Using Subscription App Benchmarks to Make Better Growth Decisions — Phil Carter, Elemental Growth

Summary

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Podcast Summary

Episode Overview

Podcast: Sub Club by RevenueCat
Episode: Using Subscription App Benchmarks to Make Better Growth Decisions — Phil Carter, Elemental Growth
Hosts: David Barnard, Jacob Eiting
Guest: Phil Carter, Independent Growth Advisor
Date: December 23, 2024

In this episode, the hosts sit down with Phil Carter—an experienced growth advisor and former VC/product leader at companies including Quizlet and iBotta—to discuss practical strategies for using benchmarks in consumer subscription apps. The conversation centers on Phil’s Subscription Value Loop Calculator, how the best companies leverage benchmarks for growth decisions, the limitations and nuances of benchmarks, and actionable examples from Phil's consulting work.

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Key Discussion Points & Insights

1. Introduction to Subscription Benchmarks and Their Challenges

• Benchmarks are controversial: Not all data is accurate; sources matter.
  • "Benchmarks, very polarizing topic, super controversial." (Phil, 02:18)
• Many widely available benchmarks are too generic or unreliable, making custom, context-specific benchmarks crucial.
  • "Some of the largest and more reliable data sets out there... are providing them globally, not for a specific country. Or they're providing them across all app categories..." (Phil, 08:33)

2. The Subscription Value Loop Framework and Calculator

• Concept: Framework divides growth into three phases—value creation, delivery, and capture.
  • "The best consumer subscription businesses are able to generate sustainable compounding long term growth through three steps: value creation, value delivery, and value capture." (Phil, 02:25)
• Calculator Purpose: Lets leaders compare their metrics to relevant, filtered benchmarks and quickly identify biggest growth opportunities via a heat map.
  • "Ideally, let's enable leaders to filter it by important variables like category and performance tier... then let's use that info to help those growth leaders more efficiently allocate resources against the growth opportunities where they have the most upside." (Phil, 02:56)

3. Data Sources and Construction of the Calculator

• Core data comes from RevenueCat: Over 30,000 apps, 290 million subscribers, ~$7B revenue.
• Supplemented survey: Fills gaps in value delivery metrics with 600+ responses; Phil plans to strengthen this with MMP partnership in future versions.
  • "For value delivery metrics... we ran a one time survey where we ended up getting almost 600 responses..." (Phil, 05:16)

4. The Role and Limits of Benchmarks

• Benchmarks as directional tools: Not the endpoint of decision-making.
  • "The way I think of benchmarks is they're one tool in a very big toolkit... to get a directional, high level view of where they're performing better or worse than other peers..." (Phil, 06:33)
• Key Limitations:
  • Inaccuracy/unreliability;
  • Overly generic/non-actionable;
  • Can point you to what areas are weak, not how to fix them.
  • "They're only a jumping off point..." (Phil, 08:33)
• Judgment is critical: Great execution means filtering and acting on the right ideas informed by data — not blindly following it.
  • "What we hand wavingly call execution is really judgment. ...Benchmarks can be a good input into those judgment decisions..." (David, 17:25)

Notable Quote:

"There's almost the midwit meme here of on the one end of the spectrum it's like, just build a good product... and then in the middle it's like, I'm going to look at 100 different metrics and I'm going to optimize every last thing..."
— Phil Carter (11:28)

5. Applying Benchmarks and the Calculator in Practice

• First step with new clients: Phil uses the calculator to quickly diagnose growth bottlenecks and create a roadmap to improvement.
  • "It's almost like X-ray vision into where is the biggest problem in the loop..." (Phil, 20:32)
• Real examples:
  • EdTech client: High retention, but low conversion/pricing issue — led to focus on onboarding, paywall, and pricing optimizations.
    • "Subscriber retention was really strong... their subscriber conversion rates were quite low, and their pricing was higher than the typical edtech app..." (Phil, 22:22)
  • Fitness app: Too much value given away for free, showing in low conversion and low price — led to reviewing the freemium/premium balance.
    • "The free product is so good. Do I actually need to pay for the premium product?" (Phil, 24:33)

6. Deep Dive: Calculator Inputs and Sections

A) Tool Overview

• User inputs: 12-month average data, app category, performance tier (P25, P50, P75, P95).
• Heat map output: Shows metric deltas vs. peer set.
  • "Once you put in your app category, your performance tier and your business metrics, the tool immediately outputs your performance delta relative to your peer set." (Phil, 25:28)

B) Value Creation

• Key metrics: Signup/registration rate, retention rates (monthly & annual), average lifetime.
  • "Activation rate would be a great metric... but it's really hard to compare apples to apples." (Phil, 31:31)

C) Value Delivery

• Key metrics: Blended and paid cost per install/trial/subscriber (CPI, CPT, CPS), payback period, LTV/CAC.
  • Blended metrics more meaningful for business health; paid metrics for marketing efficiency.
  • "As you get larger and larger, what really matters is your marketing budget, more and more so versus the cost of your marketing team..." (Phil, 43:06)
• Stage matters: Early on, organic dominates; as you scale, paid acquisition becomes crucial and economics shift.
  • "Every consumer subscription business at some point is going to hit a point where they have to spend more and more dollars on performance marketing in order to sustain growth." (Phil, 45:56)

D) Value Capture

• Key metrics: Trial start rate, trial conversion rate, install-to-paid conversion, subscription prices, plan mix (monthly vs annual), gross margin.
  • Need to balance high conversion and retention; being best-in-class in every metric is rare.
    • "It's not like they're P95 across every single metric... there are natural checks and balances..." (Phil, 49:22)
  • Companies may excel at one phase (e.g. converting users quickly) but lag slightly in others — that’s often a valid, sustainable strategy.

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Notable Quotes & Memorable Moments

• On benchmarks as input, not gospel:
  "Benchmarks can be a good input into those judgment decisions, those millions of small decisions you make along a product journey."
  — David Barnard (17:25)

• On pitfalls of fixation:
  "People lose the forest through the trees... they see one thing and they over-obsess about it and then get frustrated."
  — Jacob Eiting (10:15)

• On why best-in-class in every metric is nearly impossible:
  "By definition, if you're really, really efficient at converting free users and subscribers, you're probably going to have a harder time retaining them because you're getting some lower intent users into your subscription funnel."
  — Phil Carter (49:22)

• On early stage vs. later stage growth priorities:
  "If you're a seed stage or series A startup... focus on just being really efficient at acquiring users early on. But if you're a series C... you have to look at the metrics in totally different ways."
  — Phil Carter (36:01)

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Timestamps for Important Segments

• [02:18] — Introduction to the Value Loop Calculator and benchmarks’ limitations.
• [05:16] — Data sources for the calculator.
• [06:33] — Proper role and misuse of benchmarks.
• [10:15] — Common pitfalls: over-obsession with singular metrics.
• [17:25] — Benchmarks as input to product judgment and decision-making.
• [20:32] — How Phil uses the calculator with consulting clients.
• [22:22] — Real EdTech client example.
• [24:33] — Real fitness app example.
• [25:28] — Tool interface and filtering.
• [31:31] — Limitations of activation as a benchmark metric.
• [36:01] — Early vs. late-stage companies' use of paid acquisition.
• [43:06] — Blended vs. paid CAC and the economics of scale.
• [49:22] — Inteplay/imbalances between different funnel metrics.
• [52:05] — Duolingo case study: evolution of value capture.
• [53:40] — Future plans for the calculator: better data, more filters.

