The Exchanges

Q I mean, we could probably spend hours talking about just those few months in 22, but what were the most important things that you figured out?

A We had to have a very clear, explainable answer to the question, why should I use the commercial product and not the open source product? And it had to be digestible by someone with a C in their title. And for a long time, we actually didn't know if we were going to be a PLG business or a sales-led business. Our first, let's call it Ten million in ARR was like very PLG oriented. And so it felt like maybe that would just continue to be true in data. That's like almost never true. When you sell to software engineering, sometimes you can go PLG for a lot deeper in the journey and, you know, Stripe and Twilio and others have, have done that very successfully. But data for a variety of reasons that are probably too boring to get into ends up needing to be sales led and it, and most of the dollars come from the enterprise. And so we couldn't just build a broad set of tools that developers, that practitioners really loved. We had to really focus our efforts and build cohesive stories that we could explain to, to senior data leaders. The big unlock for us was as people use dbt for longer, the complexity of their code went up and up and up, and it became really a problem for the most sophisticated users. And oftentimes they were at the largest companies. And so there was a real opportunity for us To step in and solve that. And so we, for the first time started talking about complexity a…

AI assessment note: “solving complexity is the biggest differentiator for dbt cloud”

Q explains his one screenshot test for figuring out if a product story is coherent enough. Along with other pieces of advice for PMs looking to shape up their storytelling jobs. Thanks for tuning in. And now my conversation with Yuki. Maybe one place to start is what is your theory as to why Figma worked? Like your current theory that may be imperfect given how random and complex companies are.

A Obviously, I think the first thing was just the bet on this technology web. That was a bit speculative back in the day, and no one could imagine a professional tool as complex as Figma in the browser. And, but if it could be achieved, you know, it would be something that would be amazing from the perspective of what it opens up, because all of a sudden, every design file becomes a URL, not something that you have to download and download an application for. So that bet that Evan and Dylan took on the technology was probably the first thing. That, you know, people didn't necessarily believe that we could get to that level of fidelity or performance on the, on, in the browser. The second and maybe less obvious thing is how Dylan went around and really got the influencers on board. People in the community who, whose voice mattered, whose voice people listened to. And so he actually visualized Design Twitter into this graph And figured out the nodes that were the largest of, okay, who's following who, and then went to them and showed Figma, got their feedback, kept going back to them, not trying to sell them it, but just show them Figma and get them excited and kind of loyal because it's kind of coming back and responding to their feedback, and over time making them want to talk about Figma. And I think a important component of that was that he was supplying them with like an idea …

AI assessment note: “I think the first thing was just the bet on this technology web.”

Q What has building products across many different companies taught you about building product? And in the sense of Figma and Uber, Microsoft, Google, all, I assume, build products differently are all known for great products in sort of different contexts.

A It's, it's interesting because, you know, I think about all the different chapters of my life of different kinds of ways of Building product. Like for example, at Microsoft, I started and it was very much about the super detailed spec, um, where if you were the PM, you could be the PM just on control Z or, you know, undo feature of Excel or something. And you would kind of have to think about all the different edge cases and how they would work and document it really well. And, you know, it would have a few, we would even have a process, which we called a design change request, a DCR. Which was when you realize that your spec was wrong, and you actually had to go through a formal process to get it amended, because it was already in development phase. And obviously, you know, I'm sure Microsoft has since changed its ways, and, ah, you know, it's an artifact of kind of more boxed software. But, you know, nonetheless, it kind of was about, it was a culture of extreme attention to detail, which has stayed with me for forever. And on the other hand, you know, when I went to, for example, Google or even Uber, the area of what you're responsible was so much bigger. And so you had to really invest in kind of like the team understanding the problem so that they can make their own local decisions well, because you couldn't possibly make all those decisions. And so, you know, investing a …

AI assessment note: “at Microsoft, I started and it was very much about the super detailed spec”

Q Figma product, what are some of the most important things that you figured out That if you were talking to another product leader or CEO who has a single product company right now that's doing very well and is thinking about their next set of products, like, are, are there big ideas that you've kind of worked through now over multiple years that would be useful for them to know?

A I think about, for example, dev mode, um, which is kind of a product that we started selling this year at the beginning of this year. And I would say internally, it wasn't always clear to us that this is something that We had to do, or I think we had kind of an intuition that developers were struggling inside the Figma product and we could do better for them, but we didn't really know necessarily that like productizing that something for them is going to kind of have a really strong impact. So there's a lot of debate about that, whether it's worth it. And it turned out as soon as we launched, it was very clear that people needed it and wanted it and people were buying it. And so it's one of those things where like, We could have been a little bit less precious. I think there's this idea of, you know, the gravity of a second product or a third product, you know, and as a result of that, kind of wanting to be a little bit more thoughtful before putting it out in the world. And I kind of feel like we could have been a little bit more experimental about it to test it out, test out the waters, and not feel like pressure that, oh, this is our second or third product, and therefore it has to be a certain way. I think the other ones are, I think the best products come out of a little bit of, like, internal conflict around people standing up for a use case in a way that, like, is counte…

AI assessment note: “We could have been a little bit less precious.”

