如何为你的产品定价 | Naomi Ionita(Menlo Ventures)
How to price your product | Naomi Ionita (Menlo Ventures)
Pricing Is Never One-And-Done
Naomi Ionita: Do not set it and forget it. I see companies do this, where they labor over designs and features. And they build this perfect product that’s delightful to use. And then pricing’s sort of plucked out of thin air, and then they don’t revisit it. This was Evernote. It was many, many years before we went back and overhauled the pricing. So, think about your pricing just like you do your roadmap. Every 6 to 12 months, there’s probably something meaningful that you’re launching for users. So, treat that as an opportunity to revisit your monetization strategy and making sure you’re compensated appropriately.
About the Guest
Lenny: Welcome to Lenny’s Podcast. I’m Lenny. And my goal here is to help you get better at the craft of building and growing product. Today, my guest is Naomi Ionita. Naomi was one of the first early leaders in product life growth and monetization, having built early teams and infrastructure over a decade ago at Evernote. She was also an early contributor to Reforge when it was just getting started and helped create some of their early programs. She’s also VP of growth at Invoice2go. And currently, she’s a full-time VC at Menlo Ventures.
In her work as a full-time investor, she gets to see what works and doesn’t work across many companies. And one area that she spends a lot of time on is monetization, when it’s best to start charging for your product, how to decide what to charge, and how to evolve your pricing. And that’s what we spend the bulk of our conversation around. We also touch on a really interesting framework Naomi has been developing that she calls the Modern Growth Stack, which is essentially all the areas that new starter products can help take the load off your plate and help your product grow. Naomi is awesome, and I’m excited to share this episode with you. With that, I bring you Naomi Ionita right after a word from our wonderful sponsors.
You can also bring your whole distributed team together around wire frames where anyone can draw their own ideas with the pen tool or put their own images or mock-ups right into the Miro board. And with one of Miro’s ready-made templates, you can go from discovery and research to product roadmaps to customer journey flows to final mocks. Want to see how I use Miro? Head on over to my Miro board at miro.com/lenny to see my most popular podcast episodes, my favorite Miro templates. You can also leave feedback on this podcast episode and more. That’s miro.com/lenny.
Naomi Ionita: Thank you.
From Growth Operator to Investor
Lenny: Did you know that you’re one of the very few VCs that I’ve ever had on this podcast, and so you’re basically representing VC kind here? How do you feel about that?
Naomi Ionita: Wow. Well, thank you. I think early growth folks like us have a unique bond and a lens on startups and investing. So, my operating background is something I lean on every day and has actually informed a lot of the thesis areas where I spend time now as an investor. So, hopefully, we’ll bring it all together in that capacity.
Could Evernote Have Been Notion?
Lenny: Awesome. That’s exactly what I was just going to say, so I’m glad that you covered that. And so that’s a good segue just to… Let’s get into your background briefly. Can you talk about some of the wonderful things that you’ve done in your career both at Reforge, which you’ll touch on, and growth stuff, and then your VC life now?
Naomi Ionita: Perfect. So, I’m a partner at Menlo Ventures. I focus on early-stage SaaS from seed to series B. I started my career in engineering and consulting before getting into tech back in ‘06. Fell in love with product. Did some new product development at a big media company before business school. And while at business school, I spent time at the Design Institute at Stanford. So, this was an opportunity to kind of bridge my analytical background with this refreshing view on human-centered design and learning from the founder of IDEO.
So, brought that with me then to Evernote back in 2011, early days over there. I was there from about 10 to 100 million users. And over that arc, I shifted from more of a core product role to starting our growth product function. This was super organic. I started just collaborating with colleagues from across the business, come up with hypotheses, do user research, run experiments, drive metrics. This was a new way of building product back then. This was a decade ago, so the acronym PLG had not been coined yet. And I really just thought of myself as a user and data-driven product person.
After Evernote, I joined a bootstrapped mobile SMB company called Invoice2go. It was the top-grossing business app at the time. There, I built teams across product, data, growth engineering, design and research, and again, focused on product-led growth and monetization. Over those two jobs, I found myself doing a lot more advising and speaking on the side on these topics. And my board members used to farm me out to their companies to help their founders think through things around product growth and pricing and various topics like that. And Reforge came together at the same time, so I would come in and speak on topics through that community. So, that really accelerated my transition into venture. I realized how much I love having that portfolio view of the world and helping founders look around corners. So, I think it’s an incredible privilege to get to do the work that I do.
Three Monetization Pitfalls
Lenny: One thing you mentioned is Evernote. I don’t know how much you can talk about this, but they just got sold, right? Someone bought Evernote. And if I think back to Evernote, it feels like they could have been Notion, which is killing it right now. Any thoughts on what maybe they missed and didn’t turn into Notion along the way?
The Cost of Delaying Monetization
Naomi Ionita: Yeah. We’re going to clear some cobwebs here. It’s been a while. But one challenge that Evernote really struggled with was this evolution from single-player to multiplayer to team to enterprise. It’s a chasm that a lot of bottom-up SaaS businesses struggle to cross. Evernote was philosophically antisocial. It was meant to be your second brain, kind of your personal tool. And I think that capped the company’s growth potential. I always used to say you can’t retrofit collaboration. You have to be collaboration-first. And a lot of companies now really take that for granted. But back in mid-2000s, this was kind of a new way of building product. And so we missed that bridge.
If companies do that well, it benefits every metric. That bridge from single-player to multiplayer. Acquisition goes up. You grow organically through referrals and shared workflows. Retention goes up because now you have these shared workflows that are incredibly sticky. Employees are accountable to each other to say, “This is how work gets done.” Design in Figma, roadmap planning and ticketing in Jira are linear. It just becomes the default platform. And modernization goes up. Revenue scales with usage. And so the more people using it, the more they use it. You start tripping the wire on paying more and more over time. And so Evernote really struggled in crossing that chasm from the prosumer tool of choice that employees wall-to-wall were using, but never became this larger high-ACV contract from a sales perspective.
Lenny: Yeah. It’s always easy in hindsight to see what could have been better, what could have worked out, what didn’t work out. So, what are you going to do? You mentioned monetization. And I know that you spent a lot of time with founders working on pricing, monetization, especially using monetization as a lever for growth. And so I want to spend some time there to pick your brain about what founders and growth teams can do and how they should think about monetization in terms of growth.
The Limits of Freemium
Naomi Ionita: Perfect.
Day One vs Day 100 Features
Lenny: And then you also have this cool concept that you’ve been developing that you call the Modern Growth Stack, which is kind of this play on modern data stack. And so I want to spend some time there.
Pricing to Match Value
Naomi Ionita: Perfect.
Pricing Lessons from Evernote
Lenny: Cool. So, to dive into that first topic of monetization, if you think about when you’re starting a company, what are some of the biggest challenges you face? Start building a product, especially a B2B product, I always think about pricing and trying to figure out how much to charge, how to charge, your pricing model, how to evolve your pricing, when to charge, all these things. And so I know that you work with founders helping them figure these sorts of things out. And so maybe a first question here is just what do you find startups most often miss or get wrong when they’re starting to think about monetization?
Don’t Set It and Forget It
Naomi Ionita: There’s a lot to cover here. I’ll cover a few missteps that I think are most common. One is waiting too long to monetize. Another one is underpricing. And this isn’t just setting the base price too low, but it’s also leaving money on the table by not offering different plans to cater to different segments. And the third one is all too often with pricing, people set it and forget it. So, this idea that when your product development work is never done, neither is your pricing, and you need to combat that along the way. So, those are three areas I think we can cover here.
Maybe starting with one, I can jump right in, I think waiting too long to monetize. The beginning of a startup’s journey is all about creating something of value. Right? That’s the whole point. Hopefully, founders have some unique market insight or some authenticity around a pain point and some novel solution that’s going to change the world. So, that business value is really critical. But the other side of the same coin is being properly compensated for that value as a business. I understand the vulnerability of being a new startup. You just want people to use your product. And I view that early free beta user feedback loop as an R&D cost to make sure you’re building the best possible product and that they’re driving a lot of value.
But I see companies way too long to make that shift from building a product to building a business. And I think that’s the true signal of product-market fit, is ultimately having people open up their wallets and pay you, so looking for people to get to that end goal. And so again, these things aren’t mutually exclusive. You’re going to create business value, but you’re going to be compensated for it and prioritize your roadmap over time so that you’re building based on what people actually want and are willing to pay you for.
So, when you don’t monetize, I think you’re doing yourself a disservice. The things that I see as the pain of leaving money on the table, you’re inadvertently cheapening your product. People attribute a lower dollar value or a $0 value to what you’ve built. You’re missing out on critical feedback loops to understand what people are willing to pay. And you’re shooting your future self in the foot because this is the other problem, is at some point you’re going to start charging, and you’re going to experience some backlash. So, it’s nice to get ahead of that. A few things to think about, kind of food for thought around delaying and kicking the can down the road from a monetization-
How to Set Initial Pricing
Lenny: So, just to reinforce that, your general piece of advice is if you’re building a B2B product, start charging immediately. Don’t give it away for free. At least have some… You could probably give it away for free but make it clear, “We’re going to charge you this much soon.” How do you think about that?
Customer Research and Pricing Methods
Naomi Ionita: Yeah, I don’t think those are mutually exclusive. So, this isn’t to say that I don’t like freemium models. Evernote was the darling of freemium over a decade ago. So, I’m still a big believer in that. It’s more a question of where you put the paywall. How much do you give up for free? And then how do you price and package a paid version of your product? So, freemium is all about getting that top-of-funnel excitement, getting people to build habit formation. You’re collapsing time to value. You’re building habit formation. You’re building all these champions to use your product. But the idea is to shepherd them along into a paid version of your product and to, again, not delay the idea of, “What should our premium features even be? What should that paid plan even look like?” Again, going back to the misstep at Evernote, I think there was always a premium plan, but it didn’t really bridge into enterprise. So, we can talk more about that.
Lenny: This is kind of a tangent, I know, because you have these two other pieces of underpricing and setting it and forgetting it. Been talked about, but do you have any advice for deciding what goes into freemium and what-
Willingness to Pay Research
Naomi Ionita: If it gets you to the aha moment, that path to habit formation, that has to be free. That’s the core utility of your product. And so the idea is that in that first session or first day, someone’s getting to see the delight and saying, “Oh, my God. I’m never going back to the old way. This is how X gets done.” If you’re looking for some virality or network effect, that’s the other thing. Your free users, you might not be getting revenue from, but the idea is that they help you manage CAC. So, these are folks that are driving organic growth for you and helping reduce the incremental cost of your next set of users. So, that’s another part of the math equation to think about in giving up revenue.
The Impact of Pricing Changes
Lenny: You also have this model that you didn’t mention that you mentioned in a previous chat we were having offline of this idea of day one versus day 100, stuff people need on day one versus what they need down the road. Do you still believe in that? And what should people know about that?
Rebalancing Pricing and Packaging
Naomi Ionita: I do believe in that. That was tied to… We had done this experiment at my last company, Invoice2go, where… Typically on the demand curve, the higher you raise the price, the average revenue per user or ARPU, the lower the conversion rate. So, these things are inversely correlated. And we were able to do this rebalancing of our pricing and packaging so that we actually doubled our upgrade rate from our starter plan to our pro plan.
… We doubled our upgrade rate from our starter plan to our pro plan while also increasing the price of the pro plan. So, to actually get twice as many people to upgrade while paying something like 30% more for that new plan is pretty rare to get the compounding benefits of that. And what we did was thought a lot about what is a day one premium feature? What is a premium feature that you can get value from the very first time you engage with the product? That’s different than your day 100 features. Those are the ones that represent more advanced functionality. Maybe they’re ones where the value is derived from having a certain scale of data in the platform.
And so, those you shouldn’t waste cognitive load for your users to have to even understand or try to appreciate when they’re first getting going. Push those into a more advanced pro version of your product, and monetize them down the road through an upsell. So, big believer in how do you really keep pricing simple? And we’ve all seen those SaaS pricing pages where there’s a laundry list or just a gnarly matrix of features and functionality. So, do what you can to think about that journey for a user and how they’re going to continue to increase value with your product over time, and how you can map your pricing and packaging against that journey.
