Google Ads playbook

1. Proprietary + Confidential
Workshop for clients, agencies & partners
Perform Like a Pro: align your business goals
with Google via Smart Bidding

2. Alex Urcola
Analytical Consultant
Andoni Peña
Performance Specialist

3. ● Why is this key for any business? - 10 min
● Perform like a pro options - 40 min
● Cases - 15 min
● Summary - 5 min
● Questions - 5 min
Content

4. for Agencies & Partners
Why is this key for any business?

5. Not every customer brings the same value to a business
Customer 1
Value X
Customer 2
Value Y
Customer 3
Value Z
Some conversions do not matter as much to an advertiser’s business goals, while others
are of higher value and should be reported and optimized for accordingly.

6. You need to adapt bids based on value each
customer brings
Student
enrolled
Application
completed
Application
started
Request
info
Missed
opportunity
Student
enrolled
Application
completed
Application
started
Request
info
Reduces bids for low
expected value interactions.
Helps drive high quality leads in
a competitive landscape.
Over
investment
Conversion-based bidding Value-based bidding Value Bid

7. for Agencies & Partners
Is your mkt/business KPI aligned with
the KPI you’re using to optimize in
Google?

8. Can we go one step further?
Lead Generators Insurance
Sector
Edu
Current KPI
Leads/CPL
Final KPI
Póliza
Matrícula
Intermediate KPI
Modelo
Predictivo
Revenue Generators Hotel
Sector
Ecommerce
Current KPI
Revenue/ROAS
Final KPI
High value purchase, Lifetime Value, etc
On+Off, Margin, profit, etc

9. Perform like a pro
What’s that?
The way to achieve your business
goals via data integration + Smart
Bidding
Revenue
Market Share
Customer Business
Objectives
Volume Profit
● Optimize depending on each business
reality, not on market inertia
● Crucial way to get a competitive
advantage thanks to the value of the
exclusive data your business has
○ Bid differently depending on
who are your best users
● A global return of investment for
every € on Google:
○ Search, Display y Video
Why is so important? How are the steps?
Understand
My Business Objectives
Prioritise CBOs, and their connection to
Customer marketing objectives and KPIs
Integrate
My Objectives Into A Holistic Solution
Requires cross-product solutions
aligned to Customer objectives
Measure
My Results in My Terms
Reflect performance of the solution
in Customer terms and KPIs

10. Perform like a pro:
Talk about business, not media
Track & value success
aligned with Customer
Goals
Use Machine Learning
to maximize efficiency
Expand
to all available Inventory
Set right Budget
& Target
to maximize opportunity

11. for Agencies & Partners
Perform like a pro options

12. How can we do this?
NON PREDICTIVE PREDICTIVE
Funnel qualification Margin/profit/on+off Lead scoring Customer lifetime value

13. EXAMPLE SIGNALS
● Query
● Location
● Device
● Browser
● OS
● Language
● Time of the day,
Day of the week
● 1P audience lists
(presence,
recency)
● … and more!
● … and
combinations of
there signals
Smart Bidding activation options: tROAS &
Max. Conv. Value
Auction-time predicted
conversion value
per click
BID
Search query level
performance across your
account or MCC
Contextual signals
at auction time
ROAS Target
Budget

14. for Agencies & Partners
How?

15. Steps towards bidding to Value
Create a 360º
View customer
Bid to Value Audience
Strategy
“Let the data talk about your
business”
Use your 1P Audiences
to provide signals for
bidding
Align your
campaign bidding with
customer goals

16. Steps towards bidding to Value
Create a 360º
View customer
“Let the data talk about your
business”

17. Capturing the right data throughout the entire
consumer journey is key to enable value-based bidding
It prioritizes the actions that are worth the most to your business
Drive online sales
Generate leads
Drive offline sales
Pages visited Time on site
Brochure downloads
Newsletter sign-ups
Phone calls Form submissions
Store visits Driving directions
Purchase
Increase
awareness
Sales-qualified leads

18. Data capture for a Lead Generation business
Offline
conversion
tracking
Online
conversion
tracking
Leads
Engaged visitors Pages visited
Newsletter
sign-ups
Web Behaviour
Phone calls Form submissions
Buyers
Qualified leads Marketing/Sales qualified leads
Closed deal

