So as Sarah just said, my name's Adam Carpenter
And I head the developer business growth team at Google Play.
As mobile developers, one of the most
powerful resources we have available to us is our data.
With data, we can understand
what's happening right now
and we can also look back and try to identify why that's occurred.
Our data lets us identify problems in the game,
find solutions, and ultimately grow our businesses.
Now, in terms of this presentation, I'm gonna to talk through
some patterns, some trends, and some insights
that we've been able to identify with the data at Google Play
in each of these three areas.
Additionally I'm going to share a number of key benchmarks
across them, that you can take back to your teams,
compare to the data from your own data warehouse
and identify whether there are problems in your games
that you can maybe try and solve.
Now, to start off, first thing
I'm gonna focus on retention. And for me, like,
when you think about installs retention,
three of those key KPIs for installs performance,
is, like, the retention number itself, the buyer conversion
from your installed cohorts, and the average revenue per install.
But in my mind, retention's kind of the most fundamental metric for us,
because if you can't retain your players,
you can't make any money.
If you can retain them,
you can always figure out how to make money off of them.
And one other note I want to make for this presentation,
for the purposes of the numbers in this section,
I'm gonna call Day 1 retention as the day of the user's first session start.
And Day 2 is the next day. I know a great many of you
tend to use retention as kind of base zero, so if you're kind of used
to thinking in that nomenclature, just subtract one from each date.
To give you some benchmarks and an understanding of kind of
what retention looks like,
Day 2 retention looks like, across the top couple hundred apps on Play,
38%, that's the median value, and that's kind of a good target to shoot for
and think about it, but if you really wanna
strive for excellence, if you really wanna be in the top decile,
shoot for a Day 2 retention of about 52%.
Another way to think of this though, is that
our Day 2 retention tends to range between 22% to and 52%.
But if we flip it around, it actually means that
48% to 78% of the users
who install and play our games for the first time today won't be back tomorrow.
They just won't come back.
It's really kind of a staggering number
when you think about how much time and effort
we devote to creating our games,
how much money we spend on acquisition.
And one of those questions that I had
when I was working on the developer side is,
how can I improve my retention? Is it good enough?
What I'm hoping to do here is kind of show you some signals that occur in the
very first day, that can help you understand whether
your retention is good, and if it's not,
maybe some things to check for to potentially improve it.
So, when I was on the developer side
and we were looking at installs performance
and their retention on the first day,
generally speaking, what we would look at
is the level that users got to
or the tutorial checkpoint that they reached.
However, those tend to be game specific metrics,
and they're not really useful for comparing across games, because
people level wildly, at wildly different rates depending on what the game is
tutorial checkpoints all vary,
but a metric that we've been able to develop
at Google Play, which is much more of an
apples to apples comparison,
looks at the amount of time that users spend on their first day,
and compares it to the Day 2 retention rates.
So to give you an idea what this kind of looks like
broadly across the ecosystem, and this is for kind of the top 200 IAP games
you end up with a curve that looks like this.
Starts off at, let's say, maybe eighteen-ish percent,
and steadily trends upwards with each successive minute played.
And of course, this kind of makes sense.
There's a reasonable assumption that the more people play on the first day,
the more likely they are to return,
since the more they play, the more likely they are to be having fun.
Where this data gets a bit interesting
is when we actually take those top couple hundred games
and break them out into quartiles.
And so what we're looking at here
is the top couple hundred games broken out
based on their day two retention.
What we can actually see is that
by and large, each of these quartiles exhibits largely the same pattern
after the first ten minutes.
They all curve upwards and to the right, with a steadily decreasing slope.
But it's in the first ten minutes
where the most interesting stuff starts to emerge.
Zooming into that, we can kind of see here
that there's kind of very different shapes and experiences
and patterns that emerge just within that first ten minute window.
The top quartile, the green line here,
they start out strong and just steadily increase.
But the blue line, the second quartile down,
you actually see this short 90 second window,
where retention's largely flat and only then does it begin to pick up.
And that pattern actually increases once you get down to the third quartile.
This particular line here, looking at the quarter of those top games,
that shows retentions largely flat for the fourth minute,
but in a number of games I've looked at, you can actually see
that flat period extend out all the way to ten minutes.
