MDDM
Mas e ’s deg ee P og am in
Da a-D i en Ma ke ing
THE IMPACT OF AUTOMATION-DRIVEN PERFORMANCE MAX
CAMPAIGN, SMART BIDDING STRATEGIES, AND TENSCORE
THIRD PARTY MARKETING AUTOMATION TOOL ON
CONVERSION, ROAS, AND OTHER KPIs IN GOOGLE ADS PPC
MANAGEMENT
OKEKE MOSES
Disse a ion
p esen ed as a pa ial equi emen o ob aining he Mas e Deg ee P og am in Da a-D i en Ma ke ing
NOVA In o ma ion Managemen School
Ins i u o Supe io de Es a ís ica e Ges ão de In o mação
Uni e sidade No a de Lisboa
NOVA In o ma ion Managemen School
Ins i u o Supe io de Es a ís ica e Ges ão de In o mação
Uni e sidade No a de Lisboa
THE IMPACT OF AUTOMATION-DRIVEN PERFORMANCE MAX CAMPAIGN, SMART
BIDDING STRATEGIES, AND TENSCORE THIRD-PARTY MARKETING
AUTOMATION TOOL ON CONVERSION, ROAS, AND OTHER KPIs IN GOOGLE ADS PPC
MANAGEMENT
by
Okeke Moses
Mas e Thesis p esen ed as a pa ial equi emen o ob aining he Mas e ’s deg ee in Da a-D i en
Ma ke ing, wi h a specializa ion in digi al ma ke ing and analy ics.
Supe iso : Ma lon Dalmo o
July, 2023
STATEMENT OF INTEGRITY
I he eby decla e ha ing conduc ed his academic wo k wi h in eg i y. I con i m ha I ha e no
used plagia ism o any o m o undue use o in o ma ion o alsi ica ion o esul s along he
p ocess leading o i s elabo a ion. I u he decla e ha I ha e ully acknowledge he Rules o
Conduc and Code o Hono om he NOVA In o ma ion Managemen School.
Okeke Moses
Lisbon, July 10 h, 2023
i
DEDICATION
This hesis is dedica ed o my belo ed daugh e - T easu e Chisom Okeke-
Moses o he lo e and belie in me. This p ojec will se e as a mo i a ional piece
ha will p opel he o a ain a highe g ound in any ca ee o he choice.
ACKNOWLEDGEMENTS
I wan o exp ess my deep g a i ude o my wi e, M s. Sa ah N Okeke-Moses, o he suppo
and unde s anding h oughou he pe iod o his p ojec . She is a blend o s eng h and a
pe soni ica ion o op imism who sha es a co e alue o ha ing a uni ed on o he p og ess o ou
amily. I also wan o app ecia e my child en-T easu e, Dominion, Zinnachimdinma and Chimamanda
who endu ed my absence du ing he pe iod o his p ojec .
Secondly, I wan o hank my supe iso , P o esso Ma lon Dalmo o, o cons an ly being
a ailable o a end o my ques ions whene e I beckoned on him. I wan o app ecia e his guidance,
knowledge, and he expe iences he sha ed wi h me o ensu e ha his p ojec becomes a eali y.

i
ABSTRACT
Google ads PPC ad e ising is cons an ly e ol ing and becoming mo e dynamic by inco po a ing new
ools o au oma ize ma ke ing campaigns. S aying cu en wi h he la es ends can be challenging as
all digi al ma ke ing channels a e in oducing new echnological ends o help PPC ad e ise s mee
hei campaign goals. This s udy explo es how a new au oma ed campaign impac s key pe o mance
indica o s, especially he ROAS and he numbe o con e sions o e-comme ce ad e ising on Google
ads. The s udy ound ha he pe o mance max campaign has a mo e posi i e impac on ge ing a
g ea e numbe o con e sions han he s anda d shopping campaign. Rega ding he impac on ROAS,
his s udy concludes ha au oma ed bidding s a egy ( a ge ROAS) is a key ac o ha in luences he
ROAS o a campaign and no he ype o campaign in ol ed. The s udy p oposes u he echnological
de elopmen o upg ading Google Ads s anda d shopping campaigns such ha PPC manage s can use
sma bidding s a egies on s anda d shopping campaigns du ing he campaign se up wi hou wai ing
o ge ce ain con e sions be o e swi ching om manual o au oma ed bidding s a egies. The s udy
also ound ha Tensco e, as a hi d-pa y MA, helps educe he ime in ol ed in es uc u ing keywo ds
and ad g oups in sea ch campaigns.
KEYWORDS
Google Ads, PPC, Ma ke ing Au oma ion, Pe o mance Max campaign, ROAS, Con e sions, Sma
bidding s a egy, Shopping campaign, Thi d-Pa y Ma ke ing Au oma ion.