19. Data Capture for a Retail Business
Data Collection
Web
App
Store Client
level data
Advanced Analytics
LIFETIMEVALUE INSIGHTS
● Products bought by High LTV
customers in first purchase
● Promotion impact on LTV
● High LTV Customer Persona
● On/Off customer behaviour
LIFETIMEVALUE ROAS
● LifeTime Return per acquired
customer by channel

20. Steps towards bidding to Value
Bid to Value
Align your
campaign bidding with
customer goals

21. for Agencies & Partners
Ecommerce

22. Example: Online retailer
CPC
CPA
ROAS
Predictive
Actual
Net Sales
LTV
Margin

23. Scenario A: Retailer optimizing towards purchase value Scenario B: Retailer optimizing towards margins
Example: Online retailer - Bidding to Margin
In Scenario A, the advertiser is maximizing the purchase value Target ROAS
bidding. This allows bidding to understand the differences of each conversion
and hence increases ad spend on ‘Customer B’ and ‘Customer C’.
In Scenario B, the advertiser is going one step further and
optimizing towards margin, and hence has higher ad spend on
‘Customer B’ where there are higher earnings
Consider which data is readily available to you, to begin optimizing towards what matters the
most.
Purchase Margin
Sample Google Ads
Spend
Customer A $50 $20 $20
Customer B $100 $70 $40
Customer C $100 $30 $40
Purchase Margin
Sample Google
Ads Spend
Customer A $50 $20 $10
Customer B $100 $70 $60
Customer C $100 $30 $30
Value bidding: purchase value vs.margin value
NON PREDICTIVE

24. Example: Online retailer -
Bidding to Margin + Store Visits
FORMULA
NON PREDICTIVE
EXAMPLE
In-Store Purchase Rate
In-Store Average Order Value
Store Visit Conversion Value
(Set in GA, available in SA360)
70%
300USD
= (70% * 300USD) = 210USD
(i.e. 7 out of 10 customers who visit
your store make a purchase)
(i.e. your average customer spends
300 USD per visit)
Store Visit
Conversion
Value
In-Store
Purchase
Rate
In-Store
Average
Order Value

25. Value all customer touch points
in reporting and KPIs
NON PREDICTIVE
Online Sales
Digital Budget Online
Conv. Value
Online ROI Omnichannel ROAS
Store Visits Store Visit
Conv. Value
Offline ROI
Ad

26. Example: Online retailer -
Bidding towards LTV - What is CLTV?
PREDICTIVE
‘Lifetime’ usually starts at the day of
1st purchase
When calculating LTV (=CLV) we
must specify the time frame over
which it is calculated
Some often refer to Past Value of
their customers when talking about
LTV which then does not include the
‘predictive element’
LTV can be calculated both as a sum
of Revenue, or as a sum Profit
Order Value
$1
$4
$3
$9
$2
$8
Margin
Past Value Predicted LTV (pLTV)
$1
$4
FIRST 6 MONTH LTV
FIRST YEAR LTV

27. Example: Online retailer - Which customer
is more valuable to your business ?
CPA
Value generated in Month 1
$9 $19
$10 $10
Value generated in Month 2 $0 $10
ROI end of Month 1 11% -47%
Value generated in Month 3 $0 $10
ROI end of Month 3 11% 58%

28. Example: Online retailer -
Calculating the LTV can be very simple
Average Order Value
PREDICTIVE
Purchases per year
€36 2
AVERAGE LIFETIME VALUE OF ALL USERS IS: £72

29. Example: Online retailer -
But also very complicated
PREDICTIVE
Average Order Value Products Bought
Demographics Profit Margin Items in Basket
Product Satisfaction
Churn Rate
Return Rates
Customer Reviews
On-site
Interactions
Frequency
Postcode

30. Example: Online retailer -
You can start with simple segmentations
PREDICTIVE
Returning Customers £38
New Customers £32
“Pet Products” shoppers £42
Users with a basket value above £50 £54
3
1
5
5
£114
£32
£210
£432
Purchases Per Year Lifetime Value
AOV