Q If you think about quality of product in one dimension and quality of go to market in the other dimension, are there certain setups where one is more important?

A I think it's very different depending on who are the people that you're selling to. So LiveRamp was selling to a marketing persona. Rubric, which is where I'm at today, sells to IT and security. The buying process for IT and security is radically different from marketing. So marketing is not going to go and do like a POC on the technology and see if it actually works. Honestly, most marketers are like, hey, like maybe they'll do a very light POC. It's a very different sale. In the IT and security world, the checklist of capabilities you have do matter, and people will actually issue an RFP for like Like the hundred things that you need to do, and you need to be able to answer that well enough, but then you also need to be able to run a proof of concept and show up well with respect to your competition. And I just didn't see so much of that on the marketing side where people were making buying decisions much more, I guess, much less rigorously than they do on the IT and security side. So I think in some ways, like on the marketing side, A lot more rests on the go-to-market and having a good go-to-market story to tell and is less, a little less about the quality of the product. The quality of product obviously always matters, especially in retention and other things like that. On the security and IT side, quality of product matters a lot.

AI assessment note: “I think it's very different depending on who are the people that you're selling”

Q When you think about other hiring decisions you make, are there some where you are not willing to have someone learn the domain?

A Yes. So for the security products, it was the first direct report hire that I made when I came to Rubric. We had no one on my team who had ever built or sold a security product before. I was like, this is including me. So I was like, this is pretty bad. If we want to be a security company and we sell a lot to IT, but we wanted to do more in security. It was like, we have to have a heavy hitter that has security domain experience and can really help us figure out our vision and strategy for how do we sell into security. Zero compromise on having the domain experience. I needed that 100%, but I also needed someone who could really act as like an entrepreneur in a bigger company because we had ideas about the strategy we were going to take, but we didn't have clarity on what were the salient problems that we could solve coming from the space that we were already in and the assets that we already had.

AI assessment note: “Zero compromise on having the domain experience. I needed that 100%”

Q What about sort of continuing to build on this? Anything else for other people who are thinking about their second big product? Their first thing is a hit. They're at 50, a 105 hundred million, and they're starting to think about their next product that is sold to someone else that you've kind of debugged or figured out that would be useful for them to keep in mind.

A I think when you think about building your second big product as early as possible, identify sales, S E marketing resources that are going to be dedicated to figuring out how to get traction with this new product together. And don't make it 20% of their job, make it a hundred percent of their job. Because I think often what happens is like going and pitching this to 200 customers or 200 prospects is the easiest way to validate whether the idea, the concept of what your second product is going to be and what you're going to include in that Resonates or not. So the earlier you can do that in tangent with starting to build some thought around what the MVP is, the faster you're going to get to a solution that really can sell in the market. And I think a lot of what companies do is they're like, well, let me go do the product engineering work to build the second product and product is going to come up with some viewpoint on this. Yes, product should come up with a viewpoint, but the actual going and having those 200 conversation and having people that are really good sellers go do that, that's going to help refine The product very, very quickly in terms of what is actually table stakes.

AI assessment note: “as early as possible, identify sales, S E marketing resources that are going to be dedicated”

Q want more from Matt, we've also linked a preview of his book in the description of this episode. Now, let's dive into my conversation. I thought maybe we could sort of start at the top, which is sort of outlining how you think about growth levers, maybe what's unique about it, and what do you think most people don't get about the topic in the context of early stage startups?

A So the whole idea of growth levers, this came to me when I started, like when I first became a VC, and you're a VC, right? You get pitched and you look at these decks. And since my background was growth, I'd really focus in on their go-to-market, their strategies. And most of these plans were just lists of tick box of like ChatGPT could have written that slide. No problem. But most of these things aren't going to work. And most of them, even if they did work are going to be small and like, fair enough, these people don't have growth experience and they haven't really started to scale. It's early stage. But I thought about my time at PayPal and like, when I look back on it, all of our growth, all of like, 95% of it came from like five things. So in the beginning, before I got there, It was just getting on eBay, getting eBay sellers to start using it and that turned into a network effects loop. And then when Dave hired me, it was all about web developers because developers are building all the checkout flows. So we can just do developer relations and get out there. And so that was like S curve number two. And then shortly after that, we started partnering with all the e-commerce platforms, the shopping carts and hosts, and like the progenitors of Shopify. Because of course, you know, if you're putting up an e-commerce site, you're eventually going to need to add payments. Find th…

AI assessment note: “most startups, you just get one thing working really well”

Q When you think about the levers, you were mentioning this a second ago, is it generally just in a handful of buckets? Like there's a handful of areas you can pull from? Or every now and again, you do have true invention in something net new.