And then once we get into the bottom quartile, the worst performers,
You can actually see a retention decline
across the first few minutes and only then begin to rise.
So, to kind of give you some context
and understand just how impactful
this first five or ten minutes is,
what I wanna look at is the performance of the
top quartile, the green line, the best performers
versus the fourth quartile, the red line here.
And what this graph shows is
the amount of time played on the first day on the x-axis,
and the cumulative percent of lost players on the y-axis.
So, looking at that fourth quartile. they actually lose 46%
of their new installs by the fifth minute
and they lose 58% of their installs by minute ten.
Basically that fourth quartile, and these are all like
really strongly performing games,
half of their players don't even last ten minutes in the game,
in contrast, the top quartile, the best performers,
they see only 17% of their users lost at minute five,
and only 24% of their users lost at minute ten.
Now think of this from your own game and your own perspective
What could you do if you were in the bottom quartile
to move your retention up and effectively
be able to retain over twice as many users
on day two and of all the following retention days.
How much would that help your DAU?
And how much would that improve your revenue?
Two of the anti-patterns that we've been able
to identify in Google Play's data,
or what I've kind of started dubbing "the flats."
What we see here is largely flat retention
that persists for the first five to ten minutes
and only then does it begin to rise.
And the other one that we've identified kind of calling it "the gorge."
Here is where we actually see
retention falling for the first couple of minutes,
and only then beginning to rise.
So these are two kind of patterns
that are worth checking for in your own game data.
Get your data teams, get your data scientist,
get your business analyst to actually pull this,
and see what your game's looking like.
And if you do find that this is occurring
in your game, here's some things to think about,
because all of these have adverse impact
on that first five-to-ten minute experience.
Large secondary downloads,
especially of users on a strong Wi-Fi connection,
are gonna cause them to bounce out of your game.
Likewise, if your tutorials aren't fun,
if they aren't representative of what the game really is
that can alienate players. And additionally,
slow loading times are important to consider,
because your new users are not yet invested in their games.
So if they experience a lot of slow loading screens,
they're more likely to quit out and go to a game that loads in near-real-time.
And finally, I would say think about your lobby experience,
if a user coming out of the tutorial doesn't know how to get back into action,
how to start building their base or how to have fun,
they're probably gonna get confused
and they're probably gonna leave.
Moving on to engagement.
When you think about kind of what most of us want
and I certainly wanted this when I worked on the developer side
is we always want more users, since the more uses we have
gives us greater ability to monetize during our supply-side sales
and our demand side events. It also allows us
if we're showing ads in the game,
to show ads to a significantly great number of users.
And in addition to increasing our DAU,
we also want to maximize our DAU-MAU ratio,
since the higher that is, it means that fewer users
are churning out of the game,
and that they're coming back every single day.
In an ideal world, our DAU would be equal to our MAU,
meaning that nobody would ever churn out of the game,
they'd come back every single day.
But we know that this isn't the case.
Players, veteran players churn out of the game,
new installs churn out because of poor retention.
Even our kind of frequent users, don't come back
each and every day due to a lack of strong return mechanics.
And so there's two kinds of common stickiness metrics
that a lot of developers look at.
And these are like the DAU-WAU ratio and the DAU-MAU ratio and
kind of to give you some ideas
about what you should shoot for,
55% for a DAU-WAU ratio puts you right at the median,
so that's kind of a good starting place.
And 31% for that DAU-MAU ratio
is kind of, again, a good starting place shoot for.
But ideally you wanna be much better than that.
And something to consider when you're looking at it
and trying to improve these relationships or these ratios,
is actually the relationship between the two of them.
This is a plot that I had never done on the developer side,
'cause I didn't really have enough data points.
But in leveraging the Google Play data,
I was able to create this, and what it does is it has
the DAU-WAU ratio on the x-axis, and the DAU-MAU ratio on the y-axis.
And what we can actually see is this very strong linear relationship
between the two. And what this helps us
to understand is that if we want to
increase our monthly active users and our monthly uniques,
we can actually start not by testing changes
that function over an entire month,
but test changes that work in seven-day intervals.
And one of the big advantages of this
is you won't have to wait 28 to 30 days
before we start getting results back,
we can actually see results coming back in seven days.
And effectively, within any given window of time,
we can run four times as many tests
if we're focusing on weekly versus focusing on monthly.
