RevenueCat's Rick Hendrickman and Gusto leaders discuss how mission shapes product and growth. Hendrickman introduces the Generosity Ladder framework, showing how Patagonia, Canva, and HubSpot move from basic tactics to customer championship. A Japan market case study boosted traffic 50% by aligning pricing and community with mission.
Chapters
CTO, Invisible, Kit Colbert. Please welcome to the stage co-founder and CTO, Rubrik, Arvind Nathrakasha.
Marvin, good to see you.
All right. Well, good morning, everybody. How you doing? Try that again. I know we're the first show of the day. How you doing? All right. That was a bit better. Hold on a sec. Sorry. What's up? What slide are we on here? There we go. Okay. Thank you. I couldn't see what slide we're on. I'm blind without my confidence monitor. Okay. Well, my name is Kit Colbert, formerly CTO of VMware. Took some time off and just 3 days ago started as CTO, platform CTO for Invisible Technologies. I'm already phoning it in here at the conference. But we're very lucky to have Arvind with us today Arvind from Rubrik, who's the CTO and co-founder of Rubrik. So Arvind, maybe tell us a little bit about Rubrik for those that don't know the company or don't know them very well.
First of all, thanks, thanks for having me here. So Rubrik, we started Rubrik about 11 years ago, and we, the goal was to really build a platform that provides cyber resilience to businesses. From the time we started to now, Cyber attacks have only been increasing, and our goal is to provide a whole set of tools, both if in case there's a cyber attack, how we can recover quickly, and what can you do before a cyber attack to protect your data, which is your most critical asset. We just finished, as I said, 11 years. We rounded out the last decade by going public last year. So now we're on to the— thank you. And now we're on to the next decade of driving the company forward.
Cool.
So Let's talk about your growth and your progress to date. I think we've got a slide here with some of the high-level numbers. Amazing, amazing results. Again, congratulations. Over a billion in ARR.
Touch wood.
Yeah. So, you know, you started with a core product and then you scaled to multiple products. This is, I think, a challenge a lot of companies have as they're looking to grow and continue scaling. How do you think about that? I mean, I imagine there's challenges both from an R&D engineering perspective as well as a go-to-market perspective?
[Speaker:JASON M. LEMKIN] No, great question. I think— so with any company, right, so as you build out products, there's a certain scale at which, you know, when a certain part of the product grows to a certain scale, you can only grow it so fast, right? Because there's a natural friction. And then after that— so the way we have thought about it is that we need multiple kind of curves of innovation, right? So as one product gets to mature stage, and there's still a lot of growth ahead of it, but it's not going to grow at like 100% anymore. That's when if you can bring in a second product, which now accelerates the business, and that starts growing at like 100% every year, and that kind of— so overall, your overall curve of growth stays high. And we've been doing this. We have been doing this with multiple products. And we think of ourselves as really having multiple pillars in the data protection, data security, and now AI space. The way we have done it is, you're absolutely right, there's the R&D aspect and then there's a go-to-market aspect, which is very critical. On the R&D side, you know, we have been running a lot of the new products that we sell today in our portfolio, all started with a hackathon project. We've been running hackathons since— we just finished our 10th hackathon last year. So we've been doing it from our— from year 2 of the company. And it all starts with a few engineers spending 24 hours hacking up something and saying, "Hey, this is— here's an interesting idea." And then we have— last year, I think we had 130 submissions. Out of that, we take the top 10, we review them, and then we go into— we actually go and review all the presentations and then pick out ideas that may be interesting and then and take it to product. So almost all of them started that way.
[Speaker] Wow.
[Speaker] And then of course there's a whole question of as we get into a new space, there's the whole kind of like build versus buy question, right? And there have been cases where we felt usually when you want to build is either you don't have the skills needed or you don't have the right kind of DNA, or there's a company that's already achieved momentum and if you can bring that in. But there have been cases where we started with the build option and then realized, you know what, let's just go build this. It's easier to build it. And we've gone the other way as well. So there's the whole build versus buy question. And then as you bring these products in or even build it organically, over time as you start scaling, you have to start thinking about platformization, right? Initially you start doing stuff that's very one-off. And then you say, OK, now for example, for cloud products, earlier each team kind of built their own products soup to nuts. Now we are actually taking all the core platform areas and creating a generic platform that you can build on. So that's kind of on the, on the R&D side. And then obviously you have to make investment decisions— how much you invest in the core business versus innovation and so on. And then on the go-to-market side, what we have built is we have actually built a go-to-market incubation engine. So the way it works is when you are starting a new product, typically if it's a new buyer, It's hard for a large core sales team to go and do that because they're selling, they're used to selling in a certain way.
And the current product works well, and they know they can sell.
Exactly. And then how does it fit into the new product? It's hard to go enable like 500 sellers to do this. So what we do is initially we create a go-to-market incubation team, and these folks, they only make money if they sell this product. So they're waking up every day thinking, OK, what's the angle here? And so they can do the initial pathfinding. And then what they do is they say, OK, this is the— and obviously there's a lot of product iteration as well. So it's almost think of it as a pod. There's engineers, product managers, sales folks, maybe one marketing person. They're all iterating on the message and the product. And then once you see, OK, that this has legs, then we transition to what we call a product line sales team. And this is think of it as scaling these 2 to 3 sellers to maybe like 10, 20 sellers who can now go and really, again, they only get paid for selling this product. So they go and now scale this to the next level. So think of it as 0 to 10, 10 to 100. And then once it hits 100 million or something, then you can say now enable the entire course. By that time you've hashed out the message, you've hashed out what resonates, who's the buyer, how do you go. So the whole decks and everything, all of that have been built, right?
Yeah, and I imagine at that point the core sales team would have some conviction in this product. Conviction as well, exactly. And take it to a customer.
And the product is also probably more mature, so you're not hitting bumps on every POC that you do. So then it gets ready to scale. So that's kind of what— so we have this conveyor belt of what we call RX, which is our incubation, to product line sales, to core. And we have done multiple products in this fashion.
OK, that's great actually. I love that. Really good advice. So let's continue talking about scaling. Scaling. And, you know, a couple of numbers up there: 39% year-over-year growth, which is amazing, and 80 NPS. So hopefully people know what NPS is— Net Promoter Score. It's basically a measurement of customer satisfaction, and getting a score that high is pretty rare, actually. I don't know many others that have it. So I'm kind of curious, as you go through this really rapid scale, you're branching out to multiple products, how do you keep that customer satisfaction so high?
Yeah, the one learning for me through this journey is that with, especially with large enterprise customers, I know there's this advice that say no to 95%. I don't believe in that. I think you have to say yes to everything, right? Because if you miss a large Fortune 500 customer, it may be a $100K deal. Down the road, that's a 20, 30x lifetime value you can build with the customer. So you have to figure out, you have to say yes, but then you have to figure out What is that piece of it that they truly need and distill it down to that. In terms of supporting customers, one of my co-founders and I were actually the first support people. So from like the first beta customer we had, anything goes wrong in the field, we'd be the folks taking the call, working with the customer, figuring it out. So, and a lot of our founding engineers would do the same. So there's been a culture of the customer-first, let's make customers successful mentality that's been there from the beginning and that is kind of like—
Interesting.
—percolated through the entire organization. Even today, if a customer says, "Hey, I have a problem," engineers will drop everything and jump on it. Support, sales, engineers, everybody will drop everything and they'll jump on it. So that's definitely— part of it is culture. And we always keep emphasizing emphasizing that make the customer successful, right? Nothing else matters.
So I mean, I think it's important just to pick on that point a bit that you as a co-founder were there, CTO were there hands-on in support doing that. Because I've seen a lot of engineering cultures that have kind of an ivory tower sort of mentality. They're not as connected with their customers.
I'll give you an example. There was an early stage, there was a customer at 4 PM, they said this customer is really in bad shape. It is a law firm. There was some issue and their email system was impacted. So literally I booked a flight for that night, took the red-eye, I was on, I was with them 2 days and just sitting there until it was resolved and then flew back, right? So you got to do things like this in the early stages and you got to show that you care, right? And the other learning is that obviously as you scale, you know, there are definitely bumps in the road, not every implementation will go as planned. But what I've learned is that what customers need is, first of all, be transparent. Tell them— don't try to tell them something that you cannot do. I'll give you an example. Very large retailer in Europe. They have this huge Oracle database. They came and said, we need this, right? And without this, if you don't have it, we're not going to buy you. So initially there was a lot of question, can we do it? Then we realized this is— no, we really can't meet their requirements. So one of my engineers and I actually went in front of the customer. And said, you know what, what you want today, you want like a 7x reduction in time, it isn't going to happen today. But we will invest in this and in 6 months we'll be able to deliver it to you. But if you want to go with somebody else, I would totally understand, we would totally understand that. So you should make the choice. They said, okay, we'll think about it. Next day they came and said, you know what, we'll go with you, right? So being transparent, not trying to sell them something that later they realize there's no There, I think, is key. The other thing is there will be mistakes. Again, with customers, all they want is acknowledge your mistakes. We are very, very open about this was our— we did this, we didn't do this well, this is our bad. And we start with that, we start with saying this wasn't the experience you should have had, these are the mistakes we made, and we are doing stuff to fix that, and here are steps to that we will get you into a better place. And usually when you're honest, I think a lot of people are not able to do that and because their customers get irritated saying, "At least acknowledge the problem." So, that's what we've seen and this is something that we have a truly world-class support team, our Chief Customer Officer, I mean, he's amazing, the team he's built, and he was the one who took over from us and threw us out of the support room and said, "You guys go build the product." And he's really built this great support team. And even when you lost POCs, our customers say, "You know what? Our support team is fantastic. But that's true that it percolates all the way from sales to support to engineering.
And so, yeah, it seems like there's a deep level of customer intimacy that you guys have had from the beginning. Yeah, absolutely. Cool. Okay, so we're on the AI stage. Everyone's talking about AI, so let's talk a little bit about AI. Now, I tend to break AI down to a couple different buckets. We think about it as in terms of technology vendors, How do we build AI into our products? And then how do we leverage AI internally in terms of our internal operations and how we execute? So I'd like you to talk about both of those. Maybe start with the product. What has your product strategy been around AI?
Yeah, so I mean, you're absolutely right. We are thinking of it exactly what can we do in the product and what can we do internally? So let's talk about the product. The product, we think of it as in kind of two ways. One is our product today is not easy, right? If you think of an average customer, they're using the Rubrik platform to protect their data that could be sitting in the data center, could be a VM, could be an Oracle database. They're using it for cloud, for again, cloud VMs, cloud databases, object storage, and then they're using SaaS as well. And then there is, we have built a whole suite of security products, which very often the IT persona may not even have the skills. If there is an attack, they don't even know what to do next. So the first step is to really Can we actually provide an assistant or copilot, if you will, for the Rubrik product to really make it easy? And that's, that's what we call Ruby, is kind of this AI companion for, for, for our, for our SaaS platform. And what it does is it really says, so for example, if you say, oh, we found a piece of malware, they're like, okay, what's an IOC? And we say, okay, this is what it means. And then it's okay, what should I do next? Here's what we'd recommend. And what this does is it uses our knowledge base, documentation, support articles, wikis, and it also is able to query the product itself through APIs, put it all together and answer the question. So that's kind of what we have built. So that's the, I would say, making our product more usable. We want to do as we scale, we want more and more self-service. There's a problem, don't come to support, just handle it. So that's what customers want. Yeah. And that's what will help us scale. The second piece is really about how can we, as our customers are on this AI journey, How can we enable them to build the AI applications they need? And again, there are tons of, you know, there's lots of stuff on the foundation models and obviously that's the core of everything. But most of the time is not spending, it's not spent in foundation models. Nobody's training models. The work is in, okay, how do I get access to the data that I need to solve this particular use case? How do I make sure that first of all, am I adhering to the security permissions? That are there. And then more importantly, even if you have access to some data, what if somebody had put something sensitive in there? So financial documents exposed to all of engineering, right? And then every engineer can ask a question and will get an answer from that. So this is where a lot of the time is spent. Almost like we've heard like 80% of AI POCs don't make it to production. And very often InfoSec saying no, and we even talked to CISOs who said, we don't wanna say no, we wanna say yes. But we don't even get half good answers for the questions we are asking. And then our realization was, over the 11 years we've been building exactly all of this. A, we take all the enterprise data, put it into a single platform so it's easy to access it. It's automatically getting refreshed every day, every few hours. We understand permissions so we can actually make sure that you only see the data that you're permitted to see. And then we can automatically suppress any sensitive information, mask it. So that it doesn't go into the model. Once it goes into the model, you don't know how it's going to manifest itself on the other side. 100%. So, so we were like, wait, everything we're building is, is really well suited for the problems that the enterprise is facing. And that's why we announced a product called Annapurna, which is really around— and it's very early stage. I would say we're still exploring the right use cases and customers are also in a very early stage of innovation. But that's where we see a lot of potential there and very excited about kind of where we are.
That's cool. So what I heard is that there's actually two sort of aspects of this. There's one of how are you enhancing your product with generative AI to help the customer and automate a whole bunch of stuff, make them better at what they do. But then that second bucket seemed to be you're actually helping the customer do AI themselves. Like they have AI projects. Solve—
can we solve the data problem from it? Yes. Focus on your business use case. Focus on your workflow. Focus on building the right kind of agents, but don't worry about the data.
Yeah, which is definitely what I've heard as well, that there's a massive level of scrutiny and concern about what sort of data— do you have data leakage? Where is that going? How can you manage that? Okay, got it. Okay, let's change tactics and talk about using AI internally within Rubrik. How are you as a company adopting AI?
Yeah, so for the very same reasons, we actually at a very early stage, we set up a an AI governance committee. The goal there is to really have InfoSec, legal, everybody kind of like coming together with the goal of enabling AI adoption across the company, but doing it in a safe manner. And that has been very effective. But then there was a lot of like grassroots stuff. Somebody saying, hey, I want to try this. It would go through the committee that evaluated and say, hey, is this secure or not? And then if it's secure, they say, okay, go ahead and do it. So we started seeing a lot of projects pop up. Then we said, okay, let's bring it all together. There are lots of tools being used, maybe we can consolidate. And also we are building with Annapurna, we've said a lot of the stuff we could just build on top of our own platform. So we actually did a whole AI summit. I don't know, you were asking about like how do the legal team handle it. It's actually a VP of legal was actually running that initiative. She's like, let's get this going.
I think this is the first time, but we were talking backstage about this. I think this is the first time I've ever heard of like legal running this thing.
Yeah, so she, she was like, let's go make this happen. Let's, let's adopt a rubric-first strategy so that if we can leverage our platform, let's use that. So what we did, we brought in like people across all organizations. People were sharing notes on what worked, what didn't. We gave some basics on how generative AI works, RAG and all of that. And then now they're going back and they're coming up with— each org is going to come up with these are the business metrics I want to solve. And these are kind of initiatives I'll do, and these are how I'll measure the success of the AI initiative, like AI-specific metrics. So we're going to have that cross-functionally and then make sure that as a business we are able to do that. But one of the learnings is that even if you put these tools out there, people just don't adopt it easily. They try it once, doesn't work, they're like, "Oh no, I'm not going to go there." So you got to keep pushing people to go adopt it, saying, "No, no, no, things have changed. Let's go try it. Let's go try it. Give me feedback. Let me fix it. Now try it." you kind of have to keep pushing so that people adopt it in an organization and while balancing security and everything else that you need to do. So it's not, I mean, if you look at LinkedIn, you feel that everybody is like doing AI, you know, like crazy. But in reality, in an organization, there's a lot of work to make that happen. That's what we're focused on. It's still early, but—
Well, I imagine a lot of people don't really, haven't really internalized how different it is to like their workflows can be using AI. Yeah, like it doesn't occur to them.
And, and to be honest, definitely in on coding, we are seeing some potential, huge potential gains that we see. So we are really pushing everybody to, to start using AI for their coding. Obviously in research and any kind of product research and things like that, there's a, there's a lot of stuff that AI can help with. So yeah, so we are adopting tools and, and trying to do that in a safer manner so that there's standardized ways in which we we can do that.