31. Example: Online retailer -
Or use complex ML models to predict the value
Future
new
high
value
customer
Future
New
low
value
customer
Record
transaction
+ LTV
Record
Transaction
80$ in low value
products
(Low Value
Customer)
$
80$ in high value
products
(High Value
Customer)
$$
Smart Bidding
ML model
Ad Click
New Customer
First Purchase
(both 80€)
Model
predicts LTV
Activation in
GAds / SA360
Bid to LTV

32. Offline
Conversion import
Example: Online retailer -
How to pass the data back to Google Ads/ SA360
Use gclid and upload back
values to each click
Conversion value
rules (beta) or CFV
Adapt the value based on
geolocation device and
audiences.
Conversions
adjustments
Retracting and restating
reported conversions (Google
Ads website conversions (tag
based order id ) or offline
conversion import).
GTM or Google
Site Tag
Track conversions Real time
using GTM or Google Site Tag.
You can implement linear
models in GTM.

33. Example: Online retailer - You should keep in mind
Ensure conversions are
uploaded/tracked on a
frequent basis, ideally daily.
It’s recommended to share all
the important steps of the first 7
days of the customer journey.
You should share enough conversion volume (>50)

34. for Agencies & Partners
Lead Generator

35. Offline
conversion
tracking
Online
conversion
tracking
Leads
Engaged visitors Pages visited Newsletter sign-ups Brochure Downloads
Phone calls Form submissions
Buyers
Qualified leads Marketing/Sales qualified leads
Closed deal
Example: Education company -
identify all steps in the funnel

36. Scenario A: Optimizing towards online form completion Scenario B: Optimizing towards final sale
Example: Education company - If you value
sales deal, start by including the value of each lead
Scenario A the advertiser is using Target CPA to maximize the number of online
lead form completions. However this misses the opportunity of increasing
spend with higher value customers.
In Scenario B, the advertiser is optimizing towards the final value
of the lead, and bidding increases spend on potential high value
customers.
Online
completion
Value of Lead
(final sale)
Sample Google Ads
Spend
Customer A Y $2000 $50
Customer B Y $500 $50
Customer C N $0 $5
Online
completion
Value of Lead
(final sale)
Sample Google
Ads Spend
Customer A Y $2000 $70
Customer B Y $500 $30
Customer C N $0 $5
Volume vs. Value bidding
NON PREDICTIVE

37. Example: Education company - Go one step
further by passing all steps in the funnel
NON PREDICTIVE
Lead Marketing qualified lead Sale qualify lead
250 leads per month
Closed deal
200 MQLs per month 200 SQLs per month 25 Deals per month
CONVERSION TRACKING
MEASURED THROUGH OFFLINE CONVERSION TRACKING
Import all relevant actions into Google Ads, focus on creating a strong base of lead
quality data and the ability to choose which of these steps will be optimization drivers.

38. Example: Education company - Go one step
further by passing all steps in the funnel
NON PREDICTIVE
Lead Marketing qualified lead Sale qualify lead
4000 leads per month
$20 value per lead
Closed deal
200 MQLs per month
$400 value per MQL
100 SQLs per month
$800 value per SQL
25 Deals per month
$3200 value per deal
5% CONVERSION RATE
Customer value will help you monitor the real impact of advertising on your business and
make the right decisions to develop growth strategies, ultimately allowing you to capture
the customers that matter the most (and generate the most value).
50% CONVERSION RATE 25% CONVERSION RATE

39. Customer fit: The client has realized or modeled revenue
for leads and/or sales in their CRM.
Customer fit: The client does not have sales or lead
values in their CRM for offline conversion tracking and
they do not have an online shopping cart.
You can import conversion values into
Google Ads and pass back any value you see
fit and even manipulate the values at ease.
If you are tracking multiple conversion
actions, you can assign a static value, i.e the
same value, for each conversion action.
Static Values
Example: Education company - You can provide
either static or dynamic values
PREDICTIVE NON PREDICTIVE
NON PREDICTIVE
Dynamic Values

40. Customer
Lead
Machine Learning
model
Lead Scoring
Web behaviour analysis &
Contact center data
● Device
● Location
● Mortgage amount
● Type of house
● Job
● Other custom
dimensions
85%
Probability
25%
Probability
60%
Probability
Pre Approved
mortgage
GA360 Internal DataBase
Bucketize Scores Based on
Probability
PREDICTIVE
Example: Education company - Passing Values
Through a lead scoring system

