SMX Advanced: Reverse Engineering Google Shopping

#SMX #11B @AndreasReiffen
Reverse
Engineering
Google Shopping
–
10 Hypotheses
Tested

About…
• Data-driven online advertising strategist
• Online retail expert
• Entrepreneur
• Over €3 billion in customer revenues
last year
• SaaS product for Google Shopping &
Search
• 160 true experts in their field
• Offices in Germany & UK, new office in
NYC
… me … Crealytics & Camato

Hypothesis 1
A very granular account structure harms performance

Method to validate hypothesis
A/B/CTest:
- All in one product group
- All their own product group
- Split out product after 5 clicks
Measure how many products
receive Impressions.
Google:
“Don’t split out products too soon,
because the algorithm will apply
performance metrics to other
products in the same group.”
Question: Does a granular account
structure get more impressions?

While more products received Impressions, the difference was not
significant
# of products with Impressions & total Impressions Key insights
The approach in which
products were split in
separate product groups
from 5 Clicks had the
highest number of products
with Impressions as well as
the highest number of
Impressions in total.
However, the difference is
not significant.
1,100
900
1,000
700
800
1,019
From 5
clicks
All their
own
+6.7%
1,087
1,040
All in
one
80
40
60
100
120
140
From 5
clicks
139
+3,2%
All their
own
135
All in
one
138
# products w Impressions Impressions (k)

Product group granularity has apparently
no impact on the number of products that
receive impressions.
To test: effect on automated bidding
(200 clicks minimum for algorithm to work).
Hypothesis proved false

Hypothesis 2
SKU Price mainly influences CTR, but not impression volume

Compare performance:
2000 products above
competitor price
vs
2000 products below
competitor price
Observation: Massive drop in
traffic after slight increase in price
Question: Is this due to lower CTR
or lower Impression volume?

Impressions and clicks in Google Shopping are often very
sensitive to price changes
Clicks drop off after product price increases Chart Info
5% price increase
coincides with a 60%
decrease in clicks
0
20
40
60
80
100
120
0
10
20
30
40
50
60+5%
Pricein£
own price clicks Google product category:
Apparel & Accessories >
Shoes > Sneakers
Brand:
Days
Clicks
Price

Cheap products generate the lion’s share of total traffic
Traffic of similar products within one shop Key insights
Cheaper than competition
products generate much
more traffic.
CTR is slightly lower but the
impact is far less significant
than on Impression volume.
Due to this and higher CR,
three times more
conversions at 30% lower
CPO
134%
4.282.611
Imps
1.828.412
0.5%
2.422.41
CTR# of products
9%
2.0471.876
expensive products cheap products
40
28
-29%
CPO
274
+280%
Conversions
1,042
0,6%
CR
61%
1,0%

The CTR is roughly the same for
cheap and expensive products.
SKU prices have a heavy impact on
Impression volume.

Hypothesis 3
Price determines an ad’s position

Question:
We knew price impacts
Impression volume, but
does it also influence
position?
Analysis 1:
Analyzed two sets of
4,000 queries and
compared the position of
the cheapest products
Analysis 2:
Which factors besides
price have an
influence on offer
position?

In Google Shopping there are two types of positions
1 2 3 4
5 6 7 8
1
2
3
Product position Offer position

Product price clearly has an impact on offer position
Pos 1
Pos 2 14%
65%
Set 2
Pos 5 +
Pos 4 4%
7%Pos 3
11%
Pos 1
Pos 2 16%
62%
Set 1
Pos 5 +
Pos 4 4%
9%Pos 3
9%
Offer position* of product with cheapest price Key insights
We analyzed two sets of
queries.
In both sets, the cheapest
product reached offer
position 1 in more than
60% of cases.
*data provided by price comparison tool

Possible reasons for lower position despite better price
Seller Rating Key insights
Surfdome DE is in
position 7 despite
significantly lower price.
But:
While Pos. 1 – 6 have a
Seller rating
Surfdome DE does not
have a seller rating

Shipping Fee Key insights
sportXshop is in pos. 4
despite lowest base
price.
But:
Total price including
shipping: €29.00
Total price for
position 1: €27.90

Max CPC Key insights
Footlocker only reached
position 3 despite seller
rating AND slightly
lower price.
But:
Current bid on product:
€0.25
Proposed bid by Google:
€0.75

The hierarchy for offer position:
• Cheapest price on top
• No seller rating = no top position
• CPC secondary
Hypothesis proved true

Hypothesis 4
Bids are the most important factor for generating more traffic

Question:
If price is so
important, how
much does bid
matter?
Test 1:
Similar bids for cheap
vs expensive products.
Increase bids DoD,
compare traffic.
Test 2:
Increase prices from
lowest to highest, keep
bids stable, compare
traffic.