Got it. We have a few minutes left. I got one little question for you, but if you all have any questions for Arvind, please stand up. We've got some microphones here. So if you have any questions you want to ask them about the AI journey they've been on, what they're doing for customers, how they've been scaling over the last few years, please step on up. One question I had for you was to combine these two AI discussions. I'm curious if you've— your experience doing this yourself, putting AI into practice, And what you mentioned before about how most businesses, they have AI initiatives, but they're not successful. So what are you learning about it and how are you applying that in your customer discussions?
Yeah, I think the, the thing is that you have to bring— first of all, you got to bring all the different stakeholders together. There's legal, InfoSec. You got to make sure that they are aligned because if they are not, then you're going to run into all sorts of bottlenecks. So bring them early to the, to the conversation so that they are they agree that this is the right approach and then everybody is aligned in pushing that forward. And then I think there is a lot of— you got to put stuff out there, people have got to try this and then there's a lot of things that people can use their imagination and they're very creative, they can do all sorts of things with that. But until you get it out there, get the adoption going, you're not going to see what the real value is. So I think you need to give people time to experiment, get familiar, experiment and then The best practices will show up. So it requires— so we are within engineering, we are running it very programmatically. We're monitoring this, we're saying, how— look at the metrics, what will help drive those metrics? Where are the bottlenecks? Are there specific teams that are struggling? So you have to kind of almost run into the program. Got it.
I imagine as well, you being— oh, there's a question. You being founder or co-founder probably helps as well. You'll founder mode on it. Yeah, sometimes I have to put my co-founder hat on and There you go. All right, sir.
Yeah, the coffee's kicking in, so I'm ready to ask him. There you go.
Thank you. First of all, this has been great.
So thank you very much for that. Um, as a founder that's so intimately involved with everything from the customer-facing activities, the sales activities and marketing activities, et cetera, I'd like to know what was the biggest aha in like the last several months, say 5 months or so, that in your go-to-market motion that really surprised you, preferably to the upside, but you could also share to the downside as well, like a particular workflow or particular activity that you did, even from a messaging ICP identification standpoint.
Thanks. Yeah, in the, in the last few months, I think one of the— and we announced this recently at RSA— is that as we looked at data security, what we realized was identity is turning out to be the biggest threat vector. And while we focus a lot on data security, what we did is bringing identity and data together is probably the thing that customers are really looking for. So we almost slightly pivoted our direction to bring both together, leverage what we had done before, but bring this together. And yeah, so I think it's—
I'm sorry, by identity, tell me more. Are you talking about user identity, application identity?
All of them, right? So at the end of the day, people do automation through using various, whether it's Active Directory, Entra ID, Okta, whatever. So first of all, you've got to protect it. If I compromise the identity system, your entire business comes to a halt. Then the question is, if you see something like there's a privilege escalation for one person, could be by design, could be inadvertent, or could be malicious. If you can give visibility into saying this privilege escalation actually gives you access to to 10,000 more financial documents, which they should not. Is this what you intended, right? So surfacing this, because it's very hard for people to get this picture globe across their organization. There are tons of stuff happening, how do you, so can you surface the P0 issues that you should look at from an identity perspective and data perspective? So that's really what we are focused on. Yeah, so it's again, it's early for us, but we are seeing a lot of traction. One of the products we released, that thing is flying off the shelves, probably the fastest growing product in our history. So that's very interesting. Still early stage. In terms of— I think go-to-market is a very interesting journey. I mean, as an engineer, there's a lot of— I call Rubrik my own little MBA where I've learned a lot about how to take a product to market. It's never straightforward. And I think I talked about some of the innovation, how to take new products. I think we've learned that over time. Earlier, we'd throw it at the core team and they'd be like, What are you talking about? Core sales. And yeah, and then we have made some mistakes. For example, in some cases I think we segmented too early and we were not ready for that and that didn't work out well. And then we had to kind of almost break the segmentation and then re-segment again, right? So obviously as we scale, we want separate teams handling majors versus enterprise versus the medium-sized businesses. But the way we did it the first time didn't work, and then we had to kind of almost throw it all back into one group. And even countries, we have gone to countries where we were not successful, we had to pull back and focus on countries where we're seeing traction. Especially internationally, it becomes very complex. We also had separate EMEA and APAC. We brought them all under one single international. We have Americas and we have international and federal business role into Americas and the US. Sales team has gone through a lot of different iterations. And again, there's no one single answer. You've got to kind of like keep figuring out what works for your business.
Got it. Okay, we have just a minute left, but if you want to answer or ask a quick question, we can go into the lightning round here.
Quick question then.
So about adopting AI in the business, how do you evaluate ROIs? Do you really take care of it? Before like a POC or after POC, or you don't really care about it? Please share your experience.
Thank you. Great question. This is actually the biggest challenge because AI is not cheap. And people are saying, OK, if you're going to spend this much, what's the ROI? And this is where a lot of projects are getting stuck because they're not able to prove ROI. So the way we are doing it is we are looking at it as— so rather than just look at any AI-specific metrics, we are looking at business metrics. So for example, I'll give you an example for engineering. For engineering, we are saying, A, can we, you know, we have a plan of release, right, roadmap. Can we reduce the net delay in roadmap items delivering that to the business? So if we say, oh, we'll deliver in Q3, did we deliver 15 days later, 30 days later? What is the net delay, right? And we want to reduce that. So that's a a business metric that the business understands. The other thing is, can we make sure that while we are doing this, hopefully by moving faster, are we keeping the quality bar high? So we have what we call like a quality index that looks at defects per account and stuff like that, or cases per account. And we are saying, are we making sure that we drive those down? So at the end of the day, and then we have AI metrics where we say how much code is generated by AI or how much, what is the MTTR for certain defects and stuff like that. But that doesn't— that's an internal metric. At the end of the day, we measure ourselves by did these two business metrics actually go down, right? If they didn't, then doesn't matter. I can do 100% AI code. If that is not— if we don't meet those business metrics, it makes no difference to the business. So at the end of the day, I think you have to wear the business hat, whether you're in R&D or go-to-market, doesn't matter. You have to wear the business hat and say, are you making things better for the business? What are the metrics you track there? And then have secondary AI metrics that would hopefully drive these higher-level metrics. So at least that's— again, we haven't cracked the code yet, but that's the process we are following right now.
That makes a lot of sense. Stay grounded, focus on the business metrics, don't get caught up in the—
in the high-growth. Yeah, I know signal-to-noise is low.
All right, well, unfortunately we are out of time. Thank you all for the questions, and Arvind, thank you so much. Thank you again.
Thanks for the opportunity.
Thank you.
Please welcome to the stage CEO and founder Gusto, Josh Reeves. CTO Gusto Eddie Kim.
Hello, hello, welcome. Hello, Saster. Saster! We're excited to be here. It's not our first time. I think since I was last here, my family, we had two more kids. So the family grows, the company grows, all in the right direction. I'm Josh, CEO, co-founder of Gusto.
This is Eddie, the co-founder and CTO at Gusto.
And so we're really excited to talk about some of our learnings, lessons in the context of building Gusto, and especially with a focus and a lens on AI. But I want to start by mentioning You know, what is Gusto? And also, what are we most proud of? We're proud to serve over 400,000 companies today. Do we have any customers in the audience? We're honored to serve you. If you ever have feedback on how we can get better, please let us know. But this is our due north. When we think about technology tools, AI is a tool, it's a technology. We don't use it for the sake of it. And a big part of our mission at Gusto is how do we bring the best possible technologies, tools to small business in particular to make their life easier and better. A lot of companies, especially here, will focus on larger enterprise. We really see huge opportunity and huge need in helping and serving small business especially. So with that backdrop, we're gonna get a lot into AI technology, some of the stuff we've learned, built, how we approached it, but I really wanted to start by, by really getting to the core of a belief we have at Gusto, and maybe hopefully many of you share, which is companies don't exist for the sake of it. Gusto, we did not start this company for our benefit. We exist to go fix something, to go serve our customer and solve a pain point in their life. When we think about the journey of a small business owner, especially a huge amount of that pain ties to compliance. It ties to the different rules, requirements, regulations, local, state, federal. The US in particular is more like 50 countries. Than one when it comes to all the different rules and requirements a business owner has to navigate and go through. And so our discussion isn't about how can we go use AI for the sake of it. It's in particular things like compliance and all those rules and requirements. How can we make that easier, simpler, so that a business owner doesn't have to worry about those details in the journey of growing their company? So there's two tracks to the conversation we're gonna go through. And this is how we really think about the way this shows up for our actions. One is that we are, I think, in our view, navigating a pretty massive paradigm shift in how software gets used. So we would call this— we didn't invent the term, but we describe it as the conversational interface paradigm shift. And I think all of us are familiar with using web workflows, whether it's in a native mobile app, on a desktop, in a laptop. The power of a conversational interface is that it is more accessible, more intuitive, and for a lot of different tasks, it actually is just a better experience to have the customer. And we've all used ChatGPT, Gemini, etc. You just kind of start talking and saying what you need and want, and the software, you don't have to figure out where to click. You don't have to figure out which page to navigate to. You don't have to figure out which flow to go through, even if it's simple and easy. You just literally say and tell what you want, or the system asks you, prompts you, and it creates a more guided, more intuitive journey. Now, in our view, this doesn't replace all types of software. To be clear, we're investing a lot of time in the Gusto web app, in our mobile app. You're going to hear shortly about Gus, which is our conversational interface. We think they work in conjunction with each other, but the whole point is to make the use of software, the use of Gusto easier, more intuitive, and simpler for, again, a business owner who's really busy, really strapped for time, doing 20 different tasks, and obviously wants help to do it faster. The second track we'll talk about is automation, and that's just the ability to literally take tasks or jobs and automate them, whether it's for Gusties, which is what we call ourselves, so members of our team, we'll talk about that, or for our customers where, think about something you spend 5, 10 minutes on, if it could just be done automatically for you, time savings is cost savings for a business owner, and that's also very magical. So with that, we're gonna dive into conversational interface, and I'm gonna pass over to Eddie.
I'm really excited to tell you all about Gus today. You could think about Gus as your small business's executive assistant, and Gus is not just any normal chatbot. Of course, it's going to do things like answer questions that you might have about Gusto, but it does a lot more than that as well. Gust can generate any number of reports for you and then also take it a step further to analyze those reports and give you some really critical insights about your business. It can also save you time by you telling Gust what you want it to do. So for example, if you tell Gust, "Hey, Gus, I'd like to approve Sally's time off requests, Gust will look up Sally's time off requests, give you some details about the dates, and ask you to confirm that approval. And if you say yes, Gust will on your behalf approve those time off requests. There's also tens of thousands of compliance rules that apply to small businesses, and it's really hard to know as a small business which ones apply to your specific business. But because Gusto has so much information about our small businesses, Gus is able to tell you exactly which ones apply to you. One of the things that we offer is scheduling. So we help tens and hundreds of thousands of small businesses with setting shift schedules for their hourly workers. Now there's actually a lot of compliance behind how to shift— how to set shift schedules. For example, what kind of breaks you need to give them, how long they need to work between breaks, how long they're allowed to work, in a single day or across the course of an entire week, Gus, if you just ask it to, will set up the ideal shift schedule for your small business, and then you can sort of tweak it afterwards. So Gus is already being used by hundreds of thousands of small businesses to save them time, to help them get critical business insights, and to help them to stay compliant. And I'm happy to announce that as of last week, Gus is generally available to every single one of our customers on Gusto's platform. I'm really excited because over the past 10, 20 years, we've seen it become easier and easier to start and run a small business. And I'm proud to say that Gusto is a big part of what's enabled that. Um, but at the end of the day, with all these amazing SaaS tools that have proliferated over the past 10 to 20 years, it's still someone that needs to operate all of that software. And our goal with Gus is to take as much of that work as possible so that you, the small business owner, can just spend as much time on the things that you love about your business. So how did we actually build Gus? I'm sure you're interested in hearing a little bit of how we actually built this internally. We knew that we needed to iterate very, very quickly. We needed to have a very high rate of experimentation, and any sort of previous notions of how to build traditional SaaS-based software that's primarily graphical-based, many of those assumptions had to get thrown out. And so what we did was actually create a startup within a startup. And there are many companies that have done this before, and especially large companies sometimes call this innovation labs, but what I've observed is that they largely operate in the similar— in a similar way as other teams within that company. We really embraced this startup within a startup mindset. Of course, we started with a brand new codebase from scratch, but we also politely excused ourselves from many of our engineering team's rituals and processes. And it wasn't even until recently that we actually started to track our work in things like Jira and Asana, and before that, we would just sort of sit all together in a room, talk about what are the most important things for us to work on, and by the end of the week, we would ship it and do that again the following week. It wasn't necessary for us to have heavyweight project tracking processes. Another thing that we had to learn was how to know what problems not to solve. There's problems that are specific to Gusto that we knew that weren't gonna get solved on their own. And then on the other hand, there are problems that we knew that because the world of AI and the technology is advancing so quickly, those problems, we were making a bet, would get solved on its own through the natural progression of this technology. And so we had to be really disciplined about telling ourselves, even though these are both important sets of problems, which problems should we be focusing on, and which problems are we okay with not solving right now? So what have we learned in building Gus? I would talk about 3 things to you right now. One is that although our vision for what problems we want to solve for the next few years is very, very clear, it's crystal clear, The roadmap of what we're building cannot be more than 90 days out because we're learning so much right now. We're experimenting. And every 3 months, we learn something completely new about the sorts of things that we need to actually build as part of Gus. And so we had to stay really disciplined about just staying hyperfocused on the next 90 days and not trying to draw out too many plans beyond that. The second thing that we learned is that while it is really important to instrument your product, see how people are using it, use tools like Datadog and FullStory to see how customers are using your SaaS product, it's even more important to observe what customers are not doing on your product. And in particular, I think it's really important to observe what they're doing right before they actually start using your product. Sometimes I like to call this what is the work before the work? What's the work that they're doing to prepare everything before they start using your SaaS product? And that is a goldmine of problems that you can be solving for your customers. And finally, what we learned is that there is no single interface to rule them all. With the advent of AI, there's so many things that are done better conversationally now. But not everything is done better conversationally. In fact, there are many things that are a lot better done graphically. And so we think that the future interface is a hybrid approach, that it's at the right moment, at the right time, depending on the right task. It is sometimes conversational and sometimes graphical. Graphical. The ideal interface is a hybrid between those two.
