Lenny's Podcast: Product | Career | Growth
00:21:01 · Amol Avasare
[00:21:01] S1: I've just seen time and time again at every job I've been in growth that adding friction and adding the right steps leads to higher conversion and higher funnel completion. So you want to get rid of... And especially if you have high volume, you should test the majority of this and just learn and see, does this apply to your business as well? But you want to get rid of annoying friction that doesn't add value. But I think the most simple understanding people have is just solve time for value, cut all the steps and just get them into the product. And that doesn't work most times. I think if you've really thoroughly tested your flows, I look at the companies I've been at, MasterClass. If you go through MasterClass's purchase flow right now, you'll go through all these steps in this quiz when you land on, when you're trying to buy. And you're like, "I came here to buy and it's taking me through all these questions, what are you here for, et cetera." I think it's easy to look at that and be like, "Why do they have this? This is a terrible thing. Just cut it all." And it's like, no, that's been thoroughly tested. And actually that was a significant revenue driver because it helps users feel that the product is for them by understanding what their interests are and then recommending the content and classes there. One of the growth PMs on our team left to join Calm, Calm.com, the meditation app. If you go to Calm's, their landing page and you go through their purchase flow, their login flow, you'll see a quiz. It's not a coincidence that at Mercury we also tested, I think Immad posted on Twitter, we broke out some steps in onboarding. And just having one screen, if you have five or six different form inputs and you often break that into two screens and reduce the cognitive load to people, that is something that performs well. We added steps into the flow there that actually performed well, same at Anthropic. So I think that the takeaway to me is cut friction when it doesn't add to the experience of helping a user understand why the product is for them. But if you can help users understand a product, why product is for them and how to use it and what's most relevant to them, and it's going to add friction, don't shy away from it. Test it, confirm that it works for you, but I think this is something that most growth practitioners deeply understand.
00:23:28 · Lenny Rachitsky, Amol Avasare
[00:23:28] S0: And for them is really important there, what you described is adding friction to better understand who they are so that you know how to recommend the right thing for them. [00:23:38] S1: Correct. Yes. And that done right, it just flows through. So it helps you with activation, but then it helps you with lifecycle. You know more about those users and why they're here. And most sophisticated businesses, you can then, even if someone drops off, you can do lookalike targeting and you can get them at the ad layer as well. So that initial piece of how you understand who the user is just like it's a juice that just keeps on giving if you then use it right for the down funnel. [00:24:06] S0: Everyone's about to go do a bunch of tear downs of Claude's onboarding, MasterClass onboarding, Mercury onboarding. As a tangent, I was at this PM dinner recently and I was asking all the PMs, how has your role as a PM changed most with AI? Where has AI most impacted what you do? And one of the PM's answer that is actually doing competitive analysis, doing a bunch of teardowns of what other people are doing for pricing pages and onboarding. So it's easy to do now. Just, "Hey, Cowork..." I don't know, would you use Cowork for this or Claude? What would you use for that? Okay, this is good. Help people pick which tool to use. If you want to go do a bunch of teardowns of other competitors' onboarding flows, what would you use?
00:24:06 · Lenny Rachitsky, Amol Avasare
[00:24:06] S0: Everyone's about to go do a bunch of tear downs of Claude's onboarding, MasterClass onboarding, Mercury onboarding. As a tangent, I was at this PM dinner recently and I was asking all the PMs, how has your role as a PM changed most with AI? Where has AI most impacted what you do? And one of the PM's answer that is actually doing competitive analysis, doing a bunch of teardowns of what other people are doing for pricing pages and onboarding. So it's easy to do now. Just, "Hey, Cowork..." I don't know, would you use Cowork for this or Claude? What would you use for that? Okay, this is good. Help people pick which tool to use. If you want to go do a bunch of teardowns of other competitors' onboarding flows, what would you use? [00:24:44] S1: You can use Cowork for this, right? So you have Cowork with the Chrome extension. So if you task Cowork with a Chrome extension, go and look for these flows and give me a view of what's working, what's not. That's definitely something that Cowork can do. [00:24:58] S0: Cool. And I imagine that's one of the challenges is you have all these tools now, just which ones for me. By the way, the Chrome extension, I use it all the time. It's amazing. I want to drill a little bit further into the growth org. There's this whole meme on Twitter the other day of just like you have one growth marketer driving all growth at Anthropic and it's like, okay, that's crazy. How many growth people are there? What's the rough org structure of the growth team?