A It's generally going to be pretty similar, right? There's some network effects playbooks, there's some ad playbooks, there's some influencer playbooks. But as soon as you get more specific than that, that's where you need to get creative. And it's not always novel, but sometimes you're copying someone who solved an analogous problem in another business. But a lot of these businesses end up combining a couple of things together. There's content and inbound, and then there's a network effects flywheel or something like that. Once you've got your particular bottleneck, I'm looking at who solved this particular thing really well. So for example, multiple use case customer, heterogeneous customer segments. So Calm solved that really well with their boarding flow where you kind of have this multiple choice questions about what you're trying to achieve. So I've now used that with B to B businesses where they have multiple use cases and they go into a similar boarding flow there. No one's doing that in B to B and it works great. Einstein said, if I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem. So it's really getting super clear on what it is you're trying to solve. And then often when you get in there, like sometimes you need to invent, often you can copy something, but it probably won't be from a competitor. It'll probably be from someone who's in an…

AI assessment note: “It's generally going to be pretty similar, right? There's some network effects playbooks”

Q How do you figure that out in the context of customer development? Or is this just something you're on the hunt more broadly? It's not something you're trying to prosecute in a customer conversation.

A No, I am. So in a customer conversation, I'm like, okay, where did you, when did you first realize you needed to do this? Where did you look? Who did you ask? What did you do first? What did you do next after that? What did you do next after that? And usually in some conversation that'll come up. So I have an example in my book. There's a guy named Bob Mesta. He is the, uh, one of the co-creators with Clayton Christensen of the Jobs To Be Done Framework. And he told me this story. He had a, his business was selling houses to empty nesters. So smaller houses when the kids, you know, leave and you're going to downsize. And he was interviewing a customer and he said, you know, okay, do you remember like, you know, take me back, talk me through it. When did you decide to buy this house? And she said, okay, I was having brunch with my husband. And he's like, okay, where were you? We were in this diner downtown. What were you wearing? And she said, God was wearing a black dress and you were wearing a black suit. Why were we wearing a suit to brunch on a Saturday? Oh yeah. We would just come from a funeral. One of our friends had died of a stroke. You know, he was 53 and it was like, oh my God. And we sort of realized life is short. And then we were like, you know what? We've been thinking and hemming and aahing about buying this house. Life is short. We should just go ahead and buy t…

AI assessment note: “No, I am. So in a customer conversation, I'm like, okay”

Q So share a little bit more. You get on a Zoom where you meet one of the 40 founders you were sort of hashing this out with, and you said, I have three different directions, and you Pitch them. What did you actually do? It sounds like the sort of general concept for Crossbeam bubbled significantly to the top. What did that actually sound like?

A Yeah, it sounded like there was always a next step. With a lot of these, you know, the Escape Room one, people loved it, and they're like, oh man, that'd be so cool. They'd probably have a lot of fun playing that. Oh, well, see you later. Uh, with the Crossbeam one, it was like, you know who you should really talk to about this? Because I know they've run into this problem is so and so. Or do a field or something where you can put me on your mailing list so I get updates on like when you've actually started building this thing. I felt like coming out of the conversations where I talked about Crossbeam, I was actually cultivating a waitlist or like some sense of pent up demand. And this is why Crossbeam is so cool as a business. The business itself is intrinsically viral. We're like LinkedIn for data. Like you can't use Crossbeam unless all your partners are also on it, or at least your most important ones are. So you are Intrinsically motivated to invite your partners on and talk them into joining, even independent of us doing anything at Crossbeam. And I started seeing that virality kick in before the product even existed and we could build viral loops into the product. It just happened by a telephone chain. I would get text messages like, you know, the partner manager at our biggest partner was interested in talking to you about this idea because they desperately need some ki…

AI assessment note: “it sounded like there was always a next step. With a lot of these”

Q left off, you went to 20 or 30 founders and kind of workshopped a few different directions. Crossbeam was resonant, at least in the sort of forward momentum. You intersected somebody and they would say, oh, you need to talk to Jane or Sally or this person or that person about it. In sort of as a detailed way as you can, what was then the following six months like?

