[MUSIC PLAYING] SUMIT CHANDEL: Hello
there, and welcome to the Firebase Developer's
Guide to Google Analytics. Why the Firebase Developer's
Guide to Google Analytics? Well, chances are, if you're
a longtime firebase developer and used analytics
for your apps before, you've looked at your analytics
data in the Firebase console. And the console is
a great place to get a quick, at-a-glance view
of how your app is doing and dig into some conversion
and event reporting breakdowns. But did you know that if you
created your Firebase project after August 2019,
your analytics data is available in both the
Firebase console and the Google Analytics console as well? That means that you can use
a lot of the deeper analysis and reporting tools in Google
Analytics via the new App + Web property for your
Firebase application data. What we found is that
not a lot of developers were aware of this. And even if they were,
they either didn't know what tools
were available in GA or weren't sure about how
to use them effectively. And thus, we present to you
the Firebase Developer's Guide to Google Analytics. This is our attempt at helping
Firebase developers get more familiar and more
productive with the suite of Google Analytics tools
to more deeply understand their Firebase app data. In this video, we'll be
covering the following features in the GA console. First, the Comparison
Analysis tool, which allows you to make
side by side comparisons of different user
groups based on things like platform, language,
or more audience criteria that you can customize. We'll then present the
Segment Overlap Analysis tool, which provides a visual
and tabularized form of data to more deeply understand
your user segments and find out where they overlap. We're also going to
cover the Funnel Analysis Technique available in GA. For Firebase
developers out there who have used funnels
in the Firebase console before but we're less than
happy when they found out that they were open
funnels, you'll be glad to know that
the console provides advanced funnel analyses that
use closed funnels by defaults. More on that a bit later. For now, I think the
most helpful thing would be do take a quick tour
of the Google Analytics console to get a feel for the
tools available there and where to find them. Now, if only I had someone
who is an expert at Google Analytics to give me this tour. KEVIN LAM: Oh, hey, Sumit. SUMIT CHANDEL: Oh, hey, Kevin. For those of you who
don't know, Kevin is the product manager for
Google Analytics for Firebase. He's literally the
best person to give us a tour of Google Analytics. Thanks for dropping in, Kevin. KEVIN LAM: Yeah, no problem. So some features in the
Google Analytics console are exactly the
same as what you'd find in the Firebase console. So you may recognize a lot of
similarities between the two interfaces. But what's unique about what
you'd find in Google Analytics is that it offers a much more
advanced set of analysis tools to help you get deeper insights
into who your users are and how they're using your app. Now, let's take a look. This is Bingo Blast, a bingo
game available on both the Play Store and the App Store. And if you like playing
bingo, definitely feel free to give it a try. Now, I'm currently on the home
page for my Bingo Blast Google Analytics property. Here I can take a
quick overview of how my app is doing by looking
at some key metrics, like user engagement
and retention, which countries my
users are coming from, and which events they're
logging most frequently. There are also cards that
show the sources and mediums from which my users
are finding my app, as well as a revenue
breakdown below. Now, this home page looks
very similar to the Analytics dashboard in Firebase,
except with a new interface and more information
with easier to access filtering options and
reporting sections. But if you really miss the
Firebase reporting view, you can actually switch
to it by clicking on the dropdown at the
top left of the console and selecting
Firebase reporting. But let's stick to the Google
Analytics reporting interface for now to show you some of the
cleaner and easier to access features I mentioned. For example, you can easily
adjust the date ranges for the reporting data you
want to see in each card and jump into more
detailed explorer reports by clicking on the
link within the card. Going back up to
the top, there's also a unique
feature here called Insights, which provides
automatically generated insights into your data. You can think of these as the
headlines telling you what's going on with your app. It's our way of
surfacing up information so you don't have
to do it yourself. Now, let's take a look at
some of the other sections of the Google Analytics console. Right under the
home screen, there's the Realtime view, which
is similar to the stream view in Firebase. It provides a realtime view of
how many users have been active in the last 30 minutes, and
