BigQuery is a powerful tool for marketers because it allows for fast SQL queries across large datasets, seamless integration with Google products like Analytics and Machine Learning, and cheap pricing. The document discusses two use cases: 1) integrating CRM and Analytics data to attribute marketing channels to sales outcomes, and 2) using BigQuery ML to cluster user groups, build predictive models, and activate remarketing lists. In conclusion, the document emphasizes that BigQuery makes data warehousing, exploration, and basic machine learning surprisingly easy and affordable.

1. Why Big Query is so
Powerful

2. 2
What we’ll cover
● Introduction to BigQuery
● How does BQ works
● Use Case 1 - Integrate with CRM Data to get
meaningful insights
● Use Case 2 - Create remarketing list using BQ
ML

4. 1.
What is Big Query?
Introduction of Big Query
4

5. What is Big Query
▹ an enterprise data warehouse
▹ super-fast SQL queries
▹ leverage Google's infrastructure
▹ Cheap, pay on demand solution (easy to scale)
5

6. No hardware, easy to set up
▹ You can have a full datawarehouse running within
minutes, with virtually ZERO ongoing operational
overhead
6
BigQuery sits somewhere in between

7. So Exactly how fast?7

8. query uses over 3000 CPU cores, hundreds of disks and a
330Gb network to run a regex over 4TB of data in under 30
secs.
8

9. And.. how cheap?9

10. Why is it so powerful for Marketers?
▹ We usually have the limited dev resources to maintain
and configure data warehouse
▹ works seamlessly with google products
▸ data visualisation (DataStudio)
▸ machine learning (BQML*, Cloud auto ML)
▹ BIG PLUS: access to Google Analytics, Adwords, Youtube
data raw data* (if you have a $150k/year Google 360 subscription)
10

11. 2.
How does
BigQuery works?
Introduction of BigQuery
11

12. Big Query
Structure
12

13. Loading
Data
13

14. Loading
Data
14

15. Function &
Operators
15 Support both:
1. Legacy SQL
2. Standard SQL*
*Not all functions are supported, one of the
prominent example would be - SELECT distinct.
(Instead user will need to use Group By &
Partition By to replace these function)
https://cloud.google.com/bigquery/docs/reference/standard-sql/

16. GA Data in
BigQuery
16
▹ A new table per day "ga_sessions_YYYYMMDD".
▹ Each row represent a new session
▹ Session data is nested and stored in a
massive Big Table - works more like
arrays/subsets
Tips：
- Query by day/ month/year
- Unnest only “subsets” of data needed

17. GA Data in
BigQuery
17

18. Integrate with CRM data to
obtain Meaningful Data
Marketing use case 1
18

19. Case Study
Recruitment company, relies heavily on offline activity to
qualify leads (applicants) that comes in
problem:
▹ Misalignment in business metric (P&L) - Digital
marketing and business metrics are not aligned
▹ Attribution - Marketing channels and campaigns
cannot be accurately attributed to each placement
19

20. Solution
▹ A end-to-end bespoke dashboard to show the right
data:
▸ how marketing channels contribute to each
placements (sales) & quality candidates
▸ How offline and online activities works together
20

21. Prerequisite21
CRM
▹ You (or your client) is on Google 360
premium to access google analytics
raw data
▹ Basic standard SQL
▹ Configured an integration between
Google analytics and CRM (Customer
Relationship Management)

22. Configure GA & CRM integration
▹ User/client ID
https://support.google.com/analyti
cs/answer/7584446?hl=en
▹ using Data layer to send
back CRM user ID to match
particular sessions on GA
22

23. Approach23
Integration GA & CRM
Import CRM data
into BigQuery
Join GA Session Data & CRM
using unique identifier
Attribute GA sessions
Connect with Data
Studio

24. Create remarketing & look
alike audience using
BigQuery ML
Marketing use case 2
24

25. Case Study
Sales problem:
▹ Need a better way to prioritise leads: Offline sales
oriented nature the lead volume they get per month is
enormous (ten of thousands)
Marketing problem
▹ Struggle to improve ROI of remarketing efforts
▹ Lack understanding of the traits of high value
converters
25

27. Process
overview
27
Prepare,extract, train, evaluate (repeat)

28. 1- Collect &
Create data
sets
28
▹ Started with supervised learning
▹ Created a datasets that require a lot of
data cleaning, labeling
▹ Trying to obtain as much data points as
possible incl email, sales consultant
performance, seasonality (eg school
holiday)

29. - Desktop_flag
- Tablet_flag
- Mobile_flag
- Total time on site
- Total count of sessions
- Sum of morning visits
- Sum of daytime visits
- Sum of time spent on Product Page
*
- Sum of time spent on resource page
- Channel coming through first touch
- Channel coming through last touch
- Count of pages visited
- Days difference since the first
session
- Sum hits number
29
What data is available in GA that
we can use?

30. 2 - Clean and
categorise data
30 Verify validity and clean data to make it ready
to process. Look out for data quality
problems like:
▹ Missing data
▹ Invalid or inconsistent data types
▹ Distribution bias or uniformity
Tips:
▹ IF function to process missing value
▹ CASE statements to convert and transform data into
dummy variables

31. 3 - Cluster &
categorise
different group
of users
31 Based on our analysis and business context,
we cluster the data into 4 main categories
1. Not interested, non-converters
2. Interested, non-converts
3. Not yet interested , converters
4. Interested, converters

32. 4 - Create &
train model
32 Create a model for each group of users:
▹ BQ has a amazing integration with R
studio & Python (using code lab)
▹ Alternatively, we can use BigQuery ML
(using standard SQL function!)
▸ Linear Regression
▸ Logistic Regression(in this case)
▸ Multiclass logistic regression for
classification
https://cloud.google.com/bigquery/docs/bigqueryml-intro

33. ● Decrease complexity
● Increase speed
● Simple, easy to learn
● Export data from Big
Query may be
prevented by legal
restrictions (such as
HIPAA guidelines).
33
Advantage of using BQ ML

34. 5 - Evaluate
your model
34
▹ allow you to evaluate your model based
on simple function “ML.Evaluate”
▹ Based on what model you have chosen
you will have a different results
Example of logistic regression:

35. 6 - Predict
Outcome
35 Example of using the PREDICT function to predict
ecommerce purchase:

36. ACTIVATE
36

37. 1. Create & test out lookalike audience based on your findings eg
demographic features, location
2. Create remarketing list based on lead score
- Import lead score & visitor ID in GA > create custom metric
for lead score> create remarketing audience
https://github.com/GoogleCloudPlatform/google-analytics-premium-bigquery-
statistics/blob/master/README.md
37
Marketing activation

38. 1. BigQuery is a cheap, easy to set up data warehouse/
exploration/preparation/machine learning(!) tool
2. Data brings people together (sales & marketing) & goes a long
way if use appropriately
3. Creating and using Machine learning model is easier than you
think (with the right tool*)
38
Conclusion

39. 39
LET’S BQ THIS
Any queries?
You can find me at
gabriella@inmarketingwetrust.com.au
linkedin.com/in/gabriella-wong