41. PREDICTIVE
Example: Education company -
Check the Conversion Rate values per quintile
and apply values Based on that

42. PREDICTIVE
Customer Lead Machine Learning model Lead Scoring
Web behaviour analysis &
Contact center data
● Device
● Location
● Mortgage amount
● Type of house
● Job
● Other custom
dimensions
85% Probability
25% Probability
60% Probability
Pre Approved mortgage
Example: Education company - Passing Values Through
a lead scoring system & Steps in the funnel
Lead Marketing qualified lead Sale qualify lead
4000 leads per month
$20 value per lead
Closed deal
200 MQLs per month
$400 value per MQL
100 SQLs per month
$800 value per SQL
25 Deals per month
$3200 value per deal

43. PREDICTIVE
Example: Education company - Analyze the drop
rate by step and provide points for each step

44. Gross Leads
Net Leads
(Documentation
sent)
Super High Value (prob > 0.7)
High Value (prob > 0.5 < 0.7)
Mid Value (prob > 0.4 < 0.5)
Low Value (prob < 0.4)
10$ per conv
+15$ per conv
+20$ per conv
+10$ per conv
+5$ per conv
Example: Education company - Provide values to
each quintiles
PREDICTIVE

45. 1: Rank Net leads from highest probability to convert to lower probability to convert.
2: Bucketize into 4 segments (each segment does not need to have the same size.
3: evaluate if the given scores (share of attributed ROAS) adjust to the reality in final conversion
(share of final sales).
PREDICTIVE
Example: Education company - Provide
values to each quintiles
Minimize Difference
Error if using Leads
11.97%
46.38%

46. Optimize for
closed leads
Both click
on an ad
Both fill out
lead form
Tag counts 2
conversions
Lead gets qualified, User
B drops out
Closed lead gets
imported as only 1
conversion
Optimize for leads
User A
User B
Solution bundle:
Offline conversion
tracking
Value-based
Smart Bidding
✓ Technical implementation based
on Click ID, Order ID or Client ID
✓ Lead qualification available in a
timely manner
Example: Education company - This is
how the process would look like
PREDICTIVE

47. Brand Advertiser

48. Watch a Video
Sign up for
Newsletter
Where to buy +
Facebook
NON PREDICTIVE
Example: Funnel Qualification - Define Engagement
score Identify most valuable ‘micro-conversions’

49. Define Engagement score
Evaluate most valuable website ‘micro-conversions’
Assign value to each Engagement
Signal you want to track
Eng. Signal Value
Click to Buy 10
Where to Buy 9
Product Page 8
Newsletter 7
Live Chat 6
FB Message Us 5
Watched Video 4
Pages Visited 3
Tutorial 2
Visit > 1 min 1
NON PREDICTIVE
Watch a Video
Sign up for
Newsletter
Where to buy +
Facebook

50. Audience
Strategy
Use your 1P Audiences
to provide signals for
bidding
Steps towards bidding to Value

51. Bidding like a pro and targeting like pro should be
combined to create a good strategy for all customers
Rest of
potential
customers
Visitors
Similar
Audiences
Bidding like a Pro
Target like a Pro

52. Cases

53. Carrefour Case

54. How to leverage First Party Data to acquire High
LifeTime Value Customers - Carrefour Case Study
1P Data for Measurement
Create a 360 customer view
● Capture Online , Offline and app sales
focused on a centralized customer id.
● Understand the LTV of new acquired
users and signals related to high LTV
users. Moving to LTV ROAS.
1P Data for Audiences
Target more efficiently
● Detect patterns of High LifeTimeValue
Customers to acquire similar users
across channels.
● Leverage personalization to increase
the conversion rate.
1P Data for bidding
Improve signals for bidding
● Include LifeTimeValue as the
main KPI for bidding
● Focus on Acquisition of High LTV
customers even if prospects are
not similar audiences /
Remarketing
CASE STUDY

55. LifeTimeValue ROAS
● LifeTime Return per acquired
customer by channel
Data Collection Advanced Analytics Activation
Web
App
Client level
data
LifeTimeValue Insights
● Products bought by High LTV
customers in first purchase
● Promotion impact on LT
● High LTV Customer Persona
● On/Off customer behaviour
Investment decisions
● Product Level investment
decisions
● Channel investment
Market opportunities
● Keyword/product expansion
● Seasonal promotion actions
● Personalization strategy
store
1- Measure like a pro
CASE STUDY