But one of the other interesting things that we can do is
we can actually pull it all the way in.
This is a metric I started to work with at Google Play.
It's something I'd ever use on the developer side.
And what it does is it looks at the percent of today's DAU
who come tomorrow. And it's just trying to
understand, as a loose proxy for churn, how engaged are our users.
Now, if we take that plot from before
and instead put this DAU return percent on the x-axis
and the DAU-WAU ratio on the y-axis,
what we actually see is that, again,
it's positive and linear.
And what this kind of tells us is that
if we wanna increase our stickiness factors,
the most important thing for us to do
is start focusing at the daily rate.
It's hugely important and it's advantageous
because if you think about it,
weekly versus monthly, you can run four times as many tests.
Daily versus monthly, you can run, depending on how
you do your month, 28 or 30 times as many tests.
And so, fundamentally, the most important
thing you can do to boost your active users
and your engagement, is focus on tomorrow,
Focus on what is actually going to bring your users back
and keep them engaged.
Now, it's kind of important to understand, to think about
how do you know if you're doing well enough?
Especially kind of this new metric that
most of you probably haven't experienced.
And so here's kind of some target ratios
looking across the entire top couple hundred apps, couple hundred IAP apps,
that you can take back and compare.
And, you know, that 77% number,
that's kind of an important one to shoot for,
but really shoot for that high side 84%.
Try to figure out how do you get your users
to come back each and every day.
And a few ideas, a few things that are worth considering,
are your return mechanics, your social systems and your push notifications.
Because all of these have a huge impact.
In case of return mechanics, let's really celebrate them.
Let's make sure our users are totally aware of them,
and that the return mechanics are aspirational,
that they're rewarding, that users are really strongly incentivized,
to come back each and every day to receive them.
In the case of the social systems, let's really, really leverage them.
Because if you think about it, the games that have
very strong clan systems, alliances, good chat groups,
substantial friends benefits, in those games effectively
your players do the work for you.
If I'm a player and I have friends in the game who are relying on me,
it's in my best interest to be there.
And likewise, if they're relying on me,
I wanna be there for them.
So think about your social systems
and whether you can strengthen the existing ones
or add new ones and let your players do the work for you.
And finally, on the push notification side,
think about how you can personalize them.
How can you create personalized push notifications targeted just towards
an individual user or a segment of users,
as opposed to effectively spamming all users at all hours of the day?
Think about trying to target push notifications around
the normal time of day that a user plays,
'cause that's going to be much more compelling and much more impactful.
Now, the last thing, I want to
kind of focus on monetization.
And this, what I'm gonna be talking about,
it's a metric that I used really successfully and really effectively
when it's working on the developer side.
And what it does, what it will do is
it's gonna help us understand and answer the question of, are our payers healthy?
Generically speaking, the industry has
kind of reference singular data points
made claims to the effect of only 2% of a payers actually pay.
However, that's kind of horribly misleading as a statement,
because that's, generally speaking,
that's gonna be focused more on that daily buyer conversion number,
it takes no, it takes into account, or doesn't take into account,
the percentage of users who are
willing to only open their wallets
on a weekly, monthly or even quarterly basis.
And so what you can kind of do to take a look at,
to understand your payers' health,
is a ratio that we call PAU-DAU.
And what it is, it's the percent of your daily active users,
the users are who active today,
that have paid within the preceding x-day period.
In terms of the time frame that I usually like to look at,
the ones I found that we're most impactful
were the 7, 30, 90, and one-year time frames.
And these are extremely valuable because they allowed me
to do things like compare the users who purchase within a week
versus a month, or the quarter versus a year.
And the other thing that's really important
and really impactful with PAU-DAU,
since it's measurement of the users who have already paid in your game.
it kind of helps to you to understand
the audience in your game that's willing to pay.
Because we're always trying to convert first-time users,
but then once they're converted, we need to create incentives
to actually get them to continue
to open their wallet again on a regular basis.
So what I'm gonna talk through here
is an example game. And kind of show you
some trends and some patterns in the daily buyer conversion,
and how they've impacted various PAU-DAU ratios.
There's three events that I wanna call out on this chart.
And the first is this medium duration event that occurred basically
at the end of January to the beginning of February,
maybe a two, two and a half, three week period.
