Game Analytics 101: Why waiting "One more week" can kill your game!

So, why is retention uh the most important metric? And it's pretty obvious, but you can only monetize an install on the days they play. If they don't play your game, you can't make money off them.
Yeah, but you can't you can't buy your dinner with retention.
No. No. And you can't Yeah. Make that point very clear. You can't buy dinner with five star reviews either.
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Felix, are you going to do the intro? Hello everyone and welcome to another fantastic episode of the Two and a Half Gamers podcast where you can stay two and a half steps steps ahead of the mobile gaming industry. Today we have a very special episode because we are joined by someone who will take us where we've never been before and dive deep into analytics. But before introducing our guest, I'm Felix Braberg.
My name is Matthew
and I'm
we are
your host.
So we have the privilege today to be joined by Russell Owens. Is that how you say say it right or am I butchering that?
No, that's right.
Ovenans. Ovenans. And we used to work together a brief amount of time at Eastside Games. And one of the things that you were extremely good at was analytics. But why don't you introduce

yourself?
Sure. Well, first of all, thanks thanks for having me, guys. Uh big fan of the show since I was a little kid. Used to gather around the wireless and listen to you talk about ad placements. So to today's uh talk I asked I asked Felix like uh you know if I came on what do you want me to talk about? Should I do a deep dive into something specific that you guys are interested in? And he said no let's let's let's keep it broad. So this is everything you always wanted to know about analytics but two and a half gamers were afraid to ask. And yes so to start with I'm going to do a a quick Oh, hello. Hello there. I didn't see you. Thank you very much for coming to this episode. Uh, it's brought to you by our sponsors, PVX Partners, the simplest and most effective credit line for marketing. If you're scaling your business, uh, and need funding, here's the deal. With PVX Partners, you can

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Kind of yeah take take on my journey into games and what I've been involved with and why I think I might know what I'm talking about. The the story begins in 2007 when uh a company that I had called backstage technologies put an app out on Facebook. This was the very first days of when the Facebook API was available. And I wanted to do an app where you could buy virtual scratch lottery tickets and send them to your friends and play them

and and my my team who were all into games, they uh gified it. Um so it became a collecting uh thing where you had prize sets to collect and you know some of the prizes were you could only win with scratch tickets. So we launched this thing at the end of 2007 and by the beginning of 2008 uh we'd accidentally invented free-to-play social games because we were the first u studio to implement um microtransactions with spare chain. We were the first studio to implement an offer wall with super rewards which is Jason Bailey's former company which is why I got involved with him later on Felix.
Oh okay. And then in so and then in 2010 I sold backstage to Real Networks to Game House and left the industry. Not
pulls you back in though, right? Yeah.
Yeah. I came I came back in because you know it turns out it's as much as I like to pretend it wasn't my passion, it was

something that I was good at. And so in 2018, I came back to the industry uh to work for Jason Bailey, who is now the CEO of East Side Games. And that's where I met Felix. So Felix did he managed our ad monetization for the entire studio for over a year, I believe. So I thought, you know, he was really good and perhaps he was the reason you were before that doing
Tetris.
Yeah. So, uh, I was at EI Games at least for five years and retired and I published a book called game analytics retention and monetization in free-to-play mobile games. That was my gift back to the industry. It was basically everything I'd learned over the previous five years because one of the things about analytics is it's really hard to find out information about how to do it for mobile gaming. And uh so I I I put down uh 300 pages of what I thought was stuff that you needed to know if you were going to work.

300 pages. That's a that's a lot.
That's a good gift.
Yes,
it is a lot. So what I did in 2025, this earlier this year, I launched a website, ardow.com. And the whole point of professor arpow as it's called is that you don't need to read my book. So instead, you just go to the website and everything you need to uh all the tools are there for free. Um, so before I begin, so the the way that I've structured this presentation is just as a bunch of set of questions that I think you know people are afraid to ask, but why not? So the first one is what is analytics and is it the same or different than data science and does anybody really care? Do you three care? I'm not sure.
Not really.
Yeah, not really. Data scientists get paid more is the is the short answer.
That's a great answer. the I'm not a data scientist, never pretended to be, but I get asked uh this