where in the world and which online networks
they're coming from, and what events they're
triggering in your app. Under that is the
Report section. This is the grouping of standard
Google Analytics reports about your users and their
demographics, like country, age, gender, and interests. There is also a Behavior
report that gives you more information about
your users' behaviors, like which screens they're
spending the most time on. And below that is a
Technology report, which provides a
breakdown of your data by platform, app versions,
and device models. We also released a
new set of reports just for game
developers that you can access using the
switcher, but for this video, let's stick to the
default. Moving on, we have the Events
section, which provides a detailed breakdown
of the conversions and events that your users are
generating in your app. These reports are almost
identical to the ones in Firebase. And below that is the Explore
section of Google Analytics. This is where some of the deep
analysis and data exploration tools of Google Analytics live. This will be the
most interesting area to explore in this
video, and we'll be taking a deeper look at some
of the features in a moment. The final section of the console
is the Configure section. This is where you can configure
your analytics audiences, check on your custom
user properties, and access Debug view to
see analytics events coming in live while developing and
testing your application. Now, each of these features
is exactly the same as the one you'd find
in the Firebase console, and can be configured
here or within Firebase. Now, before we go exploring
some of the deep analysis tools in the Explore
section, there's a new feature to call
out right here that isn't available in Firebase. And that's the Comparisons tool. But before I demonstrate how
to use it in Google Analytics, it'd be really useful to
get an overview of what it's intended to do. So hey, Sumit, mind giving us
a breakdown of the Comparisons feature? SUMIT CHANDEL: Well
certainly, Kevin. So the Comparison tool lets you,
well, compare different user segments to each other. It does this by allowing you to
select different dimensions you want to include, or exclude,
for the user groups you want to select in your comparisons. And what do we
mean by dimensions? Well, it's an
intentionally general term, because it can include a lot
of different kinds of selection criteria to define
the user group you want to use in your comparison. For example, one dimension
you can select from is the automatically
collected user properties in your analytics data, such
as demographics, like age, country, and language. You can also select from
custom user properties you've set up in your app. So let's say you have a
premium users in your app, or maybe you're collecting
the amount of virtual coins your users have, like
we do in Bingo Blast. You can set custom
user properties to mark your premium users or
gamers with more than 10,000 virtual coins, and include that
dimension in your comparison as well. There are also device dimensions
to select device models, brands, or app versions. And an acquisition dimension,
where you can select campaign tracking dimension values, like
campaign sources or mediums, or ad campaign dimensions,
like the ad network your users came in from. Hey, Kevin, can you show us
how it's done in the console? KEVIN LAM: For sure. If you look up top
on the home page, you'll see that there's
a default applied labeled All Users. And right next to
that is the Add a Comparison button where I
can start creating and applying new comparisons. Now, let's say I want to learn
more about my Bingo Blast players and to see a
breakdown of which ones are most active by
different age groups, so that I can figure out where
my biggest opportunities are and where my most
active users are. I'll start by clicking
on Add Comparison and select the Age dimension. For my first bucket
of users, I want to look at those who are
between the ages of 18 to 34, so I'll select these
two ranges here. Now, if I wanted
to, I could also add more dimensions to this,
like gender or language. But for now, let's keep it
simple and stick to age. So I'll hit Apply, and then
after a couple seconds, you can see that the
comparison is showing up in the Users chart here, as well
as all the additional charts and cards below. But we still need to add
a couple more comparisons so that we can actually
gain meaningful insights from this data. So let's add another
comparison for age. I'll click on Add and add
the Age dimension once more. This time, I'll select users
between the ages of 35 to 54. Then I'll click on Apply. And now this new
comparison is also showing up and being applied to
my reporting charts and data. Now, let's add one
more comparison for the last age group
I want to include. And that will be for users
between the ages of 55 and above. I'll click on Add
