What payback should I aim for? Mastering CPI & LTV in Mobile Gaming by Matej Lancaric

we have a game genre that affects the payback period quite heavily.

So we have hypercasual games.

TPO, we had.

Oh, come on, they're still out there and still make money.

And we discussed with Felix ultra-casual games as well,

especially games coming from Southeast Asia or games and companies,

which are still earning millions of dollars per month. Felix Shacko bringing the insight we're rocking those vibes till the early daylight Matei UA master eyes on the prize tracking data through the cyberspace skies Felix acts colors like a wizard in disguise

Jackups craft the realms lift us to the highs

Two and a half gamers talking smack slow hockey stick got your back ads are beautiful they light the way

Click it fast don't delay Hello everyone, welcome to Two and a Half Gamers ASMR Insights. Well, because I'm the only one who calls it this way.
Today, I'm going to talk about, again and again, hello. Today, I'm going to talk about the CPI versus LTV equation.
And thank you very much for the feedback last time. I was talking about how to conquer a farming game market and i was playing archero 2 well i guess the decision wasn't that great right uh but here we are we i heard you this time i'm going to be playing color block jam which we also cover well based on this uh episode i guess depends when this episode comes out but we either cover or already covered it almost probably already covered it because the game is really really good okay so again thank you very much for the support and for the feedback please keep it coming this is again an experimentation from our end because we love to experiment well i'm going to talk about the cpi and ltv equation in the competitive landscape of mobile gaming understanding the dynamics between cost per install and our Lifetime value is crucial for optimizing user acquisition strategies and maximizing revenue.
So this recording goes into how CPI influences the UA spend, the calculation of LTV, decision making based on the CPI versus the LTV equation, what is the ideal payback period for different game genres,

the use of LTV prediction in UA,

and sitting on benchmarks versus utilizing LTV predictions.

Buckle up.

This is going to be an interesting episode.

Here we go.

So how does CPI influence the user acquisition spend?

Well, the CPI is, well, let's start with what the CPI is, actually. The cost per install is the average cost incurred by the game developer or publisher every time a user installs their game or app through paid advertising efforts.
CPI is a critical metric because it directly impacts the overall budget required for UA campaigns. So how do you allocate budget? Because higher CPI means acquiring each user is more expensive, requiring a larger budget to meet the UA goals.
Conversely, the lower CPI allows more users to be acquired within the same budget. No shit.
Market competitiveness also influences the CPI, because CPI rates are influenced by the market demand and the competition. Popular run-rass or keywords, because it's still if you bid on the keywords on the web to app, may have higher CPIs or keywords in the Apple search ads due to increased bidding from multiple advertisers.
And there is also the targeting options and overall quality of the users that have a very big effect on the CPIs. Broad targeting may result in a lower CPI when it attracts less engaged users.
Narrowing targeting can increase the CPIs but potentially attract higher value users. This also comes into the up event optimization.
Different events produces different CPIs and also different quality of the users. Scaling campaigns and understanding the CPI helps always in the scaling the campaigns efficiently.
If the CPI is low and the quality of users is high, it makes sense to increase the spend, right? US spending should be also calibrated so that the CPI does not exceed the LTV of the users required, ensuring the positive return on investment. Scaling in-game is not only a function of a killer user acquisition operations.
It's also a function of an LTV. If you can only scale your budget until LTV allows you to do so.
So for example, if your LTV is $5, you can run profitable campaigns until you hit, let's say, $4.5 CPIs. Any other higher CPIs that you calculated based on your margins,

that also is very important for your health of the business. I have my favorite CPI graph

from the past, which I need to definitely update. I usually use it where I compare