question periodically and uh the best answer I've come up with I believe is this that if you're a game analyst and you're working in a a game studio, you got tons of data about your players and what you want to do is be able to make predictions about what your new players are going to do and you work with cohorts. And uh we're going to talk a lot about cohorts today and because it's really the foundational piece that uh you work with as a as a game data analyst. My focus is on analytics. The book is called game analytics. It's not called game data science. If you're a data scientist, what you do is you use typically machine learning trained on historical player data to what you want to do is try and model and predict individual player behavior ultimately with the goal of tailoring uh gameplay offers whatever it might be. predict churn um on the individual user level. Okay, so in either case you're doing predictions, right? I mean analytics though uses statistics and in particular

regression as its main predictive tool. Okay,
clear does it make sense?
Yeah,
good explanation. So if we're working with cohorts, what is a cohort? But simply it's just a set of players. So we're dealing with player behavior in the aggregate, right? We want to take a set of players that say they they've come from the same source, right? So this is really important when you're doing user acquisition. So uh in particular attributing users to a particular ad campaign and you you can go as deep as you want to this particular creative um right so but we we tended to work at EI games just on the ad campaign level. So this ad network running the targeting these particular users. So a cohort is just a set of players they have something in common. The most common thing is that you look at the players who all

installed the game on the same date or had their first open on the same date. Other ways we tend to break them down is by platform and by country example. So um here's here's a definition of a particular cohort. We want to look at all the organic Slovakian Android users who installed our game in June or maybe we look at the
and then there's also one attribute missing instant on an emulator because he's a cheater. That's
yeah Jakob only plays on emulator.
Yeah. So we know we we filter them out because they're the one making all of the $99 purchases one right after the other, right? So we can tell Yeah. who's who's cracked the APK.
So why are cohorts important? I've alluded to it. It's because it's uh how we can monitor how well a user acquisition campaign is going. And and you really want to keep track of your new cohorts, right? Cohort behavior evolves over time and you want to

understand where your your good users are, what cohorts they belong to. A typical mistake some big games have made in the past is that they'll launch and they'll have this massive success in their first month and they keep buying users based on that particular behavior of the golden cohort, but their new cohorts are not behaving the same way. Right? So you can light a pilot. So we're clear on what a cohort is. Yeah, we see the golden cohort behavior too too often, I guess, still.
Yeah.
Yeah.
It's intoxicating, right? Because it's like, yeah, this game is amazing.
Super good. Yeah.
Yeah. Yeah.
Okay. So, in in the spirit of you guys' podcast, I made some 4 a.m. doodles at a hotel bar.
I was trying trying to explain what a cohorts what their behavior looks like over time. Right. So the thing about cohorts when you're looking at how they behave, you're looking at typically charts where the x-axis is an integer and it's the days since install, right?

So the origin on the x-axis is day zero. It's when the the cohort starts playing. The cohort people don't have to have the same install date like when I was talking before about all the Slovakian players in June because what we do is we just shift and looked at their behavior from their days since they started playing the game. you know what do they do on the first day what if they come back what do they do on day one etc day two three seven so some of these graphs that I've looked at is a way of looking at say DAO uh top left as a cohort metric as opposed to say just a time series when you're looking at how's your games daily active users doing as a cohort metric leads directly into how we calculate retention which is the primary topic of this presentation so the things we want know or assume these are assumptions we're going to make in a lot of the predictions that we make cohorts is that cohorts kind of behave in this way particularly once

they get to be a certain size right um then you know small cohorts you're basically looking at individuals they dominate what happens larger cohorts 100,000 10,000 users they tend to get this smooth behavior of how they uh play the game or leave the game churn um how their LTV grows asmtoically uh and how and this is the surprising one that I get a lot of people are surprised is that ARP dow remains pretty constant for cohorts regardless of how old they are right so on day one you might have an ARP dow of 50 cents day 360 you're still going to have an arpow of 50 cents for a cohort and that's because the daily conversion rate tends to stay the same uh for the cohort so even as users drop off the ones that remain they continue to uh spend money inapp purchase isn't relatively same.
What's the ideal minimum size of a cohort?
I like well I like round numbers like everyone does so I tend to say 100 like

if you've got less than 100. So what when we're looking at say cohorts based on geo by country. We'll typically filter out ones where we don't have at least 100 installs.
Yeah. And then what's the minimum cohort size on a daily basis? Let's say when you're having a soft launch game and you want to measure retention,
I would typically not look at it on a daily basis, but look at like say group them
because again it's cohort metric. You don't they don't all have to come on the same day, right? So I would wait until we've gathered enough and you can do the power calculation on your statistics and all that but intuitively we know fewer users the less reliable the uh the estimates are. Just just want to mention for people who don't do statistics daily power calculation means the statistical significance which means how many uses we actually need for the result to be statistically significant. So yeah like what what would that number be in terms of the let's say day one retention if I