A This is where the repeat founder thing gets really interesting because After talking with a lot of founders and getting some conviction around the idea, I did a couple of things in parallel. One of them was that I initially funded the company. Like I wrote a check into a bank account, created the C Corp, like basically issued a safe note to myself to initially stake the company. And one of my close coworkers from the RJ Metrics Magento days had, had left Magento and started his own development shop. His name was Buck Ryan. His company is called the Buck Codes here. And I hired Buck at the Buck Codes here to build an initial prototype of this product that I wanted to use to then, A, understand, like, the materiality of the technical challenges that existed, and, like, I wanted to pick something that was, like, actually going to be hard enough to create a defensibility moat in the technology itself, and I knew Buck was, like, the guy to really, really rapidly do that, so I went into a fairly technical mode, really for just, like, an initial month or six weeks, And then when we had like the most bare bones version of a prototype, so this is immediately next, right after this six week period is when we started meeting with the personas that we thought were going to be the buyers. It started out with RevOps people and then RevOps people led us to partnership teams. This is where I a…

AI assessment note: “I did a couple of things in parallel. One of them was that I initially funded”

Q You mentioned you were, you were kind of dual tracking these, you're basically gathering data on the three potential directions to go, and part of it was the pipeline product and doing product marketing and bringing it to market. In that process, did you get really strong customer resonance?

A That's a great point. That's a really great point. Yes, we did. And in fact, what was interesting is the form that it took, because we certainly started selling the product directly, like as new business and kind of finding These traction points where we were having a lot of conversations in the sales pipeline that were getting closed out as closed loss because people ended up buying Looker or ended up buying some other thing. And what we were able to do is basically pivot all those conversations, even ones that we had lost in the past into sales opportunities for the Argyometrics pipeline product. Because if you are using Looker sitting on top of Redshift at the time and you were like, oh crap, I really need to get my MailChimp data in here. There was no good way to do that in an automated kind of Cloud-based fashion without your engineering team writing a bunch of scripts to pull the data out of the APIs and drop it into Redshift. We were that middleware glue. So we actually made the Looker installs more valuable. We made the Redshift usage go up and the storage and compute that happened there go up by just kind of making the amount of data that was available more robust. You know, Looker, who used to be our biggest competitor over at RJ, started becoming our biggest refer of business at Stitch because the Looker sales reps knew if they were going to close a deal, Their clien…

AI assessment note: “Yes, we did. And in fact, what was interesting is the form that it took”

Q An interesting place to start would be telling the story about how you got involved with Stripe. And maybe what, if you can recall all the way back then, like, what did the first few months actually look like?

A So this was in late, and I had gotten an intro to the Stripe's founders from a colleague of mine. His now wife worked at Stripe, Christina Cordova. And my first conversation with the founders was, well, with John specifically, was telling them that they had done all of their websites wrong. And he asked me to critique a recent landing page they'd put up about Stripe Connect, the very first early version of that. Just the transfers API. And I went through it and I gave him some unvarnished feedback. I think he quite liked that. And I convinced them that they needed a marketer. They hadn't had a marketer until then and convinced them that they needed someone to think about marketing as a discipline full time. So I joined in, in late, 2013. The first couple of months, it was a crazy velocity place. Like we were launching things left and right. And I remember the very first launch that I worked on was the launch of multi-currency support. And even then, like before I started at Stripe, I already knew it was kind of special, right? In the conversations that I had with the founders, they had never done marketing before, but they already felt like they were 10 steps ahead of me. They had all of the right instincts. They were thinking about brand from day one. And I knew what I could do is kind of come in and help polish and amplify the work that they were doing, but I wasn't going to …

AI assessment note: “I had gotten an intro to the Stripe's founders from a colleague of mine.”

Q Maybe sort of building on the last point, how would you think about prioritization? You know, as that first, there could be an endless list of things that you could spend time on.

A Yeah. I mean, I was the first marketing hire at Stripe when it was 60 people. And so I saw it all the way through to around 7500 people. And in each chapter, there was a different set of priorities. But in those early days, Especially the first three years, I was the only marketer there. You know, every day was a constant exercise in prioritization. Do I work on a sales narrative that's going to be used for the next year by every single salesperson? Do I work on a blog post that needs to go out the next day? Do I work on, you know, ICP and, and buyer journey research so that we can get a little bit more crisp and articulated on who we're targeting and why? Competitive research and dynamics. So again, like the possibility space was endless in terms of what I could be doing. So When I advise startups now, what I suggest is that the job of the marketer, especially at the beginning, but actually on a constant basis, is that of diagnostician. You really need to kind of dial into what are the needs of the org today, right now. Maybe the horizon that you can take a look at is like the next three months, maybe the next six months, but at a hyper growth startup, probably just the next three months. And what you want to do as a diagnostician is to understand, again, what's the health of the funnel? You know, are we weak at the top of the funnel? And so do we need to do more demand gen th…

AI assessment note: “what I suggest is that the job of the marketer... is that of diagnostician”

Q of selling. I'm interested in maybe what else you figured out in marketing horizontal pieces of infrastructure or products relative to, you know, Stripe in the early days, you want to accept payments is very clear problem, very clear solution. It's entirely different when you have a horizontal Product. Is the answer always trying to verticalize? Are there other things that you've kind of figured out around marketing horizontal products?