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The Future of the Value Loop Calculator

• Expanding the dataset with annual updates and more apps.
• Adding more filters: Geography, platform (iOS vs. Android vs. web), company stage.
• Improving value delivery metrics: Seeking deeper integration with MMPs for more accurate data.
• Desire for full-funnel benchmarks — future versions may enable this.

"Ideally, eventually... we can filter by iOS vs. Android or even vs. Web would be another filter I'm really excited to add."
— Phil Carter (53:40)

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Final Resources and Invitations

• Phil’s Substack: philgcarter.substack.com
• His consumer subscription growth course (promo code: Sub Club at Maven).
• Interested listeners can find Phil on all platforms as "philgcarter."

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Summary Takeaways

• Context is everything: Don't take benchmarks at face value; always interpret through the lens of your app’s stage, category, and strategy.
• Benchmarks are a starting point: They highlight potential bottlenecks but only combined with your judgment and intuition do they yield actionable, high-leverage decisions.
• Best-in-class ≠ perfect in all metrics: Most successful companies are outliers in only some areas and "good enough" in the rest.
• As you scale: The importance of value capture and nuanced monetization grows, and operational focus naturally shifts.
• Continuous process: As your business evolves, so do the most useful benchmarks.

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This episode is a must-listen for subscription app founders and growth teams seeking to use benchmarks to inform—not dictate—product, marketing, and revenue strategy.

Transcript

[A]

Welcome to the Sub Club Podcast, a show dedicated to the best practices for building and growing app businesses. We sit down with the entrepreneurs, investors and builders behind the most successful apps in the world to learn from their successes and failures. Sub Club is brought to you by RevenueCat. Thousands of the world's best apps trust RevenueCat to power in app purchases and manage customers and grow revenue across iOS, Android and the web. You can learn [e-mail] let's get into the show. Hello, I'm your host, David Barnard, and with me today, revenuecat CEO Jacob Eiding. Our guest today is Phil Carter, an independent growth advisor and angel investor focused on helping consumer subscription companies scale. Phil spent the last decade as a VC and product leader at companies like Fair Quizlet and iBotta. On the podcast, we talk with Phil about how to effectively use benchmarks to aid decision making, the limitations of benchmarks, and why even the best companies aren't top quartile in every single metric. Hey, Phil, thanks so much for joining us on the podcast today.

[B]

Yeah, thanks for having me. Always a pleasure.

[C]

Phil's back. Oh, wait, sorry I interrupted. David, do my intro.

[A]

Jacob, always nice to chat with you as well.

[C]

I still can't follow directions, but I'm here, so I'm excited. Let's go.

[A]

So, Phil, we are going to talk today about your subscription value loop calculator. And the reason I wanted to do this episode and wanted to get this episode out now is that me and my colleagues are hard at work on the 2025 state of subscription Apps report and we're going to share a ton of benchmarks and I think people always are a little, I wouldn't say confused, but we get a lot of questions like, what does this actually mean? What do we do? And I think what we're going to talk about today is going to set the context for listeners of the podcast. Anyway, I hope everybody whoever downloads reports goes back and listens to this, but it's going to set the context. What do you do with benchmarks? Like, how do you actually make good decisions from all of this data? And so I love the calculator you built and excited to talk about it today. So, so let's just kick off. What is the subscription value loop calculator? Sure.

[B]

Yeah. Well, first of all, benchmarks, very polarizing topic, super controversial.

[C]

So depends if they're good or bad.

[B]

True, very true. Well, that's why they're controversial. Right? Because a lot of them are bad. But yeah, so maybe to set some context. So I think the Last time I was on this podcast, a year ago, we talked about the Subscription Value loop, which is basically this framework I've developed that posits that the best consumer subscription businesses are able to generate sustainable compounding long term growth through three steps, value creation, value delivery, and value capture. But the very next question that I get from a lot of consumer subscription leaders is, okay, this is a helpful framework in theory, but how do I actually apply it? And how can I measure the performance of my subscription value loop and understand how I'm performing relative to my peer set or my competitor group and where I have the biggest opportunities to improve and grow faster? And so the idea behind the subscription value loop calculator was let's compile a set of the most important growth benchmarks within each of those three steps. Value creation, value delivery, and value capture. Ideally, let's enable leaders to filter it by important variables like category and performance tier, eventually geography, so that you're making apples to apples comparisons, and then let's use that information to help those growth leaders more efficiently allocate resources against the growth opportunities where they have the most upside. So that's the idea behind the tool. I will say right up front, this is version 1.0, and you all know this because I partnered with Revenuecat to build it. But I think it's already better than pretty much everything else out there that I've seen in terms of getting all these metrics in one place and allowing you to get a directionally accurate view of your performance.

[A]

And you already kind of mentioned it, but where did you get all the data for this? Mostly revenuecat. And to Jacob's point, good data, great data, the best data, the best.

[C]

You wouldn't believe the data we have.

[B]

It's great.

[A]

But what other data sources did you use? And then how did you pull it all together into the calculator?

[B]

I had had this idea in my head for some time. Like ever since I came up with the framework, the next step had always in theory been let's go quantify it and let's help people look at their data and compare it against other companies. The problem was where do you find the data, right? Like you need thousands and thousands of data points if you're, if this is going to be at all reliable. Right? And that's part of why there just aren't many good benchmarks out there. Because there aren't very many sources of data that are large enough and reliable enough that you can build accurate.

[C]

You're disinterested. And some ways, right, like Apple and Google's data sets, well one, they're not going to let them out and then two, like they have a bias about how they think the world should be to serve their needs and it's not necessarily always 100% aligned with like people building subscription apps.

[B]

So totally, yeah. And so this past summer I talked to Rick, the CMO at revenuecat and basically shared this idea with him of, you know, I've got this framework, I want to quantify it, I need data in order to do that. Would revenuecat be interesting in partnering with me? And so that's what we did. We pulled data from the same data set as the 2024 State Subscription Apps Report. So more than 30,000 subscription apps that use your All's SDK, I think 290 million subscribers and almost 7 billion in subscription revenue represented by those apps. So that's a pretty large data set, right? It can always be bigger. That's a pretty good starting point. And so in partnership with Rick and a couple other members of the team at Revenuecat, we pulled together B1 of this subscription value loop calculator and we can get more into what the specific metrics are and what the numbers look like, but that's where the data was sourced. The one other caveat I'll make is for the most part we were able to get all the value creation and value capture metrics we needed from revenuecat. Obviously revenuecat doesn't get as many of the value delivery metrics right. Cost per install, cost per trial, cost per subscriber, blended acquisition cost, paid acquisition yet. And so to fill that gap for this first version, we ran a one time survey where we ended up getting almost 600 responses across all sorts of different geographies and categories. So it's, you know, it's a relatively broad data set but much smaller sample size. I think for future versions of the product we'll continue to improve on it. And one of the ways I'd like to do that is by partnering with an MMP like Apps Flyer or Adjust that can fill in some of those value delivery gaps.

[A]

And then we've already started talking about it, but the next thing we should talk about is like why benchmarks like we kind of already talked about how bad they can be, how much the source of the benchmarks is important.

[B]

The.

[A]

But how do you think of benchmarks as being helpful?