00:24:58 · Lenny Rachitsky, Amol Avasare
[00:24:58] S0: Cool. And I imagine that's one of the challenges is you have all these tools now, just which ones for me. By the way, the Chrome extension, I use it all the time. It's amazing. I want to drill a little bit further into the growth org. There's this whole meme on Twitter the other day of just like you have one growth marketer driving all growth at Anthropic and it's like, okay, that's crazy. How many growth people are there? What's the rough org structure of the growth team? [00:25:22] S1: We're roughly maybe 40 people now. And so we are, I think, structured very much like a traditional growth team in that we have horizontals of growth platform and monetization who think about the sphere of growth across the entirety of our products. And then we have more audience-focused growth pods. You can think about B2B growth, you can think about Claude Code growth, knowledge worker growth, API growth. So really these audiences to keep a narrow focus, which is the thing you have to do when you have so many different products. And then these horizontals that think about things across the board. [00:26:05] S0: And is it across a team of engineers, designers, PMs? What's the functions within this growth org?
00:26:05 · Lenny Rachitsky, Amol Avasare
[00:26:05] S0: And is it across a team of engineers, designers, PMs? What's the functions within this growth org? [00:26:11] S1: Yeah, it's engineers, designers, PMs, data. I think that overall the shape of the org is quite similar to, I'd say a traditional growth team. Probably the things that are maybe different is that I think that we index a lot more towards larger swings as opposed to smaller optimizations. If I think about a traditional growth team, I would've probably done maybe 60%, 70% of my time on small to medium bets, 20% to 30% on larger swings. And I think that for us, we flip it a lot. We do much more the other way where it's 70-30 or more like 50-50 rather than indexing towards smaller bets. That's probably one of the biggest changes, I think. [00:27:06] S0: Just to highlight that, what's interesting there is at the scale you guys are at, a 1% win is massive in the scheme of things. So it's interesting that even at the scale you're at, you're not focusing on these micro optimizations. [00:27:19] S1: It is easy. You could easily focus on these small optimizations and then you tally them up at the end of the quarter and you're like, "Look how much impact we made." And you could do that. [00:27:26] S0: Another billion. No big deal.
00:27:27 · Amol Avasare
[00:27:27] S1: Yeah. But the thing is we've been tracking at 10X year-on-year, and that's the thing that we keep in mind. And I think it ultimately comes down to our fixation of this company about the exponential. I think if you look at anyone who's talking from Anthropic about basically anything, we always talk about the exponential. It's effectively as model capabilities continue to grow on an exponential and the tools around them enable a better job of diffusing that into useful use cases, you basically just keep unlocking new markets where the value of those markets significantly dwarfs what the value of the previous markets were. And agentic coding's a great example. It didn't exist a year, a year and a half ago. And then now just the value of agentic coding is bigger than the previous market of AI coding use. And so I think that is the core thing here where just that the future product value is in order of magnitude higher than it is today. And I think about, I don't know, a normal business, number of companies that I maybe mentioned, or you think about a trading app or a grocery delivery product, many of the leading companies, they're great businesses. But if I think about what is the product value that a company, like a standard, call it a grocery delivery app, what is the product value you get as an end user today versus two years from now? I look at it as like, okay, in two years from now, even if you're shipping all these new products, as an end user, the value you get- You're shipping all these new products. As an end user, the value you get from that product maybe goes up 30 to 50% if the company's done a really good job of shipping new features. It's not exponential though. And so if I think about, "Okay, you're here today. In two years, you're going to have 30 to 50% more product value." Then as a growth team, the relative differential of the product value in two years from now relative to today, I can actually capture a decent percentage of that with the small to medium optimizations that typically have higher conviction as opposed to larger bets. But for Anthropic, it's not really that way, where because of the exponential and our products being very, very, very ... The product value coming from AI, the product value that we will deliver in two years' time is probably a 1,000x, a 100 to a 1,000x what it is today.