different genres and their CPIs. You remember Frozen City? It's a blast from the past, but it serves the purpose well as an example.
Idle games generally have lower CPIs than other categories. But what is more important here in the Frozen City example is the actual visual design and art style.
If you watch our No Bullshit Gaming show frequently, you know why certain hyper-casual games always used or use still low-poly visual style because it drives really low CPIs. I was in a lot of discussions about how fancy or more quality visual style drives higher in-app purchases because of the premium feel of the game.
Seriously, what the fuck? The visual style doesn't really impact the in-app purchases. if you have a different opinion, please let me know or join our Slack channel so we can discuss it there.
So why would a different visual style or more fancy or more quality visual style have any impact? Anyway, let's get back to the topic. So how do LTVs actually calculate? LTV represents the total revenue a game can expect to earn from a user over the entire period of their playtime.
Calculating LTV accurately is essential for making informed UI decisions. How can you actually calculate it in terms of the methods? So you have two different methods.
Let's say historical data analysis. Do you take the average revenue per user which is calculated by dividing total revenue by the number of users over a specific period of time? And you are looking at the churn rate, which enables you to understand how quickly users stop playing the game, and that helps in projecting the future revenue.
Then you look at the retention rates, and obviously higher retention generally indicate a higher LTV. Then we have a predictive modeling.
Cohort analysis, which enables you to group users based on the time they start playing to identify patterns in the spending and retention. You have machine learning models, utilizing algorithms to predict future user behavior based on historical data as well.
So LTV predictions take this concept to the next level by leveraging AI. Obviously, we are living in an AI world, so this definitely helps to find potential users with the highest LTV.
Facebook or Meta recently also brought LTV prediction tool into their suite. This feature, in general, allows user acquisition managers to optimize their strategies by targeting users that are likely to yield the highest returns.
But what is under the hood? With LTV predictions, you evaluate each user within a certain period of time, 24 hours, 48 hours, 3 days, 7 days, of joining the game and form an LTV prediction for, again, a certain period of time. It can be 7 days, 30 days, 60 days, 90 days, or one year, or even two years.
Depends on your game genre. Based on these forecasts, you can also send prospects into your ad network directly from your own data warehouse or respective tool you're using to build these prediction models, and then obviously optimize campaigns for top LTV users.
In contrast to classic optimization suggestions based on traditional metrics like time spent and engagement, the new AI-based predictive model collects and analyzes massive amounts of data around every user's potential LTV to find the highest quality leads for your campaigns. LTV predictions also let you segment users into various LTV cohorts, let's say top 5, top 20, top 50, or bottom 50, and compare them against each other.
To test the accuracy of the prediction and evaluate which method attracted the more engaged audience, you should always run the test. You always run the test.
You always rerun the prediction models and always check the prediction versus the actual performance of the campaign. So run an A-B test for a game.
The test compares two different options. Option based on on the user played for at least 10 minutes

event and then optimization based on the predictive model for the top 20 percent of paying users let's say top 20 ltv the campaign settings and budget were the same first 10 days were of the the campaign where just

spend and get the data

again

budgets and settings the same

first 10 days of the campaign were just spent and get the data. Same again, budgets and settings the same.
First days just to get the learnings and build the data set. And then next week after we collected the install data, we used the predictive modeling.
So the test showed us obviously the big advantage of the optimization on the campaign level based on the LTV.

So no shit, no shit, it actually works. But then let's also switch gears a little bit for the churn prediction because that's important.
On the other hand, in my 11 years of being a UA manager, I never used churn or churn predictions in any of my campaigns. Maybe I should.
Anyway, what is the user churn, right? So it's a user churn is a common challenge in mobile app marketing and in the whole gaming space. Identifying users that are likely to leave the game and implement proactive measures to retain them is vital for long-term success.
Hence, who is doing the churn predictions and actually using it? Let us know in the Slack channel. This enables developers and the marketing teams to identify new users who will likely churn over time

as soon as they install the app.

So, I mean, it's ideal to know

which users are more likely to churn,

which gives you an apprehension

to engaging them in preventing their exit from the app

as soon as possible.

So, quite handy.

Have you used it?

Not so much. But here we are with the factors influencing LTV.
So, the revenue, Sanina purchased from users. Then we have the ads revenue, which is an earnings from the ads that people or users or players watch in the game.
And then we have the user engagement. More engaged users are likely to spend more and stay longer in the game.
And then we have game updates and content updates because regular updates and new content can improve retention and increase the LTV. And this is very, very important

because as we are discussing,

it's CPI versus LTV equation.

And as soon as we have the CPI on the left side,

we still have the LTV on the right-hand side.

So let's talk about the decision-making

based on the CPI versus the LTV equation.