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INDEX
1 INTRODUCTION---------------------------------------------------------------------------------------------- 1
1.1 BACKGROUND 1
1.2 THIRD-PARTY MA OVERVIEW 3
1.3 RESEARCH QUESTION 4
1.4 KEY PERFORMANCE INDICATORS (KPI) 5
1.5 RESEARCH OBJECTIVES 5
2 LITERATURE REVIEW ---------------------------------------------------------------------------------------- 8
2.1 IMPRESSION METRIC 16
2.2 THE CLICKS AND CLICK-THROUGH RATE --------------------------------------------------------------- 17
2.3 CONVERSIONS And CONVERSION RATES -------------------------------------------------------------- 18
2.4 ROAS 19
2.5 Google Ads Au oma ed Bidding (Sma bidding s a egies) --------------------------------------- 20
2.6 MAXIMIZE ROAS BIDDING STRATEGY (Ta ge ROAS Sma Bidding S a egy) ----------------- 22
2.7 CHALLENGES Wi h MA 23
2.8 GOOGLE PERFORMANCE MAX CAMPAIGN ----------------------------------------------------------- 24
2.9 THIRD-PARTY MA 26
2.10 TENSCORE AS A THIRD-PARTY MA 29
2.11 PROJECT MODEL AND HYPOTHESIS 31
2.12 PROJECT MODEL EXPLAINED 33
3 METHODOLOGY -------------------------------------------------------------------------------------------- 35
3.1 RESEARCH METHOD 35
3.2 METHODOLOGY APPROACH AND CONCEPT FROM PILOT STUDY -------------------------------- 35
3.3 THE EXPERIMENT STRUCTURE 36
3.4 CONVERSION AND ROAS TRACKING 37
3.5 GOOGLE ANALYTIC INTEGRATION 37
3.6 Pmax CAMPAIGN VS STANDARD SHOPPING CAMPAIGN ------------------------------------------ 37
3.7 PMAX VS S anda d Sea ch Campaign 40
3.8 INTEGRATING TENSCORE WITH GOOGLE ADS PLATFORM 40
3.9 EXPERIMENT DURATION AND KPI SAMPLE SIZES --------------------------------------------------- 41
3.10 MATHEMATICAL MODEL FOR EXPECTED RESULTS VALIDATION --------------------------------- 42
3.11 INTEGRATING RELIABILITY IN THE EXPERIMENTATION PROCESS -------------------------------- 43
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4 RESULTS ------------------------------------------------------------------------------------------------------ 45
4.1 CONTEXT DESCRIPTIONS 45
4.2 RESEARCH RESULTS OF ALL KPI FOR TACTICAL GEAR E-COMMERCE CAMPAIGNS ----------- 45
4.3 CONVERSION METRICS RESULTS FROM THE TACTICAL GEAR E-COMMERCE
CAMPAIGNS EXPERIMENTS 45
4.4 ROAS METRICS RESULTS FROM THE TACTICAL GEAR E-COMMERCE CAMPAIGNS
EXPERIMENTS 46
4.5 CLICKS AND CLICK RATES METRICS RESULTS FROM THE SPLIT CAMPAIGNS ------------------- 48
4.6 RESULTS OF ALL KPI FOR LEADS CAMPAIGN ---------------------------------------------------------- 50
5 DISCUSSIONS ------------------------------------------------------------------------------------------------ 52
5.1 SUMMARY 52
5.2 EVALUATING THE IMPACT OF GOOGLE ADS PMAX CAMPAIGN ON ROAS ---------------------- 52
5.3 EVALUATING THE IMPACT OF GOOGLE ADS PMAX CAMPAIGN ON CONVERSION
COMPARED TO STANDARD SHOPPING CAMPAIGN --------------------------------------------------------- 53
5.4 EVALUATING THE IMPACT OF GOOGLE ADS SMART BIDDING STRATEGY (TARGET
ROAS) ON ROAS 54
5.5 Valida ing Hypo hesis-H3 56
5.6 RESEARCH MODEL AND HYPOTHESIS ADOPTION --------------------------------------------------- 56
5.7 CONTRIBUTIONS 57
6 CONCLUSION ------------------------------------------------------------------------------------------------ 59
6.1 RESEARCH LIMITATION 59
6.2 INDICATIONS FOR FURTHER STUDIES 60
BIBLIOGRAPHY ------------------------------------------------------------------------------------------------------ 62
APPENDIX ------------------------------------------------------------------------------------------------------------- 66
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LIST OF FIGURES
Figu e 2.1 Pmax campaign use su ey epo adap ed om Sea ch engine land ............................ 26
Figu e 2.2 A causal Model diag am o sea ch ads a a que y le el. .................................................. 32
Figu e 2.3 Hypo hesis 1 model diag am showing he ela ionship be ween Pmax, Con e sions &
SSC. 32 Figu e 2.4 Hypo hesis 2 model diag am showing he ela ionship be ween Pmax, ROAS& SSC
............................................................................................................................................................ 32
Figu e 2.5 Hypo hesis 3 model diag am showing he ela ionship be ween sma bidding, ROAS &
manual bidding. .................................................................................................................................. 33
Figu e 2.6 Model diag am illus a ing Google Ads’ Pmax Campaign, S anda d Shopping/Sea ch
campaign, sma bidding, manual bidding, and hei impac on Con e sions and ROAS .................. 33
Figu e 3.1 50% SPLIT: Con ol & T ea men G oups .......................................................................... 39
Figu e 3.2 Pmax and SSC as T ea men & Con ol G oups Respec i ely............................................ 39
Figu e 3.3 Tensco e in e ace showing campaign s uc u e sco e on Google Ads ............................ 40
Figu e 4.1 Cha showing he Numbe o con e sion esul s om Pmax & SSC ................................. 46
Figu e 4.2 Con e sion T ends om Pmax and SSC Expe imen om Ma 20-June 15 ...................... 46
Figu e 4.3 Con e sion Ra es om Pmax and SSC Campaign Expe imen s ........................................ 46
Figu e 4.4 ROAS Sha e be ween PMAX & SSC .................................................................................... 47
Figu e 4.5 ROAS T ends Pmax and ROAS T ends S anda d Shopping ............................................. 47
Figu e 4.6 Con e sion Values and Ad Spend om Pmax & SSC -spli expe imen ............................ 48
Figu e 4.7 cha showing he numbe o con e sions be ween Pmax & SSC Campaign ................... 48
Figu e 4.8 Clicks & Click Ra es Pmax Vs. SSC Campaign Resul s ......................................................... 49
Figu e 4.9 Imp essions om Pmax and SSC wi h De ice D ill Down a e 90 days ............................ 50
Figu e 4.10 Click end om Pmax Vs. SSC Spli Expe ience a e a pe iod o 90 days...................... 50
Figu e 5.1 ROAS alue and end om manual bidding s a egy in SSC ............................................ 55
Figu e 5.2 ROAS alue and end om a ge ROAS sma bidding s a egy in SSC .......................... 55
Figu e 5.3 SSC ROAS T ends Showing a Rise in ROAS a e swi ching o Sma Bidding om May
9 h, 2023 ............................................................................................................................................ 56
16
he digi al ad e ising. Depending on he goal he ad e ise wan s o achie e wi h he in eg a ion o
he MA such as b and awa eness, sales, o leads gene a ions a e calcula ed by hese me ics.
