A Product Market Fit Show | Startup Podcast for Founders
00:35:00 · Andrew Antos
[00:35:00] S1: You need to have that enrichment layer. I think people make the mistake that they're like, oh, I just record everything and then I'm going to reason on top of everything. Because also a lot of people don't want to reject people, right? And so you might have a very positive sales discussion, sales first meeting, but it doesn't translate into the next step, right? And so, you actually need to build the data layer with all these other types of data. So you can see, OK, well, whatever an existing customer is telling you has the highest priority, the highest weighting, right? Whatever a customer that's quickly progressing through the pipeline or progressing at the rate that you want them. We also treat that at a very high rate. If you have somebody who just gives you a first meeting and then it goes away. Then we're discounting that opinion. We're still digging into it, right? Because it could have been something, could have been our targeting. It could have been the way we pitched somebody from that specific industry and so on. So we still look at that, but in aggregate that's a little bit less valuable than what the people who are already working with you are telling you.
00:36:00 · Pablo Srugo
[00:36:00] S0: That is interesting. Obviously, stitching it together, I could see the value in that. Because then you can slice it by what you just said but also, hey, of the customers that are sending us the most customer support tickets. Who are they and also what are they saying, and so on and so forth. But let me ask it like this, what sort of things have you gotten from there in terms of end outcomes, that because you're doing this, you've seen this or you've heard that. I'm curious to get that kind of tangible piece of it.
00:36:27 · Andrew Antos
[00:36:27] S1: I mean, so for example, one of the core things is if you look at our website The entire story is really three steps. We understood how customers are talking about the problem that we're solving for them, right? That's the most important thing for every company, to actually truly understand the nature of the problem and you can start seeing these patterns of people are like, yeah, I was running this transformation project and it took $5 million and it took you eighteen months. And it took us six months just to interview a hundred fifty people in my organization to try to understand what they're doing, and it was still interviews. It was not the actual thing and then, we build something and it was the wrong requirements. So the most important part of this is actually understanding the customer pain, right? People are really good about talking about things that they know, right? And it's their problems. And so actually deeply understanding the problem across hundreds or thousands of customer conversations and seeing, are there any changes to the trend? Is the pain evolving in any way? I think that's critical and so our ability to articulate the pain to the customer and be like, is this what you're experiencing? Is this your problem? If yes, we can help you. If not, we don't want to waste their time. So that's number one. Number two is the way people talk really matters in terms of how do you articulate the problem in their own words then. I think that's really important as well, right? We had all these ideas initially. We were like, oh, should we be in this business of adaptive operations or an intelligence layer and so on. And then the customers were just describing it as, it's a transformation problem. And we would describe technically what it is. We built a context graph of how work gets done and they're like, oh, so it's like a company brain. And we're like, yeah, it's like a company brain. And they're like, why don't you just call it company brain? Now I can explain it to other people at my company. So you get these tidbits, because oftentimes when you understand the problem or your own technology in a lot of detail. You want to describe it precisely but what the customer cares about is, OK, what's the representation that I can then take and go, and talk to other people about?
00:38:40 · Pablo Srugo, Andrew Antos
[00:38:40] S0: And how do you use this regularly in the business? Is it every Monday morning, everybody sits there and looks. You know what I mean? How do you build this into the process? [00:38:49] S1: So each team, right? So marketing team, sales team, value delivery, customer success, product, they have their own agents. Where they're running the types of insights and the type of data that they want from this, right? So for example, one of the things that we really care about right now is driving the usage across our customers of our MCP connector. Because people who are using MCP are shipping more agents than people who are not, right? So we really care about driving the MCP usage across our customer base and so the product team that's working on MCP gets a report every day in the morning. And says, this is how much usage there was. This is the sentiment. This is the feedback that the customers are giving. These are the types of queries, all of these things and so, every single day they can just prioritize and iterate. And make the product better for the customer in the right direction. [00:39:38] S0: Now let's jump to the second tackler you wanted to dive into. You mentioned earlier, that the way that you perfected, like you did that launch and then the way to really perfect it, and hone in on what you now do. Which is transformation, was through this idea of four to five experiments a quarter. Four to five hypotheses that you're testing out, and you have leading indicators that you look for. [00:39:58] S1: Yes. [00:39:59] S0: Tell me how all of that is set up.
00:40:01 · Andrew Antos
[00:40:01] S1: So we do quarterly planning sessions. We don't believe in longer than ninety day cycles at the company. So we do these quarterly review sessions. It's basically with an extended executive team. We actually use this database thoroughly and basically see, OK, what are the core patterns? What are the core problems? Or what are the things that are working really well? And how can we double down on them? Make a shortlist, right? And then circulate it to everybody that we talk about it, and then we typically pick a couple of bets. And some of these bets have more of a go to market or customer success shape. Some of them have more product shape and we say, OK, this quarter we're gonna validate these. Usually it's four to five hypotheses. We assign it, we say, OK, what would success look like. I think that's critical. I think the qualitative stage of product market fit happens only very, very early, right? You launch it in an event and everybody wants to talk about it. It gives you conviction to take the next steps. But once you become a little bit bigger, once you launch more product and so on. Then it becomes tricky and you need to be very quantitative about it. And so we typically just spend a lot of time on, what's the startup metric that would give us confidence to basically resource it on an ongoing basis? So one of the things, for example, when we launched our MCP. We're like, we need one hundred users in nninety days. Otherwise, we're not going to consider this to be a huge success. Maybe we just are not that useful, right? And so then we focused, we had a clear person owning it, and we're like, OK, your job is to figure out how to get one hundred users on this. And we define the users like somebody who uses it this much, this frequently, and so on. And so setting that initial metric directionally. So then you can clearly say it's working or it's not working. So you can either kill the bet or you can resource it more heavily, is critical.
00:41:53 · Pablo Srugo, Andrew Antos
[00:41:53] S0: What are some other examples of experiments that you've run, especially things to improve? I imagine once you're over $10 million and you're kind of scaling. You're taking every single part of your funnel and you're trying to tweak things. You're like, how can we get a little more leads? How can we improve conversion from MQL to SQL? How can we improve lead, whatever it is, right? I'm curious on maybe some of those where you're like, oh, this part of the funnel is not great. Let's try two or three experiments and then kind of how you thought about that. [00:42:18] S1: I can actually give you an example. I'm sitting in one of our experiments right now, so. [00:42:22] S0: There you go.