56. 2- Target and personalize like a pro
2. Acquire High LTV
customers
Find similar audiences to high
LifeTimeValue customers
Visit / Historic
purchases
Analysis Scoring /
Clustering
Activation
Web behaviour
● Device
● Location
● Seasonality
● Steps in the funnel
● Visit num
85% Prob
Cluster 1
Cluster 5
Cluster 4
1. Avoid churn
Retain non engaged old customers
3. Personalize creatives
Adapt messages based on customer
behaviour
Historical Analysis of
purchases
● Number of purchases
● Precio medio
● Recencia
● Tipo de productos comprados
25% Prob
60% Prob
Cluster 2
Cluster 3
CASE STUDY

57. Personalize ad creative taking into account
customer preferences
Healthy life
Gourmet ECO & Bio
lovers
Caregivers
CASE STUDY

59. Source: https://towardsdatascience.com/build-your-own-recommender-system-within-5-minutes-30dd40388fbf
Taking into account customer historical data to
recommend suitable products
CASE STUDY

60. Bid to
predicted net
margin ROAS
Bid to Margin
ROAS
Bid like a Pro in ecommerce
Bid to CPC for
each web visit
Bid to
predicted
ROAS LTV
Bid to ROAS
Conversation moves from
marketing & media to business
Predictive
Actual
CASE STUDY

61. 3- How to bid like a pro in ecommerce - Acquire
high LTV customers
Future
new
high
value
customer
Future
New
low
value
customer
Record
transaction
+ LTV
Record
Transaction
80$ in low value
products
(Low Value
Customer)
$
80$ in high value
products
(High Value
Customer)
$$
Smart Bidding
ML model
Ad Click
New Customer
First Purchase
(both 80€)
Model
predicts LTV
Activation in
GAds / SA360
Bid to LTV
CASE STUDY

62. We need to work on anticipated KPIs to fasten
marketing decisions
% of qualified acquired customers - based on LTV model
CASE STUDY

64. Verti Case

65. Offline
conversion
tracking
Online
conversion
tracking
Visitors
Steps in funnel
Leads
Call Center
Aggregators
Agents
1- Measure like a pro - Verti Worked on 1PD to measure
all offline sales
CASE STUDY

66. Customer
quotation
Machine Learning
model
Lead Scoring Activation
Web behaviour analysis
● Device
● Location
● Model type
● Type of insurance
● Car model
● Kms per year
● Other custom
dimensions
85% Probability
25% Probability
60% Probability
1. Auto-bidding
Change the bidding KPI from
tCPL to tCPA
3. Reduce CPA
Align marketing campaign to
customer final goals
2. Increase Lead Quality
Auto-bidding bids higher for high
quality lead prospects and saves
money from low quality leads
Offline conv
import
GA360 BigQuery / BigQueryML SA 360
Predicted sale
Predicted NO
sale
Predicted sale
2- Verti with Making Science Created a lead scoring to
predict sales
CASE STUDY

67. Online Lead Sales
Call Center Lead Qualified lead (scoring)
X% conversion rate X% conversion rate X% conversion rate
2- Verti provided different Values to each
conversion in funnel
CASE STUDY

69. Summary

70. Global Best Practices
Align inside your company what
KPIs will be the ones used to
optimize and measure (change
your internal mindset)
Work together with Data teams
(client, agency and Google) to
be aligned
Know the technology you have
and its pros/cons before
starting
Add the new KPI value to your
pixel (Gads, GA, FL) and wait at
least 4-6 weeks before activating
Smart Bidding
After 4-6 weeks of everything
working fine, apply Max Conv.
Value or tROAS
Be patient
After other 6, start to analyze
results.
Spend time on defining your
values for each type of KPI
Analyze results
Activate SB (tROAS/Max
Value)
Add the new KPI to Google
We’re changing the KPI and
values reported previously. SB
needs at least 6 weeks.
Understand the tech you
have
Work together
Define the new KPI Alignment inside the
company
STRATEGIC OPERATIONAL

71. Questions?

72. Proprietary + Confidential
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