Marketing in the Cloud with Google It's no secret that "data" and "the cloud" presents a huge opportunity for marketers - but often it's difficult to understand how exactly these famous buzzwords can really help step change performance for a business. In this talk you will learn how Google thinks about marketing in the cloud, what the key use cases are and best practices that will help advertisers prepare for the future.

1. Proprietary + Confidential
April 2019
Carl Fernandes
Ksenia Nekrasova

2. Proprietary + ConfidentialProprietary + Confidential
● Understanding the Cloud opportunity
● Use Cases to get you started
● Making the most of BigQuery
What we’ll cover today

3. Curious Demanding Impatient

4. Proprietary + Confidential
We expect ads to be
relevant & assistive

5. Proprietary + Confidential
Three trends to consider
Privacy & User
Control
Users demand more
transparency and control
on data usage.
Volume &
variety of data
Increasing volume of data,
technology enables
cheaper storage/analysis.
Drive for
sophistication
Competitive advantage comes
from sophistication - but is
difficult to execute (resources,
costs).

6. Only 2% of brands are realising the full
potential of data-driven marketing strategies
7% 41% 49% 2%

7. CPA
ROAS
Cost Benefit
Revenue from digital marketing
AD

8. Proprietary + ConfidentialProprietary + Confidential
<0.5%Of data is analysed
Forrester, 2017

9. Proprietary + ConfidentialProprietary + Confidential
81%Of marketers don’t feel they have a
single view of their customers
200k
Unfilled data science roles
due to skill gaps
Experian, Digital Marketer Report

11. What data do you consider valuable?
CALL
CENTERS
BILLING
CRMCONTENT
LOYALTY
PROGRAMS
DIGITAL
ADVERTISING
WEBSITE

12. How to bring data sources together?

13. Proprietary + Confidential
The new “stack”
DSP
Ad Server
Creative
Advanced Measurement
Search
Tracking Solution
Data Visualisation
Website Testing
Cloud Solution

14. The 3 steps of data integration
Visualize
Data Studio
Activate
Ads
Platforms
Content
Optimization
Commerce
Platforms
Social
Platforms
Email
Platforms
CRM
Platforms
Trained Models
Transform
Analyze
Ads
Web/Mobile
CRM/Sales
Social Email Marketing
Inventory/Product
Content Media
Ecommerce
Finance
Commercial Data Sets
Ads Data Hub (ADH)
Collect
Collection Analysis Action

15. Start with a simple but
high value use case,
expand from there
Prove value first with declared,
identifiable customers then
work to close the gaps

16. Proprietary + Confidential
Improve what you
already do
Find insights that
drive performance
Innovate for
competitive advantage
Scale reporting using a data lake &
dashboard tool. Benefit from richer
(less aggregated) data - scale
insights throughout the organisation.
Enrich online data with offline - calculate
Lifetime value, propensity, churn
prediction. Using Ads Data Hub to
access granular ads data.
Use Machine learning for advanced use
cases like creative and sentiment
analysis.

17. Proprietary + Confidential
57%
of marketers say
it’s difficult to give
stakeholders in
different functions
access to data
& insights
Forrester, July 2015

18. Proprietary + Confidential
“Churn is highest on Saturday mornings”
“On Sundays in Chicago, ad spend are 30% higher
than normal but website engagement is 50%
lower”
“There has been a spike searches for “purple
mascara”

19. Proprietary + Confidential
Zalando
● Combines metrics from diverse
sources for advanced reporting
and insights
● Generates same-day results for
A/B testing to speed development

20. Proprietary + Confidential
Scheduled Email Export BigQuery BI Engine Enterprise Functionality

21. Proprietary + Confidential
Improve what you
already do
Find insights that
drive performance
Innovate for
competitive advantage
Scale reporting using a data lake &
dashboard tool. Benefit from richer
(less aggregated) data - scale
insights throughout the organisation.
Enrich online data with offline - calculate
Lifetime value, propensity, churn
prediction. Using Ads Data Hub to
access granular ads data.
Use Machine learning for advanced use
cases like creative and sentiment
analysis.

22. Proprietary + Confidential
expect brands to use
purchase history to
provide personalized
experiences.
Google/Greenberg
Survey, 2017
63%

23. Proprietary + Confidential
M&M Direct
● Adopted an advanced Recency, Frequency
and Monetary value model (RFM) to
generate an “expected lifetime value” score
for each active customer. Activated in
GA360.
● 33% decrease in CPA
● 40% increase in new display campaign rev.

24. Proprietary + Confidential
Improve what you
already do
Find insights that
drive performance
Innovate for
competitive advantage
Scale reporting using a data lake &
dashboard tool. Benefit from richer
(less aggregated) data - scale
insights throughout the organisation.
Enrich online data with offline - calculate
Lifetime value, propensity, churn
prediction. Using Ads Data Hub to
access granular ads data.
Use Machine learning for advanced use
cases like creative and sentiment
analysis.

25. Proprietary + Confidential
IPG Mediabrands
● Uses Cloud to forecast
video campaign results
● Used Video Intelligence API to
test effectiveness and
characteristics of thousands
of ads
● Studied the impact of these
characteristics on video
completion rates
Using the machine learning power of Google’s Cloud
Vision API & Alpha.One’s neuroscience techniques.