[B]

Yeah, I think where people get in trouble with benchmarks is they try to make them the end all be all right. They try to do more with them than they really should be. The way I think of benchmarks is they're one tool in a very big toolkit of things that founders, CEOs, indie developers, product and marketing leaders, individual PMs and marketers can use to get a directional, high level view of where they're performing better or worse than other peers in their set. And use that information to start to hone in on where some of their biggest strategic opportunity areas are and where they should be allocating more or fewer resources, meaning marketing dollars or engineering bandwidth in order to more efficiently capture value, more efficiently increase their a B test hit rate, increase the amount of impact that their teams are delivering. Right. But there are a number of real limitations to benchmarks. And so a few of those are they can be very inaccurate and unreliable. Right. That's the first problem to solve for is can I actually trust this data? And if you just go in Google or now I guess go to ChatGPT or Claude or whatever and you put in like what are good benchmarks for this product, you'll get something and sometimes you'll get an answer that's better than other times. But you can't necessarily rely on just whatever metrics you find on the Internet.

[C]

LLMs are really bad at kind of like understanding cave and nuances on these things. They do a really, I'll sometimes just throw some of our like revenue gets core metrics in and be like, hey, where's this put us? And like sometimes it's fine and it actually chat GPT specifically is pretty good at citing sources sometimes like where they got the data from. But yeah, it's, it's, you know, it's not the most reliable way of pulling it.

[B]

Yeah. So that's the first order problem is can I actually rely on this data? Because if you can, it's garbage in, garbage out.

[C]

How is it collecting? You really have to kind of go deep if you want to really understand which isn't, you know, at that point you should just go ask 10 apps, you know what I mean? Like the more time you spend trying to understand the qualifications of the benchmark, the less useful it is.

[B]

Yeah. So first order problem is you got to make sure they're accurate. Many of them are not accurate. The second order problem is, okay, even if you're able to find a reliable source of benchmark data, oftentimes it's too generic. Right. Some of the largest and more reliable data sets out there that provide subscription app benchmarks are providing them globally, not for a specific country. Or they're providing them across all app Categories not specific to health and fitness or media and entertainment or productivity, all of which have very different, you know, average metrics. And so that's the second order problem, is making sure that you're comparing apples to apples and getting benchmarks that are specific enough to be actionable. And then the third order problem is, okay, even if your benchmarks are accurate and even if they are specific enough to be actionable, they're only a jumping off point, right? They're not going to actually tell you how to improve your performance. They're just going to tell you where your performance is lacking. And so even in the best case scenario, they're a jumping off point. And then from there, you need to apply your own understanding of your product, your business, your target customer, your competitive set, in order to make intelligent bets on what initiatives to pursue in order to increase your growth.

[C]

One place I see people get hung up is kind of on that third step. And I think it's maybe comes to a lack of understanding of the production process, which is kind of your first, first two points of these benchmarks, which is like looking at these things in isolation, just being like, oh, my trial rate. I need to do things to make my trial rate better. And like, that's not what a benchmark is telling you. I mean, it might be correlated with that being a true and correct action to take, but it can't tell you that. All it can tell you is, like, for this measure, assuming you get the, like, systematics of it correct, here's what this measure is and where it puts you in the distribution. And I think sometimes what people do is they fail to think about the process. Like, the inputs that led to that, which can be even within a category, can be like, nature of your app, like, who you're in that cat subcategory, like, what's your differentiator? And folks can just like, dive in on, like, oh, I got to fix, I got to fix, like, conversion rate or churn. This is one where, like, I've seen a lot of people maybe spend too much time on retention, which, like, sounds like you should. Oh, retention. People look at the model and they go like, oh, it's one over. If I could just perfect retention, I'll be a trillionaire. And there are physics limitations, right, to, like, how far you can push that. Which I think that's where good use of metrics, if you can, like, understand what that particular percentile means for you, can guide you to maybe be like, okay, I can back off on this because, like, I've Maybe hit sort of like the low hanging fruit. But that's often where I see people. They lose the force through the trees, right? They see like one thing and they like over obsessed about it and then they get frustrated because they're like, oh yeah, we worked on our trial start rate for two years and we went from 10% to 12%, which maybe that's significant, but there might have been other things you could do to really push the business.

[B]

There's almost the midwit meme here of on the one end of the spectrum it's like, just build a good product. And then on the other end of the spectrum you've got the Jedi Master and it's just build a really good product. And then in the middle it's like, I'm going to look at 100 different metrics and I'm going to optimize every last thing. So this gets to like the double edged sword of benchmarks, Right. I think they can be a very effective tool in your toolkit. Ultimately, you have to pair the science of the data and the metrics that you're using for the benchmarks with the art of I'm going to apply my own intuition to understand why I'm seeing variances in certain places. And then the next question is, okay, I'm seeing these variances. Is this a gap that I can fill based off of improved product experience.

[C]

Or improved marketing, or do I actually know how to fix it?

[B]

Yeah. Or is it a gap that's exogenous? Because, for example, I'm a dating app and so I'm going to have high churn rates because if I have a good product, people are naturally churning off the platform.

[A]

Or is it inherent to the business model too? Like if you're a freemium app, by definition, your trial start rate is likely to be lower. If you're freemium, you probably shouldn't even do a free trial. And so you're looking at some of these benchmarks and you have to analyze it through the lens of like the business you've created and how you want to operate your business. You can go hard paywall, that'll juice your trial start. Right? But like, is that what you're optimizing for? It's like you need to understand them through how you run your business and how you want, like how you're trying to grow your business and what you want your business to become.

[C]

I'm sure we've talked about this probably Phil, in the past, but like the Dutch Dam analogy, right, of like, you stick your Finger in one hole and then another hole is going to pop open. Right. And it's not, it's not so binary, but there is this also this interlinked nature that often by pushing on one of these benchmarks, you're going to affect the others. And you have to like keep that in mind as well. The best thing you can learn at least is talking for me, like looking at B2B benchmarks and stuff like that, I think is it's just to like give you directional insight. Right. I think it's. Which is your point you were making before Phil. It's like feed it with your intuition also. And maybe this is as I'll say, move progressively to the left on the midwit meme. Over time I've just like more and more, let me just do it, do it, do what feels good and if feels right and then if it tracks to a metric like, that's great.

[B]

I'll draw from my own experience here. So when I was leading product growth at Quizlet, there were basically two ways I went about trying to find benchmarks. The first was the one we talked about earlier, which is go on Google. At the time There was no ChatGPT or Claude, but go on Google or find some other publicly available source of information and just ask what's a good benchmark? That was very fast and easy and broad, but unreliable.

[C]

For me it was the Series B Evernote Series B deck that everybody used that was like the only consumer subscriptions silver lining in 2014 or whatever.

[B]

Yeah, so but it's like bucket one. Find broad, overly generic, publicly available resources, fast, easy, not particularly reliable. The second bucket was on the complete other end of the spectrum, which is like I had my network of other product leaders, PMs, marketers at given Quizlet was an education company, Chegg, CourseHero, PhotoMath, Duolingo. And so we would periodically jump on a call or just sort of compare notes at a high level on, hey, roughly what's the band of what we can reasonably expect for something like signup rate, trial start rate, trial conversion rate, average revenue per user, ltv. But then you get into another problem, which is one a lot of the. Even within education, right, you have lots of different flavors of products and so it still can be pretty apples and oranges. The other problem is if you're two apples to apples, you're obviously not going to share sensitive data and information because then you're sharing data with direct competitors.

