ALYSSA PEREZ: Hi, everyone.
Thanks for joining us today for our series,
A KPI Guide for Google Play Apps and Games.
My name is Alyssa Perez, and I'm on the developer business
growth team in Google Play.
What my team does is leverage Google Play data
to help partners like you identify opportunities, improve
performance, and grow your business.
Prior to joining Google, I worked on many mobile game
products throughout my career.
I held various titles from business analytics manager
to economy and monetization strategist and senior product
manager.
But for today's webinar, we're going
to do an introduction to some of the most common metrics
that mobile developers are tracking.
So if you're new to mobile analytics
or looking for a refresher on key performance indicators
you should track, this will be a great way
to be brought up to speed on the metrics, how they're related,
and how they're calculated.
And for those of you that are familiar with some of these
already, we'll be going into some examples of how
to draw business insights from them and some guiding questions
to point you in the right direction
to understand your business's performance.
The reason we're sharing this framework
with you is because we believe that
having a strong grasp of these KPIs and how they're related
will help drive your product roadmap and grow your business.
And before we start, we also want
to say thank you to everyone who submitted a question when
registering.
You'll see a Q&A section on the page,
so please feel free to share your feedback, comments,
and questions at any time.
This is the metrics tree that we use at Google Play.
It's based all around daily revenue
because we all know we want to drive a strong bottom line.
We're going to go through this framework together, looking
at how the metrics are related, how we calculate these,
and some examples of using them.
We do want to note that not every developer will
look at their title's performance on a daily level.
So if you are, for example, more of a weekly or monthly
destination, you might set up your tree differently
to be more meaningful for the cohort of users
that are most important to you.
But we'd still love to hear your thoughts and feedback
in the chat, so feel free to share
how you look at these KPIs for your business,
especially if you see some that aren't present here
or may be slightly different.
But let's jump right in.
Let's start with daily revenue.
Obviously, we're all tracking this
because we want to understand how much money our app is
bringing in.
But the way that you can calculate this,
or the two levers that are driving it,
is you can multiply DAU, or our Daily Active Users,
by the ARPDAU, or Average Revenue Per Daily Active User.
So I have two levers there that I can
pull to drive daily revenue.
I can either increase my user base
or I can increase how much revenue I'm getting out
of each user in my title.
But we're going to start with the daily active users
branch of the tree, and then we're
going to work our way down and go
through each of these metrics and talk
about how they're related.
So daily active users--
we can grow our user base by doing two things.
We can get new users to come in or we can have
more users return to our title.
So in this case, daily active users
equates to the sum of the new installs
we see coming into our title and the returning users.
So going one step down, let's talk about new installs.
Try and answer the question of, how successful
are we at getting new users into our title?
In this case, you have two levers that you can pull.
You may have organic installs, or what
we call deep link installs.
I want to call out the difference in nomenclature
here.
Deep link is not a common term that I ever
heard when I was on the developer side.
But at Google Play, it's the only insight
we have into things that are happening outside of the store.
So if you are a mobile developer,
you probably have a deeper insight
into where your new installs are coming from.
Meaning if you're doing paid acquisition,
you can probably track which channel they're coming from.
However, at Google, because we don't have that insight,
we simply group anything that's coming
from outside of the Play Store into the deep link bucket.
So let's talk about how you would use these child
metrics, like organic and deep link installs,
to understand what are the insights into my business.
So starting with organic--
if you happen to get featured on the store, that's great.
It's a great way to get organic traffic.
But you can also think through what type of virality
your title has.
So that goes to, what type of social virality is there?
Is there a big hype that's happening?
Or do you have a social feature that's
allowing people to invite others or bring them in?
So again, you can use your own title,
and the features within that, to drive organic traffic.
For deep link, it's really important
to think about your acquisition strategy.
In terms of deep link conversion,
you want to think about testing on the Google Play Store
listing.
Is there a test you can do on the icon, the wording?
Even the screenshots that you have available
can be really powerful to drive conversion for users
who are on the store or that you're driving to the store.
On top of that, you want to make sure that any ads you do
or anything like that provide the clear value that your app
would provide to the user in order to optimize the deep link
installs you have coming in.
So in this case, new installs is the sum of organic installs
and deep link installs.
But now let's talk about the other side, returning users.
How successful are we at getting new users to come back?
So it's great to have a large top of the funnel pushing
a lot of people into the app.
But if you're not retaining them,
you may be wasting marketing dollars.
So it's really important to understand
the retention of the new users that you're bringing in.
So the way that we calculate returning users
is by multiplying the percentage of users
that return on a certain day by the cohort size
from the previous day active.
So for example, if you're looking at D2 retention,
you would look at, what percentage of the users
returned on their second day multiplied
by the size of the install cohort on the very first day.
