00:38:00 · Sandy Diao
[00:38:00] S0: Yeah. It's honestly so hard. I think a lot of the retention strategies will remain the same, and that you still need to figure out how a user continuously gets value with your product over time. And some of these are going to be figuring out, like, what is ultimately the strategy around how the longevity of your product, referencing the... Maybe the, you know, the educator tool for a second here. One common behavior that people are gonna have is figuring out how they can, like, repeatedly come back to make refinements and edits to their products, how they can store their products and view their products and share their products. So you... We have to, you know, utilize some of the same retention strategies that we would pre AI as well, which is how do you build a product experience with longevity that people can constantly come back and and reference and and continue to use. On the other hand, one of the biggest drivers of retention, the input to retention, which is new user activation or activation itself, has wildly changed. Right? AI has kind of trained us to expect value really, really quickly, and really, really fast, and very, very generously. And part of that is the fact that we've been spoiled by these big companies who have this seemingly infinite budget to burn around giving away AI credits and tokens so that people can get a taste of what good looks like. And so in order for smaller companies or newer players to keep up with that, they need to figure out, how do I let somebody peek at the value? How do I let somebody see what's behind the curtains without burning myself out? Without costing me a fortune in order to run this business? And I think that's that's really hard. But I think there are some design patterns that we can utilize to make that happen. You know, for example, one of the experiences that you get when you use any Vibe coding tool is they allow you to start typing your prompt right away. They say, what is it that you want to build? And then they let you hit, like, generate. But for the most part, most of these tools don't actually run the generation. They bring you to a sign up wall, and they try to add all of these little steps that help the user be more serious about their intention to use the product.
00:40:04 · Sandy Diao, Guillaume Moubeche
And I think that's one of the things that we need to look out for in growth, which is we wanna give away value, we wanna show people what's possible, but we also need to make sure that these are real serious users, not someone who's just gonna be casually browsing, burning us of, you know, our our our cash and our capital here in order to run these models. And, of course, that will become more and more negligible over time because the cost of running these models gets cheaper and cheaper over time. But I think, you know, for now, one of growth's responsibilities is to figure out what are those distinct points. Do we continue to identify intent early on, but still match the expectations of this ever changing consumer whose expectations are now being seriously molded by their experiences with free and fast AI. [00:40:50] S1: Yeah. I I agree. And how would, like, one full stack growth person leverage AI in the day to day? Like, what's what's the typical... What are the typical workflows that you personally leverage whenever you advise companies or... Yeah.
00:41:09 · Sandy Diao
[00:41:09] S0: Yeah. I would say the following. The first one is that, maybe unexpectedly, a lot of growth in marketing teams aren't necessarily adopting AI as much as you think they would. And and I think one of the reasons here is that, especially in the role of growth, a lot of the... Even the channels and levers we just covered are very cross functional. They're product led. They're marketing led. They're sales led. They're community led, etcetera. And one of the challenges with AI right now is getting all of that context and data to a single place in a way that makes sense, in a way that breaks past the walled gardens. You know, not every one of these tools has an API that you can use to nicely and neatly connect all of these data points together. So there's still this role of the orchestrator, if you will, who has to look across these channels, use their intuition about the business, look at the data from various sources, or at least figure out how to, generally speaking, automate reading some of these reports, and then figure out how it all coalesces. I think the manually building the stack, building the data sources still lives on, and for good... Very, very good reason. Some of the areas where agents and AI automations are starting to play more of a factor are things like automated ads reporting, automated ads optimizing, stuff that is very, I think, process oriented. You can really write out the guidelines and say, here are things that I have to do. Even things like influencer marketing outreach, where maybe your guidelines for identifying who an influencer is, you can now write into a natural language prompt. And you can, you know, build, say, a Claude skill around it so that it runs every single week, every single morning, however frequently, you know, your your outbound influencer marketing campaigns work. So I do think that every team, for the most part that I've seen, have a relatively unique stack based on who's responsible, whether they're more of, like, that general growth manager or maybe more of a performance specialist who's focused on one channel or the other. I think the opportunity here is having more of a unified growth system that has the context across all of these different systems here.
00:43:20 · Sandy Diao, Guillaume Moubeche
But I I think right now, what I'm seeing and probably the way I recommend growth teams to think about it now, is really thinking of ourselves as individuals who are responsible for growth to be general orchestrators. Figure out what is worth automating, but figure out the manual stuff first. Because if you don't have the manual stuff down, there's no point in scaling it. And that agent's only gonna be as good as the underlying assumptions and logic behind, like, why we did this thing in the first place. [00:43:51] S1: And when you join, like, a a company to to advise them, like, what's your typical process? Like, what are the steps you go through to kind of analyze what's working, what's not working, and what can be done better?
00:44:03 · Sandy Diao
[00:44:03] S0: Great question. The first step that I take when I meet a company is I just kind of wanna hear from them what their growth challenges are. And the second thing that I do is I pair that with an audit of what actually is happening in the business. And the realization that we typically have at the end of that process is that our assumptions on what's working or what our problems are are typically not actually what's happening in the business itself. So that tends to be the early on process. And then we have to align on what are the growth problems that we meaningfully want to solve. From there, it's figuring out, you know, what are the the the quick wins or the fast things that we can do to show some initial lift and impact so that we can have confidence to continue to double down or continue to invest in other things. That tends to be my road map. And then from there, we build something a little bit more long term. That's like, okay. Here are good foundational things that we should probably invest in and do, but we don't have to do right away. So an example is some quick win type of things that we tend to do early on. I mentioned this concept earlier, but what I've seen in most, probably a 100% of early stage startups I worked with is there are power power law outcomes to user acquisition. Firstly, every business wants to solve user acquisition. Even if they have tons of users, they're like, oh, I'm afraid it'll dry up at some point, and it's not sustainable, and it's not scalable, etcetera. So everyone has that problem. But what I've seen for every business I've I've worked with or ever touched is that parallel outcomes drive the results, meaning that one or two channels drive the majority of users. Again here, meaning that we need to figure out what those best fit channels are. And one of the inclinations that a lot of teams I work with tend to have is they wanna test more and more channels. So they're all saying, what do think about influencer? What do think about paid ads? What do you think about Reddit? What do you think about, you know, all these like different referral programs and everything else? And my response to that usually is, I don't think you should be testing all these all at once. We should find out which channels have the best fit for your product.
00:45:53 · Sandy Diao, Guillaume Moubeche
So for example, if you have a product that's very socially shareable, very visual, let's start with social channels first, cause you're gonna have a better fit there. Or if you have a product that has a lot of existing demand, a lot of people understand, maybe consumer product, let's focus on paid first or SEO. So it it really kinda depends on the the the setup of the product itself. And then, you know, we we launch an experiment or two, and that gets us really strong results. Either it works like crazy well, and we're like, wow. We should hire a paid specialist, or we should hire an agency, or it goes, oh, wow. I thought for the longest time that this was going to work, and it and it didn't work. Let's move on to the next thing. But that's typically how the road map looks, and it's also the same approach that I take when I'm full, you know, full time in house in a team, and my mandate is also to, you know, to grow the team. [00:46:40] S1: And what do you think when when you look at a at a growth moat for a company, what do you think have changed before AI versus now? What kind of moats do you feel will remain stronger and the one that will actually not be as strong as as they used to be?