[C]

I think this is something, I mean, I don't want to keep drawing us off the, off the trail here. But I think there's something people, unless you're like in a dead heat with somebody on like fighting over like individual user acquisition spend, it's like what's somebody going to do if they know your trial start rates lower than theirs? Like, how is that possibly going to like change their. Which is one of the cool things in consumer subscriptions I think is like when you are competing there are some like secret sauce things around this stuff. But a lot of the times you can also, nine times out of 10 you can just go look and copy what your competitor so it's not like, not like there's anything secret that like is is able to be hidden, right? Unless you're using some crazy modeling or something like this to acquire users. It's good creative, it's good app, good creative, good strategy. Like there's no super secret sauce. Right? Anyway, sorry, sidebar.

[A]

Now that we've totally thrown benchmarks under the bus, we've got a whole rest of the podcast to talk about.

[C]

Should we just not do so? Let's just, we'll figure something else out.

[A]

Let me take a stab at framing the rest of our conversation. I was actually writing this morning. I don't know if this will turn into a tweet or a blog post, but the whole ideas versus execution has always really bugged me and I haven't ever been able to like fully put my finger on it. I think this morning I came to like a piece with the idea. And here's the thing, what we hand wavingly call execution is really judgment. It's like there's a ton of ideas, ideas are a dime a dozen, but inherently good ideas aren't a dime a dozen. Like there are good ideas and so what execution really is like great. Execution isn't just doing work, isn't being good at programming or good at marketing. It's a filtering of ideas into the good ideas and then executing on those. And so I think what we can talk about with the subscription value loop calculator and where benchmarks can add value is as an input to your judgment, not as like a decision maker, not as the end all, be all, but as one of many inputs into your judgment of which idea is good, which idea is bad. I mean, if you listen to sub club podcasts, you're going to get a million ideas of like, oh, we should do this on our paywall, we should do that on our onboarding. And like you can't execute on all of those. And so the great products and the great product leaders are the ones who have the judgment to actually pick which ideas are good and then go execute on that. And I think the calculator and the benchmarks can be a good input into those judgment decisions, those millions of small decisions you make along a product journey. So that's why the subscription value of calculator, go look at it and bring it up while you listen to the rest of this podcast and make better judgments along the way.

[B]

I think that's exactly right. It's both working on good ideas, but also working on good ideas that are targeted at the right problems. And that's where a lot of companies get in trouble, is there are startups, they have limited resources and so in some cases they're executing on good ideas, but they're good ideas focused on the wrong problems, which aren't where they have the biggest upside and so they don't see a lot of impact from it.

[A]

Or they're focusing on good ideas that are good ideas two years from now that aren't good ideas today as a.

[C]

Startup, I guess you could put that under judgment, but that's also just like entrepreneurial, like wisdom or like just focus and good at resource allocation. I mean, resource allocation is really the game and like, that can be other people that could be at the beginning, right? Individual time and attention and dollars. But I'll oppose or throw a counterpoint here to like have like picking good ideas and that I think this kind of feeds into the topic, but that like, I think even the best idea pickers in the world have like 51% hit rate, you know what I mean? Or like they barely exceed the median of what everybody else does in terms of like that execution bit. It's like you need to have that edge of judgment, but then you just need to have like consistent application, right? Because that edge will compound if your competitors or your counters are only making choices 50% of the time that are correct and you're doing 51 each cycle. Each cycle is a 1% compounding advantage. And then also if you can shorten your cycle time, that's another like linear increase in your growth rate in terms of like, discovery. And this sounds very esoteric, I promise. This is like, related to like, how to use benchmarks and things like this and determining like each day, like, what is the action we're going to take today to actually move the business forward. I think we keep selling and anti selling this, this concept, but like, because I think the trap that we're trying to like tell people to avoid is that you can very easily bury the advantages of these benchmarks and like good data driven decision by like delaying decision making as well. Right. Like often if you spend too much time trying to pick the perfect decision, you've quickly like eliminated any advantage of a great decision by not doing anything right.

[A]

Next thing I wanted to talk through is how you app the subscription value loop. You know, you run a consulting business, you talk to a ton of apps about their business, you help them make decisions via data, via judgment, via shooting from the hip, but you help make decisions. And so that's kind of the next place I wanted to go with this is like, how do you use this tool? How do you use benchmarks to help make better decisions in the companies that you work with?

[B]

Yeah, it's a great question. And usually this is a tool that I use right at the outset, in the first week or two when I'm engaging with a new client and onboarding and getting to understand their business better. And there's a few advantages to that. I mean, one is it's just a good forcing function to get all of the most important growth metrics for the business in one place. And some clients I work with are very sophisticated and they've already got all of this organized. They've got a growth model, they've got their mix panel, their amplitude dashboard, and everything's dialed in. Other clients, especially some of the earlier stage clients that we're with, are indie developers. This may be the first time that they're pulling some of these metrics together and sort of seeing how all the dots connect. And so that's one advantage of doing it right away. The other advantage for me is it's just a really efficient way for me to get up and running quickly and get the sort of quick diagnosis of where are the biggest bottlenecks in the company's growth engine and where might there be some quick win opportunities to start putting points on the board. It's almost like X ray vision into where is the biggest problem in the loop? Is it value creation? Are they not actually creating a product that is resonating with users? Is it value delivery? Are they not being efficient about acquiring those users or is it value capture? They've got a great product and they're acquiring users efficiently, but they're not actually converting anybody into subscribers or their price is too low, they're not getting enough revenue back per subscriber. And so it's usually a diagnostic tool that I use in those first two weeks. We don't spend a ton of time on it honestly, it's like I share the tool with the client and in a one hour call, we pull a lot of the metrics into the dashboard and we sort of see the heat map of where the biggest gaps and opportunities are. And then I go off and do some additional analysis at a more granular level to figure out what some of the highest priority growth initiatives might be for us to work on.

[A]

Having done this with a bunch of clients now and having worked on the calculator for months and months and written a blog post about it and all that other stuff, do you have any, like, kind of key takeaways or insights that are examples of, like, what you might want to learn from putting your data in the subscription value loop?

[B]

Yeah, sure. One relatively recent example, there's an EdTech client that I've been working with where, as I said, in the first week, we sort of dug into the tool, put a bunch of the metrics in, and one thing that became very apparent was subscriber retention was really strong. Right? So they had a product that was resonating with users. They were able to retain their subscribers over a long period of time better than even the 75th percentile of education apps, let alone the average. But their biggest issues were, number one, their subscriber conversion rates were quite low, and number two, their pricing was higher than the typical edtech app in their category. And then the third problem they were facing was their paid advertising efficiency was lower than you would hope to see. And some of that was being driven by a price that was a little too high and an onboarding flow and a paywall that wasn't fully optimized. Right. And so those metrics didn't tell the whole story, but they gave me sort of a map of where we might begin to explore opportunities. How do we improve new user onboarding to convert more free users into trialers? How do we improve the paywall to maximize the excitement of those new trialers and ultimately convert them into subscribers? And then how do we optimize pricing in order to make sure that people are more willing to pay for the subscription once their trial is over? And so that basically laid out the roadmap for the first three months of our engagement. And there were a number of wins that came out of new user onboarding optimization, paywall optimization. And then we ran a subscription survey that confirmed a lot of the same stuff that we had seen in the subscription value calculator, but got into a lot more detail on the specifics of what needed to change in order to shore up some of those metrics.

[A]

Yeah. Any other examples before we move on to the talking through a spreadsheet on a podcast?

