Google Smart Bidding, tROAS, Incrementality Testing, and CLV calculations for retail ecommerce paid search advertisers.

1. Google’s Black Box Bidding Solution:
A look under the hood
March 7, 2019
1:00pm EST
Speakers:
Ginny Marvin, Editor-in-Chief, Third Door Media
Andreas Reiffen, CEO, Crealytics

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3. Today’s Presenters
Andreas Reiffen
CEO
Crealytics
Ginny Marvin
Editor-in-Chief
Third Door Media

4. Google’s Black Box Bidding Solution: A look under the hood

5. The Evolution of
Online Marketing

6. 6
Evolution of Online Marketing
Launch of first
automated third-party
bidding systems
Google finalizes its
acquisition of
DoubleClick
RLSA made available to
all advertisers
Google Shopping rolled
out globally
Introduction of machine
learning based Smart
Bidding offering
2016 2013
20082006
Launch of Google
AdWords
2000
2014

7. The Status Quo in
Paid Search
Advertising

8. 8
The Status Quo in Paid Search Advertising

9. Future Outlook

10. 10
Future Outlook
Layoffs at Zalando: „Creative Marketers replaced
by data scientiests and engineers“

11. There is nothing quite so useless, as doing
with great efficiency, something that should
not be done at all.
- Peter F. Drucker

12. Best-in-class
measurement

13. 13
The evolution of evaluating transactions
A move from merely considering transaction cost to now considering longer term gains
# Orders
CPO
Revenue
ROAS
Profit
ROI
Long term value
CLV ROI
Every order is treated equally,
no matter whether the order
value is high or low, margins
and new customers estimated
based on averages.
Focus on revenue without
taking into account that
different products have
different margins.
Products with high margins
are promoted more
aggressively, repeat purchases
from new customers are
ignored.
Lifetime Value Optimization
takes into account exact
margins and repeat purchases
from new customers.
Low data quality High data quality

14. 14
The evolution of attributing transactions
From last click to smart attribution taking into account incremental value
Last
Click
Further touch
points
Algorithmic
attribution
Incremental
value
Orders get assigned to just
one channel, usually the one
the conversion came from in
the end – ignoring any other
touch points on the way
Other touch points are given
credit, not necessarily taking
into account how valuable
each of these touchpoints was
Attribution is data driven,
giving credit to each channel
on a order by order basis.
Incrementality tests are used
to validate the outcome of
attribution as well as to modify
attribution models.
Low data quality High data quality

15. 15
Excellence in online
advertising requires
the ability to
measure and
attribute all
transactions across
all channels
Measuring and attributing the value of transactions
The ability to master the two dimensions defines online advertising success
# Orders
CPO
Revenue
ROAS
Profit
ROI
LTV
LTV ROI

16. From CPO to CLV-
centric optimization

17. 17
Maximize profits by
investing in new
customers
acquisition.
CLV optimization
makes long term
planning possible
Model budget scenarios using tracked KPIs
Longer term thinking aligns KPIs with enterprise wide goals
2.0 3.0 4.0
-1.0
2.0
5.0
-3.0
1.0
8.0
Optimize 24
month profit
Optimize 12
month profit
Maximize profit
on first order
1st order 12 months 24 months
Investment Profit over time

18. 18
The impact of switching from a ROAS to a CLV-centric approach
New customer rate improved by 58%
Ad spend growth driven by
increased efficiency
Overall ad spend grew by 20x since
we took on client management.
In 2011 our technology leveraged
efficiency gains by improving keyword
coverage, ad quality and bidding.
Accumulated LTV
profits grow fast
In 2012 we changed the investment
rationale from ROAS to LTV.
Budget was allocated more wisely to
sell more profitable products to new
customers.
Initially strong increase
in new customer rate
During the first three years the new
customer rate grew by 55%.
Despite a high market penetration of
the business the new customer rate
has been maintained at a high level.
GoogleAdSpend
AccumulatedLTV
NewCustomers%
Crealytics
20122011 20172013 2014 20162015 201420122011 2013 2015 20172016
+275%
20132011 2012 20152014 2016 2017
+58%