KPI hence o h e e ed a e ime se ies da a because hey consis o disc e e ime da a me ics
whose alues change wi h ime (Tappé & Mülle , 2022). Hence, his s udy is aimed a in es iga ing he
e ec o he impac o hese au oma ed asks on key pe o mance index o Google ads campaigns.
Acco ding o Allazo (2020), key pe o mance me ics a e moni o ed o e alua e he e iciency o
ce ain campaigns. The equi emen o he use o MA is o see posi i e ou comes on he common
pe o mance me ics. This s udy will in es iga e he impac o bo h in-house and hi d-pa y MA on he
ollowing pe o mance me ics: imp essions, click- h ough a e (hence o h e e ed o as CTR),
con e sions, e u n on ad spend (hence o h e e ed as ROAS).
1.5 RESEARCH OBJECTIVES
F om he beginning o he ise o he In e ne o online ma ke ing, PPC ad e ising has been an
ac i e segmen in main ma ke ing ac i i ies, me ging ou sides (Allazo , 2020). The ou sides o PPC
in eg a e he PPC channels (Google Ads, Bings Ads, Yahoo, Baidu, e c.), he PPC ne wo ks (display and sea ch
ne wo k), he PPC manage s known as ad e ise s and he in e ne use s who a e he a ge s o PPC
ad e isings. The need o MA is o in eg a e hese ou media o achie e enhanced esul s o all ou lis ed
ac o s. Fo he ad e ise s, i is how o achie e hei ma ke ing objec i es such as con e sions, e u n on ad
spend (ROAS he ea e ) and o he key pe o mance me ics.
The use s will be conce ned abou ha ing good expe iences while sea ching o he p oduc s and
se ices o hei in e es . Hence he esea ch objec i es o his s udy a e ou lined as lis ed below:
a) To e alua e he impac o he implemen a ion Google ads au oma ed campaign wi h ML ea u e
known as “Pe o mance Max Campaign”) on ROAS and Con e sions plus o he pe o mance me ics in
B2C PPC managemen ;
b) To s udy he impac o in eg a ing Tensco e hi d-pa y MA wi h Google Ads PPC on he ease o
achie ing epe i i e asks in Google Ads PPC managemen ;
c) To e alua e he impac o Google ads’ sma bidding s a egy – “maximize con e sion alue wi h
a ge ROAS” on ROAS in B2C Campaign managemen ;

17
This esea ch will exploi hese pas esea ch gaps. I will be based on he echnological equi emen
o s ay upda ed wi h he ending skills needed o d i e eme ging ends in digi al ma ke ing
au oma ion. I was emba ked wi h he aim o ha e a eal- ime app oach o he implemen a ions and
in eg a ion o hi d-pa y MA ools in Google Ads PPC managemen wi h a iew o using he da a
de i ed om he s udy o make in o med da a-d i en decisions o PPC manage s who would like o
in es iga e u he on he need o in eg a e hese au oma ed ea u es in hei ma ke ing s a egies.
I will also p o ide academic insigh o u he esea ch. Ma ke ing s a egis s can bene i om
academic ma ke ing’s hough leade ship o lea n how o ans o m hese challenges in o s a egic
oppo uni ies o compe i i e ad an age (Plangge e al., 2022). I is in ended o p o ide p ima y
insigh s d awn om he p ac ical in eg a ion o Tensco e as a hi d-pa y MA. I will alida e i s
ask- elie ing abili y o c ea ing Google ads PPC campaigns and he claim o imp o ed quali y sco e.
Fu he mo e, he s udy will di e deep o es ablish he impac o implemen ing he Google ads au oma ed
campaign known as Pe o mance Max campaign on ROAS in B2C agains he manual s anda d shopping
campaign in he Google Ads PPC pla o m.
18
2 LITERATURE REVIEW
A s udy on he p e ailing ends in digi al ma ke ing es ablished ha he use o a i icial in elligence
and machine lea ning o d i e digi al ma ke ing au oma ion is he cu en p e ailing ends in digi al
ma ke ing (Ko ane e al., 2019). Ma ke ing au oma ion ools ha e become an essen ial pa o mode n
digi al ma ke ing s a egies. These ools help businesses au oma e epe i i e asks, such as lead
gene a ion, email campaigns, social media managemen and PPC campaign managemen o imp o e
hei ma ke ing e iciency and pe o mance (Hammoud e al., 2019).
Thi d-pa y ma ke ing au oma ion ools in eg a e wi h a ious pla o ms, including Google Ads, o
enhance hei capabili ies and a ge pe o mance me ics. The in eg a ion o hi d-pa y ma ke ing
au oma ion ools wi h Google Ads can signi ican ly impac con e sions and e u n on ad spend (ROAS)
o businesses. This li e a u e e iew aims o explo e he impac o In-buil MA ea u es o he Google
ads Pe o mance Max campaign, Google Ads ROAS sma bidding s a egy and he impac o in eg a ing
Tensco e hi d-pa y ma ke ing au oma ion ools wi h Google Ads wi h a iew o s udying hei impac
on con e sions, ROAS and o he pe o mance me ics wi h a en ion o ease o doing epe i i e asks
highligh ing ele an esea ch and indings.
