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

Channel: Sub Club by RevenueCat
Video: https://www.youtube.com/watch?v=7QA-UtGCGJY
Duration: 57 min
Language: English
Words: 12288
Transcript page: https://viewrankai.com/tools/youtube-transcript/7QA-UtGCGJY

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[0:00] hello I'm your host David Bernard and with me today Revenue cat CEO Jacob iding Our Guest today is Phil Carter an independent growth adviser 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 iata on the podcast we talked 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 yeah thanks for having me always a pleasure Phil's back oh wait sorry I

[0:38] interrupted David do my intro Jacob always nice to chat with you as well I still can't follow directions but but I'm here so I'm I'm excited let's go 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

[1:12] 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 the 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 St data and so I love the calculator you built and excited to talk about it today so let's just kick off what is the subscription value Loop calculator sure yeah well first of all benchmarks very polarizing topic super controversial so well depends if they're good or bad

[1:43] meaning you true very true well that's why they're controversial right because a lot of them are bad but yeah so maybe to 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 deposits 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 subcription leaders is okay this this is a helpful framework in theory but how do I actually apply it

[2:12] and how can I measure the performance of my subscription value Loop and understand uh 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 benchmark marks 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

[2:47] 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 CU I partnered with Revenue cat 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 and you already kind of mentioned it but where did you get all the data for this mostly Revenue cat and and to Jacob's Point good data great data the best data the best you got you

[3:17] wouldn't you wouldn't believe the data we have it's great but what what other data sources did you use and then how did you pull it all together into the calculator I had had this idea in my head for some time like ever since I came up with a 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

[3:44] 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 right like like apple and Google's data sets well one they 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 so totally yeah and so this past summer I talked to Rick the CMO revenue account and basically shared this idea with him of you know I've got this framework I want to quantify it I

[4:17] need data in order to do that would Revenue cap 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 of subscription apps report so more than 30,000 subscription apps that use your alls SDK I think 290 million subscribers and 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 Revenue cat we pulled together B1 of this subscription value Loop calculator and we can get more into

[4:49] what the specific metrics are and 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 needed from revenue cat obviously Revenue cat 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

[5:21] data set but much smaller sample size I think for future versions of the product will 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 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 but how do you think of benchmarks as being helpful yeah I I think where people get in trouble with

[5:55] benchmarks is they try to make them the end all Beall right they try to do more with them than they really should be the way I think of benchmarks is they're one tool and 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 few resources meaning marketing dollars or engineering bandwidth in order to more

[6:35] efficiently capture value more efficiently increase their AB 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 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 chat GPT or Claud or whatever and 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 than other times but you

[7:07] can't necessarily rely on on just whatever metrics you find on the the LMS are really bad at kind of like understanding caveats and nuances uh on these things they do a really I'll sometimes just throw some of our like Revenue cats 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 sighting sources sometimes like where they got the the data from but yeah it's it's you know it's not the most reliable way of pulling it yeah so that's the first order of problem is can I actually rely on this data because if you can

[7:37] it's garbage in garbage out how was it collect 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 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 often times it's too generic right some of the largest and more reliable data

[8:07] 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 plane right they're not going to actually tell you how to improve your

[8:44] 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 one place I see people get hung up is kind of on that third step and I think it 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 and

[9:15] just being like a 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

[9:48] like who you're in that c subcategory like what's your differentiator and folks can just like dive in on like oh I got to fix sub I got to fix like a 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 get 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

[10:18] 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 forc through the trees right they see like one thing and they like over obsess about it and then they get frustrated because they're like oh yeah we worked on our trial start rate for two years and you know we went from 10% to 12% which maybe that's significant but like there might have been other things you could do to like really push the business there's almost the midwit meme

[10:48] here of like 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 a 100 different metrics and I'm going to optimize every last thing so this gets to like the double-edge sort 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

[11:13] 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 or improved marketing or actually know how to fix it yeah or or is it a gap that's exogenous because for example I'm a dating app and so I'm going to have high turn rates because if I have a good product people are naturally turning off the platform 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 premium you

[11:48] 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 pay wall 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 I'm sure we've talked about this probably Phil in the past but like the Dutch damn analogy right of like you

[12:17] 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 interl 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 this 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 and maybe this is uh

[12:51] as I I I'll say move progressively to the left on the midwit meme over time I'm just like more and more let me just do what do what do what feels good and feels right and then if it tracks to a metric like that's great 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 chat GPT or or Claude but go on Google or find some other publicly available source of information and just ask