19. 19
At the same ROAS, designers can have very different ROIs, due to margin and
new customer acquisition. Lifetime bidding will react to this.
3 weeks average before / after.
Before CLV ROI bidding Successful CLV ROI bidding
0
2
4
6
8
10
12
0.0 0.5 1.0 1.5 2.0
A
B
ROI
E
new look
ROAS
C
D
F
G
H
I
0
2
4
6
8
10
12
14
0.0 0.5 1.0 1.5 2.0
ROI
ROAS
F
new look
A
BC
D
E
G
H
I
Inform bidding
CLV ROI bidding pulls
back from weak ROI
brands, even if ROAS is
strong.
From a ROAS
perspective, many
brands would look the
same.
Average CLV ROI moves
from 0.60 to 0.90 at
stronger total revenue.
avg avg

20. Attribution and
incrementality

21. 21
Without reliable data advertising investments might never pay off
High investments for future profits are risky – how confident are you in current metrics?
FYI: Facebook dynamic product ads often have incremental value below 5%
Expectation
Reality
-50
100
45
65
52
2
-50
100
45
65
26
-24
Revenue Margin after
returns
NC value Incremental
value
Investment Profit
assumed
incrementality:
80%
actual
incrementality:
40%

22. 22
Run incrementality tests to assess the true impact of your ad campaigns
As incrementality fluctuates, we recommend running tests constantly
Split visitors in two groups Show ads only to Test group Comment
Show ad
Don‘t show ad
Test group
Control group
Split your visitors evenly into
two cohorts.
Follow the cohorts over time to
see if showing ads to the Test
group increases conversions
and revenue in comparison to
the control group.

23. 23
Continuous incrementality tests will inform attribution model to make
incrementality actionable on micro level
5
50
80
15
38
60
Facebook Paid
search
Display
prospecting
5
50
80
13
40
65
Paid
searc
h
Facebook Display
prospecting
5
51
80
7
50
76
Display
advertising
Facebook Paid
search
5
50
80
Facebook Paid
search
Display
prospecting
Incrementality
test results Attribution model 1 Attribution model 2 Attribution model 3
Attribution
Incrementality
Attribution model 3 gets closest to the incrementality test results and will best reflect causality

24. 24
Neither level is
sufficient on its
own – in
combination they
fulfill all needs
Integrating outcome of incrementality tests on macro
level only doesn’t benefit marketers
Inform in-channel bidding
decisions
Inform budgeting decisions on
channel level
Reflect causality not just
correlation
Attribute credits to clicks within
customer journey
Incrementality
(macro level)
Attribution
(micro level)

25. Step-by-step
execution

26. 26
There’s an opportunity to make an even bigger impact by enhancing the new
system with further information
Enhance tracking Make actionable
Add important metrics for
realistic ROI calculation
Product margins
Returns & cancellations
New Customers
Etc.
Ingest attributed data into
any biddable media
platform.
Optimize for incremental
long-term profits.
Test incrementality Refine attribution model
Validate outcome of
attribution system by
continuously running
incrementality tests
Adapt the attribution
model and modify
attribution rules until
results match those of
incrementality tests

27. tROAS

28. 28
Why is Google now pushing for its own bidding solution and what are the
implications for retailers?
Google needs to drive growth
Amazon has replaced Google as the
starting point for consumers’
shopping habits. Now Google needs
new ways to drive additional growth
and keep up with competition.
Better ROAS = More budget
With its own bidding Google gains
more control and promises better
advertising performance for retailers,
even if it means some transparency
is sacrificed.
The result is commoditization
A single superior solution means that
bidding is commoditized, taking away
a key competitive advantage for
smart retailers. Retailers must now
find a new way to compete and win.