This li e a u e e iew will explo e he cu en esea ch on he impac o in eg a ing hi d-pa y
ma ke ing au oma ion ools wi h Google Ads on a ge pe o mance me ics, wi h a ocus on
con e sions and ROAS me ics in B2C. I will del e in o echnological inno a ion in AI d i en au oma ion
p ocesses in Google Ads PPC managemen .
The wo d ‘Au oma ion” as ega ds o digi al space has been iden i ied as a p ocess o elimina e
manual inpu s. I wo ks wi h da a o execu e epe i i e asks using a p e-de ined se o ules (Anicca
Webina s, 2023). Hence, Ma ke ing au oma ion e e s o he use o echnology o s eamline and
au oma e epe i i e ma ke ing asks. I is aimed a helping businesses imp o e he e iciency and
e ec i eness o hei ma ke ing e o s and enables ma ke e s o ocus on highe -le el s a egies (Singh
e al., 2018). I is he u iliza ion o ma ke ing echnology o au oma e p ocesses in digi al ma ke ing
ope a ions, such as he alloca ion o campaign budge , campaign c ea ion, and gene a ion o enhanced
pe o mance epo s (Biegel, 2009).
19
Ano he de ini ion o MA by pas s udies e e s o i as he use o so wa e and echnology o
au oma e epe i i e ma ke ing asks such as email ma ke ing, social media managemen , lead
gene a ion, and cus ome segmen a ion (Rosenbe g & Cze niawski, 2016). This sea ch made a s ong
e e ence o MA as a “so wa e’ as many pas s udies ha e linked he phenomenon as such; his is
u he suppo ed by ano he s udy which de ines MA as a so wa e pla o m ha is buil o p o ide
indi idual and pe sonalized con en based on speci ic o de s placed by use s (Nilsson & Tsakmaki,
2019). MA has a la ge scope o capabili ies, such as main aining cu en and p ospec i e cus ome
da abases, moni o ing and analyzing cus ome beha io s on he websi e o on c oss de ice in e ace
and au oma ion o PPC campaign managemen (Świeczak, 2019).
In his esea ch, i is impo an o emphasize ha MA is no only es ic ed o email ma ke ing
ope a ion bu should no be con used wi h CRM as bo h a e no he same as many a e made o belie e
(Hammoud e al., 2019). A s udy by Mu phy (2018) on he eadiness o SMEs o ma ke ing au oma ion
ound ha 43% o esponden s deployed MA on PPC ad e ising. The main goal o ma ke ing
au oma ion is o inc ease he e iciency and e ec i eness o ma ke ing e o s, esul ing in highe
con e sion a es, inc eased e enue, and imp o ed cus ome engagemen (Dwye , Tanne , &
Leich weis, 2017). Ma ke ing au oma ion is also de ined as so wa e ha au oma es basic ma ke ing
ope a ions (Desai, 2019). I is a so wa e solu ion ha uses an in e ne acili y in he o m o so wa e
as a se ice (hence o h e e ed o as (SAAS) o au oma e ma ke ing p ocesses (Hamalainem, 2020).
The o igin o he ph ase ‘ma ke ing au oma ion’ can be aced o a pape p esen ed by John DC
Li le a he 2001 Choice Symposium (Heimbach e al., 2015). Acco ding o Li le (2001), MA e e s o
he use o in e ne -based au oma ed ma ke ing decision suppo . Li le (2001) o mula ed he i e
amewo ks o ma ke ing au oma ion o include: 1. da a inpu collec ed by p e ious in e ac ion o
use s (Seme ádo á & Weinlich, 2020), 2. eal- ime decision ules, 3. upda e o decision ules, 4.
eedback o si e managemen and, 5—s a egy choice. Fu he a gumen s o make hese amewo ks
possible sugges ed ha ad e ise s mus ha ness hei cus ome s' digi al oo p in s and ac o hem
in o hei con e sion unnels (Heimbach e al., 2015; Li le, 2001). Today, These digi al oo p in s a e
e e ed o as cus ome ’s da a, and he use o i g a i a es owa d da a-d i en decision ma ke ing. This
makes he con ex o MA o cen e on he au oma ion o epe i i e ma ke ing asks and he au oma ion
20
o da a-d i en decisions and s a egies. Acco ding o Seme ádo á & Weinlich (2020), ma ke ing
au oma ion has been gaining a en ion om ma ke ing s a egis s and academics’ ci cle, and i s
p ojec ed ha he MA indus y will see a ise in in es men o up o $25billion by he end o 2023
(Mu phy, 2018).
Ma ke ing au oma ion has se e al bene i s o businesses. Fi s , i allows companies o sa e ime
and esou ces by au oma ing epe i i e asks, which ees up ma ke e s o ocus on mo e s a egic
ac i i ies. Second, i helps companies imp o e hei lead-gene a ion e o s by acking cus ome s'
beha io s p o iding a ge ed messaging and pe sonalized con en o po en ial cus ome s (Co sa o e
al., 2021).
The wo aspec s o au oma ion o epe i i e ask and acking o consume beha io s leads o
e icien ma ke ing s a egies ha esul s in g owing sales and p o i abili y (Co sa o e al., 2021). Thi d,
i allows companies o nu u e leads o e ime h ough au oma ed wo k lows, esul ing in highe
con e sion a es and inc eased cus ome loyal y (Nilsson & Tsakmaki, 2019).
The leads sco ing unc ion aspec o he MA in ol es au oma ic assigning o poin s g ades o websi e
isi o s depending on he a ious pages isi ed such as any aluable lead unnels (Seme ádo á &
Weinlich, 2020). Ma ke ing au oma ion acco ding o a s udy has been p o en o a ec buye s' decision-
making p ocess h ough b and awa eness (Nilsson & Tsakmaki, 2019). Finally, ma ke ing au oma ion
p o ides aluable insigh s in o cus ome beha io and p e e ences, enabling companies o make da a-
d i en decisions and imp o e hei o e all ma ke ing s a egy (Nai & Gup a, 2020).