[13:22] what's a good Benchmark that was very fast and easy and Broad but unreliable for me it was the um ser series B ever note series B deck that everybody used that was like the only consumer subscriptions silver lining in 2014 or whatever 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 you know given quiz was an education company CH course hero photo maath du Lang and so we would periodically jump on a

[14:01] 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's 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 I think this is something I mean I don't

[14:35] want to keep Jing us off the off the trail here but I think this 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 star rate is 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 and there are some like Secret Sauce things around this stuff but a lot of times you can also nine times out of 10 you can just go look and copy what your

[15:04] competitor so it's not like it's 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 now that we've totally thrown benchmarks under the bus we've got a whole rest of the podcast to talk about should we just not do Sosa let's just we'll figure something else out 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

[15:34] into a tweet or 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 waving 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

[16:14] 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 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 podcast you're going to get a million ideas of like oh we should do this on our pay wall we should do that on our onboarding and like you can't execute on all of those and so the great

[16:46] 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 DEC decisions those millions of small decisions you make along a product Journey so that's why the subcription value 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 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

[17:20] 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 or they're focusing on good ideas that are good ideas two years from now that aren't good ideas today as a startup sure I guess that you could put that under judgment but that's also just like entrepreneurial like wisdom or like just focus and good at resource alloc I mean resource allocation is really the game

[17:51] and like that can be other people that could be at the beginning you right individual time and attention and dollars but I'll um oppose or 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

[18:25] because that edge will compound if your competitors or your ERS 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 sounds very esoteric I promise this is like related to like how to use benchmarks and things like this and determine 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

[18:59] 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 next thing I wanted to talk through is how you apply 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

[19:38] 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 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 new client and and onboarding and getting to understand their business better and there's a few advantages of 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

[20:09] 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 I work with or IND 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

[20:35] 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

[21:02] 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 1-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 together having done this with a bunch of clients now and and having worked on the calculator for months and months and written a blog post about it and all

[21:31] 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 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

[22:01] 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 pay wall 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

[22:38] opportunities how do we improve new user onboarding to convert more free users into trilers how do we improve the pay wall to maximize the excitement of those new trilers 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 road map for the first three months of our engagement and there were a number of wins that came out of new user onboarding optimization payall optimization and then we ran a subscription survey that confirmed a lot of the same stuff that we had seen in

[23:09] 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 yeah any other examples before we move on to the talking through a spreadsheet on a podcast yeah a couple more examples so there's 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 way too much value for free right and so the way that that was showing up in the metrics was their price was relatively

[23:41] low compared to their peer set and their subscriber conversion rates were relatively low and you could 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

[24:12] we should be focusing our resources 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

[24:44] that map of where opportunities might lie 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 most 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

[25:13] 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 pay wall 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

[25:45] look at all categories combined and the second filter is 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 it 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

[26:19] you've 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 road map of hey where are we over and underperforming and where might it make sense for us to make strategic bets 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

[26:55] payback period kind of th those top of funnel met tricks 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 paid CAC how do you figure all that out so the way I look at LTB over CAC ratio and payback period is there's sort of good barometers over the overall health of the business uh the strength of the unit

[27:28] omics and the efficiency of the company's growth engine right and so you're not going to be able to prioritize a road map 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 overc of 3x which is sort of the gold standard for Consumer apps and you want a payback period of ideally fewer than 3 months or even within the first month how are you performing relative to those targets and relative to the

[28:00] 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 LTB over CAC and payback period so when I look at the tool I'm looking first at LTB overc and payback period to say on the whole how is this company performing versus its Peet and then if they're over underperforming the next question is why like where are the bottlenecks what are the specific metrics where the company

[28:32] 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 value capture which looks at how efficiently they're monetizing those users I do want to talk more about LTV toac and Blended versus non 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 sign up rate you discuss this in the blog post and we were discussing this before

[29:05] 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 not done that in my dinky side project apps and have regretted it for a decade now and I still it's not something I fixed yet but most apps if you're trying to build a real business you need that's like the most that's what I tell people too but like it's like the meanest neg be like yeah if

[29:33] 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 asking Phil 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 you we don't we have some distinctions around sign up versus not but this is this is from the survey data correct 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 LTB over CAC