Envoy’s Pricing Story
Lenny: I really like that framework, because it’s so straightforward and simple. As you use it, you’ll need more enterprise features innately, because you’re sharing it more widely. Your head of security’s going to be like, “What are you doing with this thing?” Your finance team’s going to be like, “Oh, how do we pay for this thing?” And so, that’s a really nice simple way of thinking about what to put in freemium in your free plan versus not. So, glad we touched on that. Okay, so we were going through the three things that companies and founders do wrong when they’re starting to price. And so, the first you said was they go too late and I tangentized us, so I’ll give it back to you to keep going through this.
Be Bold in Enterprise Pricing
Naomi Ionita: [inaudible 00:15:53] This is by far the most common issue. And so, one framework I like to use here is matching price to value. When you do that, you create alignment with your user. So, this entails picking the right value metric. So, this is the unit of value that they derive from using your product, and it creates this natural escalator, because as people use it more, you get paid more over time. SaaS was historically built on a seat based model. That’s been historical SaaS pricing. And now with the rise of PLG, we’ve seen more of these usage based approaches gaining speed, so that’s pretty exciting to see. Whether it’s number of API calls or messages sent or terabytes of storage used or words written, this usage-based approach really matches price to value over the lifetime of a customer. The other thing that happens when you match price to value is it helps you understand who you’re building for, and it lets you target different customer segments.
In doing that, you’re able to better serve each segment, but you’re also able to maximize revenue for the business. Evernote always had a business model. From its beginning, it had $45 a year for an annual subscription. And this set the foundation for the company and tens of millions in revenue, early revenue growth, but the approach was suboptimal. So, as a growth team, we started doing surveys. I was really curious to understand why people converted from our free version to our premium subscription. And one of the most popular answers without fail was, “Well, I just feel guilty. I use it so much. I get so much value from it that I just feel obligated to pay.” And take that in for a second, because if guilt is one of the main reasons why people are paying you, then your free version is too good, and you are leaving money on the table.
So, a single premium tier is often a mistake, and you’re going to be leaving money on the table for specific segments, and it’s important to drill down and understand who those are. Our additional research helped us understand that brand-new users with low perceived value of Evernote looked at it like their Apple Notepad app that was pre-installed on their device. And so, they couldn’t understand the idea of paying 45 a year. They told us that they were getting hundreds of value from Evernote.
Here, the perceived value for avid users was far outpacing what we were asking from them. And this intuition and research really led to a bifurcated strategy of having different plans for different personas based on the value they got from the product and their willingness to pay.
Pricing Experiments for PLG
Lenny: That makes sense. When I heard you say that it costs $45 for a year, that sounds way too low. So I could see how that sets the pattern for Evernote just not making enough money over the long term. Cool. And then the third was that you don’t evolve your pricing, right? That’s like the third biggest mistake.
Naomi Ionita: Yes. So, do not set it and forget it. I see companies do this where they labor over designs and features, and they build this perfect product that’s delightful to use, and then pricing plucked out of thin air, and then they don’t revisit it. This was Evernote. It was many, many years before we went back and overhauled the pricing. So, think about your pricing just like you do your roadmap. Every six to 12 months, there’s probably something meaningful that you’re launching for users. Treat that as an opportunity to revisit your monetization strategy and making sure you’re compensated appropriately.
The Growth and Revenue Trade-Off
Lenny: What advice do you have for founders around just how to decide in your initial price? Clearly Evernote didn’t get that correct, and I’m sure you’ve learned a lot from that and then other companies you’ve worked with. How do you actually decide what to start charging?
The Modern Growth Stack
Naomi Ionita: Yeah, there’s a full pricing process here, so I’m happy to walk through it. The idea here is understanding who your customers are, why they pay you, what is it that they want or value, and how much are they willing to pay you. I’d encourage you to put together a pricing committee. This is not a single-threaded exercise that lives in one department or another. This very much is a cross-functional exercise. If you are a PLG company like companies I worked at, this was the product growth org that I ran. So the combination of PMs and data scientists, folks like that to iterate on pricing. If you are an enterprise SaaS business, of course, sales and finance and rev ops play a role. Think about who that committee should be at your company, and commit to being that cross-functional team that really owns and iterates on pricing over time.
Then, they are responsible for talking to customers. This is by far and away the most basic thing you can do to just increase those feedback loops and understand how much you can push the envelope on pricing. You do that with surveys, with interviews, there’s some questions that we like to use around understanding the relative prioritization of features. Going back to that laundry list of features and matrices on a pricing page, it’s very rare that people convert equally across all of those features. There’s typically one or two that are the main points for conversion. So it’s good for you to understand the relative rank there and how to reconcile some of your pricing and packaging accordingly. So, we would make a list of our features that we had and maybe new things we wanted to build and have people rank them as a must-have, nice to have, or not necessary that help us understand the relative prioritization.
You can also get at it with a hundred point question where you give users a hundred points and say, “Spend them across these different features.” And the more points you give a feature, the more value you’re assigning to it. This is to get to the demand or the features and functionality that you’ve created. It’s step one. It’s understanding what people will actually want and making sure that they’re not just saying everything but the kitchen sink, but they’re actually getting a good sense for what’s most important to them. And then the other side is understanding their willingness to pay. I’d say the easiest on-ramps here for companies to start digging into that is to use Van Westendorp’s method here. I don’t know if you’re familiar with that. You’re nodding a little bit.
Lenny: Yeah. Yeah. Comes up a bunch on this podcast.
Three Themes of the Modern Growth Stack
Naomi Ionita: Oh, great. So I might be repeating myself here, but…
Lenny: No, this is great. This is how we learn. We hear it again.
The Product-Led Sales Model
Naomi Ionita: If you take the packages that users designated as nice to have and must have, you make that collection of features in the survey, then ask them, “What’s such a cheap price that you start to question the quality of the product?” Ask them, “What’s a good deal or sounds like the right price for this package?” Ask them, “What’s expensive, but they would still pay?” So you’re starting to get to that level of discomfort. And then ultimately, “What’s prohibitively expensive? What would people just say, ‘Okay, that’s it.’ You’ve crossed the line of how much I’m willing to pay here.” And by plotting those four curves, you start to get a sense of how to inform your pricing. That’s a great way to marry the questions around demand and then the questions around willingness to pay.
Lenny: Awesome. I wish that survey name was simpler to say, because I can never remember exactly to pronounce it, but you got it. So Van Westendorp.
Building Experimentation Infrastructure
Naomi Ionita: You got it.
Lenny: So, say that you got a price, you launched with something. How do you think about and how do you suggest folks experiment with pricing changes? And then, what impact have you seen from making a pricing change, either in terms of revenue or growth? Because I know you work with a lot of startups on these sorts of things, so I’m curious. How big of an impact can you see from pricing changes?
Billing and Monetization Platforms
Naomi Ionita: Oh, it can be huge. Our friends at OpenView do a really good job of pumping out content and doing these great SaaS benchmarking surveys. They did something recently that showed that roughly half of companies that instituted a pricing change saw at least a 25% increase in ARR. So that’s a pretty massive step function improvement in your revenue from something that doesn’t require massive technological overhaul. I find that most companies regret not doing it sooner. ProfitWell is another group that I have friends at and have a lot of respect for them and the content that they’ve put out. They did a survey once on I think it was over 500 SaaS companies, and they looked at for a 1% improvement on acquisition, retention, and monetization, how did it impact a company’s bottom line? And they found that the impact with an improvement on monetization was 4X that of acquisition.
So, this idea of how can you efficiently improve your business monetization is really underappreciated as a growth lever. Definitely something people should be thinking about. That’s part of my goal of doing this podcast, is making sure founders are compensated in a way that they deserve. So, let’s hope everyone makes a little more money after today. And I’ve seen a lift upwards of 10X on revenue, but it’s sometimes hard to parsh just the pricing change, because usually it can be coupled with big product changes, a rebrand, a lot of PR, the launch of a new plan, like a team or an enterprise plan. So, it’s hard to sometimes understand just the pricing change in isolation, but it really can be pivotal.
Lenny: Cool. And when you think through the pricing changes that you’ve seen, is the impact often from raising the price just broadly? Is it segmenting more intelligently? Is it changing freemium versus paid? Is there a bucket you think of, like “Here’s generally where the biggest impact ends up being?”
Advice on Pricing Models
Naomi Ionita: Yeah, it comes from doing it holistically. I think it’s very rarely as impactful if you just pick a new price or just launch a new plan. I really think of it as rebalancing pricing and packaging overall. So it’s doing this whole exercise of understanding what people actually want, what their willingness to pay is, and mapping it to that user journey like we talked about from single-player mode to multi-player, that first other person you connect with and have a workflow with, spreading it to your whole teams and ultimately spreading it wall to wall across an organization. So it’s a longitudinal view of the user lifecycle and thinking about your whole business model holistically.
Gen AI and Future Growth Stacks
Lenny: I don’t know if you can talk about any of these, but is there a company or an example that comes to mind where you did a pricing change and just talking about what they changed just to make this even more concrete?
Naomi Ionita: I have a specific story there with one of our companies, Envoy. This is a fun one. He was just getting started. This is Envoy, the visitor registration tool that I’m sure a lot of people have used, especially before COVID.
Lightning Round Questions
Lenny: Probably mostly in SF, so I imagine folks in other countries don’t know about it. Maybe describe it.
Naomi Ionita: Yeah. So, if you visit an office instead of just signing in to that piece of paper in the lobby with your name and your email address and what time you checked in, it is a digital iPad based way of checking in and sharing information with the person you’re visiting. And so, in talking to Larry and getting a feel for his evolution around pricing, he tells a story that I love. He was meeting with a big hospitality company, and the conversation was going really well. This prospect was really leaning in and excited about using Envoy, and the conversation shifted to pricing. So in that moment, because Larry was feeling some good vibes, he decided to 10X the price that he was typically charging people. So, just in the moment he decided to just go for it. Go out on a limb, and ask for 10X the typical price.
And in that moment, the exec said, “Okay, sure. Sounds good.” Not a minute of hesitation, not a second of hesitation. And what he learned in that moment was that, one, he was wildly underpriced. It was very clear that he hadn’t even thought about what the ceiling was. But the truth was he probably could have pushed it even further, considering there was no hesitation. So, what I encourage users to do, especially in these enterprise conversations, is to continue to ask for more, to understand where the upper bound might be, and to understand that it’s okay sometimes to lose some deals due to price. Something on the order of 20 to 30% is reasonable so that you can get a sense for where the limit might be. The vast majority of companies are definitely undercharging like we discussed. So, go out on a limb like Larry at Envoy, and you can see that sometimes you can…
Naomi Ionita: … like Larry at Envoy and you can see that sometimes you can 2X, 4X, even 10X your price.
Lenny: That’s an awesome story and it touches on exactly what you said where people often underprice. I imagine it’s strategically smarter not to go straight to 10X and maybe go two or three X until people start pushing back because you lose a lot of data there. But that’s one way to just zoom to an answer.
Naomi Ionita: Yeah, they’re all feedback loops, so I think there’s some incrementality to it. But you got to understand who these different segments are, and if you don’t have enough data points, it’s hard to really understand how to continue to optimize.
Lenny: Any other tips that you want to leave listeners with around pricing or monetization, or even testing pricing? Anything there before we shift to our second topic?
Naomi Ionita: Yes. So we talked a bit about research methods and different surveys you can do to help inform your pricing. And with Enterprise, it’s all about continuously asking for more. But if you’re a PLG company and you have a public facing pricing page, I’d encourage you to experiment. This is something people shy away from, and frankly, there haven’t historically been great tools for companies and infrastructure to be able to do this work.
So at Invoice2go, we invested very heavily in some internal pooling. We had a whole metering and human management and experimentation system in-house. It was a big growth engineering undertaking. And with that we tested different value metrics. We tested different quota limits, price points, promotions, you name it. We tracked the consumption of our pay as you go model and looped that back into the product so we can nudge users along the lifecycle to get them to convert, or upgrade or renew, once quota limits were reached.
