. Trust me, they will love you for it.