And what we saw here is that this event provided
a substantial increase to the daily buyer conversion.
The other two events are Event B and C
Each of these occurred for about a day.
They were sales and they created a substantial 130% increase
in that daily buyer conversion.
Both, or all three of these are really, really effective,
but it's interesting to see what they did with the PAU-DAU ratios.
So, looking at the seven day ratio,
what we can actually see is that Event A
was extremely effective at monetizing a very wide number of users.
And effectively, this event, it actually caused a lot of
non-buyers or occasional buyers,
to pay and open their wallets during that time.
Versus Events B and C, which got significant increases
to buyer conversion on that date,
but it didn't do much to incentivize the occasional buyers
to open their wallets and pay.
So something that you can think about
as you monitor the performance of the game,
of the supply-side sales and the demand side events in your game,
try to understand who is actually paying during those periods.
What are the motivating factors?
What are the incentives?
What is kind of driving those players
to open their wallets?
And you can also, not just the seven-day, but I was mentioning
you have the 30-day that you can look at,
you have the 90-day that you can look at,
you have the one-year or even the lifetime,
but just always try to understand
when you're running sales and you're running events
what incentives are they creating for players? How are they,
are they aspirational? Are they desirable?
Are they excellent value-for-money?
But, what's kind of that motivation?
So, in terms of the PAU-DAU ratios, I can't give you
explicit values that you could take back,
but what I can give you is standardized ratios.
The reason is that PAU-DAU is intimately tied to your daily buyer conversion,
and some games will have a daily buyer conversion of half a percent,
some games with sophisticated monetization models
it might be as much as 3%.
But what you can do is take your daily buyer conversion,
and if you look at your 7 day PAU-DAU value,
it should be four times higher.
The 30-day value should be eight times higher,
the 90 days should be twelve times.
And if you look at that rolling 365 day window,
it should be about fifteen times higher than your daily buyer conversion.
Another advantage of, kind of PAU-DAU
is the inter-period comparisons within your game.
Take a look at, what this is, is looking at the daily buyer conversion.
in this case the hypothetical one and a half percent
and the scaling factors from the previous slide.
What we would see here is there's a reasonable expectation
that 6% of this games DAU
would have paid somewhere between 31 and 90 days ago.
In an incremental 5% of the DAU,
you would have paid somewhere between 91 and 365 days ago.
If this was your game, that would be 11% of your DAU.
So think about, how can you actually incentivize these players?
How can you shift those effectively quarterly buyers to being monthly?
Or, how can you reactivate those lapsed buyers?
Now, let's say you do kind of start monitoring a PAU-DAU-type metric
and you are
trying to understand if there's opportunities, if there's room for growth,
here's a few things to consider.
Let's say you're a game that does run supply-side offers,
figure out whether you can better segment them.
Can you put constraints on the amount that people can purchase?
Can you segment by average transaction value
and in doing so,
limit the amount of assets that users can get
so they can't stockpile and horde,
and therefore have to purchase more frequently?
Also think about it, if you see a large percentage of your users
haven't paid in an extended amount of time,
create lapse buyer offers for them,
and almost like, treat them like starter packs.
Because the primary goal
if somebody hasn't paid between 91 and 365 days ago,
is to provide them with a compelling value proposition
to get them to open their wallet, perceive good value,
and be willing to pay again.
And then for a lot of the users who are active,
think about whether you can personalize the events
that you're providing to them in the game.
And do so in a way that causes them
to drain assets quicker and therefore
is strongly incentivized to open their wallets and pay.
Now, just kind of wrap up real quick and revisit each of these sections.
When we're looking at retention,
that first five-to-ten minute period is absolutely essential
and it has such a critical effect on your Day 2 retention
and all the following days.
So I strongly suggests going back
and generating metrics like that
Day 1 minutes played versus Day 2 retention,
and see what it looks like in your game.
And on the engagement side, think about how you can focus on tomorrow.
How can you create really compelling
engagement mechanics, return mechanics,
social systems, that will bring players back into the game,
make them want to return each and every day?
And finally, on the monetization front,
think about you PAU-DAU power ratios and how they compare
to the daily buyer conversion, and use that to
generate insights as to where users are spending,
how frequently they're opening their wallet,
and the size of your player base who's only paid once and a quarter,
or haven't paid in up to a year.
Thank you very much.