So there’s a lot there. I’m excited about this new wave of modern tools to actually help you do this and not sync a bunch of engineering time into building something in-house. So that’s something we can talk about in a bit. But that investment was very worthwhile. We had huge revenue gains by being able to iterate in a way that was more streamlined.
There’s some things to watch out for though. It’s hard to test pricing. There’s a lot of different variables to isolate. So you’ve got to make sure you’re bringing a consistent test experience to the in product experience, your pricing page, maybe mobile app stores or lifecycle emails that you’re sending.
One trick you can do is we would segment these tests by geo. So we would do some tests in Canada or Australia before rolling out in the US. That was a nice way to just put some constraints around our experimentation.
And the other thing you really have to think about is the long-term nature of pricing experimentation. So knowing if you succeeded and failed often requires understanding the implications on churn. Let’s say part of your test is year one discount. You need to understand how users perform in year two and have a sense of the trade-offs around user growth, retention, ARPU. So all of these things are different levers that you want to optimize over time.
Lenny:
Beginning a SOC 2 report can be a huge burden, especially for startups. It’s time consuming, tedious and expensive. Enter Vanta. Over 3000 fast growing companies use Vanta to automate up to 90% of the work involved with SOC 2. Vanta can get you ready for security audits in weeks instead of months, less than a third of the time that it usually takes. For a limited time, Lenny’s Podcast listeners get $1,000 off Vanta. Just go to vanta.com/lenny, that’s V-A-N-T-A.com/lenny to learn more and to claim your discount. Get started today.
There’s a question I wanted to ask you, and maybe it’s too big of a question to answer simply, but it’s this question you just raised of trading off revenue versus growth. That’s one of the most common trade-offs founders have to make. Do you have any just general thoughts, advice there? Is there one you should generally index on? What have you seen works best? Which kind of direction should you lean, growth or revenue, for your, let’s say, B2B company? Like early stage?
Naomi Ionita: Yeah, if you know that you have a bridge to move up market, then giving up the long tail of individual users can be very worthwhile.
So I think Figma is a great example of that. This was a company that took a while to monetize. And even having free usage at the individual level, that was the way to just drive insane community and love for this product. Designers around the world just fell for this product overnight. But this idea that once they were using it in a more corporate setting, once they were collaborating with more people across the business, they were tripping a wire to pay. And so what happened there was you had this massive top of funnel of individual users, but knowing that design is inherently collaborative, you’re interfacing with engineers, you’re interfacing with PMs, with marketers, with researchers, with execs, more than half of Figma users weren’t even designers once it was embedded in the enterprise. And so this idea of these compounded growth loops that you got by interfacing with so many different parts of the company and making design truly collaborative and in the browser, they were able to just have this exponential curve on monetization once they shifted into more of a team or enterprise based package.
So that’s a good example of saying, they were willing to trade off on monetizing the individual because they knew that it would be so sticky and would go so wall to wall within a company.
Lenny: Got it. So your feeling is if it’s a multiplayer PLG-ish product, you probably want to optimize for growth. Let it just take over and not go charge as much as you can immediately, versus say like a sales led B2B enterprise-y product, maybe they’re focused on revenue immediately versus making it feel cheap. Is that right?
Naomi Ionita: And I think if you do have a few free users, crafting it as more of a sweetheart discount, like more of a year one discount, but getting paid over time. I mean, again, I am a big believer in having some early users being your design partners and really giving you that tight feedback loop to make sure you’re building the right product. So it’s not that you should really optimize for revenue on day one. I mean, it is a journey, but I just oftentimes see companies just take too long. Or in Evernote’s case, I mean the free version was just too good. So that’s just something to consider, where to put the paywall and be really, really strategic about that.
Lenny: Just don’t optimize for guilt in your paid driver.
So you’re talking about pricing testing and I was going to ask what tools you found that are useful for testing pricing. And that’s probably a good segue to talking about the modern growth stack. Would that fit into this concept of modern growth stack, testing your pricing?
Naomi Ionita: Yeah, let’s do it.
Lenny: Okay, let’s do it. So just to set it up, there’s this term modern data stack that I think it’d be useful for you to explain because not everyone is aware of that, and you’ve kind of been thinking more about this adjacent idea of a modern growth stack. So can you just talk about these two things, and then we’ll lead to some questions there?
Naomi Ionita: So the modern data stack is basically a collection of cloud native tools to more easily move and manage data. It consists of a fully managed ELT, data pipeline, a destination for that data. So a cloud-based data warehouse, like a Snowflake or Redshift, data transformation tool like DBT, and then finally a platform for visualization on top, so people can access the data. The play on modern data stack was very intentional. I think of the modern growth stack, or my core thesis area right now at Menlo, as the evolution of what you do with the data. So these are the workflows that the data enables to drive the business forward for product growth and revenue teams like I used to run. It’s the modern replacement for infrastructure that teams like mine built or bought. When you’re responsible for driving things like activation or monetization or retention, there tends to be a lot of these internal tools that are built because you’re really powering cross-functional teams to do this work. It’s not mapped easily into legacy departments.
Lenny: Awesome. And the general idea here is, there’s so many more tools now to help you grow with the data stack. There’s just all these tools now that make it so much easier to collect data, use data, make decisions off data, and you’re finding the same things happening with growth. What are some of the tools that you found to be super helpful? I know you’re an investor in some, you’re not a investor in others. It’d be good to just talk about, here’s just like a bunch of cool tools and how do they fit together as much as possible to help you grow your startup.
Naomi Ionita: And I want to reinforce some different themes before I get into some layers of the stack because I think it’s important to frame the benefits of the modern growth stack. So one is data, two is workflow, and three is impact.
So starting with data, the modern growth stack companies really are powered by these smart integrations and the automation that you get as a result. So with this proliferation of SaaS, it’s created this need for more data access and interoperability. We’ve all felt that pain of siloed data. Modern growth stack companies leverage reverse ETL companies, like Hightouch or Census, to break down these silos and help companies, or employees across the company, access data and be more productive. So that’s a big data theme with modern growth stack companies.
The other theme that they unlock is around workflow. Here, it’s really the enablement of people and process. So rather than employees sitting in their departmental silos, modern growth stack companies build bridges between them. So by unlocking data access, the business side can often self-serve and be more self-sufficient without relying on an engineer or a data scientist to run queries or stitch together data sets for them.
The other thing here is lots of growth work is inherently cross functional. So the efforts to drive growth requires new tools and collaborative workflows like we’re discussing. Without purpose-built software, many teams like mine felt no choice but to build in-house. So we spent sacred hours building and maintaining tooling for experimentation, personalization, billing, monetization. These were resources that could have been reallocated to building proprietary features for the business had we been able to buy something purpose-built.
And finally, these products really drive impact. So the idea here is driving hard ROI in the form of cost reduction. So automation means time savings, and oftentimes, that can be mapped directly to cost reduction for a company. But they also help product and growth and go to market teams better engage and monetize customers. So they’re driving hard ROI in the form of revenue impact too. I’m really compelled by companies that can drive hard ROI both across cost saving and revenue generation. And I think that ROI story is even more compelling now in a softer macroeconomic climate. You just have to be able to continue to retain sales and pricing power, and I think that’s derived from a strong ROI story like I described.
So those are general themes and consistencies across the companies that I get particularly excited about. So happy to talk about a few layers in the stack, and I think you might be familiar with a few of these as well, especially based on your growth background at Airbnb and tools that you probably build yourself.
Lenny: Yeah, exactly. That’s what I was going to say, that so many of these things are just coming out of startups that have built these in-house. And then they’re just like, “Hey, I could start a company doing this and provide it to all these other companies.” And so I just love that there’s all these tools coming out that just make it easier to build startups and grow startups, and do less work, and have less people. Just reminds me of the number one app in the App Store at this point. I don’t know if it still is Gas, which is just like four people, and it’s higher than TikTok and YouTube, and all the things, and Facebook, and it’s four people. And so it just shows you the power of what tools can do for you to build new startups and disrupt people, and companies that have been around for a long time.
Naomi Ionita: And you have to help companies do more with less now. There’s a lot of frozen budgets and that’s a good way to break through. So I love that these companies can do that for the buyer.
So one that’s come up a bit and gotten a lot of airtime recently is product-led sales. This idea of companies that serve PLG businesses and harness the power of all that product usage data to inform the customer facing team around which accounts are most upgradable. It’s really free money when you shine a light on an account that nobody was paying attention to and some inside sales team can drive a large account expansion. So I don’t know what better ROI you should get than that.
And there’s a bunch of companies that are doing this. Endgame happens to be one that I work with. They have customers like Figma, Loom, Calendly. There’s other players too. I think Pocus has done a phenomenal job of building content and community to help inform the market around the power of product led sales. So there’s just a lot of goodness about all the players in this space really waking everyone up to this opportunity of layering on sales to a product led motion and how to maximize revenue along the way.
Lenny: I’m an investor in both of those actually, and I’m going to, just in the show notes, note the one I’m an investor in because I’m investor in a lot of these companies, it turns out. And we’ve invested in a few, so I’m just going to keep it simple and I’ll write in the show notes. Here’s ones I’m an investor, just to avoid.
Naomi Ionita: I love that. Double- dipping means you’re a believer in the category as well.
Lenny: I am.
Naomi Ionita: … tipping means you’re a believer in the category as well.
Lenny: I am. I love it. There’s so much school stuff happening there and I’m really excited. Yeah.
Naomi Ionita: Yep. Cool. I think another layer in the stack is experimentation. So this is really critical infrastructure, in my opinion, for these cross-functional product data growth teams to A/B test hypotheses and understand their impact on the business. How do you know if you make a change in the product or your pricing, whether you succeeded or failed without having infrastructure like this along the way? Category creators like Optimizely really paved the way. I was an early buyer of Optimizely and they targeted marketing personas, and it was just game changing to be able to start to A/B test things and bring hypotheses to life.
I’m also biased as an investor, but some of the modern tools here, like Eppo, which offers experimentation for the modern data stack. So unlike Optimizely, which focused on more kind of click through metrics, Eppo ties directly to the metrics in your data warehouse. So tying an experiment result to things like subscriptions or revenue or margins, really like board level metrics that you’re trying to move. They make that full trip really convenient and understand the impact on those business KPIs directly. So it’s a lot of automation around the experimentation, results. And analysis they used to live off to the side in Excel or Jupyter Notebooks now is automated away with Eppo. I think you’re familiar with that one.
Lenny: Yeah, we definitely didn’t plan this, but Eppo’s both a happy sponsor of this podcast. I’m also an investor in Eppo. Go Eppo, but this was not planned.
Naomi Ionita: Well, this is Airbnb roots. [inaudible 00:43:41].
Lenny: Love it. It is. Yeah. It’s my colleague.
Naomi Ionita: [inaudible 00:43:42] was an early data scientist at Airbnb.
Lenny: Exactly, I worked with him at Airbnb and he was amazing and I had to invest in anything that he built. He built an awesome thing.
Naomi Ionita: Yeah, but exactly like you describe, I love these founders that have steep authenticity around the problem because they built it internally and now they’re commercializing it for the masses. And so the story of chain Eppo is a good one on that dimension. And there’s other players too. I’m also a big fan of Amplitude and a buyer of that tool as well, and I love a lot of the team there. So if you do not have a data team or a data warehouse and you still want to leverage being able to do behavioral kind of ad hoc analysis or experimentation, that’s a great tool for you as well. So a lot of different ways to solve this problem in the market.
Lenny: Also, a happy sponsor, go Amplitude.
Naomi Ionita: Cool.
Lenny: I love it. This is great. Hitting on all my favorites. Okay, let’s keep going. What else have we got?
Naomi Ionita: Well, I talked a lot about billing and monetization, so I’d have to talk about that one. I think platforms for managing, billing and iterating on your pricing and packaging, this is just such a big need. I think these will transform business models. For SaaS in particular, most companies have been seat-based like we described, so the historical incumbents like a [inaudible 00:44:54] really serve that model. But in this shift to more usage based, there’s new entrants that are servicing companies in that dimension. And there’s also lots of sort of clunky workflows when you think of bridging from engineering to product and growth to finance or RevOps.