At the same bid, cheap products generate more impressions
than expensive ones
There is a direct relationship between product price,
Max CPC bid and impressions Key insights
At each bid stage,
impressions for expensive
products were lower than
for competitively priced
products.
While expensive products
still gained volume after
six Max CPC increases,
cheap products reached
the volume plateau at a
lower Max CPC.
1.2
1,000 0.6
1.0
0.4
0.2
0 0.0
500
0.8
1,500
2,000
5-13-165-11-16 5-12-16 5-15-16 5-17-165-16-165-14-16
Max CPC
Impressions
per product
Max CPC
expensive products
cheap products

Impressions decreased massively after
changing the prices to be the most expensive
After changing the prices
from cheapest to most
expensive of competitive
set, we found that
impression volume
decreased by almost 60%.
We were able to rule out
account performance as an
influence, as overall
impressions were up by 12%
during the same period.
Impression development after increasing price Key insights
12
-59
Impressions on product Impressions on account
% change
Test Control

Invest in high CPCs or reduce product prices?
Primarily
invest in Google
budget
CPCs, cheaper
products Resulting revenue Price dominant
25
50
25
Profit
per sale
depletedMargin
25.000
High
CPCs
Lower
price
34.000
This schematic
illustration shows
that price has the
bigger impact than
just increasing bids
Further analyses
required to assess the
relationship between
price and bid
Google budget per sale
25
50
12
13
Profit
per sale
Margin depleted
Google budget per sale
Price reduction
1 2
1 2

Price can influence traffic levels more
significantly than bids.
Combined with conversion effect, price
changes seem to be more effective.
Further testing required.

Hypothesis 5
Titles are the most important feed element

Question:
Can we manipulate traffic and
queries by changing feed elements?
Is feed management the new
campaign management?
Test:
We systematically improve or
destroy main feed elements
and measure traffic changes.

Destroying product description: no effect
Evening dress
byTFNC…
Baseball cap
by Nike…
before destroyed
100% 98%
Impressions
Non-sense description No clear traffic effect

Destoying product category: no effect
before destroyed
100%
118%
Impressions
Non-sense category No clear traffic effect
Shoes > …
> Athletic Shoes
Clothing > …
> Rain Suits

Similar results visible across-the-board
Impressions
Include important missing search queries
- across many products - Traffic for “Jordan Kids” totals
Jordan 1 Flight 4
Premium
Jordan Kids -
Jordan 1 Flight…
100100
120
167
+67%
Query Level change
+20%
Control group change
AfterBefore

Including queries is beneficial if those
queries are under-represented in
comparison to market volume.
Other elements are less important.
Google openly says “category will become
irrelevant”.

Hypothesis 6
Changing titles leads to a loss of history and therefore a loss of traffic

Test:
We changed the titles of
~2,000 products and
compared DoD performance
before and after the change
History can get lost if feed
changes.
Question: Is this the case
with title changes?

Performance sees only a slight dip on the day of the
upload
Impressions before/after in comparison to total account Key insights
Some products had a slight
dip in Impressions on the
day of the upload but
picked up immediately
afterwards, showing an
even stronger uplift than
the account avg.
2017-02-24
-2.9%
-12.5%
TestGroup
Account
+6% vs +34%

Comparing each product, there is no hint that changing
titles causes a loss in traffic.
Impression development before/ after title change Key insights
These four products show
that the title change seems
to have no impact on
performance.
While some had a slight
decrease in Impressions,
others saw an immediate
increase.
What’s in common: All saw
an overall increase during
the test
-80.6%
-9.6%
+0.4%
+26.0%
Significant drop
Slight drop
No impact
Immediate increase
+13% +86%
+144% +127%

Performance is not affected
significantly.
Following uplift outweighs potential
initial dip.

Hypothesis 7
ECPC uses audience information to predict conversion probability

Test:
Before/ after test, activate ECPC
but keep audience modifiers
stable. Compare RLSA share, and
CPCs.
Use account as baseline
We assume that Google considers
user information to decide the
conversion probability of a click.
Question: Does it overwrite
RLSA modifiers?