The fundamental principle in UA is that the CPI should be lower than the LTV of acquired user. This ensures that the revenue justifies the cost of acquiring a user.
There are two scenarios. CPI is lower than the LTV, which is the profitable scenario.
UA campaigns can be scaled up. And there is another scenario, which is CPI is higher than the LTV, which is not a profitable scenario and unprofitable scenario.
UA strategies need re-evaluation. Well, and not only UA strategies need re-evaluation, but you need to look also on the product side of things.
So how can we make decisions based on these scenarios? So you can, or we can, as UA managers, it's just targeting, obviously, refine the audience segments to attract higher LTV users. Use different events.

Use purchase optimization or value optimization on different networks.

Use ROAS campaigns, in-app ROAS campaigns

or other ROAS campaigns

or if your game is actually a hybrid casual game

or not hybrid casual,

but monetization profile is hybrid,

both in-app ads and in-app purchases. You can use blended RAS campaigns.
Then what you can do is obviously optimize creatives. So improve the creatives to increase conversion rates and lower the CPI.
And we have just recorded a podcast about creative trends. You should definitely check it out.
Somewhere here here or here it's going to be added on the youtube site you can see a lot of visuals

i should check it out and then enhance the monetization well improve the in-game kpis

and just increase the ltv right because ltv always dictates the level of spend. Repeat after me.
LTV always dictates the level of spend. You can always run profitable campaigns.
But then the question is, what is the scale? So as I said, LTV always dictates the level of spend which then you can have profitable campaigns but you will spend $100 per day or you will spend $1000 a day but then the question is is that enough to run a successful business for some some games, it's $1,000 per day, or not some games, but for some studios, $1,000 per day, it's enough, because then it generates significant amount of money, which significant in this case can be $2,000. And since there are two people in the team, or three people, or four, and they are wherever, Slovakia, for example, that's enough.
If you are Supercell, Playrix, or any other gaming studio, mid-size or large, $2,000 is basically spent in 10 minutes.

So that's not going to be relevant for these types of companies. Keep that in mind.
What is the best payback period for each game or genre? So as I mentioned, different companies have very different mindsets and also different games, different payback periods. So the pay payback period is basically the time it takes for the revenue from a user to cover the cost of acquiring them.

And what are the factors that affect the payback period?

We have a game genre that affects the payback period quite heavily.

So we have hypercasual games.

TPO, we had.

Oh, come on, they're still out there and still make money.

And we discussed with Felix ultra-casual games as well,

especially games coming from Southeast Asia

or games and companies,

which are still earning millions of dollars per month.

Typically, these games have very low L LTVs ensure payback periods, often within days or weeks. But also important is that the CPI is very, very low as well.
So then the sweet spot is very important. I need to take into consideration into this CPI versus the LTV equation.
Not only the CPI you pay on the UI channel for an install, but also, let's say, cost for attribution. And attribution helps you identify the users and from what channel they're coming into your game.
And for example, if you run any campaign in tier four countries, India, Philippines, wherever else, Indonesia, and you have CPI of two cents, but then the cost per attribution for this player in India, Indonesia or Philippines

is 7 cents

your LTV needs to be way higher

it needs to be more than 10 cents

which might break

the CPI versus LTV equation

and often times

it does

then we

have hybrid casual games

which we cover

a lot on our podcast

and our bullshit gaming show

Thank you. then we have hybrid casual games which we cover a lot on our podcast and our bullshit gaming show these games have generally high retention bigger spend depth because they're borrowing the hyper casual marketability which means slower CPIs but they're actually allowing players to spend more the paybackback period usually for these types of games is anything between one to six months.
Closer to one month rather than six months but let's say this is the range that I've seen. Then we have mid-core or hardcore games which have higher LTBs with long payback periods,

usually anything between several months to a few years.

And also match-free games and casual games,

usually aiming to get the money back in two years.

Then, again, what is the best payback period?

I'm always getting this question.

What is the best payback period?

I'm going to go. then again like what is the best payback period i'm always getting this question what is the best payback period what is the payback period i should aim for well honestly i have no idea you should always look on your on the level of your money on your bank account on your cash flow flow situation.
What are your financial goals? You need to align this with the company's cash flow, as I said, because you will need that money to actually pay your employees. Also, looking at the industry benchmarks can help.
Basically, what I just said around the game genres and the games in general. And then user behavior.
You need to analyze the user spending and engagement patterns to set realistic payback periods. For example, if your retention on day 90 is 0%, you can't optimize or expect your payback or money.
And you can't optimize payback of 120 days because after 90 days, there's literally nobody playing the game. So in this case, to have a nice buffer, you should be optimizing for 60 days.