Thanks, Eddie. And, um, maybe one thing I'll add on the prior slide. So we've talked about, as a company for a long time, our goal to be not a tool, but an opinionated partner that helps to guide our customer. With this technology in particular, and with Gus, and I hope many of you have a chance to explore and use it, it feels like we're getting much, much closer to that ideal of not just us, but software in general being more like a teammate. More like a partner helping you navigate complex tasks, taking 5 or 10 hats off your head, making your life easier and simpler. It always bothered me when, you know, we would use software, I remember back in school, and it was like you had to learn how to use it, you had to train yourself, you had to kind of read a book or a manual. Software done right should be there to give you superpowers. And again, this feels like a big part of what is being unlocked here. Um, so we talked a bit about the customer impact with conversational interface. That's what we call GUS at Gusto. Um, on top of that, there's a lot of other really interesting, I think, moving parts here. In particular, we have a large team of Gusties that do customer support, that do operations. Maybe some of your companies have similar dynamics. And what's really fun is this tool, this technology, we have an internal version as well. Is really giving those folks superpowers so that, to put business terms against it, productivity can grow, right? You can now do more tasks more easily. You can access information more quickly. You're less copy-pasting stuff in Google Docs between sheets, et cetera. Things get pre-populated more quickly, and that saves time and lets us go provide a better experience to our customers. So that's one track of a lot of work underway at Gusto. Another is, and this is a beautiful dynamic of, of 24/7 self-serve, the way that customers interact with our help center, the way that they can go get the information they need. If they don't want to call in, they want to just get that more quickly. We all know, hopefully at this point it's table stakes, but these tools have made help centers dramatically more efficient, more effective, better at getting you the information you need and want. And then the last bucket is really fun. In our world, it's not just software. We're doing tax filings, tax payments, we're doing tax resolution, we're doing a bunch of health insurance enrollment. All these different third parties, organizations, they send us documents, materials. Automation is just literally training your AI to basically do that for you. So tax notices is a good case study. We have a tax notice team at Gusto. Today, over half the tax notices that come into the team get automatically flagged, organized, categorized, and then resolved without a human being involved whatsoever. And there's probably examples in your companies that map to this as well. That also is a fantastic benefit to the customer. It's faster, it's easier, and the people involved can then focus on more complex cases. So kind of just to highlight these different buckets, productivity growing, I think is a good thing for society, for the economy, definitely for a company. The better customer experience is obviously the due north. From a business running a company lens, what it literally means is we have more operating margin to reinvest into new products, into improving the experience. Gusto has been free cash flow positive for several years. We think it's— if you're at the scale we're at, running a good business means you generate revenue, you generate free cash flow. You reinvest that, you build better product, you serve the customer better, and this technology basically adds to that flywheel. Um, so kind of getting close to wrapping here. One of the biggest things that I find really exciting about working in tech, and since you're all here, I'm assuming you either work in tech or plan to work in tech, it's that the building blocks constantly are changing. So Gusto, we started the company with one other co-founder who's just as involved, just as actively engaged as we are. If two of us are on stage, that means he's back in the office cranking so that at least one of us is, you know, building the company while we're here talking. But we started the company 14 years ago, and I used to say back then, on the technology front, we were going to leverage cloud, paperless, mobile as these tailwinds. We didn't invent those technologies, but we were going to bring the benefits of those technologies to small business. And on go-to-market, I would say we're going to leverage social and search, right? These are new paradigms that 15 years ago today are table stakes. And that was a big part of our unlock to even get to the scale we're at today. AI is now a part of that, and there's going to be more in the future. And so the mindset here as a company builder is how do you not predict the future? Because no one has a crystal ball. But how do you stay on top of obsessively what the trends are? What the different technologies are, and then back to where we started, it's not using technology for the sake of it. It's you're looking at a customer problem, a customer pain point in our life that's all about small businesses, that's all about compliance, it's all about the stuff that takes up a lot of time and energy, that takes them away from building their products or helping build their team better. And then if you think that technology can be a way for you to go make that pain go away, to improve the quality of your experience, that's a fantastic reason to dive in. So hopefully you all now understand the journey we've been on. AI as a tool for us to use at Gusto started multiple years ago. We're excited to be at the place we are today with Gus and many more things coming down the pipe. And, uh, we're grateful to all of you that have joined. Thank you so much.
Founder Tiedemark Davion. Please welcome to the stage CEO and founder of Clio, Jack Newton.
All right. All right. Good stuff. This is great. This is like a mix between walking out in the Super Bowl and— Yeah, that was pretty exciting.
Fun to be here.
As an investor, we don't get a lot of that. Well, my name is Dave Wan. I'm the head of Tidemark, a growth equity firm. We invest in a number of software applications and with a real bent to vertical SaaS. Really excited to chat with Jack today.
I'm excited to be here too.
Look forward to it. Yeah, Jack's a long-term friend, Tidemark fellow investor, and, and very grateful to be an investor in Cleo. So, Jack, maybe you can start with a little bit of your personal background.
Sure. Yeah, my background's in computer science, ended up doing a master's degree in machine learning, and founded Clio about 17 years ago with my elementary school best friend, a guy named Ryan Govro. And over the course of the last 17 years, Clio has grown into a company of about 1,500 people. We're headquartered in Vancouver, BC, with offices in Calgary, Calgary, Toronto, Dublin, and Sydney. And we're, from a scale perspective, north of $300 million ARR today. Awesome, $300 million. That's great, great milestone.
Maybe start with a point of arrival and brag a little bit about Clio, what you guys do for your industry, what you do for your customers.
Sure, so Clio we describe as the operating system for legal. So if you're a lawyer, lawyer, you can run your entire practice in the cloud on Clio. You can collaborate and work with your clients with a cloud-based interface. You can invoice your clients electronically and manage basically every aspect of running your law firm in the cloud. And when we launched back in 2008, we were the very first cloud-based practice management system. And now as we move into into the age of AI, this is obviously a really exciting opportunity for us to layer some really profound AI capabilities on top of what Clio already does at the operating system level for law firms.
Awesome, awesome. Well, let's go back to 10, 15 years ago when you were getting started. There are lots of different PMS providers for law firms, both legacy as as well as SaaS. Where'd you start from a product standpoint?
So we started with a product that is now one of many products at Clio, but the flagship product that we launched back in 2008 is Clio Manage. And this is what you describe as the control point product, Dave, where we really help a law firm manage every aspect of what's going on going on in the law firm with their clients. So they need to track their matters, their documents, their to-do list. They need to keep track of the open activities with clients. They need to keep track of their billings to clients, the payments they've received. We've now attached a GL to all of the work they're doing as well. So Clio manages as that system of record and system of intelligence for a law firm. Law firm has been the backbone of our growth and our expansion story. And really, you know, lawyers start using Clio as a verb in the law firm, where they're anthropomorphizing Clio and saying, go to Clio. If we're tracking it, if it's something we need to know, it's in Clio. So it just becomes their central store of all the information going on in their law firm. So you're the control point.
You're the most important system for your customers. You have the most data gravity.
Exactly. What we hear from our customers all the time is they, uh, Clio's the first application they open in the morning and the last application they close at night. Awesome.
And one of the great things about a control point is you have an unfair right to sell multiple products and maybe all products to your customers. And so, uh, maybe share a little bit about the journey there.
Uh, what was your first expansion product? Our first expansion product, uh, was a company we acquired, uh, called Lexicata. And Lexicata was, uh, a legal CRM. And what it helped our clients do, uh, was basically intake new clients. So they might arrive at their website and decide they want to do work with that lawyer. A key part of starting that process is going through intake, getting the client into your platform, getting them through a pipeline essentially, and converted into a client. So you can think about this as HubSpot or Salesforce designed specifically for lawyers. I think one of the key strategies we launched a few years after we launched Clio was building a platform, building an open platform platform that other developers could build on top of Clio. And back in those days, we launched the platform roughly in 2013, and we've been at it, you know, for 12+ years now, and we now have 300 integration partners built on top of that platform. And I was inspired with what Mark Benioff had done with force.com, and I saw what a powerful competitive boat that created around Salesforce. And we were Salesforce customers, uh, and back then, and we still are today. And it's just such a powerful thing when you even look at something you want to add on like Marketo, and the first and often only integration point they might have is Salesforce. And we wanted to do that in legal. We thought if, if we can be the platform that other innovative companies are building on top of, that puts us in a really strong position. And the cheat code that gives us is from a corp dev perspective, you can see exactly what kind of product is getting traction with your customers. And we saw really clearly in those early days that, that Lexicata, what is now turned into Clio Grow, our legal CRM, was taking off with our customers. It was a really nice extension to into what we were delivering with that core control point of Clio Manage. And in now the 6 or 7 years since we acquired Lexicata, this is a business that on its own is doing tens of millions of ARR, has penetrated into 40-plus percent of our base, and is a wildly successful product that we were able to underwrite with a very high degree of certainty that this is a product that we could add on, bring to our customers. And since acquiring Lexicata, we've now been able to acquire 5 additional products. And all but one of those was an integration partner that we, we could see that signal from the platform that this is a fit, this is successful, this is— oh, by the way, you also get perfect visibility into what's retention look flight. You can survey customers and understand what NPS is, the kind of data that you would never be able to get arm's length from a company just surveying the marketplace. Yeah, I love the platform strategy.
I love it in respect to corp dev. We'll tack back as we talk about AI and some of the integration dynamics there. But, but so you start out with the, with essentially the Grow product. It's early in the workflow with a customer. Customers.
That's right, kind of that first stage of the, the customer journey, right? Just as a brief aside, that's one of the things we found to be really useful thinking about our product roadmap and how to frame out how our product roadmap could evolve over time, is thinking about what does the customer journey— and by customer I mean what does the client journey look like for one of our law firms' prospective clients— what's the journey they need to go through to learn about their legal issue, to find a law firm that they want to work with, to actually go through the intake process with a law firm, to sign an engagement letter, to get the legal work product and even initial consultation delivered to them, and then finally receive that end, that the final work product and pay for the transaction, and then hopefully go into some kind of advocacy and referral motion. So we looked at that whole journey and mapped out, you know, what are the series of products that we can build out to really, I think to use your term, Dave, what's the TAM shark approach to going and eating up that entire customer journey and not only delivering a ton of value to our customers, but framing what does this digital transformation need to look like in this industry to take, in legal, what is a lot bricks and mortar and analog pen and paper processes and pull that into the cloud.
Yeah, I love that. I love the customer centricity. I love follow the workflow, having that drive your product strategy. So you started there as a second product. How many products now do you sell to your customers?
We have 7 products that we sell in various configurations to our customers, including a brand new product that we acquired just a couple of months ago called ShareDew, and this is an enterprise offering that really solves for the needs of enterprise 1,000+ lawyer firms, which is a market that Clio has never really served directly, but ShareDew opens up an entirely new enterprise segment for us, and Clio Manage, Clio Grow, Clio Draft, Clio Payments, And the Calendar rules an ecosystem of related products that we sell into the core SMB products. We've got about a half dozen products we sell to our customers today.
Awesome. So you're scaling locations, you're scaling ARPU. As you think back and you reflect on the journey, was there one or two expansion products that really were a step function change for you, either in the economics of your business, the retention of your business, or how you engage their customers.
Yeah, I think about the step function as really being this, this compounding layer cake. And we talk about the fact that we're trying to deliver incremental customer value in a way that in maybe the first year we release a product, it might only do a few million dollars of ARR, but all of the new products we're launching today, we want to have line of sight to how that becomes a $50 or $100 million ARR business over the course of 4 or 5 years. And the way we frame the stage of the growth trajectory we're on today is, how do we get from $300 million of ARR to $1 billion of ARR? Basically by executing on this revenue layer cake. So the really meaningful movers thus far have been Clio Grow. This is a product product that we really believe almost every law firm needs and has been really, really successful. We've been very successful with Clio Payments, which is our payments product, and this has been able to grow into more than 20% of our annual recurring revenue today, and payments has just been an explosive opportunity. We're doing— our customers in aggregate are doing tens of billions of dollars of aggregate billing on Clio. And as payments goes through digital transformation, this is a huge monetization opportunity for us.
Well, it's a bit more than monetization, right? We've talked about this in the past. Obviously great additional ARPU, but it also has changed your retention rates. Can you talk a little bit about that?
Oh, it's— I mean, payments is probably one of the single biggest win-win-win propositions that I think we in particular, and I think in general any vertical SaaS player, can really deliver to their, their customers. Because number one, we see a dramatic increase in retention. We see our customers drive a dramatic reduction in their AR. They're able to dramatically reduce the amount of time it takes them to get paid. They're able to go back even to to clients and bills that they've written off and send a single email through our automated workflow for collections and say, "Hey, last time we asked you to pay this, we asked you to pay by check. We know that you probably don't know where your checkbook is. Can you just pay online with credit card?" And in 2 minutes, the client's paid that bill that's been outstanding maybe for a year or 2 years in some cases. So there's a dramatic and positive impact to the law firm, and then there's a really positive impact to the client experience because you've just made that payment experience seamless. We enable more— an expansion of the payment offerings and payment types you can accept. We accept credit card, we accept ACH, we allow customers to set up a vaulted card that gets charged automatically for a subscription payment, for example. It just really modernizes that payment experience and it's often actually the last interaction a client has with you and the more you can make that seamless, you're leaving a good taste in their mouth. And that's really, I think, what's so amazing about payments is it's just a win on multiple fronts and when we launched Clio in 2008, even 5 years into the journey, I'd say we had no idea what a, huge opportunity payments represented, but it certainly turned out to be one of the most important drivers of our growth and, and a really fundamental aspect of the digital transformation that's happening in legal.
Yeah, absolutely. There's been a lot of ink spilt around the attachment of payments to vertical SaaS, but every time I see it's an amazing monetization opportunity and it really does change. Absolutely. Yeah, we're actually, as you predicted, Jack, we're running running a little bit short on time. So maybe we can go directly to AI and then we can loop back onto some of the other great GTM aspects of scaling to $300 million, which I think Reagan's actually going to cover a little bit tomorrow. So yeah, it's fast forward. So lots of, lots of interest in AI here and elsewhere. And legal is kind of ground zero for it. Just absolutely professional services. High value of work. Our belief is a control point has an unfair right to cross-sell all products, including AI. And so AI is an opportunity for you guys. It's also a threat. Maybe let's start with the opportunity side. What, what, how do you think about the opportunity in AI for Clio?
Yeah, well, we just went through a big fundraise last year, a Series F that was a $900 $900 million raise and Tidemark and a bunch of other great firms participated in that round. And one of the questions we got from investors was, hey look, legal is near the top of the list of industries that's gonna be disrupted by AI. Is this a threat or is this an opportunity for Clio? And we needed to have a really clean and crisp answer answer to that question, and we've been thinking about it a lot internally as well. And look, there's no question there's a dramatic amount of disruption and change that's gonna happen in legal with AI. Goldman Sachs published a report a couple of years ago stack ranking the industries that are gonna be transformed and disrupted by AI, and legal was number 2, next only to administrative work, in general, for the amount disruption that it was vulnerable to. And there's a few data points that really help frame up how we think about the broader AI opportunity in legal. Number one, AI is gonna make lawyers dramatically more efficient, and it's gonna make them better at the work that they've traditionally been quite poor at. If you look at the average law firm, and by the way, the majority of the legal market is made up of solos and small firms. A lot of us think about suits and think about the 1,000-person law firms in a fancy downtown skyscraper, but the reality is that 80% of law firms are 10 lawyers or less and half of all lawyers are solos. So it's a very entrepreneurial, very SMB market, and like most SMBs, they're not great at marketing, they're not great at intake, they're not great at following up with clients and getting referrals and follow-on business. And I think all of this business of law type functionality is going to be agentic AI in the very near future. You're gonna call a law firm and you're gonna speak to what sounds like a human being helping qualify you and see if you're a fit for the law firm, and a lawyer is gonna be sitting down with a cup of coffee in their hand in the morning interviewing potential clients that that AI intake agent has helped set up meetings with. We're gonna see on on the practice of law side, lawyers have their productivity dramatically amplified by Agentic AI that can go do legal research for them, that can go draft briefs for them, and lawyers absolutely need to be in the loop here, but they're, they're going to be able to get a lot more done in a lot less time. The broader opportunity I see is an opportunity to dramatically expand the TAM of legal. There's a stat from the World Justice Project that I cite all the time, which is that 77% of legal matters go unresolved with the help of a lawyer, meaning lawyers don't touch more than three-quarters of the legal demand out there because lawyers are too expensive, too inaccessible, too hard to reach. In some cases, they don't even pick up the phone owner answer an email because they're so overwhelmed. So when you look at that and you look at what AI will bring, I think it's in the same vein as what the mechanical loom did to textiles or what the assembly line did to automobiles, which is it will dramatically lower the cost of delivering legal services. Concurrent with that, the cost of accessing legal services will be dramatically lowered, and that in turn, as it did with as it did with, with automobiles, the amount of demand and the number of people that can then access those goods is dramatically going to expand. And today the legal market is about a trillion dollars a year worldwide of spend, and AI I think presents an opportunity to double, triple, or quadruple the size of that TAM, and lawyers will be able to more efficiently deliver legal services, higher quality legal legal services to a dramatically expanded audience.
Yeah, I love the idea of your perspective around technology expanding the market. We've seen that in other scenarios. We were talking offstage as well about the existing market in terms of the jobs to be done by the different law firms. And what I loved about your approach was very similar to the multi-product approach, which is let's follow what these customers are doing. And you're breaking up into jobs that humans will do only, jobs that will be augmented with AI, and jobs that will be fully substituted with AI. Maybe describe your, your view on these various different jobs within legal, really just to, to bring to life the framework of how you think about it.
