Our partner Liveli, explores the wide range of insights you can gain by performing spatial data analysis on your Google Analytics data.

1. Understand your
product:
Combining Google Analytics
Data with CARTO and BigQuery
June the 18th 2020
11 AM AWST/ 1PM AEST

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5. When two Google Cloud partners join forces,
magical things start happening.

6. One conversation became a big, fun experiment

7. The result
1. The GA geo viz you
see out of the box
vs.
2. The GA geo viz you
can see via BigQuery
and CARTO

8. Understanding the workflow
1. Your Google Analytics data (a wealth of location data that already exists)
and supplementary data sources (for more insight)
2. Bring data into Google BigQuery - the interlocker in this process
3. Understanding the data within BigQuery, what are the questions you need
answers for?
4. Access that data in CARTO using their BigQuery connector, begin your
spatial analysis and answer your questions in a few clicks

9. Speakers
Dion Fleming
CARTO Consultant
Liveli
Ash Rane
Principal Analytics Consultant
Data Runs Deep
Mark Grace
Head of Data
The Big Red Group

10. Agenda
1. Google Analytics & GA360 - What is possible for businesses?
Ash Rane, Data Runs Deep
2. Making informed decisions with data
Mark Grace, Big Red Group
3. Data exploration with CARTO and BigQuery
Dion Fleming, Liveli
4. Question and answer time

11. How businesses are currently using Google
Analytics and GA360
with Ash Rane

12. How does Google Analytics Work?
Data is sent to
Google’s servers3
First-party GA cookie is read from,
and/or written to the browser by the js
code
2b
Hit is recorded
2c
Data is processed
and pre-aggregated4
Load Website1 The GA js code fires2a
_trackEvent(category, action, opt_label,opt
_value,opt_noninteraction) _gaq.push ([‘
trackEvent', 'name', 'label', value,true]);
trackEvent(category, action, opt_lab
el, opt_value,opt_noninteraction)_ga
q.push (['_trackEvent', 'name', 'lab
el‘,value, true]);trackEvent(categ
ory, action, opt_label,opt_value
opt_noninteraction)_gaq.push
(['_trackEvent', 'name', 'lab
el', value, true]);trackEven
t(category, action, opt_la
Data made available via
GA UI & API
5

13. Scope
USER
SESSION SESSION SESSION
HIT HIT HIT HIT HIT HIT HIT HIT HIT

14. What can we see out of the box?

15. What can we see out of the box? - Problems

16. Google Analytics - Extract Data via API

17. How is The Big Red Group using Google
Analytics and supplementary datasets to make
decisions
with Mark Grace

18. Supply Side Demand Side
BRG Marketplace
BRG: Who are we?
“Our vision is to sustainably deliver an experience every second somewhere on earth.”
Right audience, right brand,
right time.
A world class ecommerce
experience.
Amazing trusted suppliers
with incredible and
unforgettable experiences.

19. BRG: How we use data
WEBSITES / PRODUCTS DATA SOURCES
EVENT LAYER
ANALYTICS LAYER DATA WAREHOUSE
DATA TRANSFORMATION
REDBALLOON ADRENALINE
MARKETING
ANALYTICS
PRODUCT
ANALYTICS
EXPERIMENTATION PERSONALISATION
MODEL
DEVELOPMENT
INSIGHT REPORTING
Our ability to
understand how
customers engage
with our end to end
marketing activities.
Our ability to
understand how
users engage with
what we build /
provide to them.
Our ability to try new
things, learn and
iterate as we go.
Our ability to enable
unique interactions
with our customers
throughout their
relationship with us.
Our ability to build
data assets that
influence what and
how we do things.
Deepening our
understanding of
customers and our
business.
Repeatable outputs
that are used to
measure/track
performance.

20. BRG: What do we use GA for?
WEBSITES / PRODUCTS DATA SOURCES
EVENT LAYER
ANALYTICS LAYER DATA WAREHOUSE
DATA TRANSFORMATION
REDBALLOON ADRENALINE
MARKETING
ANALYTICS
PRODUCT
ANALYTICS
EXPERIMENTATION PERSONALISATION
MODEL
DEVELOPMENT
INSIGHT REPORTING
Our ability to
understand how
customers engage
with our end to end
marketing activities.
Our ability to
understand how
users engage with
what we build /
provide to them.
Our ability to try new
things, learn and
iterate as we go.
Our ability to enable
unique interactions
with our customers
throughout their
relationship with us.
Our ability to build
data assets that
influence what and
how we do things.
Deepening our
understanding of
customers and our
business.
Repeatable outputs
that are used to
measure/track
performance.

21. BRG: What GA doesn’t do for us
Location, Location, Location… These are a few examples of the types of questions we have on location based data that would have
tangible benefits for the BRG:
1. How are our products distributed across the country?
2. Where are people when they visit our website? Does the shopping funnel look different by location?
3. How do we see demand for product vary by city vs. non-metro customers?
4. What are the most popular products purchased/viewed and how does that relate to where the user is from?
5. How far are our users willing to travel for an experience? And what are those experiences?
6. How has this changed in a post covid environment?
FINANCIAL RECORDS ACCESS TO UNSAMPLED DATA PROVIDE A HOLISTIC VIEW A GREAT INSIGHT TOOL
GA isn’t and never will be our financial
record. We’re within 5% and we’re happy
with that.
When we need to do longer term
analysis we are hampered by sampling.
Don’t get caught out here!
GA looks at what you send it. We need
somewhere else to link all data together
- we use a DWH to help us.
GA isn’t always the best tool for
conducting deep insight, especially
when it comes to location based data.

22. Data exploration using CARTO and BigQuery
with Dion Fleming

23. Data Extract and Visualisation Process
CARTO
BigQuery
connector
+
Product Data
Warehouse
CARTO
database

24. Google BigQuery Process
Data exploration
Running queries in BigQuery console
Final data extract
Running queries via CARTO BigQuery connector interface

25. What is CARTO?
CARTO is a location intelligence platform that enables users to upload, enrich,
analyse and visualise spatial data.
Engine
Builder
CARTOframes

26. Why CARTO?
- Powerful visualisation and analytics for spatial data
- Ability to push Google Analytics data into BigQuery
- Combination of BigQuery connector and CARTO Builder combination
enabled rapid prototyping

27. CARTO Process
Data Ingestion &
Management
Data Enrichment Analysis Solutions &
Visualisation
Integration

28. Use Case 1
Product spatial distribution
Visualising product information from Big Red Group
Displaying product locations by:
- Category
- Sub-category
- Number of participants
Identifying gaps in categories by location
Answering questions such as:
Where are my product suppliers located?
Where are we lacking products in certain categories?
Where do we need more multiple participant activities?

30. Use Case 2
Understanding buyer behaviour
Visualising Google Analytics data by city
To understand buyer behaviour, we’ll look at three components:
- Visits
- Adds to cart
- Purchases
Answering questions such as:
From which cities do we see the most demand?
Where are our highest cart abandonment levels?

32. Use Case 3
Purchase/product location matrix
Visualising purchase/product location matrix - a combination of
Google Analytics data and Big Red Group product data
Lines connecting each purchase to the purchase city
Answering questions such as:
From which cities does a certain product receive the most demand?
What are the trends by product type across city centres?
What product category is most purchased by people in Victoria?

35. Question and answer time

36. Get in touch
Dion Fleming
dion.fleming@liveli.com.au
Ash Rane
ashrane@datarunsdeep.com.au
Follow Liveli on LinkedIn, Facebook and Twitter

37. Thank you