Right. So how do I use the LTV prediction in UA? Well, accurate LTV prediction enables game developers to make data-driven decisions in their UA strategies.
And where you can use the LTV prediction. So for budget allocations, to allocate more budget to channels or campaigns that attract obviously higher LTV users.
You can use it for bit optimizations, adjusting bits in advertising platforms or UA channels to target users likely to have higher LTVs. And then we have the tailored marketing messages to segments predicted to generate more revenue, also in retargeting.
So again, we have the methods for LTV prediction. We have the cohort analysis that we discussed, which identifying the patterns in user groups to forecast future behavior.

Then the predictive analytics using statistical models

and machine learning to predict LTV

based on early user behavior indicators.

So then when to use LTV prediction

and when to set up benchmarks?

Well, again, it depends. It depends how I'm saying this is because when I was working at a Pixar Federation, we had LTV predictions, and that was a really nice way how to look at data and optimize campaigns because after a few days, I was able to see the prediction and based on the prediction, if it's going well or not, then I was able to make decisions.
After I left the Big Sur Federation, not every company can actually or not every company has the LTV prediction in place. So you need to set benchmarks because then you need to make decisions.
And you can't wait until the cohorts major over time. You can't wait seven days or 14 days until you make the decision.
So you have to come up with the benchmarks because then you look at the LTV curve, you calculate or just kind of backtrack what should be the day one ROAS, what should be the day three ROAS, what should be the day seven, 14 and 30 ROAS. So you achieve 100% at certain period of time.
You can also check industry benchmarks, use CPIs, LTVs, and payback periods from the industry reports as initial benchmarks. These I wouldn't use, to be honest.
It's always very misleading. All the benchmarks and reports, they just take averages from the averages, and you don't really know if these industry reports use CPI campaigns, if it's up-evened optimized campaigns, or value-optimized campaigns, because these different types of campaigns have very different CPIs, and obviously LTV curves.
But you don't know that when you read the report. So you should definitely talk to your industry peers and also join my brutally honest newsletter where I talk about benchmarks quite a lot.
And obviously, you should check the historical performance, leverage your game's past performance data set, and set realistic benchmarks, which is basically what I mentioned with day one, day three, and day seven. And try to analyze competitors where it's available.
You won't be able to get CPS and LTVs,

but you will be able to get their retention.

You kind of can calculate how should it look like for your game.

It's not an easy exercise,

but it's definitely worth it.

So when do you use LTV prediction?

Well, with new games and updates when the historical data is limited predictive models can help us estimate the LTV it's quite again quite hard if you don't have the proper data set also if you have a portfolio of games

using an LTV prediction

from a different game

it's not really the ideal solution

but

can serve the purpose at least for

certain modeling

and

prediction of

what's going to happen with the game

more about budget allocations

and things like this

Thank you. and prediction of what's going to happen with the game,

more about budget allocations and things like this.

Then in these rapidly changing markets,

the real-time LTV prediction can help you adjust the UA promptly because you need to make decisions quickly.

Then we have the high segmented UA campaigns

and the personalization

and also for re-engagement purposes.

So last few words.

I would say the understanding

and effectively managing the CPI versus LTV

and understanding the whole equation

is essential for the financial

success of your game. By carefully analyzing how CPI influences the UA spending, accurately calculating and predicting the LTV, you make informed decisions based on these metrics.
you always optimize your UA strategies for maximum ROI.

Additionally, setting the appropriate payback periods and balancing benchmarks and predictive analytics, further refine the approach, enduring the sustained growth and profitability in the competitive mobile gaming industry. All of these things that I mentioned are critical for your game success.
And the CPI really, really, CPI LTV equation really makes a difference if you understand properly. Anyway, so that's, I think that we can wrap it up here.
Thank you very much for joining.

Please share, subscribe.

Let me know if you have any feedback.

And see you next time.

Thank you very much.

Bye-bye. Thank you.