want to see
retention or any proportion is is modeled slightly differently though in this case because the numbers are big uh retention that you don't need that many 100 would be fine but if you're looking at conversion where you only have like three or four out of the hundred that are making the purchase that's when it gets tricky and that's where the power calculation's going to reveal that you need like a thousand users because the the events of interest happen so infrequently.
Yeah. One one note regarding the great charts over there, the red curve which is growing the LTV1 99% of games in the market don't see this. They see curve that grows for 5 days and then gets flat forever.
And that's that that's how the market works. Just want to mention that.
If you see a curve,
if you see this, you see a gold mine.
Yeah. You mortgage your h you mortgage your house and you spend it on
UA that's like day 18 that's day 18

there that's not Yeah. Yeah. No, but
and Felix knows this from our time at Eastside Games is that
our narrative idol games which had really strong live box had curves like this all the way out to 90 180 days because the retention was good. That's what's really driving this and that's again the kind of the theme of today's talk.
Still the highest rewarded impto I've ever seen in any game.
Okay, good to know. So, what are we going to learn today? So, I'm going to do a little introduction to my website and Professor Arpd's free analytics tools that are going to help you model your game's retention. And once you have a model of your retention, you're going to be able to predict things like your player lifetime value, predicted LTV, and uh your expected days to break even for ad campaigns.
Ah, you have that uh clicker calculation thing there

on the website.
Yeah.
The one where you input a Oh, yeah. I really really need to do this because
Okay.
Every time I talk to people and I just tell them like, "Guys, have you modeled your CPI? Like how much money you need to throw at this?" like, oh, we think it will work out or it won't work out.
Well, I mean, no, I haven't I haven't modeled how uh if you're looking for a model of how your CPI changes with spend.
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not even that one. Just the basic one, the linear one. Okay. Even that one. No, the you can check it out and we're not going to get there today, but uh because it's kind of the more advanced one of the free tools, but yeah, what what I really want to get across today is is why your game's retention curve is fundamentally important. what it means to your game success and um you know how it it really should be your focus and when you building a game, managing the game, you're the product owner,

whatever, really focus on retention because monetization can follow.
Yep.
So
that's definitely true. which goes back. So the the question then I mean if you were expecting me to come in and talk about all the game studio metrics and what the key performance indicators are that's not what I'm going to talk about today because I think you know your audience if they're listening to you guys they're well beyond that. So really what I want to talk about is is what are the most important metrics and and how you should be looking forward. Okay. So, I'm going to start with a a quote from Jason Bailey who sat beside me for five years and how he used to always say the games teams because they would look at like topline revenue and they would look at their daily active users and they would come to the growth team and complain that their Dow was falling and Jason would tell him to go away and say you need to worry about two things and two things only. What's your retention and what's your average revenue per daily active user and everything else will follow. And why is

that? Well, obviously it's under their control, right? So, if you're managing a game as a product, what can you change? You can change the things that are going to bring your users back. You can change how your live ops is working. That will improve your retention. And then how you monetize the users you have, that's under your control, right? Once you have those two things trending in the right direction or at a baseline level, then the growth team can go ahead and profitably acquire users for you, right? And that's profitable acquisition of users for any free-to-play game is only possible if the lifetime value is greater than the cost per install. That's the basic hypothesis formula.
So it turns out though that lifetime value is just the sum of your retention times ARPA. So, well, there you go. Right there in the in one formula explains why the games team should only worry about retention and narrow because ultimately that's what's driving LTV.

And if LTV is high enough, we can grow this game relative to your cost per install.
Yeah, that's the fact I wanted to add here that in current market, the CPI thing is kind of a little bit more important than the rest of
No, everybody everybody's fixated on CPI, right? And You got to look. It's LTV is just as important. It's like, you know, uh, but people focus on CPI because it's something they think they can gain, right? Oh, no. We just need the right creative. We just need to lie to our users about what the game's like and then they'll come into the game, you know, all their their techniques there.
Yeah, definitely. And it becomes I don't want to get into why, you know, our industry isn't is troubled by the skyrocketing costs per install. What I just want to talk about is you can build a game that you can grow if your LTB is greater than whatever your cost per install is and however you want to go about fixing CPI.