A Look, like I've only had the pleasure of working at platform companies in my career at this point. And I think each of these kind of falls into slightly different axes that you might cut on. When I think about something like Stripe, even though you think of it as simple as accepting payments, there are so many differences in terms of the business model. So, you know, you have SaaS companies that need Description billing. You have e-commerce companies that are doing sort of one-time transactions. You have marketplaces like Uber or Airbnb that are not only accepting payments, but then paying out to the supply side of their marketplace. And so at Stripe, we actually started off with solutions that were based much more on your business model. And we would create these solutions for SaaS companies, for marketplaces, for e-commerce companies that would collate together a bunch of capabilities across our platform and API. At OpenAI, for example, Even though there are some horizontal use cases. So for example, you know, you might want to do something around semantics or, or you might want to do something around a better natural language processing or a better text to speech or translation type of use case. These feel fairly horizontal and, you know, a financial services company can just as easily avail of these three solutions that I just mentioned as someone in a manufacturing industr…

AI assessment note: “I think each of these kind of falls into slightly different axes that you might cut on.”

Q to cultivate a sense of playfulness and make a mess. Overall, it's a fascinating conversation with someone who has been at the forefront of some of the most important shifts of technology, Over the past few decades. And now, please enjoy my conversation with Sam. Maybe we can kick it off. I I'm curious, what is the last 30 years of building software taught you about market timing and technology?

A The one thing that I've learned that's like an indelible lesson is that the things that are worth doing are really uncomfortable. The right time to do something is when you have that like feeling in the pit of your stomach that's like, oh, this is a great idea and it's going to suck to build this because nothing's ready yet and there's all these problems. Like we had that problem With gdocs for sure when we did rightly originally and like JavaScript was there, but it was buggy and not standardized and there weren't any frameworks and there weren't any tools and the cloud didn't really exist. The mistake people make the most is, you know, they'll be like, well, that's a cool idea, but it's really hard. Let's wait three years till it's easier. When it's easier, there'll be a million people doing it and there's path dependencies in there and stuff like that. So I see that lesson all over the place right now with AI. Honestly, there's plenty of legitimate criticisms about what does and doesn't work right now. But, like, none of those really seem to matter that much to me. Like, they all just seem like engineering problems to be solved, and you can kind of see them evolving over time. Last year, the issue was it's expensive, it's slow, token windows aren't that big, whatever, whatever, you know, over time. Hallucinations are still a problem, but the grounding problem is getting deal…

AI assessment note: “The right time to do something is when you have that like feeling”

Q advising many startups like FAIR. Whether you're a founder in the early stages of building a marketplace startup or a leader looking to reignite growth in a more mature business, I think you'll find tons of insights in this episode. With that, I bring you my conversation with Casey. Maybe we could start by talking about some of your thinking on what are the requirements to make a marketplace work?

A There's a bunch of different factors that can help or hurt your ability to build a marketplace successfully. But generally the first couple you think about are, is there supply fragmentation? Meaning are there lots of different suppliers that, you know, a buyer might be interested in purchasing from if there's only a few, like say in flights. Then it's hard to have, like, a high enough take rate to build a successful marketplace. So you see with the Expedia's of the world, the bookings of the world, they actually make most of their money off hotels where there's a lot more variety and a lot more fragmentation. So that's generally the first thing you look at. The second thing you're looking at is either high frequency of need on the demand side or promiscuity, for lack of a better word, on the demand side, where people want to engage with different suppliers over time. So for Grubhub, you know, where I spent a long time, people don't want to order the same from the same restaurant. Every day or every week, they want to try Thai and Chinese and pizza. So there's a natural usage of the marketplace across different suppliers. If you're only going to work with one supplier, you know, forever, then the ability for the marketplace to add value and keep those transactions on your platform goes down dramatically. So those are kind of like the three high levels, but for most of them, the…

AI assessment note: “generally the first couple you think about are, is there supply fragmentation?”

Q customer, in most cases, maybe the top end of any marketplace, the number one or 10 or hundred don't need more customers. The top 20 restaurants in San Francisco, if you were to go to them and say, do you want more customers? They say, no, we're basically filled. But once you get outside of that, is there little work to be done on the supply side in that thinking?