Yeah, I, I think with AI we see a really dramatic shift around how you need to think about developing product from a world where you, you you listen to what your customers are telling you you should build for them to really needing to embed yourselves deeply in how a typical law office works and look for those opportunities to automate an entire role or automate some substantial portion of how somebody spends their day and allow them to focus their energy on what they're great at and what they really love doing. And the conversation I've had with our product management team is, you know, we need to get way closer to our customers. We need to understand how we do the work. Sarah Tavel wrote a great piece talking about this concept of needing to do the work. We're selling work rather than software in the age of AI. And I think in order to sell that work, you need to think not so not so much, you know, how do I, for example, support a personal injury lawyer in keeping on top of their to-do list and keeping on top of their documents, but how do I help them draft a demand letter completely automatically leveraging a deep pool of data to extract exactly the right demand that they should be making of the insurance company they're placing a demand against. And to do that, you need to really deeply understand the nuts and bolts of how a personal injury lawyer does their work and transform that into AI. So the job of a product manager, the job of your product organization in general, I think has radically changed over the last couple years. If you wanna capitalize on that AI opportunity and if you wanna be prepared prepare to evolve into the world of what agentic AI will look like.
Yeah, maybe let's switch gears a little bit and talk about the threats. Jason earlier this morning talked about the 1,000 native AI startups that are coming for you. And legal, as you mentioned, is a hotbed for this. So the typical approach we see is a native point solution, native AI point solution will integrate and surround. You have an ecosystem platform approach. How do you reconcile those two?
Yeah, I think it's a great question, and I think one of our value props to anyone innovating in legal, whether you're AI native or, you know, more of a pure-play SaaS tool, is you don't need to reinvent the wheel of what Clio as the operating system for legal does. You can tie into our APIs and you can focus on the really specific point value you're delivering to customers. And by and large, we wanna support all the native AI companies that are building on top of the, the Clio platform, but with the caveat that I think we need to be more deliberate and more strategic about how we're opening up certain aspects of our data to those partners because frankly, there's parts of that AI workflow that we're gonna wanna own because because number one, we think we're best positioned to deliver the maximum value on that front, and we're going to want to control that customer experience and control the data that supports that to some degree. Yeah. But I think there's going to be a really interesting tension that starts to emerge over the next few months and years around platforms and the AI players that are building on top of those platforms that are maybe going to demand access to data that at the end of the day, I think your thesis around vertical SaaS is absolutely correct, that as the control point and as the home to all of that client data, home to all of those workflows, we've got an unnatural right to win in a lot of these AI-based workflows because we have uniquely broad access to all of that data.
Yeah, 100%. This is a little bit simplifying it, but Clio, helps manage the business of law.
That's right. A lot of these point solutions help practitioners basically automate the practice of law. That's right, exactly.
And so as you think about those things coming together, how do you need to adapt and how do you need to think about it from a business model standpoint, go-to-market pricing standpoint?
Yeah, it's a great question. And the way we talk about this internally at Clio is traditionally traditionally Clio has been a business of law platform. We help you run the business of your law firm. The evolution that we're seeing on the practice of law and tools that support lawyers doing things like legal research, doing things like drafting legal briefs, and so on, are starting to converge. This, this world of the business of law and the practice of law are going to converge and we have a really natural— we're seeing this demand from customers already because they have the workflows in Clio where they have a task where they need to go draft a certain kind of legal document. And the question they're naturally enough asking Clio is, why can't I draft that document directly within Clio? I've got all the data that I need to draft that document in Clio. You just need a few AI-based tools to really help you to help support this. So I think the business of law tools are gonna get pulled into the practice of law over time. And similarly, I think there's a lot of practice of law tools, especially as agentic capabilities start emerging in the practice of law. You're gonna get questions like, well, how do you manage the workflows? How are lawyers supervising and orchestrating the work that these agents are doing on their behalf? And they're gonna get pulled into the business of law and this orchestration layer around what's going on in their law firm. So I see over the next 2 to 3 years a massive amount of convergence between the business of law and the practice of law. And I think in general there's a flavor of this pattern that are happening in many other verticals in SaaS that is gonna really dramatically reshape the landscape, the competitive landscape. Awesome.
Well, I think we're at time.
So we got through half that talk.
Half our time flew by. But really appreciate you sharing your journey and all Clio's success. I think you're taking questions offsite?
Yeah, I think we'll take some questions off to the side as well, but this was great, Dave. Thanks, I really enjoyed it.
Yeah, thank you, Jack. All right, appreciate it.
Former CRO Brex and the co-founder and CEO of a pre-launch startup Sam Blonde. Please welcome to the stage Chief Business Officer Perplexity, Dmitry Shevalenko.
Alright, let's do it. Excited to be here. Uh, let's see, another incredible SaaStr Annual. Big shout out to Jason M. Lemkin and Amelia and the whole SaaStr team. Uh, every year bigger and better than the last somehow. Thrilled to be here. Uh, my name's Sam Blonde. Uh, I have a series through SaaStr called CRO Confidential. I'm also the founder of a startup that's in stealth, that's an end-to-end revenue platform. Enough about me. We have one of the coolest guests from one of the hottest tech companies in the world right now. Dmitry Shevalenko is Chief Business Officer at Perplexity. I won't talk much about you or the business because I won't do it justice. Before we jump in, maybe just tell us a little a little bit about your background and tell us what Perplexity is. And then we're gonna talk a lot about how folks should be leveraging AI in sales and more.
Awesome. Well, thanks everybody for coming. Thanks, Sam, for doing this. Uh, hopefully we have some fun. Uh, so I'm Dmitry, uh, CBO at, at Perplexity. Uh, for those that haven't tried Perplexity, it's, uh, an answer machine. Uh, you, you bring your hard questions and we, do our best to answer them for you, grounding it in the high-quality sources from the internet, from your inside your organization, from high-quality external sources. And it's been a real privilege to be part of the Perplexity journey. I started my career at Facebook, was at LinkedIn, was at Uber, had tastes of hypergrowth and got addicted to that that feeling and excited to be creating it once again with Perplexity.
Awesome. Well, thank you for doing this. Uh, so let's see, coming into the show, I, I often do some research on the guests, listen to some podcasts, those sorts of things. For this one, I started with just asking Perplexity what should be— it gave it a prompt around this is who I am, this is a little bit about the show, this is my guest today, Dmitry, and his role. What are the most important topics that we could include in the list of things to discuss today? And Perplexity basically just built the show for us. Um, and so, you know, they talk about like AI coming for your job. This is one that I think is, um, pretty quickly being disrupted. Uh, with that in mind, there were, there were two really exceptional topics that I, I think you'll have maybe the most sophisticated view on than anyone in the world. The first is like, you know, you're at this cutting-edge AI company. You're also a former founder. Um, and so one of the things that I want to do is talk about where we are today and how companies, specifically tech companies, should be leveraging AI within their go-to-market organizations. So that's topic one. We'll move on to, um, the sort of intersection of product-led growth and, uh, sales-assisted customer acquisition. I, I think per Flexity has a lot of revenue with a mix of sort of product-led or folks coming directly to the website and signing up, also sales-assisted. I think that's true across like, you know, many if not most companies that are formed around AI and in B2B. So let's start with the former of the topics. Let's see, if you are a startup founder today, You're starting a new business. You've been a startup founder before. You know most of what there is to know about AI's capabilities today, the products that exist in the market and more. What are the things that you are leveraging AI for as part of demand gen and customer acquisition? And then maybe what are some of the things that we aren't yet there?
Yeah, I think we were talking about this backstage. Speed is the new moat, right? So the one, if there's one takeaway, you have no excuse to not be moving faster, right? And really you now have these superpowers at your fingertips. And you know, I think something I like to tell folks is every question that you would ask in a meeting with some, you know, third party and external party, you can be asking asking those questions of Perplexity, of different AIs in advance, right? So the— there's no excuse to not be informed. Uh, you should not actually be doing the discovery, like you shouldn't be learning the discovery questions like in real time. Now we'll get into a tactic that I like to use that, that I think kind of is, is the best of both worlds. Um, but I, I think, you know, the, the imperative is to move faster because your competitors are moving faster. I get asked, you know, why is it that Perplexity is in the position that we're in? And part of it is timing and part of it is execution speed. If we had, you know, we got lucky with timing. If we had founded the company 6 months before we did, GPT-3 would not have been good enough, the equivalent of it at that point point in time and we wouldn't have been able to create that initial magical experience of combining AI with the internet. Uh, if we had started 6 months later, there'd be somebody else on stage right now, uh, that would have like seized that, that moment. And so that, that's really kind of where I, I like to give advice for founders.
Okay, that's awesome. So I think it's sort of the, um, version of what I did with Perplexity coming into this space. Show. It's like you're a founder or you're a sales rep, you have a meeting coming up, um, just prompt AI or Perplexity, uh, with who you are, what your company does, who you're meeting with, what their role is, and ask them like, how should we structure this call? What are the things that I should know going into the call? And so this is the sort of like, um, meeting prep that used to be very manual that you can probably do in just a matter of seconds. And again, sort of like relating to, uh, pre— preparing for this show, I saved probably 90 minutes to 2 hours on just sort of like prep for what are the things that we should talk about, tell me about Demetri, just by prompting Perplexity versus doing the listening to podcasts, a bunch of my own research. You're applying that same thing to meeting preparation.
Yeah, and, and I think it's about just being with the time you you saved, you actually then ask more questions, right? And going deeper. So, you know, what are Perplexity's biggest challenges? You know, like how have they tried to solve them? You know, once you're armed with the initial information, it should spark that curiosity. One of the most popular features in the product are the related questions that we show below the answer. Because the thing, you know, there's no end stop to knowledge and learning. The, you know, a good answer just begs more questions. And you actually want to, when you're then like going from meeting prep to the actual meeting, you want to carry that spirit with you. So what I like to do is I'll just share my screen and I will in real time start asking questions about the company that I'm talking to or the person, right? So it's like, hey, what are the biggest challenges company X is facing, you know, how have they tried to solve them? So you kind of show your work in real time. And obviously it's easy for us to do it because it's showing our own product. But in some ways it's even more powerful for you all to do this because if you can prompt the right way and all of a sudden your, your solution, your product, your approach comes up in the AI-generated answer or it points to that, that, man, that is authentic. That's powerful. It's transparent. And I've just seen that work really well. And I think that's a tactic of just in this new era, you know, transparency is everything. And if a smart AI is pointing to a certain direction, that's going to help whoever you're meeting with gain conviction in that direction too. Okay.
So I would, I would categorize these things as combination of like meeting prep and meeting assistance, uh, that historically would take 10 to 20 times the amount of, uh, effort as, as it does today. Anything else? You know, again, you're startup founder, you're starting to acquire customers, you're gonna start leveraging AI in the way that you've just described. Anything else that comes to mind, uh, before we sort of, uh, progress?
Yeah, I mean, I, I think in general, you know, at the earliest stages of startup development. And this isn't just using Perplexity, but the rate at which you're prototyping and coming up with new concepts, getting feedback on them, that can all be accelerated dramatically, right? And so when you are then ready to, you know, put the pedal on the gas, you should have a lot more conviction and you've tried a lot more concepts. And so, you know, using, you know, tools like Cursor, you know, we're going to, you know, have the ability to build mini apps in Perplexity soon. Something's going to get announced called Projects. You know, it's not meant to be a coding replacement, but to quickly visualize different concepts and get feedback on them. That should no longer take a week, a month. That's like an hour. And you should be, you know, constantly running through your head. You know, how can I do in 1 hour what used to take a day, in 1 day what used to take a week, in 1 week what used to take a month? And that, that's the right mental model here.
Awesome. Um, I'll add a little bit of my own color before we get into more mature businesses and how a company like Perplexity is leveraging the advancement in this technology. Um, you know, as an early-stage founder myself, there, there are a few few things within go-to-market that I am leveraging today. The first is lead scoring. You know, historically this used to be something that was pretty complicated that you would need maybe RevOps assistance with. And it was like very manual and very trial and error. Models are quite good at this today. So as you are building out your addressable market, the scoring of that addressable market, models are very good at. I think the second thing that I am taking advantage of, and again, like, you're sort of behind if you aren't doing these things, is intent signals. So once you have a set of your database, it is scored based off of, you know, different tiers from a graphic information. You want to overlay intent signals on top of your scored database. So these are things like, where do I have connections that exist within buyers at these companies? Are they hiring for different roles? Whatever your individual intent signals are that you want to overlay on top of your addressable market, models are very good at that today. And there are several products that do this today. And then lastly, on the demand gen side, we are very good today at deciding the type of outreach to take advantage of. Given the account score and the intent signals. So if we want to prescribe, reach out to this account with this message through these channels because of, you know, these reasons, you know, these 3 different steps here. Score my database for me, overlay intent signals on top of my scored database, and then tell me what I should do to break into these accounts based off of the information that exists. In these scores and intent signals. Models are very good at that today. And so these are things that I think like if you today aren't leveraging in go-to-market, acquiring customers, you're, you're a little bit behind. And then you described the meeting prep, I think, very well. Once you do have meetings on calendars, I think that the models are quite good at preparing you for that stuff. Okay, let's talk about as we get a little bit larger and introduce more complexity in the organization, potentially, you know, management structure and those sorts of things. How is a company like Perplexity sort of taking it to the next level in terms of leveraging either your own technology or AI in general to do things that weren't possible even 12 months ago?
Yeah, I, I think one mental model is to assume that using a tool like Perplexity and other AIs, you can get to a 80% of an expert level in a lot of fields that, you know, are not your profession, right? So a good example is when I receive a contract, instead of immediately kicking over to the lawyer, I run it through Perplexity and I prompt and I ask, hey, what are the likely issues here and what should I actually discuss with my counsel? And that way when I am actually engaged with— I'm not, you know, just like pretending to be a lawyer and then and going off of what the AI says. Like, I don't think that's where the technology is. I know some founders do operate that way, but we're talking about more mature organizations. And for them, it's really about making that lawyer's time more efficient so that when a business counterpart, you know, comes to them with an ask, it's focused. They've already done their homework and you're not wasting time on the broad review and you're zeroing in zeroing in on the most pertinent issues. And so then take that same approach and think about engaging with a colleague in marketing, a colleague in design. You're able to, whatever your zone of responsibility is and you need to interface with others, you're able to anticipate a lot of what they may help you with. And so then when you do approach them, you're saving both, both sides a lot of time, right? And that's where we see incredible efficiencies. I think there's also, you know, when you have templates, right? So say you need to generate a proposal and you have, you know, 3 or 4 really good examples. People love uploading those into Perplexity Spaces and then prompting, hey, can you generate an example, a proposal in the same style, but for this other company? And where, where Perplexity really excels is we're able to pull in all the real-time information about that company, what's specific to them, and preserve the style of your templates, but update it and personalize it for, for that company, right? And you should never then just take that output and immediately hit send, right? That's not— I think that's— it's not about replacing human judgment and oversight. It's about saving you that time to get to the 80% quality first draft, and then making sure you're applying your judgment, your intuition on that last mile.