So in this case, you're going to actually multiply and then sum,
and that's because you're going to have users across varying
ages in the game.
You may have users who are brand new, but you also
we have users who have been in your title for months--
two months, three months, even sometimes a year,
depending on how mature your title is.
So again, you're going to multiply those
by each install cohort and then sum across them
to get the returning users.
Here, I like to think about returning users in another way,
as well.
It also includes reactivated users.
So not only do you want to understand early retention-- so
for new users that are coming in,
really think through what that onboarding looks like.
Do you have any long loading times,
large secondary downloads?
Is your lobby really clear or is it confusing?
That can cause people to bounce right out after they come in.
But also, you can understand which channel is driving.
So now we're linking the two sides of this tree, so DAU.
You may see stronger retention from organic users
or organic installs, and that's because they're naturally
interested in your title.
So it is important to understand the relationship across these
and break out retention of those returning users by the channel
that they came in on, as well.
As you look forward for retention-- so
we talked a little bit about early retention.
I also want to talk about later retention.
If your users are staying around for the first month,
but after the first month, you're seeing very,
very low retention-- it drops off greatly--
some things to consider are the types of content
that you have available.
How much is available to the actual user in order
to consume?
Am I continuing to see increased engagement from the users that
are sticking around?
A good leading indicator here may
be that if you see engagement dropping off
for your more veteran users, that
may be a good sign that they may be more likely to churn
because they're no longer seeing value
and engaging at a lower rate.
So really think through what type of return mechanics
you have that are bringing them back, as well as making sure
that you're providing enough content for your users.
So now we're going to look at the other branch.
And this is looking at the ARPDAU, or the Average Revenue
Per Daily Active User.
And I want to call out that some of these metrics
are a bit more targeted towards the in-app purchase
monetization model, especially because we are
looking at daily granularity.
But the idea or framework across the metrics in these smaller
clusters is going to be the same for other monetization models.
We will also have a separate webinar
that goes into more detail on KPIs
around subscriptions in the near future.
But let's start with average revenue per daily active user.
So this is helping you answer the question of,
how well am I monetizing my entire user base?
And what this equates to is the multiplication
of your daily buyer conversion multiplied by your ARPPU,
or your Average Revenue Per Paying User.
So I like to think of this metric
as a primary monetization metric because it
helps you understand how much revenue you're able to extract
out of your entire user base.
But more importantly, we can think
of daily conversion as the main primary metric
that we want to drive, trying to understand what percentage
of my active users are seeing enough value in my title
to spend on a given day.
In this case, you have two levers
to pull to drive daily buyer conversion.
You can either pull new buyer conversion, which
means you're trying to get more first-time buyers
into your title, or repeat buyer conversion, which in that case
would be someone has paid before and they're
willing to pay again.
So daily buyer conversion equates
to the sum of new buyer conversion and repeat buyer
conversion.
So when you're thinking about buyer percentage,
some things to consider are, do you
have some type of starter pack or promotion for new users
in order to get them onto that purchase cycle?
Can you get them to purchase once
in order to figure out how to get them to purchase again?
We normally see in mobile, especially for in-app purchase
monetization models, that once you can convert someone,
it's much easier to continue to convert them
because they've seen value and they've already invested,
which means they're more likely to retain and continue
to spend.
For repeat buyer conversion, again, that
goes to being able to target what the user was buying
at what price point, what they were interested in,
what was the trigger.
So really thinking through, what is that buyer interested in
and how can I create a special offer for them
to purchase again?
But you can also use this as a leading
indicator for lapsed buyers.
And what I mean by lapsed buyers is somebody who has paid once
but maybe hasn't paid in an extended period of time.
You can use this understanding, again,
to target them maybe with a lower price point that
would again get them back onto the pay or purchase
cycle you would expect to see.
Let's shift gears into ARPPU, or Average Revenue
Per Paying User.
I know we talked about daily conversion
being a primary metric.
This is a metric that we like to call the secondary monetization
metric, and that's because it's looking
at how much money you're able to get out of your buyer base.
And as we all know, on mobile, we probably
see a very small percentage of our active users
actually paying in our title.
So therefore, optimizing this metric,
or focusing on this by itself, may not
be long-term sustainable for your business.
So average revenue per paying user
equates to the total revenue of the day divided
by the unique buyers in a given day.
And you can get to it from the two child metrics
by multiplying them together.
So you would multiply average transaction value
by the average number of transactions your buyers are
making in a day.
In this case, I do want to point out
that if you have a subscription model,
you're probably not going to be tracking transactions per buyer
and that's because you're on a monthly SKU, a weekly SKU where
only one transaction is happening.