You guys can talk about that with someone else who knows anything about but that what I do know about is that your LTV is your sum of your retention time. I want to point that out in you guys are going to get tired of hearing me say some of retention in the same way that my wife is tired of hearing me say ARPD.
It's okay. It's okay.
I got back at her by by incorporating ARPDO industries.
So quickly, what's ARPO? It's just revenue divided by DAO, right? So here's a chart simple one that you know analyst at the game studio might put together. It's a time series where a calendar date is along the x- axis and then we can plot like how are we doing? What's our what's our revenue? What's our data follows naturally, right? So these are good at at revealing what your long-term trends are. But um you know again the CEO at Eastside Games used to say that you know your Dow and

revenue are vanity metrics. You go and focus on what you can control and the growth team will do the rest. So and they're vanity but they're important business. So we talked about now how do we define retention? It's one of those metrics that you know people new to the industry might have a little bit of difficulty get wrapping their head around just this is that we look at we have a cohort of users how many of them come back to play the next day what proportion of them and that's your D1 retention how many of them come back to play on seventh day you don't care about the sixth or the eighth do they play exactly on the seventh day your D7 retention so
and what do you say are good retention metrics for D1, D7, and D30
in mobile
40 and 20. If you can hit that, you're

you're doing fine.
So, EI Games just put out all game and D1 retention is like 53%.
That's that's awesome because D1 retention will anchor. D7 is going to define the shape of what happens after that as we're as we're going to see. Do you have also a benchmark for D90?
Well, you'll see what's in the next in the next slide, I'll talk about what ultimately these numbers how they should relate to each other.
So, D90 should be half your D30, which should be half of your D7. So,
okay,
spoiler alert. So, um yeah, let's go and just jump ahead and look at the next slide here. So, here's, you know, graphs you've probably seen. Uh and this is um not a time series. is what kind of is, right? The x-axis though is the install date of the players. So, we're looking at each day we get a bunch of players and uh what's their D1 retention? And

you can't you have to wait till tomorrow to figure that out. And then what's their D7 retention? Well, we have to wait um seven days, eight days actually, before you can calculate it. And then here's D30, the poor purple one, way back there a month ago. We have no idea. We call these these cohorts are not fully baked, but it doesn't mean we have to wait. We don't have to wait 30 days. We predict what for each of these days or install cohorts. We can easily predict what the D30 retention is going to be. And let me show you how. Um, but first we have to think about, you know, why do we just measure retention on D1, D7, D14, whatever? You know, studios pick their favorite days. But the numbers, this this standardization around D1, D7, and D30 retention and so on, I think really comes from Eric Seufort's book, Fremium Economics, because he he talks about this recurring pattern that he's seen with free-to-play games, which is your

D7 retention is typically half your D1 retention,
which is a wishful thinking.
I mean,
yeah, it's an idealized game. You're right. It is wishful thinking, but again, if you've got a a compelling endgame, lots of content, a live ops is pushing out events every weekend, right? These things are important. If you're just throwing out the hyper casual game with some ads that you bombard the user with in the first session,
love it. Love it.
Yeah. You're not going to see this this retention curve, right? And you're in a very different business. I'm not sure what it is, but you know this is the business that you know Esite Games has been in and you know my own studio before that longtail retention is crucial and live ops is crucial right so so this is what Eric said he pointed this out and he said isn't that interesting but what he failed to do was to actually generalize the concept and what he was describing is just a power function you just needed to do a little bit of math um and that realized that

retention follows this formula where you have two coefficients A and B and B is the exponent and A is the coefficient. A is typically the value of D1 retention and B is negative and so which means you're just dividing by the number of days and typically so I'll show you in a minute what what these these formulas derive in terms of the shape because what really we can we don't need to just measure retention for a set of industry standard dates. we can instead derive a retention curve and we can measure up for any day we like and then once we have a curve which we can usually build from like the first seven days of data we can predict what our retention is going to be out to day 360 or whatever right with varying degrees of accuracy
so just quickly I I I said you know you it's a way of of defining what a

retention profile is for a game as a whole you know If you're looking at your D1 D7 retention as every day and you're looking at a bounce up and down, you can you can get distracted by really what you want to know is what's my game's D7 retention and you can build a weighted average of these retention values through cohorts like just taking them and smashing them all together and then you can derive a retention curve which is just a formula a function and it's a compact way that you can represent a game's retention, Right? So um and if you have a a formula so the example here I give uh say your retention by day n is defined by the formula 04 n* n to the.5 right and it's actually a very typical kind of retention curve that you'll see in in games you can use that to predict what your retention is for any day right so what's day 53 retention well if we plug 53 in for n we will see that it's

