Getting Started With Google Analytics 360
Reporting with BigQuery
One of the key advantages of Analytics 360
is the ability to seamlessly export Google Analytics data
to Google BigQuery in near real-time.
This means that your organization can harness
Google’s infrastructure to power fast and
flexible queries over massive data sets.
BigQuery is a data warehouse that lets developers
and businesses tap into powerful data analytics
on demand using a familiar SQL-like
query language across terabytes of data.
Analytics 360 as well as other Google products
can easily export data to BigQuery.
Google Analytics for Firebase integrates with
BigQuery very much like Analytics 360 does,
and now, the Google Data Transfer service
lets you export data from other Google tools,
like AdWords and YouTube.
BigQuery can also be used to combine data from other sources.
This lets you tie your Analytics data
to other data from your organization.
Adding other data sources to BigQuery may require
using an Extract, Transform, and Load - or ETL - process,
but the Analytics 360 integration is seamless.
Google is doing all that ETL work for you.
Because BigQuery is part of the Google Cloud Platform,
you can use other components of that platform
to analyze, store or otherwise process your data.
BigQuery has no visualization capabilities,
but it integrates well with various visualization tools.
Google’s Data Studio has a data connector for BigQuery
that lets you access any BigQuery data source,
not just Analytics.
Let’s discuss what you can do when using BigQuery
with your Google Analytics data.
One of the biggest drivers for exporting your Analytics data to BigQuery
is the flexibility of combining it with data from other sources.
There are also additional benefits.
While Analytics 360 has much higher sampling thresholds than Analytics standard,
with BigQuery, you avoid sampling altogether.
This lets you analyze massive amounts of historical data.
When you export Analytics 360 data to BigQuery,
you have all your data at the hit level,
giving you better granularity for analysis
than what’s available in the Google Analytics interface.
And finally, you can perform deep path analysis
and other analysis that can’t be performed
using Analytics 360 data in the web UI,
like clustering or grouping, and cross-device and cross-session analysis.
The standard and custom reports in Google Analytics
aggregate users' activity to make everyday reporting easy and quick.
For features specific to a particular web application,
BigQuery provides the needed flexibility
to model the data any way you like,
thereby enabling the creation of totally new reports.
Let’s walk through a couple of examples.
Let’s say your site provides online courses.
You could use BigQuery to analyze acquisition
and retention of students, and answer questions like:
How much time passes between when a student signs up for her first course
until the time she signs up for a second course?
Or - What’s the average and distribution
around how much time passes between course signups?
The amount of time between course signups
is not readily available in the Google Analytics
standard or custom reports, but is easily calculated in BigQuery
as long as you're familiar with SQL.
You could also use BigQuery to answer questions like:
What are the most and least common page paths
that users take across the site,
and how do those paths differ between users
who sign up for a class versus those who don’t?
In the Google Analytics interface, you can't
get the raw, unsampled paths that a user followed.
With BigQuery you can essentially say
"show me the most common steps.”
In a similar way, you can use BigQuery
to analyze the characteristics common
to high-value returning customers.
Then you can use those insights to steer your
new user acquisition efforts towards customers
that are more likely to demonstrate a higher lifetime value.
Ideally you’d perform this analysis
over a large number of customer interactions.
Simply because of the volume of data involved,
BigQuery would be an ideal tool for your team.
Using this type of analysis, you might discover that:
Some cities exhibit dramatically different behavior
than other cities, so your team might want to
tailor their marketing strategy by location.
Or - You might find that a particular cohort of customers
almost always purchases after a particular type of campaign.
If you’re also recording a user ID for these customers,
you could tailor a marketing campaign specifically for them.
Or perhaps you learn that a particular news event was highly correlated
with a decrease in purchases from your company’s
most loyal customers.
These are just a few examples of the reporting capabilities
made accessible by BigQuery.
You should now understand the key benefits
of integrating and reporting with BigQuery.
In the next lesson, we’ll cover important considerations to keep in mind
as you get started using Analytics 360 with BigQuery.