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Page 1: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

We combine long

advertising experience with

in-depth digital technical

know how

Page 2: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

Google AdsSmart Bidding

How we turned machine learning data into an effective smart bidding strategy. €

Page 3: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous
Page 4: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

WHY

MACHINE

LEARNING

WHY

SMART BIDDING

HOW

SMART BIDDING

WORKS

SMART BIDDING

FOR SEARCH

CASE STUDY

PAPAKI.COM

Page 5: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

WHY

MACHINE

LEARNING

AIi s t he sc ience o f making th ings smar t

Comput ing Power

There is an exponential growth of technology capabilities that is

calculated to double its effect every 18 months.

Power Abundance o f da ta

As users we produce a huge amount of data every day. Google

announced that only via its search engine, over 3 trillion queries are

generated in a yearly basis.

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WHY

SMART BIDDING

Bidding i s a cha l l enge

Until recently, bidding was relatively simplistic:

As advertisers we placed a keyword-level bid according to the

possibility that this keyword will lead to a conversion based on last

click attribution model

Since then, more and more signals have been added as data points

such as device type, time, weekday, location that can be featured

into our bidding strategies

Today, the available signals have been multiplied and as a result,

as advertisers, we are not capable of calculating such big data in

order to come up with the best bid for each keyword or audience.

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Demographics

Search Network

Partner

Conversion

Path

Conversion

Rate / Value

Demographics

Ad Creative

Audience

Segment

Device Type

Operating

System

Application

BrowserTime

Device Weekday

Locations

WHY

SMART BIDDING

KEYWORD

BIDS

from a few signals

to many many more

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WHY

SMART BIDDING

W o r k F a s t e r w i t h

A d v a n c e d M a c h i n e L e a r n i n g

In bidding, machine learning algorithms train on data at a vast

scale and help us make more accurate predictions across

your account about how different bid amounts might impact

conversions or conversion value.

W o r k S m a r t e r

Using smart bidding strategies, we can dedicate less time to

daily campaign optimization tasks and utilize that time to

improve our client’s overall strategy

W i n M o r e

Having correctly set-up our smart bidding campaigns, we can

achieve greater results with the available budget while

maintaining control over spend.

Page 9: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

HOW

SMART BIDDING

WORKS

AdWords Core Capab i l i t es

Page 10: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

SMART BIDDING

FOR SEARCH

Steps to fo l l ow when app ly ing Β i dd ing fo r the

f i r s t t ime :

1 . S t a r t m a n u a l b i d d i n gU s e m a n u a l C P C

2 . G e t a b o u t 5 0 t r a n s a c t i o n s

o v e r l a s t 3 0 d a y s

3 . A p p l y S m a r t B i d d i n gS e l e c t a n y o f t h e a v a i l a b l e m e t h o d s t h a t

m e e t y o u r n e e d s

4 . W a i t 3 - 4 w e e k s a n d e v a l u a t e

Page 11: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

CASE STUDY

L e v e r a g i n g S m a r t B i d d i n g

s t r a t e g i e s i n o r d e r t o

t r a n s f o r m G o o g l e D i s p l a y

i n t o a p r o f i t a b l e c h a n n e l

f o r P a p a k i . c o m

Page 12: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

W e g a t h e r e d f i r s t p a r t y d a t a

f r o m o u r c l i e n t t h a t w e r e

h e l d i n i n - h o u s e C R M

s y s t e m s .

Page 13: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

W e c o m b i n e d t h e d a t a o u r c l i e n t

g a v e u s w i t h t h i r d p a r t y d a t a

f r o m G o o g l e A n a l y t i c s & G o o g l e

A d s p l a t f o r m s t o g a i n d e e p

a u d i e n c e i n s i g h t s .

Page 14: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

B a s e d o n t h i s d a t a c o m b i n a t i o n ,

w e c r e a t e d m a n y u n i q u e

a u d i e n c e s o f e x i s t i n g

c u s t o m e r s t h a t h a d d i f f e r e n t

p r o d u c t n e e d s a n d p r e f e r e n c e s .

M o r e o v e r , w e b u i l t o n t h e

e x i s t i n g c u s t o m e r s p r o f i l e s t o

b u i l d s i m i l a r a u d i e n c e s f o r

a t t r a c t i n g p o t e n t i a l n e w c l i e n t s .

Page 15: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

W e m a t c h e d e a c h d i f f e r e n t

a u d i e n c e g r o u p w i t h t h e

c u s t o m i z e d D i s p l a y R e s p o n s i v e

A d s a n d l a u n c h e d c a r e f u l l y

s t r u c t u r e d c a m p a i g n s u s i n g

M a n u a l B i d d i n g t o g a t h e r t h e

n e c e s s a r y c o n v e r s i o n d a t a .

Page 16: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

A f t e r 1 5 d a y s , w e a c q u i r e d t h e

r e q u i r e d c o n v e r s i o n d a t a t h a t

t h e G o o g l e M a c h i n e L e a r n i n g

a l g o r i t h m n e e d s a n d w e

l a u n c h e d u n i q u e a n d p o r t f o l i o

s m a r t b i d d i n g s t r a t e g i e s .

Page 17: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

A f t e r 3 0 d a y s , w e h a d t h e d a t a

t o e v a l u a t e a n d c o n s i d e r a b l y

o p t i m i z e t h e s m a r t b i d d i n g

s t r a t e g i e s t o m a x i m i z e R O I f o r

t h e s e l e c t e d c a m p a i g n s .

T h e o p t i m i z a t i o n p r o c e s s o n

t h r o t t l i n g t h e M a c h i n e L e a r n i n g

a l g o r i t h m i n o u r f a v o r i s

c o n t i n u o u s a s w e k e e p

i n c r e a s i n g

o u r a u d i e n c e r e a c h .Manual Bidding

Rule based BiddingAdwords Automated Rules

Adwords Scripts

Third Party Platform

Automated Bidding(1-2 bid refreshes per day on avg.)

AdWords

Auction-Time

Bidding

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CASE STUDY RESULTS

S i n c e t h e A I - b a s e d s m a r t b i d d i n g a l g o r i t h m e f f e c t i v e l y

g a t h e r e d c o n v e r s i o n d a t a , w e u s e d b o t h e x i s t i n g a n d n e w

c u s t o m e r s a u d i e n c e s t o y i e l d

i n c r e a s e i n C o n v e r s i o n s i n c r e a s e i n R O I

Page 19: We combine long advertising experience with in … › files › _boussias_conferences...The optimization process on throttling the Machine Learning algorithm in our favor is continuous

T h e c o m b i n a t i o n o f f i r s t p a r t y d a t a a n d t h i r d p a r t y d a t a a l l o w e d u s t o

t a k e f u l l a d v a n t a g e o f G o o g l e s m a r t b i d d i n g s t r a t e g i e s b y b u i l d i n g

u n i q u e c u s t o m a u d i e n c e s a n d u s e

n e w l y f e a t u r e d D i s p l a y R e s p o n s i v e a d s .

A s a r e s u l t , n o t o n l y d i d w e e f f e c t i v e l y

u p s e l l t o o u r e x i s t i n g c l i e n t s ,

b u t w e a l s o m a n a g e d t o

a t t r a c t n e w c u s t o m e r s a n d i n c r e a s e o u r

p r o d u c t s a l e s i n a p r o f i t a b l e m a n n e r .

CASE STUDY KEY FACTOR

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