5.5% kind of take That is because 53 is a funny number and no one would ever think, "Oh, what's your day 53ention?"
Uh, so what's a retention curve look like? So, all it is is we're just going to use statistical regression to fit a curve to uh our observed retention values. And on this graph, I'm connecting a value for D0 retention, which is always one. you play in the day you install it because the install is the first day one retention of point4 day three retention of 23.3% and a day seven retention I think I put in 18 or something 16 you end up then with a formula that fits this data and in this case it's that retention by day n is equal to 396* n^ of472 and so as you move those dots around the

shape of this curve will change. It never goes to zero. It asmtoically goes on forever which is of course not realistic but you can usually fix that by just forcing it to be zero beyond some day when you know your game never has players. But again, going back to, you know, to kind of the tires of East Side Games and Trader Park Boys came out in 2018, 17, the year before I joined, it still has players from the original Golden Cohort, right? So, they're day they're on to whatever, you know, thousands and they're still retention. They're still retaining users.
Love idol games.
Actually, day 30,000 plus.
Yeah, exactly. So again, so to take it back to that those like scribbles I did on the back of the 4 a.m. in the hotel bar, the retention curve is just cohort DAO normalized, right? Normalized in the sense that, you

know, we know how many installs were part of the cohort. If we just take every day's dowo and divide by that number, you'll get a proportion and basically calculated retention and your retention curve. They're basically how many users left are left in the game.
How many are left? You know, like what the thing about retention is it's got a funny uh statistical meaning which is like if you look at say say your day and your retention is 5%. So what that means is that if you you have a cohort and you pick a user out of that cohort at random, there's a 5% chance that they're going to play on day 90 or did play if you're looking at historical data. So it's the probability that a user will play on this day after they install, right? So if your day seven retention is 20%, there's a one in five chance that a new install is going to play on day seven.

given that we we can do some pretty uh cool things with it. So, well, first of all, let's let's take a break and and one of you or all of you go over to ardow.comretentfin and uh you can play with a tool that I built so that you can uh come up with a model for your game's retention curve and uh all you need to do is plug in some observations for what your you know early retention signals are and you know the my website will fit a curve to that data and show it to you. So for people looking at the slides, you go to the retention curve creator, you plug in some uh obser observations that you have from any cohort you like. So say we have 40.2 D1 retention and 16.7 day seven retention. I'm going to leave the D3 and D30 at zero, meaning we're going to exclude those. You need to just provide two. And obviously, of course,

the more data points, the more accurate the model is going to be. You hit submit, tell you what your retention curve is. I give you the formula for it and give you a little a little picture of what it looks like.
Nice freebie.
Tell you
Mhm.
Really, this is not important. Like how how I'm doing this is explained in detail in my book rather than like I said, you don't have to go read my book. Just, you know, go play with this tool. Um it's simple statistical regression, right? So yeah, I mean this looks pretty sweet, eh? Day 365 retention of 2.81%. That's We'll take that. How realistic is that?
Sure.
Sure.
The thing that's alluded to this as you know, Professor Argo talking on the corner of one of the slides back there is that retention and art are often in

tension with each other. What's a great way to make a user turn away is to show them an interest if they'll ad every three seconds once they install a game, right? Great for monetization. So, your ARP dial would be high, but your retention is going to kind of going to, you know, take a hit. The gold mine is uh building a game with a day 365 retention of 2.81%. uh but still has um you know monetization dollar RPL and D3 have a retention of 3% you'd be okay. Why is retention the most important metric? Felix, do you agree retention is the most important metric or do you believe ad monetization ad you know ad revenue is the most important?
I have a call at 9:00 am about exactly