A Well, I think what's interesting about that example is for OpenTable, that might be true, but for Grubhub, that's basically never true because the kitchen is essentially always an underutilized fixed asset. They can always pump out more food. 99.9% of restaurants can pump out more food. It's the front of the house that gets constrained because they're filled with reservations. They think of that as really high margin revenue because they're going to pay the rent no matter what, right? So if they can pump out more orders and pay the same amount of rent, yeah, there's food costs and stuff, but that's generally a smaller part of their expenses. Even the top end are like, yeah, I'll take more catering or delivery orders for sure. I think what you're seeing with the first generation of marketplaces was basically exactly as you said. All I care about is demand. So a lot of the product development is iterating on the sales value prop that I can convince you I can bring demand. What you're seeing with a little, a little bit more of the next generation of marketplaces is it requires more products to be in a position to drive demand scalably. Maybe you need to build out some workflow products. Or maybe you need to build out some sort of free management software to kind of unlock the ability to drive demand. And that was the case with fair of we kind of need you to start using this free w…

AI assessment note: “next generation of marketplaces is it requires more products to be in a position”

Q Everybody has reference points of how Airbnb maybe grew or Yelp grew or sort of through SEO. What are examples of like emerging marketplaces where you met or watched what they were doing in the past year or two? That's like a fresh example that brings it to life.

A I hope I don't get in trouble for saying some of these. A company Angel invested in is called Power. It's a clinical trials marketplace. Part of what they've done is, you know, a lot of people are searching for alternative methods of care due to chronic pain or things like that. And they're going to Google and they're searching these really detailed things. And they're trying to find things that their doctors aren't even aware of that maybe are going to help, you know, cure them or, you know, mitigate their problems. So they've built just the most informative pages you could possibly build on those topics. A lot of those treatments are in clinical trial stages where the people that are running those experiments are trying to find diverse groups of people to actually get into their experiments, and it's really difficult to find those people. So they're able to matchmake people that are looking for alternative forms of care with the latest and greatest clinical trials out there, and the pharmaceutical companies and the people running those experiments are desperate for that sort of audience, which is just really hard to like find its needle out of haystack. So you have a bunch of people Power is being able to raise their hand and say, like, I'm actually looking for this. So that's working really well. I have a company in the SaaS space I advise called Fermat Commerce, and they re…

AI assessment note: “A company Angel invested in is called Power. It's a clinical trials marketplace.”

Q When you think about all the marketplaces that you've studied and so many of the marketplaces that you've actually worked on, are there specific patterns of errors that you keep coming across?

A So one that comes off the top of my head is that marketplace businesses need to get sophisticated around data, usually a lot more quickly than other models. And part of the reason for that is Once you're doing more than one category or more than one city or neighborhood, the aggregate data doesn't really tell you anything anymore. The only trends that are interesting are mapping by specific geo, by specific category, by specific acquisition channel. So you need to ramp up your data sophistication a lot more quickly, or you end up seeing stuff in aggregate that's actually conflating different trends in different, you know, cities or different categories. So a lot of what I end up working on when I work with marketplaces after like series A or series B is like, okay, Let's go build up your data capability. Let's go build up the dashboards that your team should be paying attention to. Let's really make sure you're looking at the appropriate slice of data. And then I have, you know, all the examples I've used from other companies to help them, you know, do that more quickly. And, and then related to that, which we talked about earlier is what's that custom activation metric on supply and demand. A lot of times people are acquiring a lot of supply that doesn't really get any transactions and then that supply will turn. So you can generally figure out How long is the supply going to …

AI assessment note: “marketplace businesses need to get sophisticated around data, usually a lot more quickly”

Q In terms of inculcating that in the company, do you just behave that way? And everyone models that behavior after you, and so it's sort of you're unconsciously aligned, or is it something you're constantly pushing as a small team?

A I think a lot of a company's culture is a consequence of its founding team, or what have you. I think the founding team at Unblocked, and you know, same with BuddyBuild, was customer obsessed. It was funny, when we looked through our, kind of, all our support channels at BuddyBuild, the two, the people who answered the most questions over time were my co-founder and I. He beat me. Which just drives me nuts to this day. I would get up at four or five o'clock in the morning and do support for buddy builds, and it's the same thing I do with unblocked. Every piece of feedback that comes in, I see it. I respond to most of them at this point. I think that will be less likely the case as time progresses, but I will see every single one of them for sure.

AI assessment note: “I think a lot of a company's culture is a consequence of its founding team”

Q I just want to wrap up where we always do, which is when you think about sort of product building, product orgs, all the stuff we've talked about, is there someone in your career that's kind of most imprinted themself on you?

A I think the two people that have had the most impact on me are Gokul Rajaram and Alyssa Henry, both at Square. Gokul was leading product and engineering when I first got there. Gokul was extremely focused on speed and moving quickly. We were spinning up most of Square's product suite. He started in 2013. When I came there, it was basically like exactly how you described it. Like we've got this wedge product. Great. We're gonna build this business operating system on top of this wedge product. Payroll launched, customer engagement launched, appointments launched, invoices launched, all these things just like bam, bam, bam, bam, bam, bam. He had such a drive and sense of urgency to him. He was so humble and like kind, and he's been a mentor to me and my, my whole career. And like, I, I just like, I love that guy. He's a beautiful human and he, he's, and so teams felt supported, but like moving quick.