[30:00] ratio and payback period but it's an important proxy metric that's further Upstream right because in most cases if you haven't registered an account it's very unlikely that you're going to pay for a subscription which is ultimately what's driving Revenue 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 I think you know you you'd mention that activation rate like I is really the metric you would want to have in this report because you want an activated

[30:34] 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 it's so I mean sign up a sign up could be an activation it just it depends on how you define it right well 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 so far is because the activation metric across different products looks so different right and so

[31:03] 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 2 month 3 month 6 month 12 retention rate what is year 1 and year 2 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 overc and payback

[31:38] period measures 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 three one 123 62 which gives you know 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 one month drop off that's higher or lower than bench probably has more to do

[32:10] with the nature of your app right and like the sort of cyclical Natures or or seasonalities and things like that but it's good to bring that in rather than you have this metric later on that that feeds in which is the 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

[32:44] are some of the um 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 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 anual versus monthly subscribers mostly because of you know if you've got 50 bucks to drop

[33:13] you're probably just a stickier customer period it's always been kind of my assumption for that 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 outperform their peer set but their monthly subscriber reten underperform 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

[33:51] our pay wall 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 and 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 right 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

[34:19] based off of the numbers you're seeing 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 got to get retention higher yes

[34:58] but the way you're running the business and being able to get that 30-day return on ad spin 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 Raz like you can stack those cohorts over time 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 indd developers that have built really efficient engines at rapidly converting fre users into subscribers making sure their d45 Raz or even their like in

[35:41] opel's case d8 Raz 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 if not billions of dollars in evaluation 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 cat right if you're shooting for one of those really big Grand Slams then just having really efficient d8 or d45 row ads won't get you there because

[36:16] you need to find ways to continue to grow organically so that you're not overring on paid ads spin 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 serc startup and you're shooting for a billion dollar valuation and eventually going public you have to

[36:46] look at the metrics in totally different ways you're not going to get there with 30% it takes too many years I don't think it's 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 and and the corollary to what you were just saying Phil like seed Wise It's 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 CU often that can

[37:14] 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 sort of suboptimal local 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 so but if you're spending off cash to Phil's Point earlier then it's easier to kind of make those next Investments right exactly yeah you earn the right to kind of be like okay like

[37:44] how do we actually reinvest I mean that's how businesses historically not using outside capitals you generate some free cash flow through some positive roas and then you can reinvest that in R&D and like other things like that 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

[38:11] 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 on to 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 Revenue CAD 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

[38:38] 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 is it just like literally like how much should I spend on Facebook 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

[39:10] want to evaluate the efficiency of my individual Performance Marketing channels Facebook Instagram Tik Tok 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 but you have to look at them like they're so 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 a cost per X may or may not be like includ you know do that you include your own

[39:41] salary right if you're a performance marketer do you know what I mean that might be a bigger question for like Founders and stuff to determine how they do stuff my two cents would be if you're trying and I think Phil with the the vision of this is is 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

[40:10] make sure you're making that money back 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 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 Tik Tok 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 Tik toks like that's marketing spend that's pay so what do you consider like a cost of organic I

[40:41] 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 sgna cost because you want to focus on per dollar of marketing spend how many users are you getting back and the

[41:11] 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 requiring organically that could be through vir Word of Mouth it could be through posts on social media

[41:42] but installs trials and subscribers you aren't directly spending marketing dollars on averaged against your paid marketing spent across which 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 which you want to be careful of and what I mean there is like you have to think about what is your actual motion and like what percentage and 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 and they might actually be

[42:13] correlated but you're right like at scale these systems are leveraged right so one performance marketer can do you know 10K a month and add been 100K and a million probably with a similar amount of head count potentially 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 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

[42:45] 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 is 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 I think the most efficient way to answer that is it goes back to the science and the Art

[43:20] 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 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

[43:51] a really good performance marketing team and you're really really efficient at acquiring those users and there are a few other flavors of of what could be D numers 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 of 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

[44:19] 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 were over reliant on paid acquisition spend overall in terms of acquiring 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 that's a a great example and I mean even my own app I mean you know I have this side project

[44:47] weather app I talk about in the podcast a bit I haven't spent any money on paid acquisition this year I made like 100k so my uh you know if I plug those numbers in my LTV to CAC is incred incredible it's amazing nothing else again goes back to my point nothing else went into making that 100K happen at all you know it was just free money but yeah so 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