[B]

Yeah, a couple more examples. So there is a fitness product that I've worked with in the past that ran into a very common challenge that consumer subscription apps run into, particularly in the fitness category. I know Strava has run into this issue, which is giving away too much value for free. Right. And so the way that that was showing up in the metrics was their price was relatively low compared to their peer set and their subscriber conversion rates were relatively low. And you can interpret that a lot of different ways on its face. But again, we ran a subscription survey. We got into the weeds around what both free users and subscribers were saying about their free value promise, their premium value promise. And one of the insights that came out of it was, the free product is so good. Do I actually need to pay for the premium product? And what is my willingness to pay for that premium product? And so, again, it didn't give us the full picture of exactly what we needed to go do, but it told us where we should be focusing our resources.

[A]

Awesome. Well, let's dive into the calculator then. Some of you may be listening in the car, on a walk or whatever, pulling up the calculator at some point or listening and then going to pull it up would be super helpful to help all of this make sense. But the first thing I wanted to go through was, in the calculator, you have several things that the person needs to input. It's like you have to put in some metrics and I want to kind of go through those and like, why you included those and kind of the importance of them in the calculator, kind of helping to build that map of where opportunities might lie.

[B]

I'll lay out sort of the 30,000 foot view of how the tool works, and then we can drill into whatever details you guys are mostly excited to talk about. But at a high level. I mean, one of the requirements for me with a tool like this is it needed to be simple. So it's a single spreadsheet. You go in, there's a column where you enter a couple dozen metrics. Usually I recommend that companies enter the average across their last 12 months worth of data, just because if you have any seasonality in your business or if there are other sort of peaks and valleys, a year's worth of data can help to smooth that out. Obviously, the caveat would be if you had a major change to your paywall or your Pricing or some other big variable that could affect the metrics within the last 12 months. Just be careful about how that could influence the metrics. But otherwise look at the last 12 months. Take the average performance over the last 12 months, input that into the column that's your company's data and then you select the category of app you're in. So health and fitness, media and entertainment, photo and video productivity. There, there are 11 different categories or you can just look at all categories combined. And the second filter is performance tier. Performance tier. So you can look at the 25th percentile, the 50th percentile, the 75th percentile or the 95th percentile of apps in terms of how they perform. And that can be helpful as a proxy for your stage. Right. If you're an indie developer that's just getting started, I would say focus on P50. You want to be aiming for be better than average. If you're already a venture funded startup and you know you feel like you've got a pretty strong product already, you probably want to focus on P75 or even P95. So that's the second filter you can use. And then once you put in your app category, your performance tier and your business metrics, the tool immediately outputs your performance delta relative to your peer set. And it shows you a heat map in red versus green in terms of where you're over underperforming and so pretty quickly within a few hours of doing this exercise, you've got a very basic roadmap of hey, where are we over and underperforming and where might it make sense for us to make strategic bets.

[A]

Once people have done all that and they're looking at the heat map, there's a bunch of different sections that you're going to get this heat map delta. And the first section is about ltv, CAC payback period, kind of those top of funnel metrics. And so I wanted to dive into those and it's so tricky, you know, blended versus paid, you know, how much are you spending? Are you more organic driven? Like all of these things are going to factor in. So how do you think about that first section around payback period? LTV to CAC is that blended CAC is that only pay. How do you figure all that out?

[B]

So the way I look at LTV over CAC ratio and payback period is there are sort of good barometers over the overall health of the business, the strength of the unit economics and the efficiency of the company's growth engine. Right. And so you're not going to be able to prioritize a roadmap for your product or marketing team based on these metrics, but you'll get a good overall sense for the company's performance. And so to me, LTV over CAC and Payback Period are the outputs of this tool. They're what's telling you. Okay, if you want a target LTV over CAC of 3x, which is sort of the gold standard for consumer apps, and you want a payback period of ideally fewer than three months or even within the first month, how are you performing relative to those targets and relative to the targets of your peer set in your category. And then underneath that, you have the three sections in the subscription value loop. So you have value Creation, value delivery, and value capture. And each of those steps have their own component metrics that drive those steps in the loop that are ultimately leading to the output of LTV over CAC and Payback Period. So when I look at the tool, I'm looking first at LTV over CAC and Payback Period to say on the whole, how is this company performing versus its peer set? And then if they're over or underperforming, the next question is why? Like, where are the bottlenecks? What are the specific metrics where the company is most underperforming that we can go focus on first? And so then I drill down into value creation, which is measuring how effectively the company is creating value for users, value delivery, which measures how efficiently they're acquiring users and subscribers, and then value capture, which looks at how efficiently they're monetizing those users.

[A]

I do want to talk more about LTV to CAC and blended versus not and all that. But we can get into that in the value delivery section of the data. But the first section is the value creation, and there's a few metrics there. There's signup rate. You discussed this in the blog post and we were discussing this before starting the podcast. Is that what you mean by that is registration rate? How many people actually register? Because most apps these days are going to have some kind of registration wall where you want to collect an email, you want them to create an account. I mean, you know, I have done that in my dinky side project Apps, and have regretted it for a decade now. And I still. It's not something I've fixed yet. But most apps, if you're trying to build a real business, you need that's like the most.

[C]

That's what I tell people, too. But, like, it's like the Meanest nag be like, yeah, if you're trying to build a real business. I mean if we're just playing around here then then don't have a sign up. But like we were, I was ask about it because I didn't exactly know what it is. And this is one of the pieces of data that doesn't come from us, doesn't come from revenue Cat, because like we don't, we have some distinctions around signed up versus not. But this is, this is from the survey data, correct?

[B]

Yeah, this is one of the metrics from the survey data. It's basically account registration rate. So what percentage of installs are converting into registered users? And it's not directly feeding LTV over CAC ratio and payback period. But it's an important proxy metric that's further upstream. Right. Because in most cases, because if you haven't registered an account, it's very unlikely that you are going to pay for a subscription, which is ultimately what's driving revenue.

[A]

Yeah. And then if you don't have their email, you can't win them back if they don't sign up. You can't do so many of the other parts of value delivery and value capture if you don't have their attention. So and I think you know, you'd mentioned that activation rate like is really the metric you would want to have in this report because you want an activated user whether they sign up for an account or not. I mean we just said is important but what you really want is somebody who's like experienced the value, somebody who's activated but like it's, that's so fuzzy.

[C]

It'S so I mean sign up a signup could be an activation. It depends on how you define it. Right.

[B]

Well and this goes back to what we were talking about as far as the reliability of data. Right. Activation rate would be a great metric for this tool. The reason we haven't included it on so far is because the activation metric across different products looks so different. Right. And so it's really hard to compare apples to apples. And so for that reason signup rate or account registration rate is a good enough proxy for are we getting enough installs to take that first step of creating an account and then you start to get into the other value creation metrics which are mostly around retention. Right. So both for monthly and annual subscribers, what is month one, month two, month three, month six, month 12 retention rate, what is year one and year two? Annual retention rate. And then that data can be used to calculate an average lifetime for both Monthly and annual subscribers, which is obviously critical for driving those LTV over CAC and payback period metrics.

[C]

Yeah, which is an interesting way of collecting the data and like actually looking at it, not just looking at my blended monthly because that has a lot of effects on cohort composition, but then also stretching that out a little bit to 1-312-3612, which gives, you know, because some apps have really fast drop off, some apps have later drop off. And like that's probably a super good example of a case where you need to really apply context to know like if you have a six month drop off or a one month drop off that's higher or lower than bench. Probably has more to do with the nature of your app.