Comparison one last time, select the Age dimension,
and pick the age range 55 to 64 and 65 and above. Then when I click on OK
and apply this comparison, we'll now see that
the reporting data has the final comparison
added in, and I'm ready to dig into the data. So I can see some
useful data here. For example, I can see that the
number of users in the 35 to 54 age group is about 65% larger
than that of my 18 to 34 group. And my 54 and above
group is almost 71% larger than that of my 18 to 34. As I scroll down to the
breakdown by countries, I can see that the top seven for
each comparison are different. And especially so for
my age 18 to 34 group. And aside from that,
the top three countries are almost the same across
each comparison group. Scrolling farther down
to the Revenue section, I can see that the revenue
distributions across platforms by percentage are similar
across age groups, with slightly more
of the revenue coming from iOS users for my age
55 and over user group. Another interesting insight
is if I hover over the Android portion of the revenue
for each user group, I can see that although
my age 55 and over group accounts for the most
number of users, those who are in the 35
to 54 group account for a much larger portion of
my total revenue on Android. Using these insights
in combination can help inform where I should
target my growth campaigns. From this analysis, I learned
which groups are lagging and which groups are
generating the most revenue. From there, I can
actually consider if I want to use a growth
campaign to increase the number of users
in my lagging group or to further engage the
highest revenue generating groups instead. Now, another useful feature
about the Comparisons tool is that once you've
applied it, you can see the comparisons
throughout other parts of the product. For example, if
I go to the Users or the Demographics report,
I can see the comparisons being applied there as well. And that allows me to
compare and contrast regardless of where I
am in Google Analytics. Now that we've
finished this tour and checked out the
Comparisons tool, it's time to dig into
the Explore section, and to look at the
powerful techniques to help me uncover
insights about my app. There are quite a few
in here, and rather than covering them all, let's
focus on a couple that we think are most important,
or most relevant, to Firebase developers. Now first, let's talk
about funnel analysis. Hey, Sumit, I
heard that this was particularly important
to Firebase developers. Can you tell us a bit more? SUMIT CHANDEL: Ah, funnels. Yes, if there is one analytics
feature in the Firebase console that I've heard plenty of
feedback and feature requests about, it is funnels. So here's the thing,
funnels actually exist in the Firebase
console, but they're what are called open funnels. What's an open funnel? Well, I'll see your question
and raise you another and ask, what's a
funnel in general? For those of you
who don't know, I thought it might be helpful to
cover the concept of funnels, since not everyone has
encountered them before. So a funnel looks like this. If you've ever taken a
chemistry class before, it probably looks
familiar to you. The idea behind the
funnel in Analytics is similar to how a funnel
works in physical reality. As you pour in a
large volume of liquid at the top of the
funnel, it starts to narrow down as
it travels down, and gets smaller and
smaller towards the bottom. In Analytics, we use
the concept of funnels is to answer questions
like, out of all the users who have started a game in Bingo
Blast, which ones leveled up to the next stage? And out of those,
which ones moved on to make an in-app purchase? Similar to a physical
funnel, the number of users typically narrows down
as you travel further through the funnel. That is, if we're dealing
with closed files. There's also something
called open funnels, which present data bit differently. Each part of an open funnel
represents an event, just like with the closed funnel. However, in an open
funnel, the number of users counted in
each part of the funnel are out of the total number of
users in your app, and not just the number of users who
completed the previous step of the funnel. That means that
in an open funnel, you might have a
distribution like this for the same session start,
level up, and in-app purchase events we just saw. Notice how the
level up events is greater than the number
of session start events. Isn't that weird? How could there be more users
who logged a level up event than the number of
users who even opened the app in the first place? The answer is, we're
looking at open funnels. Since this is an open
funnel, the count of users who logged
level up events in the second part
of the funnel is out of the total number
of users in your app, not the number of
users who started a session in the first step. Combine that with the fact that