[45:14] that 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 more money to acquire users because they're just lower 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

[45:47] India 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 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 and looking at this data and projecting ahead to how the numbers are

[46:13] likely to change as you scale yeah so let's talk about that next section value captures you've gotten the person in the app you need to monetize them so let's talk through some of the metrics there yeah and I actually I've said this before I think value capture is is of the three steps in the subscription value 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 overd 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

[46:42] 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 fre users into subscribers and so it looks at both how efficient you are are at converting users through the subscription funnel trial start rate trial conversion rate and install the 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

[47:13] 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 Topline Revenue that you're getting per subscription are you actually retaining as a business after you've paid things like app store fees one of the things that I really want to do at some point maybe like uh state of subscription to app report 2027 when we've got like 10 data scientists and a whole team around this and version five of the subscription value Loop calculator is

[47:45] 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

[48:21] 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 metric that's a great call out and 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 have a

[48:57] harder time retaining them because you're getting some lower intent users into your subscription funnel similarly if you 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

[49:26] where you're going to naturally have higher turn 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 Robbie met as the former CPO there in my course and tender and some of these other top dating apps have gotten really really

[49:55] good and 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 right 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 3 to six months 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

[50:25] dualingo is a good example I have gone through their Q10 and 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 premium tier and they've built up this massive premium base well what are they doing now as a public company worth1 billion dollar 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

[51:04] 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 du lingo is obviously like P95 and several key metrics and then the areas where they're not good is like now at 1010 billion doll valuation there's a lot of Leverage for them to pull to get better at those I think that's exactly right and if you look at how dual product has evolved over the last few years a lot of people don't know this like dualingo spent the first 5 years as a company focused exclusively on the free user experience so they launched in I think

[51:35] it was 2011 or 2012 they didn't introduce their dual lingo Plus subscription until 2017 so they didn't start monetizing their user base really at all you know the the Elevate story right we were doing brain training apps that were paid up front and then dualingo came out that 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 yeah it becomes a real competitive mode at some point but then at this point dualingo is a really large company right they're worth over10 billion now and so the cost of acquiring

[52:03] 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 dualingo now they're adding more ads to the free user experience that means they're increasing the LTV of nonsubscribers it also means that they're able to nudge more free users into subscribers because they get

[52:31] annoyed at the advertisements and so they they they'll say fine enough is enough I'll pay for dualingo Plus or do or dualingo Max and so you can sort of see that strategy playing out in their product Evolution 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 into the calculator so it'll be updated in the relatively near future any other updates top of mind yeah well first of all I do want to thank I know this is a

[53:00] revenue cat podcast but I do want to thank Rick and the rest of the team at Revenue cat that helped pull this first version of the tool together like without the data set 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 20125 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 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

[53:25] this first version I 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 versus Europe versus Japan South Korea and a couple other geographies would be a great filter IDE

[53:56] eventually getting to the point where we can filter by iOS versus Android or even versus web would be another filter I'm really excited to add and then these will probably be more difficult but eventually being able to filter by company stage and a couple of other variables would would be great to do as well and then the last thing what 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 rbat SDK and so either finding ways

[54:25] to get that data from revenue cat or finding another partner like appsflyer or adjust that can pull in the value delivery metric data awesome that I'm really looking forward to the future of this calculator and uh I think you know internally at Revenue cat we've been talking about more and more ways we can help even in our dashboard kind of surface aspects of this to 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 to productize aspects of these ideas but anything else you want to share as we're wrapping up I know you

[54:55] actually have a new live course launching in January right 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 philg Carter across substack LinkedIn and x and so if you're interested in the blog post I wrote on subscription value Loop or the subscription value Loop calculator you can check that out at Phil garter on substack you mentioned the course so I do have a live course that I now teach on Maven the next court actually launching in mid January and so you can check out consumer sub growth

[55:26] course at maven.com and actually if you use promo code subclub I'm offering a 10% discount um so feel free feel free to use that and then lastly is you know my core business is being a full-time growth adviser 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 uh a more intensive basis would would love to hear from you awesome Phil thanks so much for joining us this was uh super

[55:55] insightful and thanks of 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 uh building better businesses yeah well likewise I mean I said this when I was at the uh Revenue cat 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

[56:21] 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 well we'll have you back for Christmas next year I'm sure sounds great that seems to be the uh the timeline for us yeah all right thanks Phil 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. club.com to join our private community [Music]

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