[B]

Right.

[C]

And like the sort of cyclical natures or seasonalities and things like that. But it's good to bring that in rather than you have this metric later on that feeds in, which is the monthly average, monthly periods, which also we were talking earlier, it's like a tricky one because to calculate, to have a true understanding of because that number tends to float and have biases based on like, you know, amount of time you've been collecting data. But you can put in a rough idea that is helpful in terms of estimating ltv. But yeah, I guess like Phil, when you pull in that curve, what are some of the conditions you might look at there to be? Like, oh, we should maybe this customer needs to do X or Y, like if they have like short term drop off or like maybe long term drop off. Like are there, are there examples you can think of where there's some interesting variants?

[B]

Well, one very interesting example I'll give you is I had a client I looked at somewhat recently where annual subscriber retention rate was much better than monthly subscriber retention rate. And obviously in general you're going to get longer retention from annual versus monthly.

[C]

Subscribers mostly because of, you know, if you've got 50 bucks to drop, you're probably just a stickier customer, period. It's always kind of my assumption for that.

[B]

But yeah, they're higher intent users and they've committed more upfront. But even relative to benchmarks, right. Relative to their peer set, their annual subscriber retention outperformed their peer set, but their monthly subscriber retention underperformed their peer set. And then combining that with an insight from the value capture component of the tool, which was that their annual subscription price was higher than their peer set, the insight was we should look at our annual subscription price relative to monthly and potentially offer a more generous annual subscription discount. And we should run additional optimizations on our paywall to try to nudge more users from monthly into annual subscription plans. Because we know that if we get a user to convert into an annual plan, we are significantly outperforming our peer set on annual subscriber retention. But the opposite is true for monthly. And so there is a lot of upside, even more so than the normal upside, to converting more users into annual subscriptions. And so you can start to see how some of these pieces fit together. It is hard talking about a spreadsheet on a podcast, but you can see how the pieces fit together and how it leads to actionable next steps a company can take based off of the numbers you're seeing.

[A]

This is when I wanted to dive into some of the caveats, though. I was talking to an app recently that has below median retention rates. I think their annual retention rate was something like 30%, but they're driving a ton. They're driving most of their traffic through paid ads and they're getting like day 45 return on ad spend. I think this is like, you know, what business are you in, what business are you building, how are you operating your business? And when you see that and you think, okay, that's where we need to focus, like we gotta get retention higher. Yes, but the way you're running the business and being able to get that 30 day return ad spend, 45 day return on ad spend, you're inherently bringing in a lot more people and lower intent users. And man, if you're retaining 30% and you're getting 45 day ROAS, like you can stack those cohorts over time.

[B]

This brings up another great point, which is this is oversimplifying things, but there's a little bit of a dichotomy I've seen between earlier stage consumer subscription businesses, in some cases indie developers that have built really efficient engines at rapidly converting for users into subscribers, making sure their D45 ROAS, or even their, like in Opal's case, D8 ROAS is really, really efficient. Right. And so they are printing money in the short term and that's great, that's a great small business. But then on the other end of the spectrum, you have the larger consumer subscription businesses that are trying to get to hundreds of millions, if not billions of dollars in valuation. Right. It's a much smaller ecosystem, but that's where what got you to greatness on the first category won't get you to greatness on the second category. Right. If you're shooting for one of those really big grand slams, then just having really efficient D8 or D45 ROAS won't get you there because you need to find ways to continue to grow organically so that you're not over reliant on paid ad spend to begin with. You need to find ways to increase subscriber retention because that's the foundation for everything else. Right? And so this is where one of the other filters we want to add to this tool is company stage. Because if you're a seed stage or series A startup, by all means focus on just being really efficient at acquiring users early on and making sure you're getting fast payback periods. But if you're a series C startup and you're shooting for a billion dollar valuation and eventually going public, you have to look at the metrics in totally different ways.

[A]

You're not going to get there with 30%. It takes too many years.

[C]

I don't think it's possible because what happens is as you scale that, right, you're going to hit, you're going to expire those users and like, because you didn't build a reliable. And the corollary to what you were just saying, Phil, like seed wise, this is like what game are you playing, right? Like what's your game? I've seen, seen this. It's not necessarily a bad thing to be like, yeah, let's rush to get some sort of engine of something here because often that can be the chip that or like the sort of lifeline you need to then like worry about the next stage. Double sided sword again, sometimes you can get in there and then you get stuck with this like, like sort of suboptimal locale like that you get stuck in and now you've maybe traded in some brand that's hard to like recover and things like that. But again depends on the game, depends on the game you're playing.

[A]

So but if you're spinning off cash to Phil's point earlier, then it's easier to kind of make those next investments, right?

[C]

Exactly. Yeah. You earn the right to kind of be like, okay, like how do we actually reinvest? I mean that's how businesses historically not using outside capital as you generate some free cash flow through some positive roas and then you can reinvest that in R and D and like other things like that that can ideally like I mean the comment about Gears, right? But it's like you start in first gear and it's maybe just like this rapid sort of just get Some money back and then, okay, great, we've expired, we've topped out in first gear and now we have to think about, okay, what's second gear? Like how do we like increase our leverage and actually go a little bit further and that will change. Right? This is why, this is not a like do once and forget sort of thing. So maybe we should, we should jump. I want to keep us moving, but maybe we should jump onto like sort of the value delivery, the inputs to this. Because I think this is, this is interesting. You talk about like cost per install and it made me think about like revenuecat system is different but like when I think about CPI or like cost of acquisition or whatever, I just take bit like number of installs which be for us like a sign up and I just divide it by my entire sales and marketing budget. So I'm curious, like how do you suggest people like kind of put together a rough number for that? Is it very inclusive or just, is it just like literally like how much did I spend on Facebook?

[B]

The way I think about this is there's sort of aggregate cost per install and so that can be across all of your different paid acquisition channels and can also include organic acquisition. Right. So you're taking an overall blended cost per install and that's what's most helpful for looking at the summary metrics of LTV over CAC and payback period as an entire business. But then if I'm a performance marketer and I want to evaluate the efficiency of my individual performance marketing channel channels, Facebook, Instagram, TikTok, Google, whatever the case might be, then I'm looking at a very different set of metrics which is my paid cost per install through each of those individual channels so that I can compare the efficiency across all of them.

[C]

But you have to look at them like, they're certainly useful in relatives with each other because like, and this is where benchmarks break down as you zoom in, right? Because like, you know, other people, what they consider a cost per install or cost per X may or may not be like include, you know, do that, you include your own salary. Right. If you're a performance marketer. Do you know what I mean? And that might be a bigger question for like founders and stuff to determine how they do stuff. My 2 cents would be if you're trying, and I think Phil, with the vision of this is it's to be very high level. So if I were you, I would, I would probably include whoever's working on day to day, ad, creative, all that stuff that's probably like all should go in. Maybe not. If you're just like trying to evaluate a model for just trying to like as you said on the performance marketing, like rip a big, you know, ad spend and make sure you're making that money back and, and it stays above the line or whatever. But if you're actually thinking about running a business, you probably should be considering those costs as well.