AI assessment note: “I think the two people that have had the most impact on me are Gokul Rajaram”

Q If I were to listen to the last 200 interviews you've done for People that are in the shape of the CSM you're talking about for kind of a zero to one CS function. Are there other types of questions that you constantly go back to over the years?

A Yeah, there are three. So the first is think of a, a brand that, you know, likely a consumer brand that you think delivers excellent customer service. And oftentimes they'll say something like Nordstrom, Four Seasons, you know, my corner deli. And I'll say, okay, tell me the, the aspects of that great customer service and what does it feel like When you get great customer service, and what does it feel like when it breaks? And that tells me, like, how empathetic are they to a given person's experience, and how, like, reflective are they on their own experience as a customer themselves in some context. So I love, I love that. We use that one very often. The next go-to for me, especially in very fast-growing organizations, both for ICs and for managers, is I'm really looking for some self-awareness, because that drives leadership behaviors, that drives An ability to work well with a very varied set of people internally. And that also gives me a clue about somebody's ability to grow with the organization. So my question there is, tell me about the most impactful piece of feedback that you got in your last performance review, or maybe I'll say in the last two years, depending on their career level. And that's all they, that's all I tee up and whatever their answer is, usually people go to like negative feedback and then I'll dig in and I'll say, okay, so how did that land with you?…

AI assessment note: “Yeah, there are three. So the first is think of a, a brand”

Q What is a V-one? Again, it's super early days, couple million dollars in recurring revenue. What do you think a V-one of the implementation process looks like?

A I think it's in a Google Sheet or maybe a monday.com workplace. It's, it's a templatized, you know, ten-step process. The details of that implementation are different depending on the product, but it always includes something like a kickoff call, a requirements gathering conversation, ideally a conversation about Who is involved on the customer side and who are going to be the users? A conversation about what are the integrations we need to do? And then it's planning out each of those steps. What data do we need to gather and who's gonna, who's on point to actually deliver those files or to build that API? Next is, you know, who's on point to build the communications, the actual communications that are going, going to go out to our users when we roll out this product. The last step that often gets missed is how are we going to measure success? If organizations can get in the habit of asking this question from the very, very early days of the business, they are going to be set up way better when the business starts to scale, because you're going to have a stronger understanding of how, like, what is the path to value for a customer? And you can start to build in, like, more value-based conversations with customers in a way that avoids the CS team being really reactive and only, like, ticket focused. But oftentimes that, that question gets overlooked because it's hard to define. …

AI assessment note: “I think it's in a Google Sheet or maybe a monday.com workplace.”

Q And maybe to wrap up where we always do, and this may be particularly tricky for you, given your career, but is there a person or persons that had an outsized impact on the way that you think about all these topics that we explored?

A Yeah, it was the first engineering manager at Netscape, a guy named Tom Paquin. He was the first engineering manager. I was kind of a new engineering manager in a different team at Netscape and Netscape once again, high buzz, instantly found that relevant party. And they were all hockey players and I was a hockey player. So playing hockey with all the founders for like two years. Um, and he was part of that crew. I left Netscape when, uh, just before they got bought by AOL and he actually came, this is, I really admired this guy, engineer from SGI and just super terribly smart. You know, these folks, you're like, Oh God, you're so much smarter than me. I'm not going to be like this ever. And, and he was just, he was just willing to give his time. And I was at the startup as their first Engineering leader. And he came over to consult for us. He was like our interim VP for like, I don't know, seven months or something like that. He said something that changed my life. And this is the moment that there was, it's the reason you and I are sitting here right now, by the way, he said, I was sitting in the ping pong room at Icarion, which is the name of the startup. And he goes, Hey Lop. I don't remember the reason we were having this conversation. He's like, you're a pretty good engineer. I'm like, oh, thanks. He's like, you're never going to be a great engineer. You're never going to…

AI assessment note: “Yeah, it was the first engineering manager at Netscape, a guy named Tom Paquin.”

Q You joined Figma in the early days. You were the first salesperson, sales leader, and I'd be really interested to hear about what the first few months looked like and would love you to sort of share in as much detail, even if it feels mundane or boring in terms of what are the things that you did when you showed up for the first day?