this with a client. But uh I do agree that retention is exactly the most important with this particular client. We've actually managed to double the ads and the day one retention is exactly the same.
Uh but
amazing
very slow base, right? Yeah. But still
Yeah. Well, that's good. All right. So, so why is retention the most important metric? And it's pretty obvious, but you can only monetize an install on the days they play, right? If they don't play your game, you can't make money off them. And so,
yeah, you can't you can't buy your dinner with retention, though. So,
no. No. And you can't Yeah. Make that point very clear. You can't buy dinner with five star reviews either.
Yeah. Yeah. Exactly.
Yeah. Yeah. You need you need to uh but what what you need to be able to do is and here's the analogy I use. So I have this this recurring cafe in my book. So kind of ground these ideas in bricks and mortar businesses. So imagine you own a cafe and you have a customer cross the

threshold. Well, you're they're they're going to be you're going to monetize them. They're in your store. They're going they're coming in to buy a coffee, whatever. What you really are trying to sell them though is a return visit because you you're going to monetize them today but you want to monetize them tomorrow as well. So it's that whole tension between you know provide them good service provide them a compelling reason to want to come back and to make money off them today as well. So, but within mobile gaming or free-to-play gaming, what we want to know is how many days is an install going to play, like how many how many chances am I going to get to monetize them before they churn? And it turns out that the expected number of unique calendar days that one of your new users is going to play your game is simply the sum of your game's retention curve.

blew my mind when I realized this. You know, after playing with analytics data and leading analytics at Nissa Gamester for years, this really obvious, it's it's obvious in hindsight, but when you are working in the industry without any mentors or guidance or this might be something that takes you a long time to discover or you may never discover it. So, I put it in my book. I'm like, here, this is something that's pretty cool. You fit a curve to your observed retention. That's your retention curve. Uh, then if you take the sum of that, that's the number of unique calendar days an install is going to play your game. Why is that important? Well, as we just said, you can monetize them when they play. So, expected player days is what I call that. acronym is PB for player day. It's the sum of the retention curve. So, as part of the output on the professor

AR site that I include with the retention curve is not only just predictions for what your day and retention is going to be, but how many days will a player be expected to play unique calendar days that they'll play the game by that time frame. So you can see here for this slide that by day 180 given this particular retention curve we expect that users will have played the game 13 different days. You have 13 cracks at monetizing them. It's very much an average in the sense of the average is the fulcrum of which you balance histogram. So, if you were to look at actually how many if you were to look at a histogram of people play your game, you're going to see that most installs play only one day and churn, right? They install, they kick the tires, they go, "This game's not for me," and they're gone. And then out at the other end, you've got a bunch of players who play love your game, and

they play every day. And you got this massive valley in between, right? So, players either play once and quit or they play all the time. Why is it important to know the expected player days?
What do we derive from it?
Yeah. How can you use it?
You're going to derive LTV from it? Because if you if if so going back to this example here, by day 180, we know that that player will have played 13 times. So, we know what our and we know what our RPOW is. You multiply the two and that's your LTV 180. Okay,
it's there's nothing magic about that. It's just the algebra. Devote an entire chapter of my book to showing the equivalence between monetization monetization curves and the way that we typically look at uh data regarding revenue.
Sorry, I'm trying to
Yeah, go ahead. wrap my head around it a

little bit because usually when I I don't balance economies or stuff like that, I balance it mostly on like session time, session length, which gives me daily play time on calendar day and from that point on I know like how much actual you know battles the guy does or whatever. Therefore, I can estimate like by this point in I don't know 200 days he had the old gacha this many times which means this has this percentage of collection yada yada yada. But what's the translation of this number regarding that? So, this has nothing to do with actual play time, does it?
No, it just has to do with they fired up the game.
And yeah, it's it's it's a bit of a more it's a blunt object, but it's one that user acquisition managers would use as opposed to economy game design. like you're you're looking at the data through a kind of a different lens in terms of how engaged is this player and that engagement

will impact art now for everyone but you can't engage them unless they open the app right they have to come back and you know and play it but really what you're starting to get look at there Jacob is I think the to my point at the beginning looking at individual user behavior is very different than looking at cohort behavior.
Mhm.
Yeah. So, you're using a microscope and I'm using like a magnifying glass.
Okay.
Yeah. Kind of the difference.
So, let's go to the next
to average it out by whole cohort. That's the point.
Yes.
Okay. I was missing
and LTV is an is a cohort metric. It's the average, right? It's Yeah. So,
which I can't do because I'm modeling it for every single user,
right? So this player days thing is so here's the the running sum of retention plotted as a curve and it looks like LTV curve it has the same shape as the LTV curve