[A]

You know, I think that gives for a better blended metric too because the whole idea of the blended metric is that you're accounting for organic. So if somebody's like, you know, creating TikTok videos and you're not putting money behind them, it's not part of your paid spend, but you're paying for those installs. So if you have 20 people creating organic TikToks like that's marketing spend, that's.

[C]

So what do you consider like a cost of organic?

[B]

I think there's two different ways of looking at this depending on what your goal is. Like if you are the VP of finance and you want to understand exactly where all the dollars and cents are going, then absolutely you want to find a way to load in the cost of your marketing team or the cost of your sales team in the case of a B2B business. But if you're looking at the health of the business in terms of the unit economics, then generally you're not going to layer in SG and a cost because you want to focus on per dollar of marketing spend, how many users are you getting back? And the reason for that is as you scale into a larger and larger company, then the percentage of your cost basis that's coming from things like a marketer's salary gets to be a smaller and smaller and smaller percentage. And so the way I've built this tool, it's very much focused on the unit economics. So I'm not looking at the cost of the sales and marketing team in these value delivery metrics. I'm looking at the blended average cost per install, cost per trial, and cost per subscriber. When you look at users you're acquiring organically, that could be through viral word of mouth, it could be through posts on social media. But installs, trials and subscribers you aren't directly spending marketing dollars on averaged against your paid marketing spend across it.

[C]

Which I guess actually is maybe the inverse of the question of using these benchmarks correctly. Again, which is like, because at a smaller scale and what you want to be careful of and what I mean there, it's like you have to think about what is your actual motion and like what percentage of your efforts and energies? Because that's what ultimately you're trying to do is like, get some insight about how you should invest that incremental energy. And like, where is that falling now on these inputs? And they might actually be correlated. But you're right, like at scale, these systems are leveraged. Right. So one performance marketer can do, you know, 10k a month in Aspen, 100k and a million, probably with a similar amount of headcount potentially.

[B]

Exactly right, yeah. As you get larger and larger, what really matters is your marketing budget, more and more so versus the cost of your marketing team. And so the tool is meant to look at it from a unit economic standpoint.

[A]

How do you think about kind of bringing it back to the earlier section? How do you think about payback period and customer acquisition cost in relation to all these things? And then especially kind of the blend between paid and organic as well. Because I think for some companies who have amazing organic and start spending on paid, it's easy to look at the blended and then let those payback periods extend further and further out into time because the money's just rolling in because you have such great organic base. But how do you think about balancing all those things of like, how much you spend, what you expect of your spend versus what you expect of your organic, and how do you balance all those things?

[B]

I think the most efficient way to answer that is it goes back to the science and the art of interpreting benchmarks like this. So if you look at your value delivery metrics and you see that you're very, very efficient on cost per install, cost per trial, and cost per subscriber. There are a few different reasons that could be driving that. One is you're getting the vast majority of your acquisition through organic. You might be really, really inefficient at paid marketing, but that's being offset by a huge percentage of your users. And so subscribers coming through organic channels. So that's one possibility. A second possibility is sort of the opposite. You're getting a good number of your users and subscribers from paid channels, but you're, you've got a really good performance marketing team and you're really, really efficient at acquiring those users. And there are a few other flavors of what could be driving numbers like that. And so you start with the science, which is like, let me look at the numbers and let me look at where I'm over and underperforming. But then you got to get into interpreting why is this the case? And so another example I'll Give you a client I've worked with where we specifically looked at these value delivery metrics was they were acquiring a significant percentage of their users through paid channels. So they were very, very efficient. If you looked at their paid cost per install, cost per trial, cost per subscriber, you know, they would have been among the best in class at acquiring users through Facebook, but they were over reliant on paid acquisition spend overall in terms of acquired users relative to organic. And so one of the first things that I worked on with that client was figuring out how to get them off of the drug of paid acquisition and how to acquire more users through word of mouth or SEO or other non paid channels.

[A]

That's a great example. And I mean even my own app, I mean, you know, I have this side project Weather app I talk about on the podcast a bit. I haven't spent any money on paid acquisition this year. I made like 100k. So my, you know, if I plug those numbers in, my LTV to CAC is incredible. It's amazing.

[C]

Nothing else again, goes back to my point. Nothing else went into making that 100k happen. All, you know, it was just free money.

[A]

But yeah, everybody has to figure out kind of what business they're building, how they're going to build it, why the blend that they're seeing, why the numbers make sense and how they want to move forward from that. So great context for all of that.

[B]

When you're an early stage startup, number one, you're going to get more. Hopefully you're able to get more user acquisition organically than you will once you start to get beyond your early adopters. And you have to start paying more and more money to acquire users because they're just lowering intent. You're outside of your ideal customer profile. And then the second thing is, across all of the other metrics, value creation and value capture, early adopters tend to outperform the later majority. Right. And so what that means is you have to interpret these metrics through the lens of is this sustainable? And so to use your example David, like if you're an indie developer, an early stage startup, and the vast majority of your acquisition is organic, and so your LTV over CAC is like to the moon. That's great in the here and now, but it won't last. Like eventually if you scale to a certain size. History tells us every consumer subscription business at some point is going to hit a point where they have to spend more and more dollars on performance marketing in order to sustain growth. If that's what they want to do. And so it's another great example of where you have to apply your own intuition in looking at this data and projecting ahead to how the numbers are likely to change as you scale.

[A]

Yeah, so let's talk about that next section, Value Capture. You've gotten the person in the app you need to monetize them. So let's talk through some of the metrics there.

[B]

Yeah, and I actually, I've said this before, I think value capture is of the three steps in the subscription value loop, I actually think it's the one that most often gets overlooked. Right. Because especially early stage companies, you have a lot of product driven founders, they want to over deliver value for their customers. And so they're rightfully so investing a lot of their resources into just building a great product and getting people to talk about it. But at some point you have to capture resources back from your best users who are generally your subscribers, in order to reinvest into the business and keep growing. And so value capture is all about that. It's all about converting for users into subscribers. And so it looks at both how efficient you are at converting users through the subscription funnel, trial start rate, trial conversion rate, and install to paid conversion rate. It also looks at what your pricing looks like. So what is your annual monthly subscription price right now I don't have weekly subscription plans in the tool. That's another thing we could add in the future. But it looks at your price for each of your different subscription plans and tiers. And then it also looks at things like your subscription plan mix. So what percentage of your subscriptions are annual versus monthly and your gross margins, like how much of the top line revenue that you're getting per subscriber are you actually retaining as a business after you've paid things like App Store fees?

[A]

One of the things that I really want to do at some point, maybe like state of subscription to App Report 2027, when we've got like 10 data scientists and a whole team around this. And version 5 of the subscription value loop calculator is that I would love to do a full funnel benchmark because when you get to this stage and you're starting to say, oh well, my trial start rate's really low. But then your trial conversion rate is amazing. And so overall your trial open to paid is really high, but your trial start rate's really low. I think if we were able to like do full funnel benchmarks and like look at the apps that have, you know, higher stats here and then going all the way through Retention, not just looking at subscriber acquisition, but actually going all the way to, you know, two, three years down the road. And then that's where you start to see the benchmarks. Like very few people are going to be P95 on every single metric along the entire chain. Usually you're going to have a strength somewhere in the chain that you're the real outlier in that and that makes the business work. Even if you're below benchmark on another.

