A Product Market Fit Show | Startup Podcast for Founders
00:03:09 · Pablo Srugo, Colin Zima
[00:03:09] S2: What kind of ACVs were these, by the way? Just to know the stakes? [00:03:12] S0: So now we're maybe 45k, at that point. We were just trying to get 15k, and the idea was as low as possible where we could still justify a sales driven motion. But 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, let's get rid of every bug that we could possibly find. So rather than 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 Blond talking about the business, things like that. We 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. 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 you get your first good customer. The first person that's using the product and finding things that you hadn't thought about, and just sort of discovering new ways of using it. That was when the flywheel really started happening and that was all over the span of maybe four weeks.
00:03:12 · Colin Zima, Pablo Srugo
[00:03:12] S0: So now we're maybe 45k, at that point. We were just trying to get 15k, and the idea was as low as possible where we could still justify a sales driven motion. But 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, let's get rid of every bug that we could possibly find. So rather than 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 Blond talking about the business, things like that. We 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. 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 you get your first good customer. The first person that's using the product and finding things that you hadn't thought about, and just sort of discovering new ways of using it. That was when the flywheel really started happening and that was all over the span of maybe four weeks. [00:04:25] S2: So I have some questions, some follow-ups but maybe this is a good moment for you to tell us a little bit. What does Omni do? Because that's going to help set the context for this.
00:04:25 · Pablo Srugo, Colin Zima
[00:04:25] S2: So I have some questions, some follow-ups but maybe this is a good moment for you to tell us a little bit. What does Omni do? Because that's going to help set the context for this. [00:04:32] S0: So, I mean, we were founded February 22. So 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, ninety percent 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. And that was the product that we sort of pitched, and were selling from day one. It was, get rid of all of your other BI, we can do it all and it came from this place where 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 kind of what we slowly built over that year.
00:05:24 · Pablo Srugo
[00:05:24] S2: But yeah, it's a funny space. It gets reinvented every, well, probably every ten years. But now I feel like it's every two or three years and then I don't even know what the future's gonna 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 2, 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? And the difference between what I'm offering, and what's there today is just. It's just not worth, the juice not worth the squeeze sort of thing and then you kind of got 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?
00:06:16 · Colin Zima
[00:06:16] S0: Yeah, I mean, it's going to sound a little bit silly, but 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 and it's sort of, you know, your mom thinks that you're beautiful type thing. 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 an unaffiliated user could not. But there were still the bones of really good product. 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 angel investors. 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. We set up his ETL, we set up his BI, and I was just answering questions for him on a daily basis. They would just 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 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, and I think to make product, you have to do enough good stuff. We had felt convicted that we were doing those things and you got to be intellectually honest. Customers will lie to you and they'll say, 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 pay you money. They're sort of showing you that it's not differentiated in an interesting enough way.
00:08:07 · Pablo Srugo
[00:08:07] S2: 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 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 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. And I always think, 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 trying to learn as fast as possible and it can't work. It genuinely can't 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.