26. Proprietary + Confidential
April 2019
Ksenia Nekrasova

27. Looks familiar?
Lack of AI/ML Adoption often causes
Issues with providing tailored
recommendations and services
...Leading to unhappy
customer experiences
Wrong
product
Wrong
character
Wrong
material
Small plush Mickey toy

28. Full Data Journey
Visualise ActivateCollect Transform Analyse

29. Data Types: Structured vs. Unstructured
Structured Data
● Predefined and Machine Readable:
○ Files with Raw or Aggregated Data
(Excel, Sheets, CSV, Avro, JSON,..)
○ Database Tables, Sheets
○ Anything else organised, labeled and
easily accessed
Unstructured Data
● No Predefined Structure
○ Image, PDF, Audio, Video, Podcast,
Streaming
○ EMail, Social Post, Product Review,
Support Call, Magnetic Tape
○ Anything else without identifier to be
recognised by search functions
< 50%
Used to make decisions*
< 1%
is analyzed or used at all*
* Harvard Business Review magazine; May-June 2017

30. Who can actually
use AI today?
10K
DL researchers
2M
ML experts
+23M
Developers
+100M
business users
Our goal:
empower more users to help
enterprises put AI into production
Very few people can create
custom ML models today

31. Making ML Accessible for all Audiences
Developer SQL Analyst Data Scientist Use cases and skills
TensorFlow and
CloudML Engine
DataLab (research)
● Build and deploy state-of-art custom models
● Requires deep understanding of ML
and programming
BigQuery ML
● Build and deploy custom models using SQL
● Requires only basic understanding of ML
AutoML and
CloudML APIs
● Build and deploy Google-provided models
for standard use cases
● Requires almost no ML knowledge
Data Analytics Com
plexity

32. Before we get into BigQuery ML, let’s get a quick recap
on BigQuery itself
Real-time analytics on
streaming data
Unique!
Encrypted, durable, and
highly available
Gigabyte- to petabyte-scale
storage and SQL queries
Built-in ML and GIS
Unique!
Fully managed and
Serverless
Unique!
Google Cloud Platform’s
enterprise data warehouse
for analytics

33. More Room for your Data Party
Where I live...
Where I celebrate my next Birthday

34. Quick Look in the User Interface
Explore and quickly analyze
collected datasets in BigQuery
Save queries and views
for future analysis
Explore Data Directly
In Data Studio
Datasets with Tables
and Views

35. BigQuery Transfer Service and Integrations
Cloud Datalab
Cloud Machine Learning
Google SheetsBI/Analytics
Tools
Advertisers
Publishers

36. Few interesting new Features, like
BigQuery GIS
● Accurate spatial analyses with Geography
data type over GeoJSON and WKT formats
● Support for core GIS functions –
measurements, transforms, constructors,
etc. – using familiar SQL
Analyze GIS data in BigQuery
with familiar SQL

37. Few interesting new Features, like
“For analysts spread across the
globe, this is a blessing. They can
now collaborate easily with a
streamlined flow for sharing their
insights.”
-- Nikhil Mishra @ Yahoo
Unlock big data for all
users with BigQuery
& Sheets
gsuite.google.com/bq-sheets

38. Few interesting new Features, like
Tight integration in the BigQuery UI
brings visual exploration of your query
results in one simple click.
See your BigQuery
data in one click with
Data Studio Explorer

39. And of course BigQuery ML
Build, train, refine and
predict within familiar Tool
and SQL format!
Easy ML Access
with BQML
cloud.google.com/bigquery/docs/bigqueryml

40. ML your Analytics360 in 4 easy Steps
Create
Create your model
and train it based on
GA data in BigQuery
1
Evaluate
Evaluate your
model to check
the accuracy
2
Predict
Use your model
to predict high
value visitors
3
Activate
Send high value cookies
back to GA for
remarketing
(via Data import or Measurement
Protocol)
4

41. Machine Learning in a Nutshell
Data
preparation
Training data
Machine
Learning
Model
evaluation
Trained
model
Data split
Validation/Test
data
Trained
model
Predictions
Model Training
Model Serving
Publish model
1
2
3 4

42. BigQuery ML: Just 2 Steps
cloud.google.com/bigquery/docs/bigqueryml

43. ● Scale: BigQuery’s processing power to build and use a Model
● Algorithms: Regression, Categorisation, Clustering, Matrix Factorisation, etc.
● Data: Just a SQL Query Result (Analytics360 Dataset Query would do it!)
● Parameters: Auto-tuned learning rate, Auto-split of data into training and test
● Data Preparation: Standardization of numeric features, one-hot encoding of strings
● Custom: Custom learning rate, adjust train/test split, L1/L2 regularisation etc.
BigQuery ML: Behind the Scenes and advanced users

44. BigQuery ML: Demo, unless it breaks

45. BigQuery ML: Demo, unless it breaks
Create
Create your model
and train it based on
GA data in BigQuery
1
Evaluate
Evaluate your
model to check
the accuracy
2
Predict
Use your model
to predict high
value visitors
3
Activate
Send high value cookies
back to GA for
remarketing
(via Data import or Measurement
Protocol)
4

46. BigQuery ML: Activation
fullVisitorId UserId Converted # Hits # Sessions
Avg. Session
Duration
(min)
LTV
380066991751227408 abc123 1 10 1 3.56 300
712553853382222331 abc123 0 7 3 1.68 259
881288060286722202 abc123 0 3 2 5.66 65
1677140157296205498 def456 1 12 4 9.54 430
Summary / Calculated Fields
Illustrative
Analytics360 BigQuery Export
Predict
3

47. Use Data Import to ingest LTV scores into GA360
ga: propScoreLifetime Value LTV
Activate
4

48. Create audiences in Analytics360 based on LTV Scores
to reach them in Google Ads and DV360
propScoreLTV
Activate
4

49. Proprietary + Confidential
Cloud becoming part
of “new stack”
Cloud use cases
drive performance
BigQuery ML makes
machine learning
accessible
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