[B]

Metric, that's a great call out. And you see that anecdotally with a lot of the top consumer subscription apps in different categories. Right. It's not like they're P95 across every single metric. In fact, it'd be really hard to do that because by definition, if you're really, really efficient at converting free users and subscribers, you're probably going to have a harder time retaining them because you're getting some lower intent users into your subscription funnel. Similarly, if you were, you know, really, if your prices are really, really high, so you're in the highest percentile on how much revenue you're getting per subscriber, that might negatively impact your ability to convert subscribers in the first place. So there's sort of natural checks and balances on some of these metrics. I think the important thing, and what I generally tell my clients is ideally you're finding a way to be good enough across the majority of these metrics. You don't want to have too many areas where you have glaring deficiencies. Again, there are outliers like dating apps where you're going to naturally have higher churn rates. That's part of why you have the category filter. But you want to do your best to be within striking distance of at least average, if not P75 across as many of these metrics as you can be. And then more often than not, there are like one or two metrics where you are just outstanding. That's where you really outperform. And so Tender is a great example of a company. I teach a case study on Tender with Ravi Matt is the former CPO there in my course. And Tender and some of these other top dating apps have gotten really, really good at both converting new users into subscribers and offering different subscription tiers that maximize the amount of consumer surplus they're capturing because they know that they're only going to be able to retain subscribers for so long and so their subscription retention rates are never going to be as good as a lot of category leading apps and other categories. But they can make up for that if they're really, really efficient at capturing value from users in their first three to six months.

[A]

The other lens, I think to think through this section with is Stage as well, which you brought up earlier, is that, you know, I think Duolingo is a good example. I haven't gone through their Q10 and looked at their publicly shared stats to try and verify this, but I would assume they're like app open or their sign up rate, their activation rate to subscriber rate historically has been on the lower side because they've, they're driving a ton of usage in that freemium tier and they've built up this massive freemium base. Well, what are they doing now as a public company worth $10 billion, that's now their opportunity to improve on that, the massive free user base, to start monetizing them better through ads, through converting into subscriptions, through other methods, but then also to get better and better at converting new free users coming in into paid subscribers. So there's also like a time component of this. It's like, you know, Duolingo is obviously like P95 and several key metrics. And then the areas where they're not good is like now at $10 billion valuation, there's a lot of levers for them to pull to get better at those.

[B]

I think that's exactly right. And if you look at how Duolingo's product has evolved over the last few years, a lot of people don't know this. Like, Duolingo spent the first five years as a company focused exclusively on the free user experience. So they launched in, I think it was 2011 or 2012. They didn't introduce their Duolingo plus subscription until 2017. So they didn't start monetizing their user base really at all.

[C]

You know the elevate story, right. We were doing brain training apps that were paid up front and then Duolingo came out. That's kind of. And then it was like, okay, we can't win that. They did a really good job of turning venture capital into like a monopoly, essentially.

[B]

Yeah, it becomes a real competitive mode at some point. But then at this point, Duolingo is a really large company. Right. They're worth over $10 billion now. And so the cost of acquiring incremental users gets higher and higher. They fight against that by creating a better and better and better product. And now they're expanding into math and music. And so that's unlocking early adopters in new categories. But still on the whole, they're having to expend more resources to acquire the marginal customer, which means they have to get more efficient about capturing value from their subscribers or from their free users. And so if you look at Duolingo now, they're adding more ads to the free user experience. That means they're increasing the LTV of non subscribers. It also means that they're able to nudge more free users into subscribers because they get annoyed at the advertisements. And so they'll say, fine, enough is enough. I'll pay for Duolingo plus or Duolingo Max. And so you can sort of see that strategy playing out in their product evolution.

[A]

So I did want to give you a chance to talk briefly about the future of the Value Loop calculator. I know you've got some stuff in the works and the 2025 report will be out soon and you're going to incorporate that data to the calculator. So it'll be updated in the relatively near future. Any other updates? Top of mind, yeah.

[B]

Well, first of all, I do want to thank I know this is a revenuecat podcast, but I do want to thank Rick and the rest of the team at revenuecat that helped pull this first version of the tool together. Like without the dataset the tool wouldn't exist. That was a great experience and I'm really excited to partner with you all again next year once the 2025 State of Subscription Apps report is ready to do v2. I think a number of things that will happen with V2. I mean the first and most obvious one is, is you guys are growing quickly and so the sample size of apps is just going to keep getting larger and larger. I think it was 30,000 apps for this first version. I assume it will be a larger number for V2. I think that the second thing we want to do is add more filters. Right. So I mentioned before just how critical it is to compare apples to apples and make sure that you're looking at apps within your category, within your performance tier. But you also ideally want to be able to look at apps within your geography. Right. We know that US users tend to monetize at higher rates than international users, but they're also more expensive to acquire. So being able to cut by US vs Europe vs Japan, South Korea and a couple other geographies would be a great filter. Ideally, eventually getting to the point where we can filter by iOS vs Android or even vs Web would be another filter. I'm really excited to add. And then these are will probably be more difficult, but eventually being able to filter by company Stage and a couple of other variables would be great to do as well. And then the last thing that we talked about a little bit earlier was there are a couple of weaknesses in this first MVP version of the tool. Right. The biggest one being that those value delivery metrics are coming from a survey. They're not coming from the data in revenuecat's SDK. And so either finding ways to get that data from Revenuecat or finding another partner like AppsFlyer or adjust that can pull in the value delivery metric data.

[A]

Awesome. I'm really looking forward to the future of this calculator. And I think, you know, internally at revenuecat we've been talking about more and more ways we can help even in our dashboard kind of surface aspects of this, like help developers spot areas of weakness and things like that. So looking forward to your work and looking forward to what we can do to productize aspects of these ideas. But anything else you want to share as we're wrapping up? I know you actually have a new live course launching in January, right?

[B]

Yeah, just a few things. So number one, if you're interested in learning more about the work, I do, I have a website, it's just phil g.carter.com and I'm at Phil G. Carter across substack, LinkedIn and X. And so if you're interested in the blog post I wrote on the Subscription Value loop or the Subscription Value Loop calculator, you can check that out at Phil G. Carter on Substack. You mentioned the course. So I do have a live course that I now teach on Maven, the next course actually launching in mid January. And so you can check out consumer subscription growth [e-mail] and actually if you use promo code Sub Club, I'm offering a 10% discount. So feel free, feel free to use that. And then lastly is, you know, my core business is being a full time growth advisor and angel investor at this point and so I work with about half a dozen different clients at a time. I'm at full capacity right now, but I'll likely have capacity opening up in Q1 next year. And so if you're interested in potentially working together on a more intensive basis, would love to hear from you.

[A]

Awesome. Phil, thanks so much for joining us. This is super insightful and thanks for the work you're doing in the community too. Like, you know, we provided the data but you built the tool and I think it's going to be super helpful to a lot of folks. And then thanks for being so generous kind of sharing how you use it and how to apply it in building better businesses.

[B]

Yeah, well, likewise. I mean, I said this when I was at the revenuecat annual event, but you guys have really become thought leaders in the community. You've aggregated this wonderful group of people. And, you know, it can be lonely building apps. And so having this community of other people to compare notes with and share data with is really helpful. And it's always a pleasure to partner with you guys.

[C]

Well, we'll have you back for Christmas next year, I'm sure.

[B]

Sounds great. That seems to be the the timeline for us.

[C]

Yeah. All right. Thanks, Phil.

[A]

Thanks, Phil. Thanks so much for listening. If you have a minute, please leave a review in your favorite podcast player. You can also stop by chat.subclub.com to join our private community.