So there’s a lot of just streamlined workflow that these new tools can offer. Some early breakout players in the world of usage-based billing are Metronome and Orb. There’s also more room to handle the full monetization infra-layer. So for example, I like how Orb marries the billing component with the data infrastructure to actually inform what your pricing and packaging iteration should be and help you forecast and optimize revenue. So there’s other players that are doing different components from this journey of the metering piece all the way through to the experimentation piece, and it’s been really, really fun to get to know players in that space. And I wish I could have been a buyer of them many moons ago.
Lenny: Not an investor in these yet. And so that’s cool. Quick tangent, do you have a strong opinion on pricing models, usage based versus seed based versus something else? What’s your guidance to founders? Is this the way to go, usually one of them, or is it super dependent? What do you recommend?
Naomi Ionita: Yes, this is a similar answer I had before. I don’t think that they’re mutually exclusive. And so if you look at all the companies that in different pricing models in SaaS, a small sliver less than 10%, around roughly 5% have just pure natural escalator kind of usage-based model. The vast majority have a hybrid approach. And so what I mean by that is they’re typically some good, better, best subscription model where there’s some consumption component across each tier, like some quota limit for your given value metric. So in Slack there might have been number of messages sent or Dropbox number of terabytes of storage. Invoice2go might be number of invoices. There’s some dimension that’s been sort of packaged in with a given pricing plan, and once you reach that limit, it is a trigger to get you to upgrade to the next plan over, or sometimes there’s overages that you can pay for.
So I don’t believe that you should just be seat-based or just be usage-based. I think one challenge with purely usage-based models is that’s not always how CFOs want to buy. I think buyers sometimes want predictability. They want to be able to budget for your tool, and I’ve lived that. I remember using tools like Mixpanel and Segment and even Jira to an extent where I was paying a cheap amount to get going, and all of a sudden I realized we had grown quickly and I looked at all of our SaaS spend and I was blown away by how much more we were paying. So it’s the other side of this… I’m advocating for people getting paid and compensated for the value that they’re delivering, but there can be a breaking point. And so how do you think about packaging a fixed and variable component so that people can more predictably buy your software?
Lenny: Any other layers of the stack that you want to touch on slash which would you be most excited about in the future? Do you think people should be paying more attention to that maybe they’re not paying attention to?
Naomi Ionita: I mean, I wouldn’t be doing my job as a VC if I didn’t mention Generative AI right now. It’s really having a moment. So there’s a bunch of breakout applications there that sit within this theme of the modern growth stack. When you think of using AI to create images or text or code or audio or video, these capabilities change the way teams work. So writing a blog or writing copy for an ad, SDR is doing their work and can outbound sales efforts. There’s just a lot of these touchpoints where it’s humans kind of tinkering and iterating and laboring over every word. And if the machine, if AI can tell you what’s going to be a more performant version of something, that’s a very, very hard ROI exercise there. You save time and hopefully you’ve improved your performance across the various marketing or sales campaign.
Lenny: Where do you think AI will be the most help on growth in terms of growth? Do you have an idea there?
Naomi Ionita: I think what I described around marketing and sales, just because they really touch the dollars. It can be this ROI story around saving time, but also driving revenue. There’ll be plenty of really effective examples within things like customer support. I mean the cost savings potential. There’s going to be massive. We’ll see what happens in engineering, which generating code. I think there’s a lot of areas where it is going to touch the enterprise, but from a modern growth stack standpoint, I think something that’s really revenue generating and can point to attributable ROI on that dimension is going to be pretty relevant to where I’m spending time right now.
Lenny: I’m excited. Any last thoughts before we get to a very exciting lightning round?
Naomi Ionita: I’m happy to hand it over to the lightning round here.
Lenny: Well, we’ve reached the very exciting lightning round. I’m only going to have four questions for you. I’m going to ask them pretty quick. We’ll go through them fast, whatever comes to mind. No pressure. Question one, what are a couple books that you’ve recommended most to other people?
Naomi Ionita: My buddy Madhavan from Simon Kucher’s wrote a book called Monetizing Innovation. This is a great read. He and others there have done pricing engagements with hundreds of tech companies, so there’s a lot of stories and practical tips there. I often gift that one to founders, so I can’t do this whole talk without giving a nod to my friend, Madhavan, and his bible.
Lenny: Awesome. I just recorded an episode with Madhavan, and so that’s a great pick. Question number two, favorite recent movie or TV show that you really enjoyed?
Naomi Ionita: I have little kids, so I don’t know if this is going to be as interesting for folks, but we like Story Bots on Netflix. They’re these little cartoon characters that answer kids questions. So people sort of call in and ask questions and they do a whole episode on why is the sky blue? How do airplanes fly? How do I see? And I inevitably learned something from watching those. So those are very kind of playful and educational shows. I critically need a new-
Lenny: No, those are… I don’t know the answer to any of those questions. I need to watch this. Okay, so question three. I’m looking at my notes and I’ve never asked this question before, so I don’t know where this came from, but I love it. Who’s been the biggest inspiration to you in your life?
Naomi Ionita: I mean, this one’s pretty easy. For me, it’s my parents. They’re from South America originally and lived on three different continents before immigrating to the US for graduate school. It’s a pretty cliché American dream, but they came here with nothing. Just this idea of building a family and taking advantage of the educational and professional opportunities in America. They progressed through school and building their career in three different languages with no financial support, no entrenched kind of resources or networks to lean on, and I just can’t imagine doing that. Just the stress or cognitive load of kind of restarting your life in whole new geographies and cultures and languages and just betting on yourself and figuring it all out along the way. So my drive has always been rooted in their story and I’m forever indebted to them.
Lenny: I need to ask this question more often. That was an amazing answer on the spot. Naomi, we have reached the end of our chat. Two final questions. Where can folks find you online if they want to learn more, maybe pitch you startup ideas, contact you if they want to ask you questions, and then finally, how can folks be useful to you?
Naomi Ionita: I’m a partner at Menlo Ventures, so you can find more about me in the firm at menlovc.com or else on LinkedIn or Twitter. My DMs are open.
Lenny: Amazing. Naomi, thank you so much for being here.
Naomi Ionita: My pleasure. I look forward to talking to more folks who are building things across workflow automation, data AI, and the modern growth stack. So thank you. It’s always a pleasure.
Lenny: All right, DMs are coming in as we speak.
Naomi Ionita: Thanks, Lenny.
Lenny: Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.
Glossary
| English | 中文 |
|---|---|
| A/B testing | A/B 测试 |
| ACV | 年度合同金额(ACV, Annual Contract Value) |
| aha moment | 顿悟时刻(aha moment) |
| Airbnb | Airbnb(民宿短租平台) |
| Amplitude | Amplitude(产品分析平台) |
| ARPU | 每用户平均收入(ARPU, Average Revenue Per User) |
| ARR | 年经常性收入(ARR, Annual Recurring Revenue) |
| CAC | 客户获取成本(CAC, Customer Acquisition Cost) |
| Calendly | Calendly(日程安排工具) |
| Census | Census(反向 ETL 平台) |
| CFO | CFO(首席财务官) |
| data warehouse | 数据仓库 |
| DBT | DBT(数据转换工具) |
| ELT | ELT(提取-加载-转换,数据管道方式) |
| Endgame | Endgame(产品驱动销售平台) |
| Envoy | Envoy(访客登记管理工具) |
| Eppo | Eppo(面向现代数据栈的实验平台) |
| Evernote | Evernote(笔记应用) |
| Figma | Figma(在线协作设计工具) |
| freemium | 免费增值(freemium) |
| Gas | Gas(社交应用) |
| Generative AI | 生成式 AI(Generative AI) |
| Go-to-market | go-to-market(推向市场) |
| Hightouch | Hightouch(反向 ETL 平台) |
| human-centered design | 人本设计(human-centered design) |
| Invoice2go | Invoice2go(移动端发票与收款工具) |
| Jira | Jira(项目管理工具) |
| KPI | KPI(关键绩效指标,Key Performance Indicator) |
| Larry | Larry(Envoy 创始人) |
| lightning round | 快问快答 |
| Loom | Loom(视频消息工具) |
| Madhavan | Madhavan(Simon Kucher 合伙人,《Monetizing Innovation》作者) |
| Menlo | Menlo(Menlo Ventures,风险投资公司) |
| Metronome | Metronome(按使用量计费平台) |
| Mixpanel | Mixpanel(产品分析平台) |
| Modern Growth Stack | 现代增长栈(Modern Growth Stack) |
| Monetizing Innovation | Monetizing Innovation(商业定价策略著作) |
| OCR | 光学字符识别(OCR) |
| OpenView | OpenView(风险投资公司) |
| Optimizely | Optimizely(A/B 测试与实验平台) |
| Orb | Orb(计费与变现基础设施平台) |
| PLG | 产品驱动增长(PLG, Product-Led Growth) |
| Pocus | Pocus(产品驱动销售平台) |
| pricing committee | 定价委员会(pricing committee) |
| product-led growth | 产品驱动增长 |
| product-led sales | 产品驱动销售(product-led sales) |
| ProfitWell | ProfitWell(SaaS 收入管理平台) |
| prosumer | 专业消费者(prosumer) |
| Redshift | Redshift(AWS 云数据仓库) |
| rev ops | 收入运营(rev ops, Revenue Operations) |
| reverse ETL | 反向 ETL(reverse ETL) |
| ROI | ROI(投资回报率,Return on Investment) |
| SaaS | SaaS(软件即服务) |
| SDR | 销售开发代表(SDR, Sales Development Representative) |
| seat-based model | 按座位计费模式(seat-based model) |
| Segment | Segment(客户数据平台) |
| Simon Kucher | Simon Kucher(全球定价咨询公司) |
| Snowflake | Snowflake(云数据仓库) |
| SOC 2 | SOC 2(服务组织控制类型 2,安全合规标准) |
| Story Bots | Story Bots(Netflix 儿童科普动画) |
| usage-based | 按使用量计费(usage-based) |
| value metric | 价值指标(value metric) |
| Van Westendorp | Van Westendorp(价格敏感度测试方法) |
| Vanta | Vanta(安全合规自动化平台) |
Reformatted by reformat_english.py
如何为你的产品定价 | Naomi Ionita(Menlo Ventures)
如何为你的产品定价 | Naomi Ionita(Menlo Ventures)
文字稿
定价不能一劳永逸
**Naomi Ionita:**不要定了就忘。我看到很多公司花了大量精力打磨设计和功能,构建出体验完美的产品,但定价却像是凭空拍脑袋定的,之后再也不会重新审视。Evernote 当年就是这样,过了很多很多年我们才回去彻底改革定价。所以,请像对待产品路线图一样对待你的定价。每 6 到 12 个月,你大概都会为用户推出一些有意义的新东西,把这当作一个契机,重新审视你的商业化策略,确保你获得了合理的回报。
嘉宾介绍
**Lenny:**欢迎收听 Lenny’s Podcast。我是 Lenny,我的目标是帮助大家在产品构建和增长的技艺上更上一层楼。今天的嘉宾是 Naomi Ionita。Naomi 是产品驱动增长和商业化领域最早的先驱之一,十多年前就在 Evernote 搭建了早期团队和基础设施。她也是 Reforge 刚起步时的早期贡献者,帮助创建了他们最初的一些课程项目。她还曾担任 Invoice2go 的增长副总裁,目前是 Menlo Ventures 的全职风险投资人。
**Lenny:**在她作为全职投资人的工作中,她得以看到众多公司中哪些做法有效、哪些无效。她花大量时间研究的一个领域就是商业化——什么时候最好开始对产品收费、如何决定收多少钱、以及如何演进定价。这也是我们今天对话的主要内容。我们还会聊到 Naomi 一直在打磨的一个非常有趣的框架,她称之为”现代增长栈”(Modern Growth Stack),本质上就是各类新兴工具可以帮你分担工作、助力产品增长的所有领域。Naomi 非常棒,我很高兴能把这期节目分享给大家。好了,接下来就是我和 Naomi Ionita 的对话。
(广告段落已跳过)