Ma ke ing au oma ion and i s implemen a ion ha e limi a ions, especially among middle- and
small-scale businesses. These challenges ange om cus ome da a collec ion and he expe ise needed
o implemen and in eg a e he MA ((Seme ádo á & Weinlich, 2020).
While ma ke ing au oma ion has many bene i s, he e a e also se e al challenges ha companies
may ace when implemen ing i . One o he bigges challenges is ensu ing he echnology is p ope ly
in eg a ed wi h exis ing sys ems and p ocesses, which can equi e signi ican ime and esou ces (Chen,
Chen, & Hsiao, 2019). In addi ion, companies mus ensu e ha hei da a is accu a e and up o da e o
a ge e ec i ely and segmen cus ome s. Ano he challenge is a oiding o e -au oma ion, which can
lead o impe sonal o i ele an messaging and esul in lowe engagemen a es (Rosenbe g &
21
Cze niawski, 2016).
Companies should ollow se e al bes p ac ices o ensu e ha ma ke ing au oma ion e o s a e
success ul. Fi s , hey should s a by clea ly de ining hei ma ke ing goals and objec i es, and hen
selec he app op ia e au oma ion ools and pla o ms o achie e hem (Nilsson & Tsakmaki, 2019).
Second, companies should ocus on p o iding pe sonalized and ele an con en o cus ome s based
on hei p e e ences and beha io , a he han simply sending gene ic messaging (Kim, Lee, & Yoon,
2018). Thi d, companies should egula ly analyze hei da a and me ics o op imize hei ma ke ing
e o s and make da a-d i en decisions (Nai & Gup a, 2020).
Ma ke ing au oma ion has become an in eg al pa o digi al ma ke ing and i ’s widely used o
s eamline and op imize a ious ma ke ing ac i i ies. I has been seen as a ma ke ing ideology o
deli e pe sonalized ma ke ing and inc ease con e sion a es (Rae, 2016). One o he a eas whe e MA
has made a signi ican impac is PPC managemen . Acco ding Anicca Webina (2023).
MA is used in PPC o s eamline p ocesses such as accoun se up and managemen bu equi es
some le el o human in e en ions o se up. PPC (pay-pe -click) digi al ad e ising has become
inc easingly popula in ecen yea s as a way o businesses o each and a ge speci ic audiences
online. PPC ad e ising allows businesses o c ea e and display ads on di e en pla o ms, such as
sea ch engines, social media, and websi es, and only pay publishe s only when a use clicks on hem
(Desai, 2019).
S udies ha e shown ha PPC ad e ising can e ec i ely inc ease websi e a ic, gene a e leads,
and boos sales (Mu phy, 2018; Smi h, 2019). One o he p ima y jus i ica ions o using PPC in digi al
ma ke ing is i s abili y o deli e highly a ge ed a ic o a websi e. This is achie ed using keywo ds
and ad placemen a ge ing, which allow ad e ise s o a ge speci ic demog aphics and in e es s.
Acco ding o a s udy by Google, PPC isi o s a e 50% mo e likely o make a pu chase han o ganic
isi o s. PPC me ics a e easie o measu e han SEO, which makes i easie o ack ROI and budge
alloca ion (Val e Me e , 2023).
Howe e , businesses need o ha e a well-planned and execu ed s a egy o see he desi ed esul s
(Plangge e al., 2022). A s udy by Szymanski & Lipinski (2018) examined he ac o s ha in luence he
e ec i eness o PPC ads. The esea che s ound ha ad ele ance, ad posi ion, and ad o ma we e he

22
mos signi ican ac o s in de e mining he success o a PPC campaign. Addi ionally, he s udy ound
ha a ge ed keywo ds and ad copy also played impo an oles in he success o a campaign. Pay-pe -
click (PPC) ad e ising has become a popula ma ke ing channel o businesses o all sizes due o i s
a ge ed and measu able na u e. In a s udy by Ru z e al. (2009), he au ho s examined he impac o
ad copy and landing page design on PPC campaign pe o mance.
The esea che s ound ha a combina ion o compelling ad copy and an e ec i e landing page could
signi ican ly imp o e he con e sion a e o a PPC campaign. The s udy also sugges ed ha es ing
di e en ad copy and landing page designs can help ad e ise s iden i y he mos e ec i e
combina ions. Ano he ac o ha a ec s PPC ad e ising is he digi al audience a ge ing which can
be ha nessed wi h digi al analy ics (Kabi aj & Joghee, 2023). The au ho s examined he impac o
audience a ge ing on PPC campaign pe o mance. The esea che s ound ha a ge ing speci ic
audiences can imp o e campaign pe o mance by inc easing ele ance and engagemen .
The s udy also sugges ed ha using da a om p e ious campaigns o iden i y high-pe o ming
audience segmen s can help ad e ise s achie e g ea e success in u u e campaigns. E ec i e PPC
managemen is c ucial o achie ing success in digi al ad e ising. The s udies e iewed in his li e a u e
e iew highligh he impo ance o ac o s such as ad ele ance, ad copy, landing page design, machine
lea ning, and audience a ge ing in imp o ing campaign pe o mance. Ad e ise s should con inuously
moni o and op imize hei PPC campaigns o achie e he bes esul s. All hese ac o s when in eg a ed
can lead o an enhanced PPC pe o mance in ela ion o a ge KPIs.