because the players that play are the ones that you monetize is relatively constant Felix for an ad driven game your ad is pretty constant right it doesn't really change like if a player's play if it's the 180th day they're playing your game, they're still going to generate roughly the same amount as they did first day.
Yeah. The only difference is
how close it is to Christmas, right? Or Black Friday.
That's right. That's right. So, the professor on the right there is chiming in to say, just sort of reiterating what I said on slide. Since you can only monetize a user on the day they play, a player days by N is how we predict LTV. It's also how we predict DAO and revenue. So all of the predictive tools that are freely available on arpdow.com use the monetization curve the Swiss army knife of of doing your

predictions as to where you're going to get it. I actually give you the formula for the you know player days curve which again these are all stated an easy way for you to just plug them into Excel and you can play with them yourself if that's what you want. So yeah to um you know wait we can predict LTV with the retention curve like it's kind of surprising but if again if you look at it it's just a way of defining what LTVM is. Lifetime value is the average total revenue derived by a user during their first 10 days after they install your game. Well algebraically it's just the number of days they play times arpal. So it falls out of this notion that PDN is the expected number of distinct calendar days they're going to play the game and ARPO is just how much money an active player generates each day that they play the game. So if you want to improve your LTV, which is necessary to grow because you want an LTV to be greater than cost per install, you need either increase

retention or you need to increase ARP out and you need to do one without affecting the other. Ideally, you improve both. But
easier said than done. Yeah.
Yeah.
Or or you don't.
Yeah. So, the second free tool on the professor art website is this LTD predictor. And so, you can go and and play with that while I'm um talking about it here. And it works very similar to the retention curve creator. All you do though is you also add in your ARPA,
right? So if you if you have an estimate of your retention curve and you have an estimate of what your arpow is and LTV is just the two multiplied together and so I give you like the LTV curve um so you can get estimates of where you're going to be like you know what's your LTB90 going to be in this case $468 and so on and so you you can see that that

you know if you increase your ARP value you're going to increase revenue if you increase retention depending on how you increase retention retention curves are weird though, right? Like if your D1 retention was 80% but your D7 was only 16, what would that mean? That means people are dropping off like crazy, right? And then you know the you would expect that that trend would continue and you'd have no users by day 90, right?
Yeah. But then then you need to show millions of intersters on day one and then you're fine.
You do just broke the game economy. Love it.
Yeah. Exactly. But that's that's the right if you've got 80% D1. Yes. And seven.
Maximize that day.
Exactly. Yeah. And you know, well, we've only got them for a week.
Carpetm carpet.
Is there a there a CPI input somewhere? Because on the side, I see the seven ROS target to break even by here's the here's the cool thing. The

seven ROS targets don't have anything to do with CPI or LTV. like they're based on your ratio of what your uh your LTV7 is to your cost per install. And that can be like a $5 LTV7 and a you know $50 cost per install or it can be you know five cents on the 50 cent CPI. It's it's about the ratio of those two and the monetization to that point is going to follow the shape of the retention curve. So the two are are divorced. So here, yeah, let's go to the next slide and see what you're talking about here. On that LTV prediction tool, I include what your LTV by day end is predicted to be based on, you know, your current retention and ARPA. Uh, and then another column at the end is what your D7 rorowes target should be if you want to break even by day end. So for example, N for 90. So day 90, we know by then each of our installs has

played an average of nine calendar days. Their LTVN by day 90, LTV90 is $4.68. 28.03% is the D7 rorowaz target. So if we have an ad campaign that by day seven we're seeing a rorowaz of 28% or greater, we know it's likely to break even by day 90. And that has and it doesn't matter what the CPI is
but the curve needs to go up. That's the assumption.
The curve is going to follow the same shape. You know the retention curve and the monetiz curve is going to follow the trajectory of the retention curve the sum of the retention curve.
And rorowaz here not to be confused for our avid listeners is not the rorowaz hotel in Bodram Turkey but return on ad spend.
Exactly. I assume your uh your your listeners are very intelligent and they know what a lot of these things are. And my apologies if I'm assuming names are understood. Thank you for

it's just a prominent prominent game CEO in our industry who after was selling his gaming company, he named a hotel ROAS. He bought a hotel.
Okay, perfect. I like it. That's an actual hotel. So, I was hoping Roz meant something in Turkish. Maybe it does. One of your listeners can uh can can let us know. So, let's see what what's on the next slide here. Oh, yeah. Yeah. So, this is um one of the things that I was interested in. So I have included it as sidebar on that LTV tool is basically what what happens if you miss your D7 rorowaz targets. What's its impact on your days to break even? Because a lot of times you're starting a a rorowaz campaign and your um uh you know rep from the