从增长操盘手到投资人
**Lenny:**Naomi,欢迎来到播客。
**Naomi Ionita:**谢谢。
**Lenny:**你知道吗,你是我这个播客上为数不多的 VC 嘉宾之一,所以你基本上是在代表整个 VC 群体出场。对此你怎么看?
**Naomi Ionita:**哇,谢谢。我觉得像我们这样做早期增长的人之间有一种独特的纽带,对创业公司和投资也有独特的视角。我的运营背景是我每天都在倚仗的东西,实际上也深刻影响了我现在作为投资人重点投入的许多论题方向。所以,希望能把这些经验融会贯通地带到今天的对话中。
**Lenny:**太好了,这正是我刚才想说的,很高兴你先提了。这也正好引出下一个话题——让我们简单聊聊你的背景。你能谈谈你职业生涯中做过的一些精彩的事情吗?包括你提到的 Reforge、增长相关的工作,以及现在的 VC 生活?
**Naomi Ionita:**好的。我是 Menlo Ventures 的合伙人,专注于从种子轮到 B 轮的早期 SaaS 投资。我的职业生涯始于工程和咨询,2006 年进入科技行业,然后爱上了产品。在商学院之前,我在一家大型媒体公司做过新产品开发。在商学院期间,我在斯坦福设计学院(d.school)学习,这是一个将我的分析背景与人本设计(human-centered design)这一全新视角结合起来的机会,还能向 IDEO 的创始人学习。
我把这些经验带到了 Evernote,那是 2011 年,公司还处于早期。我在那里经历了从大约 1000 万用户到 1 亿用户的增长阶段。在这段历程中,我从核心产品角色转向了创建我们的增长产品职能。这个过程非常自然——我开始与公司各部门的同事协作,提出假设,做用户研究,跑实验,推动指标提升。这在当时是一种全新的产品构建方式。那是十年前,PLG(Product-Led Growth,产品驱动增长)这个缩写还没有被发明出来。我当时只是把自己看作一个以用户和数据驱动的产品人。
离开 Evernote 后,我加入了一家靠自有资金运营的移动端中小企业公司 Invoice2go,当时它是收入排名第一的商业应用。在那里,我搭建了横跨产品、数据、增长工程、设计和研究的团队,同样专注于产品驱动增长和商业化。在那两段工作经历中,我发现自己越来越多地在这些话题上做顾问咨询和演讲。我的董事会成员们经常把我”外派”到他们的投资组合公司,帮助创始人们思考产品增长、定价等各种问题。Reforge 也在同一时期成型,我会通过那个社区来讲授相关话题。这些经历确实加速了我向风险投资的转型。我意识到自己有多么热爱以投资组合的视角看待世界,帮助创始人们预见拐角后面的风景。所以,我觉得能从事现在这份工作是一种极大的荣幸。
Evernote 本可以成为 Notion?
**Lenny:**你提到了 Evernote。我不知道你能说多少,但它刚被卖掉了对吧?有人收购了 Evernote。回想 Evernote,感觉它本可以成为 Notion——而 Notion 现在正风生水起。你对它可能错过了什么、没能变成 Notion 有什么想法吗?
**Naomi Ionita:**好的,我们得清理一下记忆的蛛网了,毕竟已经过了很久。但 Evernote 当时真正面临的挑战,是从单用户到多人协作、再到团队、再到企业级的演进。这是很多自下而上的 SaaS 企业都难以跨越的鸿沟。Evernote 在理念上就是反社交的——它的定位是你的”第二大脑”,一种个人工具。我认为这给公司的增长潜力封了顶。我过去常说,协作能力不能事后补装,你必须一开始就把协作作为核心。现在很多公司觉得这是理所当然的,但回到2000年代中期,这还算一种新的产品构建方式。所以我们错过了那座桥。
如果企业做好了这件事——从单用户到多人协作的跨越——每项指标都会受益。获客量上升,你会通过推荐和共享工作流实现有机增长。留存率上升,因为这些共享工作流粘性极强——员工之间形成了约定:“事情就是这么做的。“Figma 做设计、Jira 做路线图规划和工单管理,一切顺理成章,它就成了默认平台。变现也会上升,收入随使用量增长——用的人越多,用得越深,你迟早会触发越来越高的付费阈值。所以 Evernote 在跨越这道鸿沟时确实很挣扎——它成了员工上下人人都在用的专业消费者(prosumer)工具,但从销售角度来看,始终没能变成那种更高 ACV(年度合同金额)的大型合同。
**Lenny:**事后看总是容易——什么本可以做得更好、什么本该奏效、什么出了问题。那又能怎样呢。你提到了变现,我知道你花了很多时间和创始人一起研究定价和变现,特别是把变现作为增长的杠杆。所以我想在这方面多花些时间,请教一下创始人和增长团队在变现与增长方面可以做些什么、应该怎么思考。
**Naomi Ionita:**好的。
**Lenny:**另外你还有一个正在发展的很酷的概念,叫做现代增长栈(Modern Growth Stack),有点像是对现代数据栈的一种发挥,我也想花点时间聊聊这个。
**Naomi Ionita:**好的。
变现的三大误区
**Lenny:**好。那我们先深入聊聊变现这个话题。想想创业的时候,面临的最大挑战有哪些?打造产品,尤其是 B2B 产品时,我一直在想定价的事——定多少、怎么收、定价模型是什么、定价怎么演进、什么时候开始收费,诸如此类。我知道你在帮助创始人解决这些问题,所以第一个问题:你觉得初创公司在开始思考变现时,最常忽略或搞错的是什么?
**Naomi Ionita:**要讲的内容很多。我分享几个我认为最常见的误区。第一,等太久才开始变现。第二,定价偏低——这不仅仅是基准价格定得太低,还包括没有提供不同方案来满足不同客群,白白把钱留在桌上。第三,定价时人们经常设完就不管了。产品开发永远不会结束,定价也一样,你需要在过程中持续应对。这是我们可以展开的三个方向。
迟迟不变现的代价
那就从第一个开始吧,我认为等太久才开始变现这个问题可以直入正题。初创公司旅程的起点,核心就是创造有价值的东西,对吧?这就是全部意义所在。创始人应该对市场有某种独特洞察,对某个痛点有切身理解,并有某种新颖的解决方案要改变世界。所以商业价值至关重要。但同一枚硬币的另一面是:作为一家企业,你要为这份价值获得合理的回报。我理解初创公司初期的脆弱——你只是想让人用你的产品。我把早期免费 Beta 用户的反馈循环视为研发成本,用来确保你在打造最好的产品,而且他们确实在获得大量价值。
但我看到太多公司花了太长时间,才完成从”打造产品”到”打造企业”的转变。我认为产品-市场匹配的真正信号,归根到底是有人愿意打开钱包付钱给你——你要找的就是走到这一步的人。所以再说一次,这些事情并不矛盾:你创造商业价值,同时为此获得回报,并随着时间推移基于人们真正想要且愿意付费的内容来优先安排产品路线图。
所以,不变现其实是在亏待自己。我看到把银子留在桌上的代价是:你在无意中贬低了自己的产品——人们会赋予你做的东西更低甚至零价值;你错失了关键反馈回路,无法了解人们愿意为什么付钱;你还给未来的自己挖了坑——因为问题在于,总有一天你会开始收费,届时就会遭遇反弹。所以最好提前应对。这些就是关于延迟变现、不断把问题往后推的一些思考——
**Lenny:**所以,再强调一下,你的总体建议是:如果你在做 B2B 产品,立刻开始收费,不要白送。至少……你也可以免费提供,但要明确告诉对方”我们很快会收这么多钱”。你怎么看这个平衡?
免费增值模式的边界
**Naomi Ionita:**对,我认为这两者并不矛盾。这不是说我不认可免费增值(freemium)模式。十多年前,Evernote 曾是免费增值模式的标杆。所以我依然是这个模式的忠实拥趸。问题更多在于付费墙设在哪里——你免费提供多少功能,然后如何对付费版本进行定价和打包。免费增值的核心是在漏斗顶部制造兴奋感,让用户建立使用习惯——缩短价值实现时间,培养习惯,培养一批产品倡导者。但目的是把他们引导到付费版本,而且同样不能推迟去思考”我们的高级功能应该是什么?付费方案应该长什么样?“回到 Evernote 的教训,当时确实一直有高级方案,但并没有真正延伸到企业级。这个我们可以再聊。
**Lenny:**这有点跑题了,我知道,因为你还有另外两点——定价偏低和设完就不管。不过刚才已经提过了。但对于决定什么放在免费增值里、什么……你有什么建议吗?
**Naomi Ionita:**如果某个功能能帮用户到达顿悟时刻(aha moment),走上建立习惯的路径,那它就必须免费——这是产品的核心功用。理想情况是,在第一次使用或第一天,用户就能感受到那种愉悦并说出:“天哪,我再也回不到以前的方式了。事情就该这么做。“如果你在寻找某种病毒式传播或网络效应,那是另一方面。你的免费用户你可能没有从中获得收入,但他们能帮你摊薄客户获取成本(CAC)。这些人在为你驱动有机增长,帮助降低下一批用户的边际成本。这也是免费增值模型中需要纳入计算的另一部分。