Howe e , managing a success ul PPC campaign can be ime-consuming and complex. To help
ma ke e s op imize hei PPC campaigns and achie e hei desi ed e u n on ad spend (ROAS), a ious
ools and s a egies ha e been de eloped, including ma ke ing au oma ion and a ious bidding
s a egies such as ROAS bidding. A s udy by Miklosik e al. (2019) es ima ed ha 2.5 quin illion o da a
a e being gene a ed on a daily basis o e he in e ne and he esea ch explo ed he use o machine
lea ning echniques in PPC managemen as an au oma ed p edic ion ool in digi al ad e ising. The
au ho s ound ha machine lea ning algo i hms can be used o op imize bidding s a egies and ad
a ge ing, esul ing in imp o ed campaign pe o mance. The s udy also highligh ed he impo ance o
inco po a ing his o ical campaign da a in o machine lea ning models o op imal pe o mance, whe e
23
bo h inbuil and hi d-pa y MA a e ound o be e y e en ul. Thus, machine lea ning is a key unc ion
ha empowe s PPC campaign op imiza ion h ough au oma ion (Hall, 2022). Thus, in he esea ch
conclusion made by Miklosik e al. (2019), machine lea ning can be used o p edic digi al ad e ising
by ex ac ing la ge amoun s o da a o da a-d i en decisions in PPC ma ke ing.
The machine lea ning p ocess used in ma ke ing au oma ion can ei he be passi e o ac i e lea ning
p ocesses whe e passi e au oma ed lea ning in ol es he use o pas campaign da a o clicks eams.
In con as , ac i e lea ning in ol es di ec ly asking ques ions o en ich he ma ke ing au oma ion da a
(Jä inen & Taiminen, 2016). To complemen hese in e nal ma ke ing au oma ion sys em ac i i ies,
hi d-pa y ma ke ing au oma ion is made a ailable by hi d-pa y endo s.
Thi d-pa y ma ke ing au oma ion ools a e designed o help ad e ise s op imize and au oma e
hei PPC campaigns. Acco ding o a s udy by Wilson (2019), hi d-pa y ma ke ing au oma ion has a
signi ican impac on PPC managemen as i helps ad e ise s s eamline and op imize hei
campaigns, leading o inc eased ROAS and imp o ed campaign pe o mance.
Ad anced hi d-pa y MA u ilizes cloud ma ke ing au oma ion so wa e hos ed by hi d-pa y
companies wi h he ad an ages o sa ing ad e ising he cos o hos ing hei own se e , da a
in eg a ion, and sys em main enance as he ad e ise s will only in eg a e hese al eady made sys ems
in o hei digi al ma ke ing channels (Smi h, 2016). The s udy also ound ha hi d-pa y ma ke ing
au oma ion ools p o ide ad e ise s wi h ad anced ea u es, such as bid op imiza ion and audience
a ge ing, which help inc ease hei ads' ele ance.
Many s udies ha e in es iga ed he impac o in eg a ing hi d-pa y au oma ion ools wi h PPC
managemen and ha e made some no able esea ch conclusions. Fo ins ance, Hall (2022) p o ides
insigh in o au oma ion ools in au oma ed PPC, including hi d-pa y ma ke ing au oma ion ools. The
au ho concludes ha hi d-pa y au oma ion ools can imp o e PPC pe o mance by educing manual
asks and imp o ing ad a ge ing. O he esea che s in es iga ed he impac o ma ke ing au oma ion
on PPC ad e ising and concluded ha hi d-pa y au oma ion ools can imp o e PPC pe o mance by
educing cos s and imp o ing a ge ing (Smi h, 2016). This au ho also examines he impac o PPC
ad e ising on business g ow h and concludes ha hi d-pa y ma ke ing au oma ion ools can imp o e
PPC pe o mance by educing cos s, imp o ing a ge ing, and inc easing con e sion a es. Hall (2022)
24
examines he impac o ma ke ing au oma ion on PPC ad e ising and concludes ha au oma ion ools
can imp o e PPC pe o mance by educing manual asks, imp o ing ad a ge ing, and inc easing
con e sion a es by op imizing campaigns using his o ical con e sion da a.
A u he s udy suppo ed he indings ha au oma ion ools can imp o e PPC pe o mance as a
iable ma ke ing channel wi h he abili y o enhance o ganiza ional s a us and boos hei equi y alues
h ough s eng hening he cus ome ela ionship by using ecip oci y in communica ions, which is a
undamen al and e ec i e p ocess o cus ome acquisi ion and e en ion mechanism (Świeczak, 2019).
I is e iden ha in Google ads PPC channels, cus ome acquisi ion is boos ed using e a ge ing
campaigns while le e aging he cus ome audience segmen a ions o deli e loyal y p og am h ough
au oma ed a ge ing.
One o he mos common ypes o PPC is Google Ads, which allows ad e ise s o bid o esul s
anywhe e on he Google sea ch engine (Desai, 2019). Google Ads PPC is one o he mos popula
ad e ising pla o ms, wi h 92.96% o all sea ch engine ad e ising ma ke sha e wo ldwide (S a is a,
2023). Google Ads gene a es an a e age o $8 o e e y $1 spen on ad e ising (Google, 2021).
Fu he mo e, Google Ads is esponsible o 28% o all websi e isi s om paid sea ch ad e ising
(Sma phone Use Beha io Repo , 2021). Many s udies ha e shown he e ec i eness o Google Ads
PPC as a leading PPC channel. Fo example, a s udy by Wo dS eam ound ha Google Ads PPC has an
a e age click- h ough a e (CTR) o 3.17% o sea ch ads and 0.46% o display Ads, wi h abou 82% o
Ad spend alloca ed o Google ads, acco ding o hei epo pulled om 18,000 ad e ise s on Localiq
(McCo mick, 2023).
Addi ionally, a s udy ound ha 64.6% o people click on Google Ads when hey a e looking o buy
i ems online, wi h paid sea ches bea ing ou o ganics in he a io o 2;1 o sea ch keywo ds wi h
comme cial in en s in he Uni ed S a es (Kim, 2022). Resea ch by Allazo (2020) s a ed ha cus ome s
om Google sea ch engine ad e ising ha e mo e ansac ions han o he s om ela ed channels.