A When I joined the company, it just raised our series B a few months earlier. I think there was about 40 to 45 employees, give or take. No sales team. We had this product that was in a beta. So when I joined, the first thing I wanted to do was, was meet customers, get closer to the product and understand like how people were using it. There wasn't any really formal documentation of notes. So Dylan and Claire, who was on your show in the past and a few other folks had been talking to customers and working with customers and converting customers over the years. So even though they didn't have a sales team, founder led sales, community led sales was kind of happening in some ways. But there was no documentation. I couldn't come in and read the notes on working with Microsoft and what that relationship looked like. So that was a challenge. I had very limited information on what was happening in the field. And the other thing that Dylan asked me actually to do in the first month of joining was to join the support team. He asked me to not do anything on the sales side, spend four months with the support team in the support queue, working on bugs, understanding the ins and outs of the product. And at first, You know, I was a little taken aback. I was just hired to help this team build their business, drive revenue, hire a sales team, and now I'm going to go hang out with the support te…

AI assessment note: “Dylan asked me actually to do in the first month of joining was to join”

Q there other rituals in the early days that you all did that were kind of interesting or unique? You talked about sales tales and you just talked about in the interview process, Each potential rep had to demo the product for you and Dylan and others. Are there other sort of unique little rituals or part of the operating rhythm of the sales team that might be interesting to explore?

A Yeah. I think one thing we thought about was that, Hey, we're trying to sell to an audience and we have some of the best people in this profession at the company. And in the early days we were all in San Francisco, you know, we, COVID had not happened. We were very much And office culture, and so we would actually go to design crits, design critiques, where designers and product managers would come in, and they would tear down things that people were building and shipping, and it was so interesting because we got to see all of the level of detail that they were tearing down these products, and you start to open up your mind, you know. I mean, I think of myself as a little bit creative, not a lot a bit creative, but when you spend time with creative, and you see what they see, it's like a whole different world, and so I think that curiosity and spending Time with people where they were like, yeah, totally. Bring your sales team in. So be myself, six other reps, and we'd come in and they'd even like, let us ask questions. We don't want to derail the meeting, but a design crit is a huge part of the development process and the design team getting together and kind of sharing ideas, critiquing them, of course, tearing them down, building them back up and coming out more aligned on, all right, what we're going to go do and what we're not going to go do and not going to go spend time …

AI assessment note: “we would actually go to design crits, design critiques”

Q Are there downsides to that or, like, local maxima problems you run into when your primary company value is peace of mind and or fear-mongering security and not much else?

A Yeah, this is, this is what I call the, the SAML, you know, SSL hostage play that many companies employ, including my own companies in the past. Here's the downside. The downside is Very likely, your upside case is essentially you have securitized and stabilized your initial organic user base. So at Dropbox, it was very common was we would see people pay, like, seven-figure deals, right, for the enterprise-grade version of Dropbox that has all the security bells and whistles, but it was primarily for just that 30% of employees. You don't really develop trust And the right relationships with the shared services and procurement and IT to really, like, deploy out wall to wall. Of course, you try, right? And I would say maybe five percent of your enterprise customers are able to actually leverage that access to IT's interest and security to drive a bigger deployment. A lot of times you're basically trading seats for a discount. You know, traditionally, traditional enterprise software, you think of Yourself as a company, as a strategic partner to the, to your customer. How can I help you actually achieve your company goals? And the hostage play, you're not setting up yourself for that kind of relationship. And so the local maximum, I like the way you, you framed it. Oftentimes the local maximum is your existing deployment and whatever happens organically, and you don't get pulled. L…

AI assessment note: “Here's the downside. The downside is Very likely, your upside case is essentially”

Q know, and you're getting together with the founder and they're trying to figure out what the next few years are going to look like and how do we think about enterprise and all the other types of things that we talked about. What are the most important things that you tend To convey or kind of most crystallized pieces of advice that you would tend to share with that founder?

A It comes down to that user value, uh, conversation we had earlier, which is, so I think there's actually two junctions from some PLG companies, which is some PLG companies start as an individual. Their product is really good for the individual usage, and now they want to go into teams. The value for a user versus the value for a team May require significant product changes and packaging and go to market changes. And then what we've talked about for the past hour is really going from small teams to big enterprise. Again, right? That value is very different. I always ask founders, even early on, even if I'm like, uh, evaluating a seed investment, I ask like, what's your hypothesis, right? Of what is that? If it's an individual, if it's like a, Productivity app, like one of those email or calendar apps today, right? It's like, okay, what's your hypothesis of what that team level user value, team level value will be? What are the problems you're solving on the team level? And then what are the problems you're solving on the enterprise level? You have to start generating these hypotheses as soon as possible and as early as possible so you can test them, test them, and validate as you go. Otherwise you end up with a company like, uh, like Evernote. Um, Evernote was a tremendous individual productivity app. It was, like, one of the first PLG companies. It's like, I still have it. I th…

AI assessment note: “You have to start generating these hypotheses as soon as possible and as early as possible”