第一天与第一百天的功能划分
**Lenny:**你还有一个之前我们线下聊天时提到过但没在这里展开的模型——“第一天”与”第一百天”的概念,用户第一天需要的功能和后续需要的功能之间的区分。你现在还认可这个框架吗?大家应该从中了解什么?
**Naomi Ionita:**我确实还认可。这要追溯到我在上一家公司 Invoice2go 做的一个实验。通常在需求曲线上,价格越高,每用户平均收入(ARPU, Average Revenue Per User)就越高,但转化率越低,两者呈反向关系。而我们对定价和打包方案进行了重新平衡,在提高专业方案价格的同时,把从入门方案到专业方案的升级率翻了一倍。也就是说,让两倍的人愿意升级并多付大约 30% 的费用——同时获得复合收益,这是相当罕见的。我们的做法是深入思考:什么是”第一天”的高级功能?什么是用户第一次接触产品就能从中获取价值的高级功能?这和”第一百天”的功能是不同的。后者代表更高级的功能,其价值可能来源于平台中积累了相当规模的数据之后才能体现。
因此,不要在用户刚开始使用时就浪费他们的认知负担去理解或尝试体会这些高级功能。把它们推到产品更高级的专业版本中,通过后续追加销售来实现变现。所以我非常信奉”保持定价简单”这个原则。我们都见过那些 SaaS 定价页面——上面是一长串的功能清单,或者一个令人头疼的复杂功能矩阵。所以你要尽量站在用户旅程的角度去思考:他们如何随时间推移从产品中获得越来越大的价值,以及如何将你的定价和打包方案对应到这条旅程上。
**Lenny:**我非常喜欢这个框架,因为它非常直观且简洁。随着使用深入,你天然会需要更多企业级功能——因为你会把产品分享给更多人使用。你的安全负责人会跑来问:“你用这东西到底在干什么?“你的财务团队也会来问:“这东西怎么付款?“所以,这是一个非常好的、简洁的思考框架,用来决定什么放在免费增值(freemium)的免费方案里、什么不放。很高兴我们聊到了这个。好,刚才我们在梳理公司和创始人在开始定价时常犯的三个错误。第一个你说的是”定价太晚”,然后我把话题带偏了,现在把话筒交还给你,继续往下讲。
按价值匹配定价
**Naomi Ionita:**定价太晚是迄今为止最常见的问题。我在这里喜欢用的一个框架是”按价值匹配定价”。当你做到这一点,你就与用户实现了利益对齐。这意味着选择正确的价值指标(value metric)——也就是用户从你的产品中获得的那个价值计量单位。它会形成一个自然的增长阶梯,因为用户用得越多,你获得的收入就越高。SaaS 历史上是建立在按座位计费模式(seat-based model)之上的,这是传统的 SaaS 定价方式。而随着产品驱动增长(PLG)的兴起,我们看到越来越多的按使用量计费(usage-based)方式获得了发展势头,这很令人兴奋。无论是 API 调用次数、发送的消息数、使用的存储容量(以 TB 计),还是撰写的文字数量,这种按使用量计费的方式能够在客户的整个生命周期中真正做到价格与价值的匹配。按价值匹配定价的另一个好处是,它帮助你理解你在为谁构建产品,并让你能够针对不同的客户群体进行差异化定位。这样做,你既能更好地服务每个细分群体,也能最大化企业的收入。
Evernote 的定价教训
Evernote 从一开始就有商业模式——每年 45 美元的年度订阅。这为公司奠定了基础,也带来了数千万美元的早期收入增长,但这个方案并不是最优的。作为增长团队,我们开始做用户调查。我非常想了解人们为什么从免费版转化为高级付费用户。其中最常出现的答案之一,毫无例外地是——“我就是觉得愧疚。我用得太多了,从中获得了太多价值,所以觉得有义务付费。“消化一下这个回答——如果”愧疚”是人们向你付费的主要原因之一,那说明你的免费版本太好了,你正在把钱留在桌上。所以,单一的高级付费层级往往是一个错误,你会在特定客户群体面前留下钱。重要的是深入挖掘,搞清楚这些群体是谁。我们进一步的研究发现,那些刚注册的、对 Evernote 感知价值较低的新用户,把它当作设备上预装的 Apple 记事本应用。所以他们根本无法理解为什么要为 Evernote 付 45 美元。但当我们访谈那些重度用户时——这些人在桌面端和移动端跨客户端使用,覆盖所有设备,工作和个人生活都在用,利用光学字符识别(OCR)能力和网页剪藏功能——Evernote 真正成为了他们的第二大脑。他们无法想象没有它的生活。这些人听说一年只需 45 美元时都惊呆了。他们告诉我们,从 Evernote 获得的价值有数百美元之多。在这里,重度用户的感知价值远远超出了我们对他们的收费。这种直觉和研究成果最终促成了一项分层策略——针对不同用户画像,根据他们从产品中获得的价值和付费意愿,提供不同的方案。
**Lenny:**说得通。当我听你说一年才 45 美元时,确实觉得太低了。我能理解这如何为 Evernote 长期收入不足埋下了伏笔。好,第三个错误就是你不去迭代定价,对吗?这是第三个最常见的错误。
不要”设完就不管”
**Naomi Ionita:**没错。千万不要”设完就不管”。我见过很多公司是这样的——他们在设计和功能上反复打磨,打造出一个令人愉悦的完美产品,然后定价却是凭空拍脑袋定的,之后再也不会去重新审视。Evernote 就是如此。过了很多很多年我们才回去彻底重构定价。所以,请像对待产品路线图一样对待你的定价。每六到十二个月,你可能都会为用户推出一些有意义的新功能。把这些时刻当作契机去重新审视你的变现策略,确保你获得了合理的回报。
如何确定初始价格
**Lenny:**对于创始人来说,关于如何确定初始价格,你有什么建议?显然 Evernote 在这一点上没做对,我相信你从中以及你合作过的其他公司那里都学到了很多。你实际上应该如何决定开始收多少钱?
**Naomi Ionita:**好的,这里有一套完整的定价流程,我很乐意梳理一下。核心思路是理解你的客户是谁、他们为什么为你付费、他们想要或看重什么,以及他们愿意付多少钱。我建议你组建一个定价委员会(pricing committee)。这不是一个只存在于某一个部门的单线程工作,而是一项非常跨职能的协作。如果你是一家像我之前工作过的那样的产品驱动增长(PLG)公司,这项工作由我负责的产品增长团队来承担——也就是产品经理和数据科学家等人员的组合,来迭代定价。如果你是一家企业级 SaaS 公司,那销售、财务和收入运营(rev ops, Revenue Operations)也要参与其中。想清楚你公司里这个委员会应该由哪些人组成,并致力于组建一个真正能够长期拥有并迭代定价的跨职能团队。
客户调研与定价方法
**Naomi Ionita:**接下来,他们负责与客户交流。这是你能做的最基础的事情——缩短反馈循环,了解你能在定价上把边界推到多远。你可以通过问卷、访谈来做,我们有一些喜欢用的问题,围绕功能相对优先级来理解用户需求。回到之前说的定价页上那一长串功能和矩阵,用户几乎不会对所有功能一视同仁地转化。通常有一两个功能是主要的转化驱动点。所以你需要理解这些功能的相对排序,并据此调整你的定价和包装方案。我们会把现有功能和可能要开发的新功能列出来,让用户按”必须有""有了更好""不需要”来排序,帮助我们理解功能的相对优先级。
**Naomi Ionita:**你还可以用”百分点问题”来获取信息——给用户 100 个点,让他们说,“把这些点分配到不同功能上。“给某个功能的点数越多,说明你赋予它的价值越高。这一步是为了搞清楚你创造的功能和产品的需求情况。这是第一步——理解人们真正想要什么,确保他们不是什么都要,而是真的能区分什么对他们最重要。
支付意愿调研
**Naomi Ionita:**另一面是理解他们的支付意愿。我觉得公司切入这个问题最简单的起点是使用 Van Westendorp 方法。不知道你是否熟悉——你在点头。
**Lenny:**对,对。这个在这个播客上出现过好几次了。
**Naomi Ionita:**哦,太好了。那我可能是在重复了,不过……
**Lenny:**没关系,这很好。我们就是这么学习的——多听几遍。
**Naomi Ionita:**你把用户标记为”有了更好”和”必须有”的那些功能打包成一个方案组合放在问卷里,然后问他们:“什么价格便宜到让你开始质疑产品质量?“问他们:“什么价格听起来划算,或者说正好是合理的价位?“问他们:“什么价格你觉得贵,但你还是愿意付?“这样你开始触及他们不舒服的区间。最后问:“什么价格贵到你直接放弃?什么价格让人们说,‘好了,到此为止,你越过了我愿意支付的底线。‘“通过绘制这四条曲线,你就能对定价有清晰的判断。这是将需求问题与支付意愿问题结合起来的绝佳方式。
**Lenny:**太棒了。我希望那个调查方法的名字更好念一些,因为我总是记不住怎么发音,不过你念对了。Van Westendorp。
**Naomi Ionita:**你说得对。
定价变更的影响
**Lenny:**假设你已经有了一个价格,也已经上线了。你如何考虑、又如何建议大家对定价变更进行实验?以及,你见过的定价变更对收入或增长有什么样的影响?因为我知道你和很多创业公司合作做这类事情,所以我很好奇——定价变更能带来多大的影响?
**Naomi Ionita:**哦,影响可以非常大。我们的朋友 OpenView 在持续输出优质内容和做很棒的 SaaS 基准调研方面做得很好。他们最近做了一项调查,显示大约一半实施了定价变更的公司,年经常性收入(ARR, Annual Recurring Revenue)至少增长了 25%。这是一个相当大的阶梯式收入提升,而且不需要大规模的技术改造。我发现大多数公司都后悔没有更早做这件事。ProfitWell 是另一个我有朋友在、也非常尊重他们输出内容的团队。他们曾经做过一个覆盖 500 多家 SaaS 公司的调查,研究在获客、留存和商业化三个维度上各提升 1%对公司利润底线的影响。他们发现,商业化提升带来的影响是获客的 4 倍。
**Naomi Ionita:**所以,如何高效提升商业变现能力这个想法,作为一个增长杠杆真的被严重低估了。这绝对是大家应该思考的事情。我做这个播客的部分目标就是确保创始人们获得他们应得的回报。所以希望今天之后每个人都能多赚一点钱。我见过收入提升高达 10 倍的情况,但有时候很难单独归因于定价变更,因为它通常会和重大的产品变更、品牌重塑、大量 PR、新方案的上线(比如团队版或企业版)捆绑在一起。所以有时候很难单独理解定价变更的效果,但它确实可以成为关键的转折点。
**Lenny:**明白了。回顾你见过的定价变更,影响通常来自广泛提价吗?是更精细的客群细分?还是改变免费增值(freemium)与付费之间的策略?你觉得有没有一个类别可以说,“影响最大的通常是这里?“
整体定价与包装的再平衡
**Naomi Ionita:**是的,影响来自整体推进。我觉得如果你只是换一个新价格或只是推出一个新方案,效果很少会那么显著。我真正把它看作是整体上重新平衡定价和包装。也就是说,要完成这整套流程——理解用户真正想要什么、他们的支付意愿是多少,并将其映射到我们之前说的用户旅程上——从单人模式到多人协作,从你连接的第一个人和你们之间的工作流开始,扩展到整个团队,最终覆盖整个组织。这是一个纵向的用户生命周期视角,需要整体性地思考你的整个商业模式。
Envoy 的定价故事
**Lenny:**我不知道你能否谈论其中任何案例,但有没有哪家公司或什么例子让你印象深刻,你做了定价变更后——哪怕只是谈谈他们改了什么,让这个事情更具体一些?
**Naomi Ionita:**我有一个具体的案例,是我们投资的一家公司,Envoy。这个故事很有趣。那时候他刚刚起步。Envoy 是一个访客登记工具,我相信很多人用过,尤其是在疫情之前。
**Lenny:**可能主要在旧金山使用,所以我估计其他国家的人不太了解。也许你可以描述一下。
**Naomi Ionita:**好的。当你拜访一间办公室时,不再是前台那张纸上签上你的名字、邮箱和到达时间,而是一个基于 iPad 的数字化签到方式,并将信息分享给你拜访的人。在和 Larry 交流并了解他在定价上的演变过程中,他讲了一个我很喜欢的故事。他当时在和一个大型酒店集团会面,交谈非常顺利。这个潜在客户非常积极,对使用 Envoy 很兴奋,然后话题转到了定价。在那一刻,因为 Larry 感觉气氛很好,他决定把通常的报价直接乘以 10。就当时当场,他决定放手一试——大胆一回,要了平时 10 倍的价格。
企业定价中敢于要价
Naomi Ionita: 那位高管当下就说:“好的,没问题。“没有一分钟的犹豫,甚至没有一秒的犹豫。他在那一刻学到的是,第一,他的定价严重偏低。很明显他从来没有想过天花板在哪里。但事实是,考虑到对方毫无犹豫,他当时可能还可以再往上推。所以我鼓励用户,尤其是在企业级客户的对话中,持续要求更多,去了解上限可能在哪里,并且理解有时候因为价格而丢掉一些交易是可以的。大约 20% 到 30% 的流失是合理的,这样你才能感受到极限在哪里。绝大多数公司确实都收费过低,正如我们讨论过的。所以,像 Envoy 的 Larry 那样大胆一回,你会发现有时候你可以把价格翻两倍、四倍,甚至十倍。
Lenny: 这个故事太棒了,而且恰好印证了你说的——人们往往定价过低。我想从策略上来说,不直接跳到 10 倍,而是先试两三倍直到人们开始反弹,可能更聪明,因为那样你能获得更多数据。但这不失为一种快速找到答案的方式。
Naomi Ionita: 对,这些都是反馈循环,所以我认为其中有一定的渐进性。但你必须了解这些不同的细分群体是谁,如果你没有足够的数据点,就很难真正理解如何持续优化。
产品驱动增长公司的定价实验
Lenny: 在定价、变现或者定价测试方面,你还有什么其他建议想留给听众的吗?在我们转到第二个话题之前,关于这些还有什么要说的吗?