29. 29
Retailers should choose the ideal campaign + bidding solution based upon
the selection of a CLV vs. ROAS approach.
Bid
Time RM List
Location Device
OS
App
Browser
Creative
Language
Query Partner
History
Control and manage the data input to
include conversion value (e.g., CLV,
margins and returns) plus attributed
data delivered via touchpoints.
Data Input
Structuring and negative management
to secure competitive advantage.
Automatically anticipate performance
peaks driven by external influences.
Performance
Exception Management
In a tROAS world, pricing is the new
bidding. Leverage insights from Google
Shopping to identify optimal price points
and promotions.
Give retail teams additional levers for
non-ROAS driven strategies such as
promotional, seasonal, and inventory-
related decisions.
Price Optimization
Google Automated Bidding Promising Enhancements
Signals available via
bid modifiers
Signals available
exclusively via Google

30. 30
Click growth is greater than 100% when D&E ends at same CPC. That proves
that some traffic was not available to the regular campaigns due to D&E.
Confidential
D&E ended at 0:00 midnight. Comparing two days before (Mon/Tue) vs two days after (Wed/Thur)
Explanation
If D&E had worked correctly, when paused, clicks should
grow by ca. 100% on the remaining sibling, if the CPCs stay
on a similar level.
A click growth > 100% indicates that previously, a share of
traffic has had vanished.
Besides unknown problems with D&E, there are two
obvious reasons for traffic missing during test phase:
• Existing customers randomized into NC campaign pair,
where the regular campaigns had audiences exclusions
with -90%, therefore missing eligibility by design.
• Generic traffic in the designer campaign when the
regular campaign was refraining from bidding on by
design.
Clicks growth on regular campaigns
when D&E ended in DE
64%
Before
42%
Generics
Designer
58%
36%
After
+143%
EC NC
Generics
33%
67%
After
66%
Before
34%
Designer
+101%
CPC 0.39 0.39 0.24 0.23

31. 31
With D&E paused, without any other changes,
conversions grew faster than clicks.
Confidential
ExplanationBefore vs after on non-tROAS campaign
Clicks Conversions
NC
Before
91%7%
93%
9%
EC
After
+104%
EC
Before
22%
78%
77%
23%
After
NC
+121%
With the test sibling campaigns paused, clicks were
expected to grow ca 100% at same CPC. EC had a lot
stronger growth rates but at low volume.
However, conversion rate growth was unexpected. One of
the reasons: In shared customer journeys spanning over
both A and B, tROAS won the conversion in 40% of the
instances, while current bidding won the conversion in
21%
Now, the current bidding can act as introducer and not lose
the conversion credit.
CPC 0.39 0.39 2.9% 3.2%CR%
40%
21%
tROAScurrent
bidding

32. 32
Determine actual smart bidding advantage by correct A/B test and secure
efficiency gains over other advertisers using the same algorithm
Geo-split Test
Use a Geo Split test in order to validate
the accuracy of D&E and decide which
testing method is most reliable:
• Run campaigns in different geo
locations that correlate in spend and
revenue
• Avoid cookie-overwriting, campaign
priority effects and other potential
D&E artefacts
KPI target changes
Measure how quickly tROAS reacts to
target changes and if too frequent
changes lead to performance decline.
Test: A & B both run on tROAS. “A” goes
through different target changes, while
“B” is kept at original target.
Campaign set-up
Test if machine learning improves
further by providing pre-knowledge on
query behavior or best-sellers. Test
tROAS in priority campaign setup for
• Designer queries
• Website best sellers
• Combination
Compare against regular tROAS without
priority split.
1 2 3

33. 33
Determine actual smart bidding advantage by correct A/B test and secure
efficiency gains over other advertisers using the same algorithm
Include profit data
What is the performance impact of
ingesting order profitability into the
Google bidding?
• Through synchronization with Google
Analytics (360)
• Via AdWords conversion pixel
Will tROAS subsequently react to higher
new customer rates or higher product
margins?
Control audiences
Can we control audiences?
• directly, with Google’s help
• indirectly, by ingesting attributed data
into Google?
For example, if we set ingest conversion
values of 0 for low-funnel users, does
Google stop spending money on website
visitors 1 day?
Price intelligence
With Google advertisers using tROAS,
price becomes the new bidding.
How much additional revenue and profit
can be generated by algorithmically
changing prices?
• Test price elasticity of traffic and
revenue across channels
• Deduct scenarios, test volume and
profit benefits of scenarios
4 5 6

34. Q&A

35. Thank You!
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https://www.crealytics.com/