Awesome. I'll add a little bit of color as well. I think, you know, as companies mature a little bit, you get more data. And I think you can train a model about historical engagements that you've had with different companies and data on, sort of like how to position your product. One of the things that Jason brings up a lot is how— Jason from Saastr brings up a lot is how effective their AI is at sort of like selling for Saastr. You know, they have on their website, come talk to the like chatbot. And I think like I'll go a little bit further on that specific example, but I think generally speaking, Um, transactional sort of commodity-like sales will be disrupted in ways that support has been disrupted, where more and more revenue will be generated from just pure models converting, uh, website visitors to revenue without the sales touch. And I think that will continue to become increasingly true. Um, you know, as I said, Jason Jason was raving about how Saastr is leveraging their AI, and I wanted to like play with it, see what all the hype is about. Um, let's see, beginning of last week I went to Saastr and it was just like, hey, it feels like I'm pretty late, but is there still any sponsorships that I could take advantage of for next week's annual? And interestingly, what, what the model did is something that I think a sales rep would never do, and it, it, It sort of like down-sold me, and what I mean by that was, you know, it sort of outlined, here are the types of sponsorships that you can still move forward with that are available. Like, I actually would, I being the model, would recommend that you move forward with like either a gold booth, a silver sponsorship, or one of these brand-type sponsorships, because one of the more advanced sponsorships, I don't know if it was the platinum or or what it was exactly, it was like, you know, it's going to be pretty tough for you to take advantage of all that, that, that sponsorship level has to offer, given that we're 8 days away from the event. And like, you know, that just built a lot of trust with the AI that I was speaking to. It's the type of experience that I just can't imagine that that is what a sales rep would be recommending because of course they like want to maximize the amount of revenue that the company generates and ultimately their commission. So I think like, you know, that is maybe some proof of models and AI replacing salespeople, especially at the sort of like transactional or almost commodity-style sales. You know, other end of the spectrum, you have the, I don't know, Andurils of the world that are selling weapons to the government government, probably need a human for that for the foreseeable future, and then everything in between. The second thing that comes to mind is—
just building on that, I think the, the most essential quality for AI products and AI-first products, as you project out, is what you were referring to, which I call trust, right? And, you know, you really have to trust a salesperson to, you know, have confidence they're not selling you something you don't need. But the trust threshold for an AI is even greater. And, you know, that informs everything we do at Perplexity. How do we create and preserve trust with our users? And trust will be the scarce asset of the agentic internet and a lot of the products that are built on top of it. So, you know, it's easy to create AIs that manipulate people, but that's a very short-term strategy. And for folks that want to build for the long term, you have to build trust in at the, at the foundation level of, of your product and how you prompt these different AIs.
Yeah, I, I, I think that's smart. And then the second thing that comes to mind as companies mature a little bit, it's in the category of converting demand or opportunities to revenue. So closing revenue. And you know, you might be a founder that manages some salespeople. You might be a sales leader that manages a team of salespeople. And you know, this typically manifests in what I would describe as pipeline reviews, where this can either be ad hoc or casual, where it's sort of like, you know, how's the opportunity with Perplexity going? And you get an update from the sales rep. Um, over time it becomes a little bit more formalized where You know, my experience, and this was true even up to something like 12 months ago, you have like this incredibly sophisticated RevOps influence process around— you have a 30-minute meeting with each of your sales reps, and in that meeting there is within your CRM all of the deals that have the sales rep notes that are like ready in different categories for you to talk about, you know, each of the sort of risks and, you know, highest probabilities with the different deals. And then through that, you kind of get to a forecast. And, you know, everything is like so perfect about this process. And RevOps has set up this like beautiful spreadsheet in CRM. The only problem is it's entirely wrong. And, you know, you have like a sales rep that is adding their own like subjective experience about the account. Like they may want to influence the sales manager to think that the deal is going to close, or they may be forgetting about what actually happened on the call. And, you know, back to your point around trust, today you should have a model that is reading all communication with the buyer, the, the, the opportunity, the customer. Um, and that's over generally email. You also should have an AI that is joining all of your calls. And, the AI should be the one that is preparing for that meeting. And so when you come in, it's not actually the sort of like sales rep subjective information about the opportunity. This is like very matter of fact. You have a prompt. Given all of the data that you have in the model, what are the risks about this deal? What do you think the probability of this deal closing is? What are the suggestions that you have for the sales rep? And so as if you are not you're not doing this today with a model, this like pipeline review, sort of pipeline hygiene type application, you are a bit behind in something that wasn't totally possible 12 months ago.
Yeah. The one thing that you can rely on in terms of the direction of AI progress is the context windows will expand, right? You'll be able to feed more into the prompt and get a useful output. Right now, yeah, they claim, you know, 1 million tokens, 10 million tokens. When you start going up to that size of input, the quality deteriorates on the output. But that's not, you know, you have to assume it will get better. And yeah, you'll be able to just feed in, not necessarily train a new model, but just in the prompt, pass in every conversation you've had with a potential client. And get that, you know, honest readout and benchmark it against the other prospects.
Okay, we're all moving, we're moving just so fast with the progression of the technology and then the way that people are coming up with applying it. And, you know, we talked about a lot of these things that even 12 months ago, they were either less good than they are today, certainly less trustworthy, or not possible. And, you know, in your seat, being at truly the cutting edge of you probably having even access to things that aren't available publicly today, you know, if we're sitting here this time next year, I think it's, you know, really hard to project out beyond 12 months. What are the types of things that you're excited about that we can expect over, you know, the course of the rest of this year?
Yeah, well, I like to answer that by saying what won't change because in some ways that's, you know, a harder challenge. And I think the, you know, going back to where we started, coming up with the right questions to ask will become the critical skill, right? And you can say that's a form of like prompting, but, you know, preserving the spark of curiosity and knowing kind of, you know, where to take the next question, And that's because the answers will keep getting smarter. The products like Perplexity will be better at incorporating many different sources, but it won't solve the cold start challenge. Right. And so the cold start spark, that's the thing to focus in on. That's the thing that you can project will remain an evergreen problem. I think where we want to take the technology technology is to become a lot more proactive rather than being reactive, right? So instead of you having to always ask for the update of what's happening with this account, uh, we're able to detect those changes that are meaningful and surface that to you, right? And whether that be your personal life, your professional life, uh, transcending there, uh, having these technologies become more personalized, become more proactive I think that's, that's, you know, what I would expect 12 months from now.
Awesome. I'll try and layer in a couple of tactical things that I think will change with the way that companies, how companies leverage technology. You know, I talked about the different piecemeal things that today models are very good at: scoring accounts, overlaying intent signals, determining what the best outreach strategy to ultimately generate a meeting and close revenue is. You know, today these things sort of manifest in like a very piecemeal approach where you have maybe one product or service or one approach to doing the account scoring, and then you have to sort of like check the work and see if it's correct. And then you have like a different model, different company, different product that is doing the intent signals, then you have a different model or product, however you want to think about it, that's doing the outreach strategy. I think there will be a sort of like unified platform that comes together that takes all of these sort of disparate piecemeal processes and basically combines them. So you can effectively say like, this is my TAM, I want meetings, and the Output can be— these things are still happening behind the scenes, but they're all happening in one sort of unified end-to-end platform. And I think because of that, what will start to happen for technology companies, for technology founders, so much more of the success of the business will be predicated on finding product-market fit, like delivering customer value, rather than being exceptional at developing and scaling go-to-market, because I think that the models are going to start to become really good at that. And so for technical folks, I think that's hopefully music to your ears, where it's like, you know, you can really focus on solving customer problems, building the product, finding product-market fit. And gosh, like the risk around hiring the right SDR leaders and teams is far more abstracted away because the technology can get so much better at this stuff.
Yeah, I mean, I think we're a good example of, of how this may play out. We have around over 5,000 enterprise clients for Perplexity Enterprise Pro, and we have 5 sellers. And so that, I mean, that also speaks to like, you know, the PLG motion of, of having a consumer-facing version of Perplexity that's very popular and that effectively reaches every enterprise that could potentially become an enterprise customer. But I think that's, you know, there's a version of that that's true for many businesses. How do you have— use AI to bridge that, that kind of enterprise version of what you want to sell with the easy to get, the 80/20 of the value, consumer accessible version that may distribute itself itself. And that's, you know, and AI may help in crafting that, but that feels like a strategy to unlock. Yeah, I think it's really smart.
Sort of piggybacking off of your comment around something like, yeah, I think I don't want to butcher this. Was it 5,000 customers from 5 sales reps? Yeah, exactly. So like, you know, incredibly efficient go-to-market model that you have in place. I think, you know, I'm I'm now leading, you know, the product that I describe is what I'm hopefully, and we are hopefully, building. But I now have a startup, and I've gone through sales cycles buying products for the company that I lead. And there have been several instances where it was sort of like a commodity transactional sale, and I was just like, the sales rep adds unnecessary negative friction, and like I have to wait on them to respond and get back to my email, and they want to take a call, and it's like, gosh, like I just want to buy the thing. Um, and maybe I have some questions, but these are the types of questions that the, the Jason Saster model that I described earlier, they're going to have better answers than the, like, you know, low-paid sales rep that's selling this transactional commodity. And so I, I think like one thing that maybe slightly more mature businesses, and certainly businesses that have been around for a long time should start to think through is like, is the way that we are leveraging sales reps, salespeople, is this the future? Or like, are we actually adding negative value by paying sales reps to try and serve and convert these customers? And like, you know, maybe start trying to disrupt yourselves a little bit. And what that will, what that will do is it will enable the salespeople to focus on the opportunities where somebody is truly needed. Like, I really want to talk to a sales rep versus the buyer like me on the other end saying like, I don't want to talk to a salesperson, but you're forcing me to. And I do think that we're, we're, you know, collectively probably a little bit behind given the capabilities of models today.
Yeah. I mean, where sellers end up being being still very essential is things like security and compliance review, where you're actually— it goes back to trust, where, you know, somewhat arbitrary process, you know. Hopefully one day those become, uh, more automated too, uh, but that, that's where, you know, you need the human glue, uh, to, to fill the gap. And, uh, Sam, I hope you won't get upset by, uh, me sharing this, but, you know, in your self-startup, one of the things you're using is actually Perplexity's Sonar APIs, which I think is, you know, for folks here kind of at the earliest stages of building, you know, new products, being able to pull in real-time information, which is kind of our bread and butter, but synthesize and reason with, with, with AI and LLMs. That's what we're unlocking with our APIs. It's growing really fast. We have, you know, 75,000 developers using them and, you know, very much in the space where Sam is building, where we're seeing a lot of traction. So I was really happy to hear that you guys are finding value there.
We are happy customers. For the record, Perplexity was not one of the companies that forced us to talk to a sales rep that I was really disappointed with. It was incredibly easy to onboard. You know, unfortunately, I think one of the things we wanted to to talk about was product-led growth, the intersection of product-led growth and sales-assisted growth. And I think we don't have time, but, you know, your business is one that does an incredible job with that. I think we're going to start getting the, like, music to kick us off the stage. Dmitry, you are awesome. Huge round of applause for Dmitry. Thank you. Thanks, everyone, for attending.
Mango Mint, Marshall Mooney.
Hello, Saastr.
I'm Marshall Mooney, VP of Sales at Mango Mint. We do very deep vertical SaaS for salons and spas. How many of you in here are building vertical SaaS? Okay. How many of you know a vertical SaaS founder you kind of like? Okay, cool. They're glad you're here. I am I am a hairdresser salon owner turned sales in SaaS professional. When I first started with MangoMint and I had heard about Saastr, I thought SaaS was just like you had attitude. So I had a lot to learn, and I attribute much of that to Jason and the company he's built. And today I'm gonna run you through the 10 biggest lessons that I have learned learned selling vertical SaaS to SMBs. So we sell to a very small mom-and-pop shop, salon-and-spa-type customer, but many of these can be applied across to more mid-market enterprise motions as well. So it's been something that I've learned in vertical SaaS that some people think it's just a tighter ICP, a cuter name, a cuter logo, But the reality is the way you sell to vertical SaaS is going to be a very different playbook. I like to say that it's flipped upside down. So tomorrow you have the opportunity to hear Kyle Norton of Owner.com. I'll never forget hearing him say, you know, he understands the difference between a pizza shop owner and a sushi owner and what they need for their customers. And these are the things that really become fundamental, and we're going to dive into them. I've got 10 slides up here, but the reality is if I just shared some slides, you wouldn't need me. So I really want to get into the conversation about it, how I think about it, and we'll jump right in. So number 1 is deep domain cred. What do I mean by that? I mean our founders, Daniel, who's here. You can find them later. Poke his brain on this. But really understand the jargon, the words that our customers use before they even went into shipping a product to them. I think this is number one so important. Your founding team has to have deep insider knowledge of the vertical you're serving. When we think about what we're doing, we're solving a problem for them. You know, We're looking at years of antiquated playbooks for many of these verticals, and they don't know what they don't know. So your product has to be the thing that answers that for them. When I say on here, you know, credibility, what does that mean? This is how we build trust instantaneously. From the moment your customer lands on your website, they should feel like the language they see is the language they speak. In many cases, and I'll get into this, for us that means being very particular about words that need to be agnostic. Number 2 is master your niche. This is something, like I said, SaaStr being my own playbook and understanding what SaaS is, this is something Jason talks about time and time again. Focus is where you will win. So nailing every edge case of of your market. We've got to have monogamy first, poly-vertical later, if you're into that type of thing. When I think about this, I think about expansion being easier once you know you've nailed your niche. Jumping right into number 3, win by eliminating choice. I said earlier today, what are we doing for dinner? We could do sushi, we could do Thai. As soon as I I already have two answer or two like options. I'm in analysis paralysis. I can't figure it out. Our customers feel the same way in vertical SaaS. You know, one thing that I remember and the theme of this week for me is how are we injecting AI into every step of our go-to-market in a way that actually speeds us up and isn't just feeding into any sort of hype cycle. And when I think about when I started with Mango Mint in 2018, it was very common that every 10th call I had was unseating pen and paper. So your haircut is scheduled on a book, and if you cancel, your Ticonderoga better have a good eraser, because otherwise it's not going anywhere. Today, it's maybe once a year that I hear someone is on pen and paper, so when I I talk about eliminating those choices, we are now going after a vertical. I haven't come across one demo that I've reviewed where that customer is using any sort of AI product except for using ChatGPT to decide if they want to eat sushi or Thai tonight. So jumping into number 4, hire vertical athletes. I'm not saying don't find someone who's sold SaaS and done it but what I am saying is you need someone agile enough to understand who the customer is and why that is so important. They need to operate at the level of the vertical you are selling to. If I brought in tomorrow an enterprise rep from Salesforce who has the big logo on the resume and then I throw them into the mix with a hair salon owner that is doing their demo demo while there's actual bleach processing 5 feet from them and they're going to get off the call to shampoo that person and ask if you'll wait, it's probably not gonna be a good match. So we need to think about these things when we're building out, especially in the early days, who those people are that are selling to your vertical. I really think about when we're onboarding reps, the focus that I have shifted from is I wanna hire people with great sales acumen. They understand the assignment we're selling here, but my job is to immerse them in the trade and know that they can be at that altitude, speak their language, pick up on the jargon without having me have to beat it into them. Um, there are ways to expedite this, and this is a great way to layer in AI to your go-to-market. There's probably someone here today that is building an AI an AI simulator. Who's using one? I expect to see more hands. I saw Daniel's hand. That's about it. An AI simulator where you can take actual conversations from your vertical, plug them in, and rep, rep, rep until your AEs are fluent, your BDRs, your SDRs, whomever, in the language your vertical speaks in. So if this is, you know, the thing that you're stuck on. I'd love to chat more about this later, 'cause this is also the puzzle I'm putting together. So number 5, sub-vertical dominant sprint. What do I mean by this? I mean, we're go, go, go, we're fast, but