Naomi Ionita: 有的。我们之前谈了一些研究方法和不同的调查方式来帮助制定定价。对于企业级客户,关键就是持续要求更多。但如果你是一家产品驱动增长(PLG)公司,并且有公开的定价页面,我鼓励你做实验。这是很多人回避的事情,坦率地说,过去一直没有很好的工具和基础设施来帮助公司完成这项工作。在 Invoice2go,我们在内部工具上投入非常大。我们有一整套计量、用户管理和实验系统,全部自建。这是一项庞大的增长工程投入。在此基础上,我们测试了不同的价值指标(value metric),测试了不同的配额上限、价格点、促销等等。我们追踪了按使用量计费模式下用户的消费情况,并将数据反馈到产品中,这样就能在用户达到配额上限时,沿生命周期引导他们转化、升级或续费。
这里面有很多内容。我对这波新兴的现代工具感到很兴奋,它们能真正帮你做这些事,而不需要把大量工程时间投入到自建系统上。这个我们稍后可以再聊。但那笔投入确实非常值得。通过更加高效的迭代方式,我们获得了巨大的收入提升。
不过也有一些需要注意的地方。定价测试很困难,有很多不同的变量需要隔离。所以你必须确保测试体验在产品内体验、定价页面、可能是移动应用商店或你发送的生命周期邮件中保持一致。一个小技巧是,我们会按地区来划分测试。比如我们会先在加拿大或澳大利亚进行测试,然后再在美国推出。这是一种为实验设定边界的好方法。
另一件你真正需要考虑的事情是,定价实验具有长期性。判断成功还是失败往往需要理解对流失率的影响。假设你的测试中包含首年折扣,你需要了解用户在第二年的表现,并对用户增长、留存和每用户平均收入(ARPU)之间的权衡有清晰的认识。所以所有这些都是你想随时间持续优化的不同杠杆。
增长与收入的权衡
Lenny: 我有个问题想问你,也许这个问题太大了不好简单回答,但你刚才提到的那个收入与增长之间的权衡问题——这是创始人最常见的取舍之一。你有什么总体性的想法或建议吗?一般来说应该更偏向哪个方向?你看到过什么效果最好?对于你的 B2B 公司,比如早期阶段,应该倾向哪个方向——增长还是收入?
Naomi Ionita: 如果你明确知道自己有一条通往向上拓展市场的路径,那么放弃个人用户的长尾是非常值得的。我认为 Figma 就是一个很好的例子。这家公司花了一段时间才开始变现。甚至在个人层面提供免费使用,这就是驱动社区对这款产品疯狂热爱的方式。世界各地的设计师几乎一夜之间就爱上了这个产品。但关键在于,一旦他们在更正式的企业环境中使用它,一旦他们与公司内部更多人协作,就会触发付费机制。所以那里发生的情况是,你拥有了庞大的个人用户漏斗顶端,但设计本质上是协作性的——你需要和工程师打交道,和产品经理、市场营销、研究员、高管打交道——一旦 Figma 深入企业,超过一半的用户甚至不是设计师。正是这种与公司那么多不同部门接触所带来的复合增长循环,加上让设计真正实现协作化和浏览器化,使得他们在转向基于团队或企业的套餐后,变现曲线呈现出指数级增长。
所以这是一个很好的例子——他们愿意在个人用户变现上做出牺牲,因为他们知道产品会非常粘性,会在一家公司内全面铺开。
Lenny: 明白了。所以你的感觉是,如果是一个多人协作型的产品驱动增长产品,你可能应该优先优化增长,让它自然蔓延,而不是立刻尽可能多地收费;而如果是销售驱动的 B2B 企业级产品,也许应该立刻聚焦收入,而不是让人觉得便宜。对吗?
Naomi Ionita: 我觉得如果你确实有一些免费用户,可以把它设计成更像是一种优惠折扣,比如首年折扣的形式,但随着时间推移逐步收费。我想再次强调,我非常赞同让早期用户成为你的设计合作伙伴,给你紧密的反馈循环,确保你在构建正确的产品。所以并不是说你应该在第一天就优化收入。这确实是一个过程,但我经常看到公司花了太长时间才开始收费。或者以 Evernote 为例,它的免费版本实在太好用了。所以你需要认真考虑付费墙应该放在哪里,并且要对这个决策非常、非常有策略性。
Lenny: 总之就是别把”让付费用户觉得不亏”当作你定价的优化目标。
现代增长栈
你刚才提到了定价测试,我本来想问你觉得哪些工具对测试定价比较有用。这恰好可以顺接到聊聊现代增长栈的话题。定价测试是否也算在现代增长栈的概念范畴内?
Naomi Ionita: 对,我们来聊这个。
Lenny: 好,开始吧。先铺垫一下,有一个概念叫”现代数据栈”,我觉得由你来解释一下会很有帮助,因为不是所有人都了解这个概念,而且你一直在思考一个与之相关的想法,就是”现代增长栈”。能不能先聊聊这两个概念,然后我们再展开一些问题?
Naomi Ionita: 现代数据栈基本上是一组云原生的工具集合,用于更便捷地移动和管理数据。它包括一个全托管的 ELT 数据管道、数据的目的地——也就是云端数据仓库,比如 Snowflake 或 Redshift;数据转换工具,比如 DBT;最后还有一层可视化平台,让人们能够访问和使用数据。“现代增长栈”这个名称是对现代数据栈的有意呼应。我把现代增长栈——也就是我目前在 Menlo 的核心研究方向——视为”你用数据做什么”的演进。这些是由数据驱动的工作流,用于推动业务前进,服务于产品增长和收入团队,就像我以前带的那种团队。它是过去像我这样的团队自建或采购的基础设施的现代替代品。当你负责推动激活、变现或留存这类指标时,往往会需要搭建很多内部工具,因为你真正要做的是赋能跨职能团队来完成这些工作,而这些工作并不容易归入传统的部门架构中。
Lenny: 太好了。这里的总体思路是,现在有越来越多的工具帮助你基于数据栈来驱动增长,各种工具让收集数据、使用数据、基于数据做决策变得容易得多。你发现增长领域也在发生同样的事情。你发现有哪些工具特别有用?我知道其中一些你有投资,另一些没有。希望能聊聊——这里有一堆很棒的工具,它们如何拼在一起帮助你增长你的初创公司。
现代增长栈的三大主题
Naomi Ionita: 在深入讨论栈的各个层级之前,我想先强调几个不同的主题,因为我觉得它们对于理解现代增长栈的价值很重要。三个主题分别是:数据、工作流和影响力。
先说数据。现代增长栈公司真正依靠的是智能集成以及由此带来的自动化。随着 SaaS 的激增,产生了对更多数据访问和互操作性的需求。我们都感受过数据孤岛带来的痛苦。现代增长栈公司利用反向 ETL 公司,比如 Hightouch 或 Census,来打破这些孤岛,帮助公司——或者说全公司的员工——访问数据、提高生产力。这是现代增长栈公司的一个重要数据主题。
第二个主题是工作流。这里的核心是赋能人和流程。与其让员工困在各自的部门孤岛里,现代增长栈公司在它们之间搭建桥梁。通过开放数据访问,业务侧通常可以自助获取数据、更加自主,不再需要依赖工程师或数据科学家来跑查询或拼接数据集。
另一个要点是,很多增长工作本质上是跨职能的。推动增长需要新的工具和协作式工作流,就像我们正在讨论的这些。没有专门为此构建的软件,很多像我这样的团队觉得别无选择,只能自建。我们花了宝贵的时间来构建和维护用于实验、个性化、计费、变现的工具。如果当时能买到专门为此打造的产品,这些资源本可以重新分配去构建业务专有的功能。
最后,这些产品真正驱动的是影响力。核心理念是以成本节约的形式带来硬性 ROI。自动化意味着节省时间,而且这通常可以直接映射为公司的成本节约。但它们同时也帮助产品、增长和 go-to-market 团队更好地触达客户并实现变现。所以它们也在以收入影响的形式带来硬性 ROI。我对那些能同时在成本节约和收入创造两个维度上驱动硬性 ROI 的公司特别感兴趣。我认为在当前偏软的宏观经济环境下,这个 ROI 叙事变得更加有说服力。你必须能够持续保持销售和定价能力,而我认为这正源自我刚才描述的那种强大的 ROI 叙事。
这些就是我对这些公司特别兴奋的通用主题和共性。接下来我很乐意聊聊栈里的几个层级,我想其中一些你应该也熟悉,尤其是考虑到你在 Airbnb 的增长背景,以及你可能自己也构建过的工具。
Lenny: 对,完全正确。我正想说,这些东西很多都是从初创公司内部自建的工具中诞生的。然后他们就想着:“嘿,我可以为此创办一家公司,提供给所有其他公司。“所以我非常喜欢现在涌现的这些工具,让创建和增长初创公司变得更容易,做的事情更少、需要的人也更少。这让我想到目前 App Store 排名第一的应用。我不知道现在还是不是 Gas,那个只有四个人的团队做的应用,排名比 TikTok、YouTube、Facebook 都高。这就展示了工具的力量——帮你创建新的初创公司、颠覆那些存在已久的 incumbents。
Naomi Ionita: 而且你现在必须帮助公司用更少的资源做更多的事。现在很多预算都被冻结了,而这是一个很好的突破方式。所以我很喜欢这些公司能为买家做到这一点。
产品驱动销售
最近被频繁提及的一个方向是产品驱动销售(product-led sales)。这个概念是指服务于产品驱动增长(PLG, Product-Led Growth)企业的公司,利用所有产品使用数据来帮助面向客户的团队判断哪些账户最有升级潜力。当你把一个无人关注的账户挖掘出来,让内部销售团队推动一个大型账户扩展时,那简直就是白捡的钱。我想不出比这更好的 ROI 了。
这个领域有很多公司在做。Endgame 恰好是我合作的一家,他们的客户包括 Figma、Loom、Calendly。还有其他玩家。我觉得 Pocus 在内容建设和社区运营方面做得非常出色,帮助市场理解产品驱动销售的力量。所以这个领域的所有参与者都在做一件好事——唤醒大家对在产品驱动模式上叠加销售这一机会的认知,以及如何在此过程中最大化收入。
Lenny: 其实这两家我都投了。我会在节目备注里注明我投资了哪些——结果发现这类公司我投了不少。我们投了好几家,所以我就简单处理,在备注里列出来。注明哪些是我投资的,免得产生利益冲突。
Naomi Ionita: 我喜欢这个做法。双重下注说明你对这个品类是真正有信念的。
Lenny: 确实是。我很看好。这个领域正在发生太多精彩的事,我真的非常兴奋。
实验基础设施
Naomi Ionita: 好。我觉得这个技术栈的另一个层级是实验。在我看来,这对于跨职能的产品数据增长团队来说是至关重要的基础设施——用来做 A/B 测试验证假设,并理解其对业务的影响。如果你在产品或定价上做了一个改动,没有这样的基础设施,你怎么知道是成功还是失败?像 Optimizely 这样的品类开创者确实铺平了道路。我早期就是 Optimizely 的用户,他们面向的是营销人群,能够开始做 A/B 测试、把假设变成现实,在当时确实是颠覆性的。
我作为投资人也有偏向,但这个领域的一些现代工具值得关注,比如 Eppo,它为现代数据栈提供了实验能力。与 Optimizely 更关注点击类指标不同,Eppo 直接与你数据仓库中的指标关联。所以可以把实验结果直接与订阅量、收入或利润率等真正想要推动的董事会级指标挂钩。它让整个链路变得非常便捷,直接理解实验对这些业务 KPI 的影响。围绕实验和结果分析的大量自动化工作,以前需要放到旁边的 Excel 或 Jupyter Notebook 里手动完成,现在被 Eppo 自动化了。你应该对这家公司很熟悉。
Lenny: 是的,这绝对不是我们提前策划的,但 Eppo 恰好是本播客的赞助商,我也是 Eppo 的投资人。加油 Eppo,但这真不是事先安排的。
Naomi Ionita: 嗯,这是 Airbnb 的渊源。
Lenny: 没错。他是我的前同事。
Naomi Ionita: 他是 Airbnb 早期的数据科学家。
Lenny: 对,我在 Airbnb 和他共事过,他非常出色,他做的任何东西我都必须投。他做了一个了不起的产品。
Naomi Ionita: 是的,正如你所说,我特别喜欢这类创始人——他们对问题的理解有着极深的真实感,因为他们在内部亲手构建过这套东西,现在把它商业化推广给更多人。所以 Eppo 的故事在这个维度上是一个很好的案例。当然还有其他玩家。我也是 Amplitude 的大粉丝,也是这款工具的用户,我很喜欢他们的团队。如果你没有数据团队或数据仓库,但仍然想进行行为层面的即席分析或实验,Amplitude 对你来说也是一个很好的选择。所以市场上有多种不同的方式来解决这个问题。
Lenny: Amplitude 也是我们的赞助商,加油 Amplitude。
Naomi Ionita: 好。
Lenny: 我太喜欢了。太棒了。全是我最喜欢的公司。好吧,继续。还有什么?