players can log multiple level up events and you end up
with a situation where the second part of the funnel
is larger than the first, and you have a conversion
rate higher than 100%. Now by default, funnels
in the Firebase console are open funnels. And this is something that
a lot of Firebase developers were sometimes confused about. In fact, we receive
plenty of feedback to provide closed funnels
to analyze Analytics data, since developers typically
expect funnels to be closed. But while these aren't available
in the Firebase console, they are available
in the GA console. Also, in addition to that,
funnel analysis reports in Google Analytics
have a lot more features that can get into much
more detailed breakdowns of your data. Let's take a look. OK, so we're back
in the console, and we're about to take a
look at the first analysis tool in the Explore section. So I'll expand the
Analysis menu here and then select the Funnel analysis
technique from the selection below. Now I'll create a
new funnel analysis by clicking on the plus
button right up here, and then selecting
Funnel analysis. OK, I'm ready to start. So let's take a look
at the previous example I was looking at. Let's say I want to perform
a funnel analysis to answer the question, how many users
who start a game session then go on to level
up, and then go on to making a purchase
in Bingo Blast? At the basic level,
what this amounts to is creating a
series of steps, with each step building
a part of the funnel. The first step will be a
user starting a session. The second step will be
triggering a level up event. And the last step will be
completing an in-app purchase. Now, at first glance, there's
a lot of stuff going on in this interface, and we'll
take a look at it in a moment, but for these basic steps
to create our first funnel, all we need to look at is the
Steps section highlighted here. So let me edit the steps
for this funnel analysis. If you've used the audience
builder in Analytics before, you'll notice that this
interface is very similar. First thing up top is to name
the first step of my funnel. I'll put the name,
Session Start, and now just below that I can select
the condition for this step. For the condition, I just want
to select the Session Start event that gets logged
when a user starts playing Bingo Blast from
the Events menu here. So I'll select Events, and
scroll down to Session Start, and select it from the menu. Now already, there are
a couple of cool things that happened here. First, see that Summary
card at the right? This is actually updated
with the total number of users and events
counted in my funnel based on current conditions. It will also automatically
update as I add more. This is a unique feature in
the Google Analytics console that isn't available
in the Firebase console when creating a new funnel. And there's also
another cool thing that happened here as well. You might have noticed that
when I selected my condition, there are way more than just
the events to choose from. I could also create conditions
based on dimensions. These include
automatically collected user properties, like age,
country, and device category, as well as custom user
properties registered to my game. So let's say I wanted to take
a look at a specific age group and apply that to the condition
to the first step of my funnel, for example, users in
the 18 to 34 age group. Well, I can just add another
condition, select Dimensions, and then my automatically
collected user properties, and then select Age. I'll then select the 18 to
24 and 25 to 34 age groups as values to include,
and it's done. My funnel is now including
all users within the 18 to 34 age group who have logged
a Session Start event. Also note that the
Summary card to the right has been updated with
the user and events counts based on the
newly added condition. The ability to select
more than just events when creating a
funnel is something unique to Google
Analytics, and can come in handy for more
fine grained analysis of your Firebase
app analytics data. To continue with
my example, though, I'll remove the age
condition and continue adding on my second step in
my funnel, which I'll name, Leveled Up. Also, you'll notice
that just above that I have an option to choose
how this second step should be counted. I can choose if I
want to count it if it occurs anytime
after the first step or if it occurs immediately
after the first step. I can also select
the time window for how long after
the first step I want the second step to occur. In this case, I want
to include users who leveled up anytime
after their session started, so I'll set step 2 to count if
it indirectly follows step 1, and won't specify a specific
time window for when step 2 occurred. Now I'll add the condition,
which is basically when a user has logged a level
up event, indicating that they have indeed leveled up. So I'll click on
Add new condition, select Events, scroll down