But if you are an IAP and you have multiple currencies
available in your title, it is important to track this metric,
because usually, the more currencies you
have for sale for real money can impact this metric's
average transaction value.
So that goes to trying to find your optimal pricing
for your title.
And you want to balance not only the optimal pricing in order
to get the maximum value per transaction,
but also maximizing conversion.
So now let's talk about these two metrics in relation.
Daily buyer conversion is obviously
related to average revenue per paying user.
One thing we commonly see is that as daily buyer conversion
is growing for a title, you may expect
that average revenue per paying user is stabilized
or dropping off.
And depending on how many price points
you're offering in your title, that
could be because you're getting a larger number of users
to pay a smaller amount as opposed
to being heavily reliant on a small number of users paying
a large amount.
So if we go through an example of,
is it better to drive my average revenue per daily active user
up by increasing conversion or average revenue per paying
user?
It's a lifelong question.
I think it's one that takes a lot of balancing.
But in our opinion at Google Play,
it's really important to focus on that primary metric.
It's much healthier to have a hundred $1 buyers than it
is to have one $100 payer.
And that's because your risk goes down.
If I lose one $1 payer in that hundred-buyer pool,
I only lose 1% of my revenue, versus if I lose that one
large buyer, it's going to have a larger
impact on my bottom line.
So it really is about finding the optimal balance
between how many users can you convert and show them
value for paying in your title, but also
optimizing what is the right price point for each user.
Now that we've gone through the tree,
let's take a look at the full visual again.
So here you can see it in its entirety,
and I really want to explain, now
that we've gone through each of the clusters,
how to read it in its entirety.
So every metric is going to have a parent.
And if two metrics have the same parent,
you're going to multiply them together in order
to get the parent value.
The only time that this isn't true--
and we went through these examples--
is when there's a dotted line around the metric.
So for example, daily active users
equates to the sum of new installs and returning users.
So that dotted line is an indicator
of summing the children in order to get to the parent value, as
opposed to multiply them.
So let's jump into Q&A. So we have a question coming in.
Should I care more about how much paying users are spending
or how many people are paying at all?
Is one more important than the other?
It's a really great question.
I did kind of touch on it a little bit,
but it is finding the optimal balance for your title
in terms of how many users are converting
and how much you're getting out of those users.
In our opinion, it goes back to that primary and secondary
metric.
It's always safer to invest in trying to figure out
how to get more breadth and more of your user base spending,
as opposed to relying on a small subset of users
that are paying a lot of money.
So it's up to you, but we like to say,
focus on the primary metrics.
And once you've optimized how many users
can spend in your title, you can then
figure out how to optimize the secondary metrics, or average
revenue per paying user.
We also have another question.
Does time of day matter, and for which ones?
So if you're tracking any of these metrics in real time--
I'll use DAU, or Daily Active Users, as the biggest example
here--
it is important to take into account which markets
you're heavily prevalent in.
So for example, if you are a developer based out of the US
but you are targeting APAC markets,
it is important to understand the time
difference between there.
You may see very, very low daily active user counts
during our middle of the day, but that's
because it is the middle of the night in APAC.
So it is important to take into account,
which markets you're targeting or are making up
a majority of your user base.
Do you recommend calculating monthly users
as 30 days or 28 days?
This is a really, really great question.
It's one that I've worked at companies that we've done both,
so I think as long as you're consistent across all
of your metrics, it's up to you.
I have done some where it is calendar month.
In that case, it is a little bit more
difficult to see daily performance because you're
waiting for the end of the month to occur before you actually
see your true MAU for, for example, October.
But if you do a rolling 28 days, it
may be easier to see quick changes in any of the metrics.
So it's up to you guys, but as long
as you're consistent across what that definition is,
you'll be fine.
The next question-- how does the right side of the tree
look different for subscriptions?
Another great question.
I did highlight, we are going to have a webinar
fully on subscriptions monetization key performance
indicators.
But just to give you an idea, there
is still that optimal balance of how many users are converting
to a subscriber, what that price point is,
and that price point can be tested on the Google Play
Store.
But in that case, for subscriptions, you also
have to take into account subscription renewal.
So you can think of that as return buyer rate,
so how many users are returning to buy.
You really have to focus on, how can I retain as many
of those subscribers each period when their SKU is expiring?
Feel free to stick around.
We'll be available to answer any questions, comments,
or feedback via chat for the next 30 minutes.
But thank you for joining the Intro to Metrics webinar.
We will have additional webinars that's
doing a deep dive on acquisition and engagement KPIs.
And you can also check out our Medium posts
and our previous webinars on Bridging
the Gap, Games Approaches to Engagement and Monetization.
Thank you.