计费与变现平台
Naomi Ionita: 之前我谈了不少关于计费和变现的内容,所以得聊聊这个。用于管理计费、迭代定价和打包方案的平台,这个需求非常大。我认为这些平台将改变商业模式。对于 SaaS 来说,大多数公司一直采用按座位计费模式(seat-based model),就像我们之前描述的那样,所以历史上的传统厂商就是服务于这种模式的。但在向更多按使用量计费(usage-based)模式转型的过程中,出现了服务于这一维度的新进入者。而且当你考虑从工程团队桥接到产品和增长团队,再到财务或收入运营(rev ops)团队时,存在大量笨拙的工作流程。
这些新工具可以提供大量精简的工作流。在按使用量计费领域,一些早期脱颖而出的参与者是 Metronome 和 Orb。此外,处理完整的变现基础设施层还有更多空间。比如,我很欣赏 Orb 将计费组件与数据基础设施结合起来,实际上可以指导你的定价和打包方案应该如何迭代,并帮助你预测和优化收入。还有其他参与者在处理从计量环节一直到实验环节这段旅程中的不同组件。认识这个领域的参与者真的非常有趣,我希望很久以前就能用上这些工具。
Lenny: 这些我还没投。很酷。顺便问一下,你对定价模式有什么强烈看法吗?按使用量计费(usage-based)还是按座位计费(seat-based),还是其他什么?你对创始人有什么建议?通常应该选其中一个,还是高度依赖具体情况?你怎么推荐?
定价模式的建议
Naomi Ionita: 这个问题的回答和我之前的一样。我认为它们并不互斥。如果你看 SaaS 中采用不同定价模式的公司,只有很小一部分——不到 10%,大约 5% 左右——拥有纯粹的自然递增式按使用量计费模式。绝大多数采用混合方式。我的意思是,通常会有某种”好、更好、最好”的订阅层级,在每个层级中都包含某种消费组件,比如针对你的价值指标(value metric)设定某个配额上限。比如在 Slack 中可能是发送的消息数量,在 Dropbox 中可能是存储的太字节数,在 Invoice2go 中可能是发票数量。有某个维度被打包进给定的定价方案中,一旦你达到那个上限,就会触发你升级到下一个方案,或者有时你可以为超额部分付费。
所以我不认为你应该只做按座位计费或只做按使用量计费。纯按使用量计费模式的一个挑战是,CFO 们并不总是想按这种方式采购。买家有时想要可预测性,他们希望能够为你的工具做预算。我对此有亲身经历。我记得使用 Mixpanel、Segment,甚至在某种程度上用 Jira 时,起步时我只付很少的钱,然后突然间发现我们增长很快,回头看所有的 SaaS 支出,金额之高让我大吃一惊。所以这是另一面——我主张人们应该为他们交付的价值获得报酬和补偿,但可能会存在一个临界点。那么你如何考虑打包固定组件和可变组件,让人们可以更可预测地购买你的软件?
生成式 AI 与增长栈的未来
Lenny: 技术栈还有其他你想谈的层级吗,或者你对未来最兴奋的是哪个?你认为有什么是人们应该更关注但目前没有关注的?
Naomi Ionita: 如果我不提生成式 AI(Generative AI),那我就没尽到 VC 的职责。它现在确实正处于风口浪尖。这个领域出现了很多突破性的应用,它们坐落在这个现代增长栈(Modern Growth Stack)的主题之下。当你想到用 AI 来创建图像、文本、代码、音频或视频时,这些能力正在改变团队的工作方式。写一篇博客、为广告写文案、SDR 做外呼销售——有很多这样的触点,过去都是人工逐字逐句地斟酌、迭代、打磨。如果机器、如果 AI 能告诉你哪个版本的性能会更好,那就是一个非常有说服力的 ROI 实践——你既节省了时间,又有望提升各种营销或销售活动的表现。
Lenny: 你认为 AI 在增长方面最大的助力会在哪里?你有什么想法吗?
Naomi Ionita: 我觉得就是前面提到的营销和销售领域,因为它们直接触及收入。它可以成为一个关于 ROI 的故事——既节省时间,又推动营收增长。在客户支持等领域也会有非常多高效的案例,毕竟成本节约的潜力是巨大的。工程领域也会看到变化,比如代码生成。我认为 AI 会触及企业的很多领域,但从现代增长栈(Modern Growth Stack)的角度来看,那些能够直接产生收入、并且在那个维度上可以归因到 ROI 的应用,是我目前最花时间关注的方向。
Lenny: 我很期待。在进入非常令人兴奋的快问快答之前,还有什么最后的想法吗?
Naomi Ionita: 很开心,直接进入快问快答吧。
快问快答
Lenny: 好的,我们已经到了非常令人兴奋的快问快答环节。我只准备四个问题,会问得比较快,我们快速过一遍,想到什么就说什么,不用有压力。第一个问题:你有哪几本最常推荐给别人的书?
Naomi Ionita: 我的朋友 Madhavan 来自 Simon Kucher,他写了一本书叫 Monetizing Innovation,非常值得一读。他和团队在那里为数百家科技公司做过定价项目,所以书里有很多案例和实用建议。我经常把这本书送给创始人,所以不能在整场对话中不向我朋友 Madhavan 和他的”圣经”致敬。
Lenny: 太棒了,我刚刚和 Madhavan 录了一期节目,所以这个推荐很棒。第二个问题:最近最喜欢的、让你非常享受的电影或电视剧?
Naomi Ionita: 我家有小小孩,所以这个答案可能对大家来说没那么有趣,但我们喜欢 Netflix 上的 Story Bots。它们是一些小卡通角色,回答孩子们的问题。人们打电话进来提问,然后他们用整集来探讨天空为什么是蓝色的?飞机怎么飞?我是怎么看见东西的?我每次看都不可避免地会学到点新东西。所以那些是非常有趣又有教育意义的节目。我确实需要一个新的——
Lenny: 不不,这些都挺好的……那些问题我一个都答不上来,我也需要看看这个。好的,第三个问题。我看了下笔记,这个问题我从来没问过,不知道它是从哪冒出来的,但我很喜欢。谁是你生命中最大的灵感来源?
Naomi Ionita: 这个对我来说很简单,就是我的父母。他们原本来自南美洲,在移民到美国读研究生之前,在三个不同大洲生活过。这是一个相当老套的美国梦故事,但他们真的是白手起家来到这里。就是这种建立家庭、充分利用美国的教育和职业机会的信念。他们在三种不同的语言环境中完成学业、建立事业,没有经济支持,也没有现成的人脉资源或网络可以依靠,我完全无法想象自己能做到这些。那种在全新的地理环境、文化和语言中重新开始生活的压力和认知负担,以及孤注一掷地相信自己、一路摸索前行——我的驱动力始终根植于他们的故事,我永远感激他们。
Lenny: 我需要更经常问这个问题。这个即兴回答太精彩了。Naomi,我们的对话已经到了尾声。最后两个问题。如果大家想了解更多、向你推介创业想法,或者有问题想联系你,在网上哪里可以找到你?最后,大家怎样能帮到你?
Naomi Ionita: 我是 Menlo Ventures 的合伙人,所以你可以在 menlovc.com 上了解更多关于我和公司的信息,也可以在 LinkedIn 或 Twitter 上找到我。我的私信是开放的。
Lenny: 太棒了。Naomi,非常感谢你来参加节目。
Naomi Ionita: 我的荣幸。期待与更多在流程自动化、数据 AI 和现代增长栈领域打造产品的人交流。谢谢你,总是很愉快。
Lenny: 好了,私信已经在涌入了。
Naomi Ionita: 谢谢,Lenny。
Lenny: 非常感谢你的收听。如果你觉得这期节目有价值,可以在 Apple Podcasts、Spotify 或你喜欢的播客应用上订阅。也请考虑给我们评分或留下评论,这真的能帮助更多听众发现这个播客。你可以在 lennyspodcast.com 找到所有往期节目或了解更多关于节目的信息。下期再见。
术语表
| 原文 | 中文 |
|---|---|
| A/B testing | A/B 测试 |
| ACV | 年度合同金额(ACV, Annual Contract Value) |
| aha moment | 顿悟时刻(aha moment) |
| Airbnb | Airbnb(民宿短租平台) |
| Amplitude | Amplitude(产品分析平台) |
| ARPU | 每用户平均收入(ARPU, Average Revenue Per User) |
| ARR | 年经常性收入(ARR, Annual Recurring Revenue) |
| CAC | 客户获取成本(CAC, Customer Acquisition Cost) |
| Calendly | Calendly(日程安排工具) |
| Census | Census(反向 ETL 平台) |
| CFO | CFO(首席财务官) |
| data warehouse | 数据仓库 |
| DBT | DBT(数据转换工具) |
| ELT | ELT(提取-加载-转换,数据管道方式) |
| Endgame | Endgame(产品驱动销售平台) |
| Envoy | Envoy(访客登记管理工具) |
| Eppo | Eppo(面向现代数据栈的实验平台) |
| Evernote | Evernote(笔记应用) |
| Figma | Figma(在线协作设计工具) |
| freemium | 免费增值(freemium) |
| Gas | Gas(社交应用) |
| Generative AI | 生成式 AI(Generative AI) |
| Go-to-market | go-to-market(推向市场) |
| Hightouch | Hightouch(反向 ETL 平台) |
| human-centered design | 人本设计(human-centered design) |
| Invoice2go | Invoice2go(移动端发票与收款工具) |
| Jira | Jira(项目管理工具) |
| KPI | KPI(关键绩效指标,Key Performance Indicator) |
| Larry | Larry(Envoy 创始人) |
| lightning round | 快问快答 |
| Loom | Loom(视频消息工具) |
| Madhavan | Madhavan(Simon Kucher 合伙人,《Monetizing Innovation》作者) |
| Menlo | Menlo(Menlo Ventures,风险投资公司) |
| Metronome | Metronome(按使用量计费平台) |
| Mixpanel | Mixpanel(产品分析平台) |
| Modern Growth Stack | 现代增长栈(Modern Growth Stack) |
| Monetizing Innovation | Monetizing Innovation(商业定价策略著作) |
| OCR | 光学字符识别(OCR) |
| OpenView | OpenView(风险投资公司) |
| Optimizely | Optimizely(A/B 测试与实验平台) |
| Orb | Orb(计费与变现基础设施平台) |
| PLG | 产品驱动增长(PLG, Product-Led Growth) |
| Pocus | Pocus(产品驱动销售平台) |
| pricing committee | 定价委员会(pricing committee) |
| product-led growth | 产品驱动增长 |
| product-led sales | 产品驱动销售(product-led sales) |
| ProfitWell | ProfitWell(SaaS 收入管理平台) |
| prosumer | 专业消费者(prosumer) |
| Redshift | Redshift(AWS 云数据仓库) |
| rev ops | 收入运营(rev ops, Revenue Operations) |
| reverse ETL | 反向 ETL(reverse ETL) |
| ROI | ROI(投资回报率,Return on Investment) |
| SaaS | SaaS(软件即服务) |
| SDR | 销售开发代表(SDR, Sales Development Representative) |
| seat-based model | 按座位计费模式(seat-based model) |
| Segment | Segment(客户数据平台) |
| Simon Kucher | Simon Kucher(全球定价咨询公司) |
| Snowflake | Snowflake(云数据仓库) |
| SOC 2 | SOC 2(服务组织控制类型 2,安全合规标准) |
| Story Bots | Story Bots(Netflix 儿童科普动画) |
| usage-based | 按使用量计费(usage-based) |
| value metric | 价值指标(value metric) |
| Van Westendorp | Van Westendorp(价格敏感度测试方法) |
| Vanta | Vanta(安全合规自动化平台) |
此文档由 AI 分片翻译(translate_long_document)