RLSA CPCs increase more significantly than
Non-Audience CPCs after activating ECPC
% increase in CPC RLSA vs non-RLSA Key insights
While there was an overall
increase in ad spend during
the test, the avg. RLSA CPC
increased more
significantly.
Also noticeable: The gap in
CPC growth became
stronger each week during
the ECPC algorithm
learning phase.
16
1515
23
20
17
Week 1 Week 3
+44%
Week 2
+13%
+33%
RLSA
Non-RLSA

Google pushed lower funnel audiences due to higher
conversion probability
Relation CPC RLSA vs non-RLSA by audience type Key insights
Google increased the CPC of
lower funnel audiences such
as cart abandoners and
purchasers as these are
more likely to convert.
Audience CPCs increased by
16% in comparison to non-
RLSA despite similar bid
modifiers.
159
102
184
101
Upper funnel Lower funnel
-1%
+16%
After
Before

Google’s learning curve shows
reliance on user information.
Caution: Use ECPC carefully if generating
new customers is your main objective.

Hypothesis 8
Raising bids in Shopping
increases share of less relevant Search Terms

Tests:
Significantly bid up products
(+200%) and analyze search queries.
Repeat across multiple designers.
Question:
Do higher bids attract
poor-quality traffic?

Share of less specific traffic increases over
proportionality
Most obviously, the share of
less specific traffic increases
over proportionally. This
has been validated across
multiple designers.
Secondly, we observed a
hike in avg. CPC on designer
only traffic – even if volume
did not change significantly.
This shows overbidding can
be expensive in Shopping.
Traffic volume Chi Chi London before / after bid increase Key insights
0.6
4.3
2.1
0.4
1.3
bid = 0.50 bid = 1.50
0.7
5.4
designer only [chi chi london]
designer + category [chi chi dress]
generic terms [party dresses]
0.40
0.09
0.22
0.85
0.25
0.63
CPC
Max CPCs, impressions (k), avg. CPC

New Test: Using query length as an indicator, what
happens to traffic quality when increasing bids?
Raising bids attracts a
higher share of shorter
search queries, a similar
observation as with broads.
Long tail queries increase at
far lower growth rates.
Luxury designer core brand
terms tend to have a low
word count as well, but they
require very high bid levels
to win.
Traffic by word count and bid level Key insights
22.6%
0.0%
39.5%
0.6
73.0%
44.0%
17724
12.0%
4.0%
24.0%
2 words
3 words
1 word
2.5
339
4 words 1.5%6.7%
67.6%
1.2CPC bid
impressions (k) and share of impressions

Bidding up leads to a higher share of
generic traffic; specific traffic is
maximized first.
Higher bids may lead to the same
traffic becoming more expensive.

Hypothesis 9
Matching between query and product is purely based on text match

Question:
Does Google use elements
for matching that are not
present in the feed?
Test:
Extract queries by product
and compare to feed, URL
and website

Google can match queries to information that is contained
in the image, but not in feed or website
Title:
BoyTracksuit by Adidas Rose SatinTrackTop
Query matched:
Adidas leopard print jacket kids
Title:
Nike Free Flyknit - Women Shoes Multi Size 38.5
Query matched:
rainbow Nike shoes

Similarly: The information about skirt length is not
present anywhere in the feed or on the landing page
Title:
Lipsy - Lace Bodycon Dress - Navy
Query matched:
Lipsy dress short
Title:
Lipsy - Cap SleeveV Neck Bodycon Dress - Red
Query matched:
Lipsy dress short

Examples suggest that Google uses
more than the feed for query matching.
Other sources might include:
• Image recognition
• GTINs
• KPI based machine learning

Hypothesis 10
Out of stock products should remain in feed or history is lost

Test:
Compare products with same ID which
a) remain in feed
vs
b) get deleted
Google: “History is lost if out of
stock products are deleted from
feed.
Therefore keep them in feed for 30
days.”

Products that remain in feed pick up same traffic levels
while deleted ones take time
Impression levels DoD after being in stock again – per
product Key insights
It does not seem to
influence traffic levels if
products with the same ID
are taken out of the feed
while being out of stock or if
they remain in the feed.
Lower Similar Higher
Lower Similar Higher
Avg. Before
After
Deleted while out of stock
In feed while out of stock

Google recognizes same IDs if the
products re-enter the feed within 30
days.
It doesn’t matter products remain or
are deleted.

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SMX Advanced: Reverse Engineering Google Shopping

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