Many e e ences in academic and indus y publica ions also suppo he e ec i eness o Google Ads
PPC as a leading PPC channel. Fo example, a s udy by Cho and Lee (2018) ound ha Google Ads PPC
is one o he mos e ec i e o ms o online ad e ising. Addi ionally, a s udy by To ge son (2022) ound
ha Google Ads PPC is a cos -e ec i e ad e ising me hod o small businesses. One o he p ima y
25
bene i s o Google Ads PPC is i s e ec i eness in d i ing websi e a ic and con e sions. Ano he s udy
by Wo dS eam ound ha businesses using Google Ads PPC had a con e sion a e o 3.75%,
signi ican ly highe han he indus y a e age o 2.35% (Wo dS eam 2018). Acco ding o Hanapin
Ma ke ing, Google Ads PPC gene a ed an a e age o $2 in e enue o e e y $1 spen on ad e ising
(Hanapin Ma ke ing, 2018). These indings sugges ha Google Ads PPC is a highly e ec i e o m o
digi al ad e ising, deli e ing a s ong e u n on in es men (ROI) o businesses.
Based on hese dis inguishing ac o s o Google Ads PPC on in o ma ion ex ac ed om he pas
esea ch abo e, his li e a u e e iew explo es he e ec i eness o Google Ads PPC as a leading PPC
channel wi h he aim o in es iga ing he impac o i s inhe en o in-house au oma ion ea u es in
“Google ads pe o mance max campaign,” i s sma bidding s a egy and in eg a ion wi h Tensco e as
a hi d- pa y au oma ion ool on Con e sions, ROAS and o he key pe o mance indica o s (he ea e
e e ed as KPI). Se e al s udies ha e highligh ed he bene i s o ma ke ing au oma ion in Google Ads
PPC managemen o B2C businesses. Acco ding o a s udy by Geh e al. (2018), ma ke ing
au oma ion can help businesses sa e ime, inc ease e iciency, and imp o e cus ome engagemen .
The s udy ound ha businesses ha used ma ke ing au oma ion in hei Google Ads PPC campaigns
could gene a e highe click- h ough a es (CTR), educe bounce a es, and inc ease con e sion a es.
Ano he s udy by Digi al Ma ke ing Ins i u e (2022) ound ha ma ke ing au oma ion can help
businesses pe sonalize hei messages o cus ome s, leading o highe engagemen and con e sion
a es. The s udy ound ha 83% o ma ke e s who used ma ke ing au oma ion we e able o inc ease
hei con e sion a es h ough leads nu u ing. This s udy c i ically e iews pas li e a u e ega ding
he e ec o MA on some pe o mance indica o s on Google Ads PPC managemen .
2.1 IMPRESSION METRIC
An imp ession is said o be made when an ad eques by a use is comple ed and displayed
(Shandilya e al., 2023). The i s KPI mus exis be o e all o he KPIs can be measu ed in digi al
ad e ising because wi hou imp essions, he e won’ be a click o con e sions. An imp ession is
coun ed when an ad is se ed as pa o sea ch engine esul s in sea ch engine ma ke ing (hence o h
e e ed o as SEM) and his is based on he numbe o imes he keywo ds a e sea ched which is
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has been p o en o ha e esul ed in a 15% inc ease in ROI compa ed o manual bidding (Baldwin e al.,
2020). Ano he s udy ound ha using Maximize ROAS bidding s a egy esul ed in a 12 % inc ease
in ROAS compa ed o manual bidding (Baldwin e al., 2020). I is impo an o no e. This esea ch
p oposes a hypo hesis ha ha pe o mance max campaign, a Google ads AI’s d i en au oma ed
campaign has mo e posi i e impac o numbe o con e sions han s anda d shopping/sea ch
campaigns in B2C.
H3: Google Ads’ sma bidding -maximize con e sion alue wi h a ge ROAS s a egy has mo e
signi ican posi i e impac on ROAS han manual bidding s a egy.
2.7 CHALLENGES Wi h MA
Despi e he bene i s o ma ke ing au oma ion in Google Ads PPC managemen , implemen ing i can
be challenging o B2C businesses. One o he main challenges is he lack o expe ise and esou ces.
Acco ding o a s udy by Aspa wa e al. (2020), many B2C businesses do no ha e he necessa y
expe ise o esou ces o implemen ma ke ing au oma ion in hei Google Ads PPC campaigns. The
s udy ound ha businesses ha lacked he necessa y esou ces and expe ise we e mo e likely o
expe ience challenges such as poo da a quali y, low engagemen a es, and educed ROI. Ano he
challenge o implemen ing ma ke ing au oma ion is he need o ongoing op imiza ion. Acco ding o
a s udy by Kim e al. (2021), businesses ha used ma ke ing au oma ion in hei Google Ads PPC
campaigns had o con inuously op imize hei campaigns o ensu e ha hey we e e ec i e. The s udy
ound ha businesses ha did no op imize hei campaigns we e mo e likely o expe ience educed
engagemen a es and lowe con e sion a es.
Bes P ac ices o B2C Businesses: To o e come he challenges o implemen ing ma ke ing
au oma ion in Google Ads PPC managemen , B2C businesses can ollow bes p ac ices. Acco ding o a
s udy by S ini asan e al. (2019), some o he bes p ac ices o B2C businesses include: a) de ining clea
objec i es and goals o he Google Ads PPC campaign; b) segmen ing he a ge audience based on

33
demog aphics, beha io , and in e es s; c) de eloping pe sonalized messages ha esona e wi h he
a ge audience.