to the level up event, and then select it. Notice how after
I've selected it, the Summary card
updates once again at the right of the screen. Now I'm ready to add the
final step of my funnel, which is the action of making a
purchase in Bingo Blast. So I'll click on
Add step once again and name the step Made Purchase. This step should be
counted no matter when it happens after the
level up event in step 2, so I'll leave this set as
is indirectly followed by, and leave the time
window unspecified. Now I'll add the
condition, which is when a user has
made a purchase and logged an in-app
purchase event. So I'll click on
Add new condition, select the Events category,
and then scroll down and select the in-app
purchase event. OK, my funnel is complete,
and the Summary card has been updated
to show that I have about 100 users who have
taken all the steps defined in my funnel. Now that my funnel
is ready, I'll hit Apply at the
top right, and I'm taken to the Funnel
Analysis view where I can start
digging into the data. First, let's take a look at
the funnel graph analyses. So I can see that I have
about 6,600 active users who started a game session. Among those, about 3,200
have leveled up in the game. That's about a 47.7%
completion rate, which I can see up top up here. And it's also a drop of about
3,500 users between starting a session and leveling up. For the 3,200 users who did
level up, about 100 of them actually made a purchase,
leading to a 3.1% completion rate, and a drop of
about 3,000 users between leveling up
and making a purchase. Just below the funnel graph is
a tabular breakdown of my data. I can see the number of
users included in each step, the completion rate going
from one step to the next, and the number of abandonments
and the abandonment rate between each step. Now let's look at some
of the additional tools in the funnel analysis
feature that we didn't look at before back
in the left-hand side. So first we have the
Tab Settings pane. Here we have a Segment
Comparison section, along with a prompt to drop
or select a segment. Then we have the Steps
section, which we just used. And below that, a
Breakdown section, with another prompt to
drop or select dimension. The Segment Comparisons
and Breakdown features that get applied to our
funnel are quite powerful, and are unique to
Google Analytics. Let's look at the Segment
Comparison feature first. So using this feature,
we can segment the users in each step of the funnel to
figure out things like, say, the number of users from
different countries, and how the funnel graphs
for these users compare. The full list of
available segments is in the Variables pane to the
left of the Tab Settings pane. You can also create
any custom segment you'd like by clicking
on the plus sign. There are also ready-made
suggested segments and template segments you
can choose from as well. But let's say in my
case, what I want to know is what the funnel
breakdown looks like for users in my top Asian countries,
and how the data compares. These countries would be
Japan, India, and Thailand. So let me create
those segments now. So I'll create my first segment
and select User Segment, and add a new condition. Here it will be the
Country ID user property, which I'll set to JP for Japan. Now I'll do the same for India,
except this time the Country ID value will be IN. And do the same for
Thailand, setting the Country ID to TH this time. And now that I've got my funnel
data segmented by country, I can compare them in both the
funnel graphs and the tabular data below them. Now let's look at the breakdown
section in the Tab Settings pane. So here I can break down
all the tabular data shown for my funnels by
additional dimensions to get even more
insights into my data. The full list of dimensions
I can choose from are here in the Variables pane. I can add new dimensions from a
number of different categories as well, like Acquisition
Channels to see which sources and
mediums my user came from; Advertising to
see a breakdown of users from different ad conversions;
User Behavior, like which other events they triggered
while playing the game, or which screens they
spent the most time on; E-commerce events;
Generic events; and more. In this case, let's say I want
to get a breakdown of users in my top Asian
countries by platform, and see how many Android users
and how many iOS users I have in each step of the funnel. So I'll navigate to
the User section, scroll down to Platform, and
then select it and hit Apply. Now I've got Platform listed
as an additional dimension in my Variables pane. So now I can drag and drop
the Platform dimension into my breakdown in
the Tab Settings pane to have it applied to my data. So I'll do that. And voila, I can now view
the platform breakdown for each of my users
in each country and in each part of the
funnel in my tabular data. I can also view the completion