ad network is like saying, "Oh yeah, you're really you're close." because you're saying, "Well, we need a D7 rorowaz of 28% to break even by um or 28% to break even by day 90." Well, our rorowaz is only 20%. On the campaigns, and they're like, "Oh, that's close enough." Well, is it? Uh our our days to break even just went from 90 to 178. And if your day seven rorowaz is only 15% in this particular example, suddenly you got to wait 316 days. And if it's 10%, you're never going to see it, right?
Sounds more realistic.
And you know, you get a lot of pressure from account reps to just let let the campaign run. It's learning. It's still learning, right? And you know, and you need to spend this amount of money for the to get enough signals for it to learn. And you know, meanwhile, you're and then even then after like two weeks of

spending and spending, you're still you're close, but you're not you're not at your rorowaz target. So, you just need to be aware that gets real here. Like, you're you're not going to see a return on that ad spend for, you know, it does it doesn't change linearly. It goes up exponentially. So, if you miss, you're going to have to wait a long time. And even then, if you're lucky, right? Like again, if your retention curve uh does have a terminal day, you know, so people just never play your game after say 300 days and you're, you know, looking at a 316 day back window, you're probably never going to see the money back, right? At least not. So don't let your account reps bully you. And to give it just one more week.
Yeah. But we should end on a positive note for sure. So hopefully we got through it.
We're at the point we're at, it's the end of the lecture and homework for all

of you, which is to go to ardow.com and play with the tools there and do some experimenting with looking at what's what's better for your game. Is it a 10% lift in your D7 retention or is it 10% lift in your you know because I see a lot of product managers really kind of fixate on squeezing more and more money out of the out of their users battle passes more placements because they want to increase that ARP down and yeah that's important but would you be better off finding a way to increase your retention by a similar left.
Battle passes actually can decrease your up.
Yeah.
Yeah. Yeah.
I agree. I agree. And you know, or pay to remove ads. If you do that wrong, you're going to hurt yourself.

Oh, no. You may increase your retention. So, you may want to be a negative.
So, that's your homework. Expect
Awesome. you know, you guys to to get back to me to that with a with an answer.
And here, last slide is so if that's wet your interests at all in analytics for the games industry, where can they go to learn more? Obviously, you can go to art.com for the free analytics tool. Stay for my geographical pricing tool, which maybe I'll come back one day and we can talk about that. Probably not so much in you guys' wheelhouse. You can buy my book. My book's great.
It's the Bible.
There you go. I have a copy downstairs. Yeah.
Did I buy it for you, Felix?
No. No. I bought it for myself.
Thank you very much.
I owe you a beer.
So, which is about how much money I make

on each copy. Jeff Bezos gets most of the most of the revenue on this baby. You can buy my book, How to Find It. Just Google Oven's game analytics or you can visit books website which is game analytics.ca. I'm Canadian not.com which is that uh the good uh analytics um company or you can follow me on LinkedIn where I post uh about stuff like this uh as as things occur to me and I learn more and more I'd like to share my learnings with the community of art.
Yeah. Thanks. We'll put the all the links in the show notes and if you have any questions or comments just yeah put them under the video join our Slack channel or just be a ping on Russell on LinkedIn or on the
DM me directly.
Yeah. Yeah. I love Yeah,
I got nothing better to do
at this point.
Yeah, pretty much. Mhm.

Thank you very much for for sharing all the info and the graphs and everything and that though both of those tools I will I will definitely play around.
Yeah, some new metrics today.
Yeah, exactly.
Yeah.
Well, you know, if you guys can't find any value in in what I've done, then I need to just shut it down. Go
quietly into the into the sunset.
Good night.
Yeah. Exactly. Yeah. Yeah, but I'm hoping that you know and and hey, if you guys have any feedback about, you know, would be nice is if you did this or that, but the um I think you know, you want to you want to probably look at that days to break even calculator because I think that's um
Yeah, that's very interesting.
That's very interesting.
I've seen a lot of LTV calculators, but I don't think so. I've seen the one with this edition.
Yeah.
Very cool.
Very cool.
There you go.
Thank you for listening and yeah, see you next time. See you next time. Cheers.

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