what does that look like? When I look at micro-niche fluency, we need to look at the vertical and go, wait a second, a hair salon owner is not actually using the same word for their client as the med spa owner. Why is that important? Inside of your vertical, you're gonna find these little sub-verticals, and each one of them thinks that they're unique and special, and we're gonna treat them like the unicorn that they think they are. We're going to break through the silence that exists within each one of those sub-verticals. So if you don't speak the language that they speak. I have literally seen a demo end because one of our reps called their patient a client, and to them that was unacceptable. So sometimes it might just be asking those questions, but it wasn't noise that we had to cut through at Mangament. And I love this, this moment that Daniel had this picture in his mind that it's actually silence. Each one of these sub-verticals lives in their own bubbles. A hair salon is not joining the same Facebook group that a med spa is in. These are completely different people and we cannot look at them the same. So you've got to dominate within the sub-vertical and go just that deep, know the words they use, the things that they say, and train your reps on that with pure efficiency like 100%, they've got to know it inside out. This has been one that I've seen come up about 5 times in just the past 24 hours here, and it's this idea that, you know, I've heard some PLG is dead, you know, we're back to SLG, and it's this idea that you have to be one or the other, or that the hot ticket of PLG from 2019 is now not a thing. I think of this very differently. So at MangoMint, we have a free trial. It's not a freemium product. It's a 21-day trial, and we use this as a tool for the sales team. Now, that would seem like a PLG motion. They can get into the trial. They can begin using the product. But what we're not using that trial to do is to sell MangoMint. We're using that trial to inform our AEs through data within the software of what could be a great conversation starter. It's not a likelihood to buy. It's a did Sally from the waxing studio go straight to the memberships tab, rage click, and then leave her trial? Because my text with a blue bubble is gonna say, hey Sally, I'm Hi, I'm Michelle with Mangament. Thanks so much for starting a trial. Looks like you're building memberships. What memberships do you currently offer? Would you like to see how Jessica's Spa down the street does $30K a month in memberships using Mangament? See where I'm going with this? Your trial becomes the tool that informs your team of how to approach. That is personalization using PLG. Proves the humans frame the value. Here, this is really something that I've been a big part of taking over at MangoMint. I believe it was 2022 where our customer success team really morphed and became our onboarding team and we moved them into the new revenue organization. Why is this important? Important. Again, I'm serving very SMB. These people don't want to pay to get their data over. We are unseating software where not only do they have client lists, client history, in some cases medical history, but they've also got sales history, service menus, staff schedules, uh, they're setting up their payroll. This is deep vertical SaaS, and nobody Nobody wants to pay to have to get the things that are mission-critical to their business into a new software. So we remove that altogether. We actually onboard our customers completely complimentary, white glove service, every detail within that free trial. So they have not even paid us a dime in many cases, but what this does is allows you to build that trust and then later Later you can layer in charging for those outcomes, those things that you eventually want to grow onto. So trust is number one. It's also the number one company value. And that is by design, not by default. This one to me should be abundantly obvious, but being proudly not for everyone in vertical SaaS is a crucial positioning. You need to know that you are not pushing into markets that are not pulling you there. And if you find yourself getting into that hype cycle, to know that you need to say no. We are so niched down that if I see someone that even gives me like the, the itch on my spine that they could be a franchise, I'm going to proactively have that conversation and understand if they're a franchise and let Let them know we're not great for them before they self-identify that within the trial. So growth loves focus. We've got to beat that into ourselves, and it's even more important when you're really trying to be the default leader for a specific vertical. This one's been fun, especially this week, because I mentioned earlier we killed our CS team. But that doesn't mean we've killed CS within the company. We've actually made it more important. As an organization, shared ownership means that CS is built across the entire organization. And that can sound like a nice thing to have, a nice idea, an aspirational thing to have, but it's actually foundational. Making customer success everyone's job. And the simple framework to this is simply that the product owns the success of the customer. The product has to own time to value, making that happen quickly from moment that they get into the trial, and then sales comes in and owns the reality of that. So me, franchise owner, might log into the trial, see exactly what I'm looking for, but then I get a message from an AE that says, well, you know what? We actually don't have, you know, this type of sub-location reporting that you would need for a franchise. So they're owning the reality. On the other hand, that can also be helping them see what they don't see so they understand the reality of how the product can serve them. And then if you have a chance to meet Iris, our VP of customer operations, she is here, and we have a live in-app support team that owns the problems that will inevitably come up. Now, I have heard about 4 times today alone, including the CEO of HubSpot, who I greatly admire, say that support is done. It is AI owned. And I looked to Iris and I said, how, how often are we using Finn? And her face kind of went sideways. And I was like, okay, okay, what's up with that? And she said, "Well, Finn doesn't know who canceled the haircut on Tuesday at 2 PM with Jessica." And I said, "They're alone. Vertical SaaS needs a different playbook. You have to think about the problems that will inevitably come up that might not even be a software problem. It's a vertical problem. And you need to show them that you can be there and you can help them with this and you can teach them to do this on their own over time. So good news is we have a report for that. That person will not be reaching out again to find out who canceled that appointment. So last but very much not least, this is the most important point, and for me, why I am so loyal to Bangoment and how we have built this product and built this team is you don't want to be just another software. You want to be the only software that they will need. So right now our mission is to make every salon and spa more profitable. We want to be best in class, the default software for our ICP, and down the line we're We're going to be the software that teaches every salon and spa to bolt on AI. All of you will in the future book your appointments through an agent, most likely, but that's going to be us getting that trust, building that in with our vertical, and ensuring that they feel safe migrating in that direction and that they don't feel like they're ripping out humans. I sat getting ready for here Lovely woman in the speaker room, she goes, "Well, what are you speaking on?" And I told her, and she mentioned, you know, the Wi-Fi password if you haven't seen it, and I said, "How does it feel to have a job that AI cannot take from you?" And she said, "Oh, well, there's actually a shop down the street that opened up and it's robots doing eyelashes." So we had a good laugh. Those are my 10 takes. I did say I have 8 minutes for us. If anyone has any questions, I'd love to hear what you're building. And anything, I'm here for that. I know there's 2 mics if anybody wants to jump in. If not, take a picture, hit me up on LinkedIn. I am happy to share. Question. Yeah.
How do you think about pursuing transactional revenue opportunities this one-platform vision inevitably involves taking advantage of those. How do you think about those types of opportunities?
Just so I'm clear, are you talking about payment processing?
Yeah, just any— hello?
Any place where bits of money are going back and forth, then you can clip off some.
Yeah, I mean, I am a firm believer that when you're looking at the playbook of a vertical SaaS business, you you have to have like the broad picture of everything that happens in their business. So when I think of going to a salon or spa, it needs to be really fucking easy to book and really easy to pay. So all of that needs to live in the platform. And I think if you can't answer that deep into your vertical, you won't win. That's my opinion there. Hi. Hello. I was just wondering if there's any opportunity to meet your yourself or Daniel after the talk? Absolutely. Amazing. Yeah, I'll hang around here, and then I know apparently I'm playing poker, which I have learned that by not knowing how to play poker, I'll probably win because those of you who are looking for my towel, you'll be like, what has she got? I don't have anything. Was there one more question?
Hi, how are you? Hi, I'm Brendan Cassie.
Nice to meet you. Nice to meet you finally.
You talk about the importance of vertical expertise in hiring salespeople. Did you A/B test that with like people with like deep domain versus sort of SaaS sales generalists? Yes.
We are still testing that. So by no means would I ever give up on a fantastic candidate that has deep sales acumen from prior SaaS experience. By no means. I want— it's the persona that is an absolute monster. Like, that is the person I'm looking for. I'm looking for like the grade A, top-tier, USDA-approved beef, grass-fed, all of it. That part of the persona is what is most important. It is not the logo, the specific experience, so yes, across the board. Across our team now, we do, all of our top performers do have prior vertical experience. That is true. But I also know that I could throw them into any of your companies and that they could do the same thing there. So very good point. You've got to be holistic about your approach to building the team. Hi.
Is this working?
There we go. Yeah. Dan Davidow. Just a question.
I have a question on the trial experience.
What have you learned adding human intervention, in other words salespeople, into the trial experience? How has that worked?
When is the best time to give the business a call? Kind of what's that journey look like?
Right away. Yeah. So the SLA is within minutes and there's no exception outside of that. It is something that I've been passionate about since the moment we introduced the trial. The reason is, if I am a business owner operator, which is the most common ICP that we serve, I am taking 30 seconds out of my day to go, oh, I'm just gonna go search software. If I don't call them within the minutes that they're actually looking at the trial, I've probably missed the initial window and I'm not going to be able to get back in touch with with them. Not to say we don't recycle and we can't get deals that way, we do, but the ideal is having a touchpoint within minutes, and we are always experimenting on where that should come from. So an automated message, always a person reaching out by phone call, and then a text to follow up. That is what works in our ICP, but I think you kind of have to be constantly experimenting on that, but the faster the better. Thank you. Sweet. Well, if that's it, thank you all so much. Um, I'll hang around after and we can chat as well. Please welcome to the stage co-founder Hypergrowth Partners, Guillaume Cabane.
This is quite the walk. Okay. Hey everyone. Hey, thank you all for coming. Thank you for making it. It's going to be a great event. My name is Guillaume Cabane. Everyone knows me as G, so like you can refer to me as G. Last year I was the interim CMO at Ramp while we passed half a billion in revenue, and I was also the strategic advisor at Reddit alongside running Hypergrowth Partners. Just a quick word on that before we move on. Hypergrowth Partners, like, what is it? Like, We don't invest capital, so we're not a VC, but we're like a VC, right? We like finding the highest potential startups and get long-term incentive alignment through equity. We're like a talent firm. We supplement the skills gap, but we don't place people. We're like a consulting firm, but we don't have junior staff, right? In the end, like, we, Hypergrowth Partners, we're the number one GTM Advisory Collective. We've worked with over 30 companies last year alone, including multiple unicorns. You can see Ramp, Vercel, Retool, Together AI. In short, we bet with founders. We fill the GTM gap fast and we don't win unless growth happens. Cool. That said, let's get on to today's agenda. What this What this means about Hypergrowth Partners is that we see, I see a lot of what's going on in the market. We have a unique chance of observing multiple transformations at the same time. And so today I wanna share some of those observations. And we'll start not with a problem, but with a real issue. This picture here is a picture of the initiation well in Sintra, Portugal, for those who know. The concept of the well is it's at the ground level, right? And you leave the light to go into darkness, right? So that's our agenda today. Today we're going to leave the light and go into darkness because we've got a real problem. So let's start with what's going on. And I'm going to tell you it's truly insane. We've always had technology cycles. All right, a new technology comes in, enables a tactic or a channel. If you made the right choice, you can extract some amazing benefits, right? Higher conversion, lower CAC. And then as everyone adopts the technology, the market gets fatigued, right? The conversion starts dropping. So our job is to gracefully hop from one peak to the next upwards trend. And it, it's difficult, but like if it happens every like 2 or 3 years, it's, it's doable, right? Well, the problem that today that we see with AI, especially if you're an investor, right, is that the investments have gotten much riskier. There's more startups, there's more capital, only a few will survive. And so the same is true as technology buyers, for our MarTech investments. The amplitude and the frequency of those cycles are increasing as the time goes. Changes are faster and they bring more impact. Our 2-year-long cycles are now quarterly ones. So there's 3 consequences with that. The first is that technology buying decisions are are becoming almost impossible. Whatever you choose today is unlikely to be the right choice next quarter. What is the right choice if, like, established vendors are way behind the curve and emerging tech dies at a fast rate? What is even a safe choice? We are getting choice paralysis. The second problem, second consequence, is that tactics have an incredibly short shelf life. Fatigue sets in faster and faster. And the third consequence is how do you hire? How do you structure your org? All right. What skills do we hire for? Do you need engineers in your growth team? Do you need content editors? Like, who knows what technology is going to replace them like next month? So our model as marketing leaders is kind of breaking. So let's look at an example here. Let's look at outbound, which is a channel I know extremely well. You know, I built like the outbound engine at Ramp. It's over 50% of the pipeline that comes from there, and so like Ramp is over $600 million in revenue, right? So I've built that engine over and over again at multiple companies over the past couple years. It's an incredible moat, but is it still a moat today? When everybody is sending emails, right? Let's have a show of hands here. Let's have, let's see, who got less than 50 outbound emails last week? Did anyone get less than 50 outbound emails last week? No one, okay. So we have a huge issue. Think about this, think deeply. If you are 100% sure this email does not come from a human, do you care? Are you going to respond? Do you read the email? No, of course not. What if you no longer can tell the difference between an agentic email for outreach and another email? What's gonna happen is that you're gonna start ignoring all emails. We have desensitized our audience, and we created a trust issue in the channel themselves. So by using Agentic Solutions to send gazillion emails, we are killing the email channel, not outbound, the email channel itself, and buyers are losing. So that's our problem. I'm saying we here, but I kind of have to take ownership. It's the royal French sense of we. So I did this. Like, I told everyone for the past couple years that this was a tactic that was working really well, and the one thing I didn't realize is that you all were listening and you all did the same thing, right? And so I just hope my legacy isn't that I'm the guy who broke email as we know it. That would not be fun. It's not just outbound. Just look at SEO. Like, I'm sure you've heard about zero-click SEO. It's real. Zero-click SEO is real. Like LLM traffic represents about 5% of the traffic to our B2B SaaS clients. We estimate a much higher number never gets to our website. The decision process is done in a back and forth conversation within GPT. How do we influence buyers? Who is the buyer actually? Who is making the decision? Is it the human? So the, the problem that we're facing here is that the AI is breaking marketing concepts as really like at the core concept of marketing. So you think about marketing, it is, right? It's about distorting the reality of putting the spotlight on something. And so if the market had perfect information, you wouldn't need marketing at all. The issue that we're facing right now is Right, is that more and more the buyers are delegating their choice of what technology to buy to an LLM. They're using GPT Deep Research to pick the software. That's what's going on, right? Who are we marketing to? We're marketing to the people who no longer are decision makers. We're marketing to the humans. We're not marketing to the LLM agents. And by doing that, we fail. That's a problem, and that is a problem that is not easy to solve. The second issue is the trust in the channels. We talked about SEO, we talked about email. If people no longer believe, they no longer trust the emails that we're sending at all, then we no longer can communicate with our buyers, and we have the wrong buyers. So what are we to do? Right? So by now, I hope you have a— you understand the reason for this pit of darkness image I had at the beginning. Right? It's kind of gloomy, right? But I don't have all the answers. But I can tell you what I'm seeing work at the top 1% teams that we work with. Okay? So let's go back to RAMP. All right? Remember the cycles? RAMP craves speed. They have over 200 experiments per quarter. They hop from one cycle to the other. Right. The cycles are pressuring competitors. You know them. The cycles are pressuring competitors not to ramp. Okay, let's look at their org. The growth and marketing functions are separate entirely. The growth team scope is very large. It took over parts of sales, part of content. Part of engineering. They use a hybrid approach for automated outbound. The automated approach has perfect market data with custom AI scrapers. It's all stored in like this internal database, which is merged with internal data. And here comes the magic part. They use 90 SDRs to find micro campaigns in an internal app. It's based on Retool, right? 