rates and abandonment rates for each of these users across
the steps of the funnel too. So the funnel analysis is
a very powerful technique you can use on your Firebase app
data through Google Analytics. And the best part
is that, yes, they are closed funnels,
which is a big deal and big ask from
Firebase developers. But if ever you
miss open funnels, you can always use
this toggle here to turn this into
an open funnel, or just use funnels in
the Firebase console. Hey, Kevin. I think there was another
technique you wanted to show us in Google Analytics. Is that right? KEVIN LAM: Yeah,
Segment Overlap. While the funnel
analysis technique is great for figuring out how
many users move on or drop off from critical steps of
my flow, sometimes you may just want to get a better
understanding of your user base as a whole. You may, for
example, want to know which traffic sources your
users are coming from, what age groups they
belong to, and which platforms they're using,
as well as maybe which countries they're coming from. Knowing all this information can
tell you what kind of audiences you should be targeting
for your next campaign to get even more
users into your app. But now let's say you only
have a limited budget to spend. Should you spend the money
on users in the 18 to 34 age group, users
who are on iOS only, the intersection of the two, and
how do you factor in countries? To answer questions like
this and to figure out how you should target
your next campaign, you need to figure out
the right combination to get you the largest and
most opportunistic user group. To do that, you can use the
Segment Overlap technique. This technique allows
you to create and choose segments you want to use to
identify overlaps between them, both visually and
in a tabular form. You can also add breakdowns
across different dimensions and metrics, just as you had
done with the Funnel Analysis technique. Now, let's use this technique
to solve a real problem. In Bingo Blast, we
haven't invested a lot of money in
pay traffic before, but I want to change that. I want to start by figuring out
a good audience I can target that will provide me with
both additional users coming into Bingo Blast and insights
into whether my campaign strategy is working. So let's go into the
Google Analytics console and see how using segment
overlaps can help. So now I'm back in
the Google Analytics console and looking at a new
Segment Overlap technique. You'll notice that the
Variables and Tab Settings panes to the left include
Segments, Dimensions, and Metrics, just
as you had seen when Sumit was looking at the
Funnel Analysis technique. But in the Analysis section,
we see a visual representation for our very first segment,
which is all users in my app. What I want to do is
to start figuring out the characteristics that make
up the majority of my user base so that I know how to target
my marketing campaign. First, let's see what
interests are the most common within my user base. I'll create a new segment to
apply the overlap analysis by clicking on the plus
button, selecting User Segment, and then adding a
condition for the segment. In this case, the
condition I want apply is the User Interest
category, so I'll go into the
Dimensions, Automatic, and scroll down to
Interests, then select that. As for which one
to pick, I'm pretty sure that Media and
Entertainment will encompass the majority of my users, but
there are multiple categories here. Would it be the one with
Gamers, Casual and Social, or the Comics and
Animation Fans? My intuition is that it'd be
the Gamers interest group, but let's validate
that with our tool. I'll start by selecting
the Gamers, Casual group. I can see the
Summary card update and tell me that there are
about 4,100 users included in the segment before
I even apply it. Now, let's see what
it looks like if I switch to the other one. Wow, this group actually
has 6,000 users, which is even more
than the last one, and definitely not what I
initially thought would happen. But it's a good example of how
a tool like Segment Overlap can help you validate or
disprove your assumptions. Now, let's move on and apply
this segment to my analysis. I'll name this
Comics and Animation, and click Save and Apply. OK, now I get a nice
visual for how my users stand amongst my global base. But before I go ahead
and decide that these are users I want
to target, there are some other important factors
I want to fold in as well. For example, I want
to make sure that I'm targeting users who are most
likely to spend in my game, so let me apply another
segment for purchasers. And let's verify that
with the overlap. So I'll create another segment
by clicking on the plus button, select User Segment, and
add another condition. This time, the
condition is going to be based on the
in-app purchase event. So I'll select
Events from the menu, scroll down to in-app
purchase, and then select it. Now, let's name