90 SDRs in 2025. That's crazy, right? We have all those like AI SDE agents automation out there and they have SDEs. Why is that? Are they crazy? They're not crazy. If you look at their SDR workflow, it's obvious that 30 to 50% of their work is automated and it's over, it's It surpasses, it overperforms the completely automated workflows, and it overcomes, let's say, the fully human. What's going on is the SDs are capable of creating a super high volume of micro campaigns. Each SD is doing in the order of like 20 to 30 different campaigns per day. Let's look at another company. Let's look at Gorgeous. Okay, a CS platform for e-commerce. They built a workflow that extracts pain points and trends from their sales calls. They are stored alongside the firmographic of the buyer, matched with deep firmographics. The data is seeded into LLM prompts for super targeted outbound or redirection campaigns that always refresh. Every new call is a seed, the beginning of a micro campaign. I'm sure you can start sensing the theme here. Let's look at one of the example emails here, right? The opener is tailored to the last interaction, okay? The hook is anchored on the recent competitor news. The body is anchored on recent product updates, right? And there's a custom CTA with relevant buyout offer. Your call data is a goldmine. Every objection, pain, differentiation, is recorded daily. This is the source of your differentiation. All right. If you look here at Airopps and Attention, that's exactly what they were able to build together. All right. The Attention bot listens in, tags the topics, scores the urgency. Their Airopps workflow puts the hottest topics and checks the search volume and difficulty. There's a workflow that then drafts net new SEO posts and avoid duplicates. And there's a human editor. Yes, a human again. That gives it the once-over. The result is a self-fueling content engine that publishes fresh, high-intent articles every day and ranks faster than any other. So what are the learnings here? What, what do we think of all of this? Well, we can summarize the learnings in 3 persistent modes. There's perfect data as a source, there's a hybrid workflow with a human in the loop, and there's AI interactions, right? And there is speed. There's a modular architecture, okay, for modular teams. Why Perfect Data? Because AI workflows, those hybrid workflows we're going to cover, they require Perfect Data. There's a moat in finding what makes the smallest possible group in your audience tick, okay? What is the smallest common denominator, okay? So you need to have like proprietary data that is going to enable unique outputs and increases variability. Okay? We need a lot of data for that. Okay? And so for that, for example, you can build like a very simple TAM building exercise where you start with the accounts like we discussed, right? You can find the regular like data points like employee counts. You find the LinkedIn ads, LinkedIn ads counts. Alright? And more importantly, you know, you can look at the video the video counts, right, which is more interesting in this case, right? You can do the same for Facebook, right? And then you can start doing some scoring and run some LLM on top of it, right? You can do some, some fit reasoning, right? That is going to make the SDR and the AE life much easier, all right? And then you come up with the case study, right? And so all of that is like processed automatically and then used either for SD, um, as the in the seed of the campaign or in sales calls. So I think what's important here is to realize that hybrid is today's solution. There's this N-shaped curve, lowercase n I've been told, of efficiency right now on the spectrum from traditional, like the human SDR, to full AI. So let's look at human. Human is gone. Human-only is gone. All right. Today, today you get like lower quality and higher costs versus hybrid. Okay. Put aside like enterprise strategic accounts and SDR agents. I know there are a lot here, but like, remember last year, if you were here last year, like, I know last year feels like a decade ago on some fronts, but if we focus on SDR agents, the hype from last year, we were— I was convinced I'm sure a lot of you were convinced that now we'd have gotten SDR agents entirely and SDR as a role would be gone. It did not happen. It didn't materialize, right? Like, sure, there's a lot of like SDR-like platforms, but they don't work. Like the fully automated SDR doesn't work yet. Okay. They're out there. There's like 31 different startups of which like 25 3 and 4 are still active. They do volume, but they don't drive the conversions. It still feels like low quality. There's no reciprocity. Customers of those platforms are churning. So if you multiply quality by volume on the prior graph, you get overall output, and it becomes obvious. The best efficiency is happening at the intersection, the hybrid workflow. And so interestingly, This is not only applicable to outbound automation where we observed it, but also in other channels like SEO. And that's the change from last year. Agentic, full agentic did not pan out yet, and humans can't win even on quality. The ones who are winning are the hybrid workflow platforms. You know, whether that's like AmpleMarket for outbound, whether that's AirOps for SEO, those are the platforms that are winning right now. So the AI workflow platforms, I think they're the ultimate form of growth, right? There's a human at the beginning and at the end, right? The human is the, the seed of this variability that we need in the LLM workflow. All right, it kind of reminds me of, um, the— for those who know, the wall of lava lamps at Cloudflare for like security. Like, that is— it's the same thing. We need this variability that we We need to get it from somewhere. And there's a human at the end of the process for review. What's kind of funny is that, you know, last year we said like, hey, we got this AI magic kind of stuff. Like, we don't know how it works, it's magic. And this year, like, we have this human magic. We still don't know how it works or why it works, but we observe higher performance, higher conversion as soon as we have this like a mushy human brain in the middle of the process. Right. I kind of love this, like human magic, right? If you move to modular architecture, what it means with this cycle acceleration, it means you can switch vendors every quarter. Yeah, yearly contracts are so dead. And so it means that you need to be able to swap in and out a different tech stack in the middle of your workflow. The same is true for your teams, all right? Which means, who do you hire? You're not gonna hire like deep engineers, backend engineers. You need like a GTM engineer who has like a lot of expertise across different functions and can pivot. The teams need to pivot, the people need to pivot very fast. Okay, so you need to be very flexible. It also means that, and that's a kind of a shameless plug, but like yes, you need more advisors, more contractors, Smaller teams, more outsourced. Okay, the pattern matching they can get you is very, very valuable. So how would I do it today if I had to do it again? The one thing is I'd say growth has a large scope and they've taken over some jobs to be done which used to be in sales and marketing. We talked about the ramping example. Alright, so growth teams need less engineers, more GTM engineers. Okay, GTM engineers, not martech babysitters. If your next marketer can't diagram an AI workflow, keep interviewing. You got the wrong guy or girl. Alright, so you need more AI-native workflows and fewer campaigns. You need loops that build trust. We're gonna go into that. Alright. You need to de-risk your choices, right? You need, again, like, to find out how you can, like, spread your risks across multiple places. So what's the framework, right? Obviously, hybrid workflows whenever possible. As good as possible data. Unique data to feed your workflows. Don't skip the humans. I'm the culprit here. I've been trying to skip humans forever. It's not working right now. Now. Okay, so don't skip the humans entirely. Okay? Not when the end product is meant for human consumption. And we'll get into what that means, what human consumption is. But last, like, last is like the trust. You need to invest on some channels which are AI resilient, AI proof. So yeah, marketers of 2025 don't run campaigns, they debug workflows. What's after that? Well, I need to take a sip of water for that. After that is tough, right? Because it's been a while I've been making predictions. It's been almost 10 years since my infamous Growth Hackers San Diego conference where I predicted a future where campaigns would auto-adapt to every prospect in the TAM. I'm not sure if I believed my own prediction back then, but boy, did it happen to be true, right? With the cycle speed, I'm hard-pressed to come up with a new long-term vision I believe in, but if I think about it, I want to circle back to the opening slide on AI is breaking marketing as we know it. This is still in Portugal, by the way. This is at the end of the initiation well. There's a tunnel back to the light, so let's see if we can bring you back to the light. You go back to the core concepts of marketing. We talked about this distortion, right? We distort perfect information. Okay, well, if you look at Google Ads, for example, like, they don't lead you to the best answer. They lead you to the answer that paid. And humans, like, lazy, right? Marketers, persuasive. So we click, right? And just like that, like, we distort the market. Okay, so what other things that we can invest in. AI resilient is AI-proof channels. Let's look at any examples of that. We're here. When are you folks, I, going to be like replaced by an AI bot in person? Very unlikely. Referrals? Very unlikely. All right. Channel partnerships? Very unlikely. So we need to evolve and invest in some of those channels. Okay. And the second thing is, if we go back to like choice has been delegated, distortion is gone, we need to be able to like influence the AI agents. And there are some ways to do that now. So we need to create trust with the audience on some channels, and we need to influence the AI agents. So where do we go from here? All right, one, we can invest in what is likely to stay out of reach, right, as we talked about. And we need to influence our buyer agents. We need to experiment. We need to find ways. I need to do that, right? That's the challenge for all of you. The one hero who will find how to influence an AI agent will win this next cycle, and it's going to be a huge win. So here's to the next winner. Here's to the person who's going to take some risks. Here's to the person who is going to be able to find how to market to LLM and distort their reality and make marketing survive. And with that, I want to thank you. If you're a fit for what we do, you can find us at Hype Growth Partners. We cater to like post- Series A B2B startups, please reach out. Yeah, don't be next year's Caution Retail. Reach out to us and we can help you. Thank you very much. We have a few minutes for questions. There's mics in the middle if you have questions. If you're too freaked out by the doom and gloom, that's okay. I can provide hope. One, two.
Yes.
I love the human magic piece. What have you seen with the clients that you're working with in terms of how they're thinking about the balance of the human versus AI workflow?
How they're thinking about the balance between the human? Yeah, we are seeing the balance be about 90/10. So 90% workflow AI and 10% human. That's where we see it. As I said, it's really the beginning and the end where we see the best. So create variability and review. The middle part is really aggregation. It doesn't need humans. So that's kind of the balance where we see it. About 10% humanness is where we get the best return on investment. About 10%. Yes, ma'am. Hey, thanks.
Should we keep writing for Google knowing that the LMs are picking it up?
Yes. I'm not giving you all the answers for next year, but if you want to know how do we influence AI agents, the answer is, it is obvious that the training data relies on SERPs, search engine results. There are strategies, tactics about what is most efficient there. I'll give you one hint. The search engine, the LLM training data seems to be below the fold, which is the opposite of what we've been trying to do on SEO, which is also much easier. So it is fascinating if you want to try and like keyword stuff, page stuff, the SERP for LLM, try below the fold. Yes.
You mentioned about tools that—
not tools, but like you mentioned about like softwares that are out there that are trying to be fully agentic, but they don't achieve the outcomes. Have you used any tools or tactics that have helped achieve outcomes as opposed to just like fulfilling—
Sorry, can you speak closer? Have I tried what?
Any tools or strategies that have helped achieve outcomes instead of just like hitting leading indicators?
Yeah, there are cases where full agentic makes sense. Not when you're trying to mimic a human. So if you're trying to impersonate a human, full agentic doesn't work because it doesn't feel human yet. It still feels like robotic. All right? But if the expectation is that it is a robot, CS for example, then that works. That works great, because people, the expectation is fine. All right? So that's, we can't trick humans yet or anymore, whichever way you want to put it on the curve. So yeah. I'm at time. I'll be out in a couple minutes. Thank you so much, and see you around.
of growth, RevenueCat, Rick Hendrickman.
Hello. Hello. Thank you all for coming out. When I walked into this venue yesterday and I saw the size of this stage. I was a little bit overwhelmed because when they asked me to do a workshop, I imagined like 50 people in a room. This is not 50 people in a room. And it would have been really, really awkward if I was up on the stage and there was nobody there. So I'm glad you came. I'm not actually going to click onto my slides yet, and it really confused the people backstage. And that's because I want to tell you a little anecdote first. And I'm telling you an anecdote because I'm on the SaaStr AI stage and my talk is not about AI at all, but I have an AI-based anecdote. I told Jason M. Lemkin this yesterday and he told me I had to tell it on this stage. My hotel is roughly a 50-minute walk away from this venue, and I know that it's a 50-minute walk because I've made that walk 3 times this week. And yesterday, before I made the walk for the first time, I opened up my MacBook, pulled up my slides for today, and practiced the talk as I was planning to do it for you today. I had my iPhone open on the Voice Notes app, and the Voice Notes app, now that Apple AI is out, actually does a really, really good job at transcribing your audio. So what I did with the audio is I fed it into ChatGPT, not to ask ChatGPT for feedback, because then what happens is that you get a ChatGPT talk, but to have ChatGPT read me my transcript as I was walking to the venue via voice mode. And whenever it said something that didn't quite work, wasn't entirely consistent, a little confusing, I would literally just tell it, "Okay, like, take a beat, go back a little bit, and let's tweak it like this." And then it would start reading again. And I did that, well, 3 times. I went here and back yesterday, and I did it this morning. So this talk, polished, really, really polished. The flip side is that I have a terrible recollection of these kind of things. So there's an amazing script. I'm not going to be able to repeat it at all. So if after this talk all this falls flat or I mess up, just reach out and I will actually send you the transcript. And also, if none of this turns out to be interesting, you have a really good AI hack for your next presentation. Alright, what you see here— there— is Times Square. This is, I want to say, early last year or maybe even a little before, and on the right-hand side you see Lil Artist. Lil Artist is an app built by a student in India, and this was one of 10 apps that we put up on this giant billboard in in Times Square, and there's not a RevenueCat logo in sight, and there's not a RevenueCat logo in sight because we spent the budget on them rather than us. And the result was there was like a million passersby over the course of a week. We had 4 million impressions on social just from the buzz of people talking about the fact that we did this. And it's an example of you putting your customers on stage and them returning the favor. I'm Rick Hondergmon. I run marketing, sales, and success for RevenueCat. I'm gonna talk a little bit today about mission-driven marketing, which is when you grow your revenue without feeling you need a shower afterwards. I've been in growth for 20-ish years, 3x the last 3 startups. Like I said, I'm now at RevenueCat, and when I joined RevenueCat very, very early on, I think this might have actually been week 1, I asked our top AE, which at that point was our only AE, to tell me what made deals stall or what lost us deals, and he told me that When he got into that conversation, when he got into the room, leadership was on board, product was on board, marketing was on board, but there'd be a developer in the corner of the room just kind of shaking their head, right? They didn't believe. And what that drove home was the notion that we needed everybody in that room to buy into our mission and believe that we really they cared. Because those developers, they're a skeptical bunch to begin with, but they're a skeptical bunch for a good reason. It's because most of the vendors just don't care about the same things they care. Quick note about RevenueCat, uh, we're an in-app monetization platform. We're used by tens of thousands of apps: VSCO, Ladder, ChatGPT. If you have a mobile phone, you probably have at least a couple of apps on your phone today that use RevenueCat, and what we do is we make it easy to implement monetization, run experiments, run analytics, uh, really handle anything that touches revenue in your app. Uh, we do that for tens of thousands of apps, handle $8 billion in revenue annually, and the majority of that $8 billion actually comes from apps that started at zero. When they were on RevenueCat. I had one of our data people run this number this morning. It was actually 5 out of 8 billion people grew from zero on RevenueCat. And part of the reason that that works is because we are relentlessly mission-driven. Our mission is simple. We help apps make more money. And that mission translates even into our pricing. Where we will only charge apps when they make money, right? We charge fractions of a cent on every dollar. So the incentives are really, really cleanly aligned. And ideally, you'll see some of that in the rest of these slides. Then what we're going to do together over the next— well, probably not 30 minutes because I spend a lot of time talking about the AI thing. We're gonna stress test your mission, we're going to explore some generosity or mission-driven marketing in action, we're gonna spend some time doing a little exercise together because it's a workshop after all, and then you're gonna pick a thing to ship in 30 days. Then let's talk about trust for a minute. Trust is not just a buzzword, trust is on the checklist for your customers' purchases. This is the outcome of research done by Edelman Trust in 2024. They found that 65% of people globally said that sharing values with the people you buy from is very, very important to them, that percentage increases when the audience gets younger. So it actually goes up to 80% when we're looking at just the 18 to 34s. And what that means is that when your values and the values of your customers are aligned and they know those values are aligned, your CAC, your cost of acquisition, goes down and your LTV, the amount of money that you make off of people, goes up. Let's go and do a little exercise, and this is the t-shirt test. Maybe some people know this. What I want you to do is raise your hand if your company mission, like for us it's help apps make more money, is something that your customer would wear on a t-shirt voluntarily, right? Is it something they care about enough to put it on a t-shirt? Miguel, raise your hand. Raise your hand. Thank you. That's not a lot of hands, right? And that's because in a lot of cases, company missions are, well, not increase shareholder value in that many words, but it's kind of what it boils down to. And when you compare that to HubSpot helping millions of organizations grow better, or Canva, or Slack making work life simpler, more pleasant, and more productive, more productive. RevenueCat helps apps make more money. That's— those are things you will find on swag, right? And you will find on swag that people proudly wear. This is a simple little model to kind of keep in mind as we go through the rest of this talk, and this is the chain from mission to revenue. And we're going from mission to to believability, to generosity, to revenue. When you can talk about your mission to your customers and you're believable, right? They believe you care. They believe you're not just bull— They believe that you're not making stuff up. Then you get a license. They allow you to be generous. And what I mean by allow you to be generous is that No, first let me define generosity. What I mean with generosity is not swag, right? Developers like swag, everybody likes swag. What I mean by generosity is you sharing your unique knowledge, sharing insights, writing a super interesting blog post, building a utility that helps them, taking them on a study trip to Tokyo. This will come back later. That generosity, it only lands if people believe that it comes comes from a shared sense of values, right? If I would take that developer that's in the corner of the room and I would tell him, you know, come and spend the next week with 10 RevenueCat people in a hotel in Tokyo, they will only do that if they believe that you actually care about the same things. And that generosity compounding allows you to generate more revenue. It gets that developer in the corner of the room nodding their head instead of shaking their head. And here's a test, right? If you think about your mission, would your best mission— would your best customer quote this mission verbatim? And I don't know if you noticed, but when we looked at that Times Square slide a couple of minutes ago, in the top left hand corner was a quote by Matt, the CEO of PhotoRoom, on LinkedIn. And I'm gonna paraphrase a tiny little bit, but totally unprompted, Matt on LinkedIn said, "RevenueCat does the most mission-driven marketing I've ever seen. It's impressive to see a company that's truly this mission-driven," right? And that's the kind of Goodwill. That's the kind of exposure that money won't let you buy. It comes from a place of believability and generosity. All right, your mission. Does it actually improve your customer's life before it improves your ARR? Right? Your ARR going up should be a consequence of you fulfilling the mission, not the other way around. Not to toot our own horn too much, but that's what happens with RevenueCat, right? We help developers make more money and then they pay us more. And lastly, can you actually progress towards achieving that mission with numbers rather than adjectives? I'm not going to force anybody to put up their hands again. Just kind of keep this in mind because what I'd like you to do is take a moment. If you have like a little notebook, pull out the notebook. If it's the Notes app, it's the Notes app. Write down your company mission. If you don't know your company mission, that's a whole other, that's a whole other problem. Write down your company mission and then give some thought as to how you would rewrite that mission to better fit on a t-shirt, right? To better adhere to those 3 principles we looked at earlier? And keep that around. Well, actually, maybe just send it into the company Slack and provoke some reaction and provoke some thought. We're gonna come back to that later. Before we come back to that, we're gonna run through some examples of mission-driven marketing at work. Bunch of these examples are us, because we're very, very good at this. But some of these examples are also from others because we didn't actually invent it. First, and this is probably the easiest form of mission-driven marketing or generosity at work, is sharing insights, right? We do that with the Sub Club podcast. We launched this podcast in October of 2020. We recorded— well, I finished these slides a month ago, so by now it's probably 128 episodes, over half a million listens, 25,000 listens in the last month alone. And it fits the mission because the only thing we talk about in this podcast is about helping apps make more money. And if you listen to any SubClub episode, you will maybe you hear RevenueCat mentioned once, which is actually not about us, it's about helping the audience achieve our mission. And the result of that, it's like authentic and it's believable. This shows up in 20% of prospect calls. And I don't know if there's anybody from Grain around, but I wanna give them a shout out because the only reason I know this is because I set up a Grain alert that flags transcripts from prospects whenever they mention Subclub or the podcast, but 20%, that's a lot, right? It shows up in unprompted job applications almost daily. Then if we take that a step further and start building tools or utilities, things that people can actually use, we do this via the Last Data Subscription Apps. This is our annual report where we pull data from every single app on RevenueCat, all those billions and billions of dollars of revenue, and we turn those into insights, right? Benchmarks, best practices, figure out what works. This report was downloaded 8,000 times in the first month. And that's a good number. It's a really interesting number when you consider that it's not actually gated. You do not need to download this report, right? Because we want to be mission-driven. We're giving people this tool. The download is totally optional. You can read all of it without downloading. So the actual number is a factor 10 higher. It got over 50 unique press mentions, hundreds of social mentions, We use this with, actually the PDF, we use with customer success to run workshops with our customers. We use it a little bit in sales outreach where no strings attached, we run these workshops. We turned it into a workshop in a box where we generated a template for the deck, gave people the PDF, people an entire script and sent that to all our agency partners who run these workshops with their customers, often even charging for those workshops. And it's 263 pages. When we published this, a friend, another growth person somewhere, who knew the history, and the history is when I first joined, this report was 70 pages, the year after was 160 pages, this time it was 260 pages. He asked me, what's the ROI of the incremental 100 pages? Because this is 4 months of work for a lot of people, right? So what's the ROI? And I told him, the ROI is in the mission. We wrote those 100 extra pages because we truly believe that those 100 pages contained information that would help apps make more money. So what we did is we made the content fit the mission rather than some downstream lead goal. And, you know, chances are next year this is going to be 500 pages, and the year after is going to be 1,000, and we're just going to increase as long as we have unique and useful things to share that fit the mission. Then the Times Square takeover. I won't spend a ton of time on this, we already did it. I think the main thing here to take away is if you authentically, believably, this is not a growth hack, if you treat it as a growth hack, it's not gonna work. If you authentically put your audience on the spotlight, the audience is going to put you in the spotlight in return. It's not gonna work if you fake it. Care about the mission. If you don't care about the mission, go join another company. And this is probably one of the more high-touch examples of RevenueCat doing this. Last month we did a joint study trip to Tokyo, and a little bit of context if you're not in mobile is that Japan is the number 3 mobile market market in the world after China and the US. Japan is also a uniquely closed market, right? In Japan, Japanese apps are successful. Most of them can't make it out of Japan. And the reverse is true. Foreign apps don't really manage to make Japan work. And that's actually an opportunity for both sides. So we were ideating how can we kind of address that opportunity. So what we did is we took 8 Western non-Japanese app developers to Tokyo for a week. We put them up in a hotel, a bunch of RevenueCat people came along, we organized meetups with the Japanese app development community, founders, builders, multiple workshops. We had a light-up truck drive through Shibuya covered in Trip@nd logos while blasting the RevenueCat theme song. That's a thing. Look it up on YouTube. It's very, very good. And the first sales opportunity, because this was a mix of customers and prospects, first sales opportunity came in the week after, right? The moment we were back, the engineers for this prospect were poking around in the product trying to figure out if this could work for them, because they knew we drove home the notion that we care about the same things. Also, since, 50% more monthly users in Japan. Japan is now our number 5 market in terms of traffic, and it was number 10 before we began this. And all of that is good, and all of that is downstream from actually achieving the mission. Because I think the biggest and most useful thing we got got from this is that we got people to build connections, right? We got these Western app developers and these Japanese app developers, also up to a degree our team, to build relationships that will allow them to help each other be more successful over time. And we don't have to mediate that conversation anymore. And if you can, again, believably, without bullshitting, right, if you believably make those types of connections, these people will never ever leave RevenueCat. Never, never, because they know we care about the same things. Before they leave, they'll come to us and they say, hey, we care about the same things, you're not doing this thing for me, can you please do it? Some examples from outside of RevenueCat, because we're definitely not the inventors, right? There are a lot of examples of other companies companies doing this successfully over the years. It's consumer, I still think it's relevant because it's a very good example. Patagonia, their mission is to save our home planet and the most sustainable product is actually the one you already own, right? And that's a tough message to put out there if you're actually selling jackets, but they did because it fits the mission. And the result is sales dropped because people were actually buying fewer jackets on the short term. But what it also did is build relationships, build this group of diehard repeat Patagonia buyers that buy Patagonia because they know they share the same values. Really good example of sacrificing some short-term gains over long-term sacrificing some short-term sales for long-term gains. Then Canva Design School. Canva's mission: empowering the world to design. So what they did is build a school to make that really, really easy and accessible. Design School features free video classes and templates, pulls in 2 million students a month, 40,000 back- backlinks, great for top of funnel, but actually what happens is that they're educating this community that when they come into Canva is way more likely to succeed, and then upgrading or paying feels obvious. Similar playbook, slightly different ICP, a lot of people know about this, HubSpot and HubSpot Academy. HubSpot's mission is to help millions grow better. So they have a very, very, very popular blog, practically invented inbound marketing, but they also have an academy with hundreds of free lessons and daily how-tos. Pulls in millions of users, but actually, this is where things get really interesting, when somebody earns a HubSpot Academy certificate, they see a jump of 107% in inbound leads within 6 months. It's a good example of like caring about the same thing, helping your customers achieve their goals, and when you help customers grow, you grow because incentives are aligned. Now a little model to help us go into the next exercise. The conjoined triangles of mission-driven marketing. This is what I was gonna use until I realize that actually Silicon Valley used this gag before. If you haven't watched Silicon Valley, watch it, Google it, it's very, very funny. Instead, we're gonna climb a ladder, and we're gonna climb the generosity ladder. And this sounds a little cheesy and cringy because it is a little cheesy and cringy, but it's also quite useful, right? If this provokes thought, or even better, provokes some kind of action, it's done the thing it needs to do. The generosity ladder allows us to map tactics, map mission-driven tactics on a ladder where each rung increases in effort but also deepens the intimacy and becomes way harder for your competitors to copy and while being believable. We go from insights That's what most people do. You're sharing, teaching what you know. You do a blog, you do a podcast. Nothing wrong with that, works really, really well. It just shouldn't be where you stop, right? Second rung is utilities. It's where you build reusable tools, assets, calculators, benchmarks. Then the third one is where you give them the mic, right? You spotlight them. Put them in a guest post, put them up on a billboard in Times Square. Fourth rung, built— building together. We didn't really touch on this in the examples, but this is where you have open source projects and you're collaborating, you're working together, doing joint research. Fifth rung is experiences, super high touch. You can, you can really only do that if you're believable. If you take something away, let it be the believability. This is where you learn side by side, so you're doing boot camps and study trips. And the last rung, and arguably the most difficult or the most treacherous, is championship. And what I mean by championship is putting your customer in a position where they lead. And it's different from putting them in the spotlight. The spotlight is passive, right? You're still steering, you're still deciding your direction. In championship, you create a customer council that does more than have fancy dinners, but the customer council actually has some sway over your roadmap. Very, very, very scary, but if you can do it, right, if you can do it believable because you care about the same things and your customers are walking the walk, right, they're doing it on a day-to-day day basis, giving the reins over every once in a while is actually very, very, very smart. So the generosity ladder, and what I'd like to do now is do a little exercise, right? You've hopefully, well, none of you did, but maybe you did it in your head, right? You've written that mission sentence and you tweaked it to be more believable, And now we're gonna try and list 5 of your current tactics. What are the things you're doing today? Do you have a blog? Do you have a podcast? Do you put customers up on your website? List 5 of the tactics you're doing today. Map them on the generosity ladder. So just like, this is 1, this is 2, this is 3, this is 4, this is 5. And tweak it to move it up a step. And this is usually easier than you think. So while you are doing this, or while some of you are doing this, I'm gonna run you through some examples, right? Say you have a very, very popular blog post and you can think of a way to turn that blog post into a tool. You went from 1 to 2. Say you have Maybe you have a fancy SaaStr booth, right? Tons of space. You take half of your booth, turn it into a podcast studio, pull in customers to talk about what they learned, right? Suddenly you're in Rung 3. You're spotlighting. Maybe you built a template gallery, right? You're spotlighting customers that have built things for your platform, and now you're paying people for building templates, or you give over the reins to the template gallery, to the community. You went from 3 to 6. A really easy one that probably everybody here can do. If you've written a case study with a customer or you've done a customer story, you're right on 3, right? Take that customer with you to the next conference, put them on a plane, fly them to Europe, Put them at your booth or put them on the stage. Don't have me talking, have a customer here and do an AMA with the audience. And what you've done is you've gone from a spotlight to an experience. So this is actually way easier than a lot of people think, right? As long as you have the framework in mind and you understand that higher is better, then this is something you can do. And then I would like you to or the ones of you that did this, to literally just pick one and ship it in 30 days. Didn't touch on it, one of RevenueCat's core values is always be shipping. Shipping beats speaking. So rather than come up with amazing tactics that never see the light of day, just pick a thing and do it in 30 days. And if it's too big, narrow down the scope, but do it in 30 days. If you go to this QR code, I actually posted about this talk behind the stage as I was waiting to come up, and if you're feeling very, very brave, you can thread a comment with the tactic that you're going to deploy over 30 days, and 30 days from now, I'm going to actually check back in with you, and I'll be like your accountability partner. It'll also have the slides. You'll know where to find me now if none of this made sense and you actually want the transcript that ChatGPT produced. And then I actually think that we might still have a little bit of time for questions because I spoke very, very fast. Oh, you want to go back? There we go. All right, I'll keep this up, and then if there are questions, there's a mic There's a mic there. There's a mic there. If there are no questions, I could do a little bit of stand-up. Oh no, there's a question.
Great content, by the way. Thank you.
I think all of this is very beautiful to see, and not just beautiful, but it's meaningful about your work.
But at the end, And any company has to make money, be profitable. And how do you correlate this in your daily basis, like in your annual revenue growth targets? You know what I mean? When this become money at all, at the end?
Well, for us it's easy because what we did first, and I think probably one of the most important things we did, was we aligned our pricing to match the mission, right? If we when we help apps make more money, we make more money automatically. And it's not— so for context, way back when I ran growth for mobile games, all of that's very mathematical, right? You have user acquisition, you need to make it back in 7 days. That's not really how we do things, right? We really focus on the mission and we trust, and the proof's in the pudding, downstream that's going to turn into revenue. And, and so far it does. And, you know, to maybe get back to AI for a tiny little bit, I wouldn't be surprised if that type of marketing has a resurgence over the next couple of years, because the single biggest source of new users for us is the LLMs. It's like two-thirds of users, which is which is crazy when you realize that a year, a year and a half ago, that just wasn't the case. And that's because we've trained the LLMs to know that we believe and we care about the same things that the people care about that are typing in these questions, right? The LLM is just looking for the best possible answer. And as long as that best possible answer is RevenueCat, because we care about the same things our customers care for, then everything's fine. I think it works out.
Building a little bit— sorry, building a little bit on the last question, given how demand-focused a lot of companies get, people get internally, how have you helped people see the long game of what you're doing, which I interpret as you're building brand and demand and reputation and experience and connectivity and community over time, but how do you get people to get past the short-term thinking or the immediate ROI expectation to play the long game?
That's a good question. The question was, because I don't know if the audio completely caught it, it's how do you get the rest of the company to buy into the idea that this is a thing that matters, right? When everybody's looking at the numbers and the number needs to go up and you're talking about let's do mission-driven marketing and somebody's where, where are my installs or where are my registrations. And I think that's a company culture thing, to be honest, because I don't think I would have been able to pull this off, I don't think we would have been able to pull this off unless the entire company truly believed this. And like another anecdote, I've done a ton of these, is that when I joined RevenueCat, our pricing was actually very similar to a lot of the SaaS pricing pricing you see around, right? We had a good, better, best package. It was still tied to company revenue, so it was somewhat mission-driven, but there were still those tiers. And what we locked behind those tiers were things that we very purposely built for people to make more money, right? But if what we build for people to make more money actually helps them make more money, then they'll pay us more regardless. And this was very early on, I think in the first couple of months. I brought this up, we had a conversation, everybody kind of looked at each other and was like, well, that actually kind of makes sense. And we changed our pricing in a matter of months. And when we changed our pricing, that best tier was way more expensive than what we charge today. What happened initially is that we lost 10% of revenue in a month. And we knew, and everybody was on board because we knew it would make us more mission-focused, right? It sounds kind of radical, but it works, right? But you can't fake it. It's not a growth hack. It's building a company you believe in that matters for the people that you build it for. All right. I think that's— oh.
I can ask. Yeah, yeah.
When you're doing some of these generous acts for your customers, are you encouraging them to share about it? Are you asking them? To talk about your company in any way? No. You're just kind of waiting for the response from them.
Well, I'm not waiting for the response. I just know that if we care about the same things, they will do that, right? And think about that. It's not a revenue cat thing per se. Think about that HubSpot certificate, right? You have a HubSpot certificate and suddenly you have 100% more inbound leads. That's a gazillion times more powerful than you at the bottom of an email say, "Hey, was this interesting? Please share it with your friends." You're caring about the same things, right? You're chasing the same goals. They will share, but you need to care about the same things. Oh, there's another one. How did you apply the generosity lesson ladder to this talk that you're doing right now, or you're coming to SaaStr, or, you know, your marketing efforts here? Oh, that's really interesting because I literally just came up with the generosity ladder for this talk, right? That's like a chicken and egg thing. The next time I do this talk, I will apply the generosity ladder, but I needed some kind of framework to help people understand the progression more than anything else, right? Okay, I think that was actually it. I'm going to be around for a bit. If you see me, come say hi. I won't bite. Thanks.
More from Jason
Why Your Next Hire Should Report to an AI, Tragedy Apps, and Too Many AI SDRs? | The Agents #004
Our Agents Drove Our Salesforce Bill Up 80%, Marketo Hates Agents, and We Stealth-Churned Off Notion
Managing 20+ AI Agents: Lazy Agents, Our $500K AI Bill, Stealth Churn & the Death of 60% Solutions