this one Purchasers and click Save and Apply. Cool. I've got another
interesting insight here. Despite the fact that
the majority of my users are in the Comics
and Animation group, there's a significant
amount of purchasers who are outside of that. And looking at the
Totals table below, I can see the breakdown
of users for each segment. Specifically, I see that the
total purchasers in my app is 127, but the total users who
are both purchasers and comics is only 91. So if I were just to
target the Comics group, I would miss a
significant portion of my potential purchasers. I'll have to make a
slight adjustment here. And for the purposes
of this video, let's say I'm OK
with the fact that I might lose some purchasers,
and let's just dig even deeper into that overlap. There's a useful feature
in this view that allows me to create a new
segment from any combination in the table below. In this case, I want to combine
the users in the Purchaser segment and the Comics segment. So I'll right-click on that
row and click on Create segment from selection. This will open up
the Segment Editor, which allows me to combine the
conditions from the segments I previously selected. Now let's click on Save. And ta-da, I have this
new combined segment instantly created. At this point, you
may be wondering, why am I combining segments and
not just keeping them separate and then moving forward? Well, part of the reason
is that you can only have three segments at a time. By combining the previous
two, I can effectively discard them and
add even more layers on top of my current analysis. The other reason,
and this ties back to why I'm doing this
in the first place, is that I can create an audience
out of the intersecting segment and use that for
targeting purposes. So now that I've
combined my purchasers and comic enthusiasts,
I can remove their individual segments. And we'll delete them
from my list of variables. So let's go ahead and do that. First, deleting my
Purchaser segment and then deleting the Comics
and Animation segment. There, now I can add the
combined segment to my analysis by dragging it over to the
list of segment comparisons. I can probably remove the
All Users segment as well, so let's clean that up too. OK, now that I'm just looking
at my comics purchasers, I can break it
down even further. Let's see what the breakdown
is between my Android and iOS users. I already have these
segments created, so it's as simple as dragging
and dropping them in. Looking at the
breakdown below, I can see that virtually
all comics purchasers are on Android. And that's pretty
powerful, because when I think about how I want
to optimize ad campaign, it allows me to
really keep focused. To take it one
step even further, I'll clean up my workspace
by removing the iOS users and consider, alas,
one more factor, age. This time, instead of
adding a new segment, I can actually add a
new dimension directly in the table. To do that, it's as simple
as dragging and dropping the Age dimension from
the Variables pane onto the breakdown. And after letting Google
Analytics process the data, the tabular view is
updated so that I can analyze it even further. And looking at
this information, I can see that the
majority of my users are within the 25
to 64 age groups. That's great, because now I can
actually refine my ad campaign. As you've just seen, this
was just one quick example of how you can use the
Segment Overlap tool. But there so many
more use cases where this tool can come in handy. So we invite you to check
it out for yourself using your own Firebase data, and
to perform your own analysis using the automatically
collected events or the custom events that you think are most
important to your business. And with that, Sumit,
are we ready to wrap up? SUMIT CHANDEL:
Yes, we are, Kevin. It's about that time. Well folks, we hope some
of the tools we covered in the Firebase Developer's
Guide to Google Analytics were helpful for you. Now, we only covered
a handful of the tools available in Google Analytics,
but there are plenty more. For example, you may have
seen the Path Analysis tool in the Analytics menu,
which lets you get a closer look at how your
users are navigating through the different
sections of your app. Or you might have seen the
User Explorer tool, which allows you to perform analyses
at the individual user level, rather than at the
aggregate level, to get another perspective
into your user data. Please check out the docs
linked in the description to learn more about these
tools, as well as the tools we covered in this video. And hey, if you'd like us
to do another video on more of these tools in the future,
let us know by liking the video and subscribing to the
channel, and maybe we'll be back with a part two. In the meantime, have fun
analyzing your Firebase app data with Google Analytics,
and get out there and build something
great for your users. Bye for now. KEVIN LAM: Bye. [MUSIC PLAYING]