Using a a ie y o ad o ma s such as ex ads, display ads, and ideo ads and implemen ing
con e sion acking o measu e he e ec i eness o he campaign and con inuously op imizing he
campaign based on he pe o mance da a. As digi al ad e ising con inues o e ol e, Google Ads PPC
will likely see some changes. One po en ial de elopmen is he use o a i icial in elligence (AI) and
machine language (ML) o op imize ad a ge ing and bidding s a egies. A s udy by Kenshoo ound ha
using AI and ML in Google PPC campaigns esul ed in a 30% inc ease in con e sion and a 33% dec ease
in cos pe acquisi ion (CPA) (Kenshoo, 2019). The Pe o mance Max campaign is one o he Google Ads
campaigns wi h ull in-house ma ke ing au oma ion ea u es powe ed by ML algo i hm.
2.8 GOOGLE PERFORMANCE MAX CAMPAIGN
Google Ads Pe o mance Max campaign is a new global-based campaign ha allows ad e ise s o
place ads au oma ically on all Google ads in en o y by s ee ing he campaign inpu s h ough ma ke ing
au oma ion. I uses sma bidding, a ibu ion echnology, machine lea ning, and au o-gene a ed
asse s o enhance campaign audience signals (Google, n.d.). Pe o mance Max campaign shows ads on
all Google ne wo k anging om display, sea ch, YouTube, shopping, and disco e y. The main
di e ence be ween o he ypes o Google ads campaigns and pe o mance is based on he ac ha
pe o mance ads will au oma e he in o ma ion been p o ided by he ad e ise (Bishop, 2021).
Pe o mance Max campaign is designed o help businesses achie e hei desi ed ROAS by using
ad anced machine lea ning algo i hm o au oma ically adjus bids and budge in eal- ime based on
he pe o mance o each ads (Flo es, n.d.).
A ecen su ey by sea ch engine land e ealed ha 67% o ad e ise s who pa icipa ed in he
su ey a e cu en ly using pe o mance max campaign. 62% o he esponden demons a ed hei
us a ion wi h he keywo ds and sea ch e ms epo ing insigh as he e is no keywo d epo ing as
sea ches a e based on au oma ed a ge ing (Ad hena, 2023). The pe o mance max campaign has
only wo bidding s a egies o selec om, including maximizing con e sion alues and con e sions
(Sande s, 2022). The e o e, he ad e ise has he op ion o using a ge cos pe con e sion (TCPA) o
34
se a equi ed amoun he wan s o pay o a gi en con e sion o a ge cos o ROAS o se bid o he
maximize con e sion alues o a gi en con e sion alues. Acco ding o Google (Google, n.d.), he
bene i s o a pe o mance campaign a e unlocking new cus ome s ac oss he channels, d i ing be e
esul s ac oss ad e ise s’ goals, simpli ying campaign managemen , and ge ing mo e anspa ency
insigh s. Kim (2021) p o ides an o e iew o he ea u es o Google Ads pe o mance and how hey can
bene i ma ke e s. The au ho highligh s he pla o m’s machine-lea ning capabili ies ha op imize ad
placemen , bidding, and c ea i e elemen s o d i e be e pe o mance. Ano he s udy ha explo ed
how Google Ads Pe o mance Max can help ma ke e s imp o e hei e u n on in es men (ROI)
es ablished ha Pe o mance Max can au oma e Ad c ea ion and a ge ing, educing he ime and
e o s equi ed o PPC managemen (Amanda, 2022).
Pe o mance campaign has g ea is p ojec ed o ha e g ea impac on PPC managemen elying on
how he Pe o mance Max campaign au oma ion ea u es can educe he need o manual
op imiza ion and allowing ma ke e s o ocus on s a egy and c ea i e. The Pe o mance Max
Campaign u ilizes he powe o au oma ion o iden i y he bes op imiza ion s a egy in B2C (Amanda,
2022). I is o no e ha pe o mance max campaign does no imply 100% au oma ed asse s in e ms
o campaign se up as he e a e le el o con ol o be e ec ed by he PPC manage such as budge
alloca ion, ad scheduling, loca ion a ge ing, and asse se up (Bishop, 2021). Pe o mance Max
Campaign can also educe he lea ning cu e by au oma ing campaign se up and op imiza ion,
h e eb y e du c in g h e le a ni n g c u e a nd he i me e q ui ed o P PC
m an ag e me n b e c aus e G o o g le a d s w i ll a u o m a e h e a s se s h e PP C
m an ag e p o i de s (Bishop, 2021).
Ma ke ing au oma ion can ei he be used as an in-house ool o a hi d-pa y ool in PPC
managemen . Acco ding o a epo by Ma ke s and Ma ke s, he global ma ke ing au oma ion
so wa e is expec ed o g ow om $3.3 billion in 2019 o $6.4 billion by 2024, wi h a p ojec ion o $9.5
billion by 2027, his g ow h is being d i en by inc easing demand o ma ke ing au oma ion ools,
pa icula ly he hi d-pa y ma ke ing au oma ion ools (Ma ke And Ma ke s, n.d.).
35
Figu e 2.1 Pmax campaign use su ey epo adap ed om Sea ch engine land.
2.9 THIRD-PARTY MA
Ma ke ing channels such as Google Ads, Me a o Business and o he s ha e hei own in e nal o
inhouse-ma ke ing au oma ion sys em, while o he s a e being con igu ed using hi d-pa y
companies’ ools. Thi d-pa y MA ools help o complemen in-house au oma ion ools. Thi d-pa y
ma ke ing au oma ion e e s o he use o au oma ion ools de eloped by hi d-pa y companies as
opposed o being de eloped in-house by he business i sel . These ools a e designed o help
businesses imp o e he e iciency and e ec i eness o hei ma ke ing e o s. They can include
ea u es such as email au oma ion, lead nu u ing, and PPC campaign managemen (Pa el, 2022).
Acco ding o Me a (2023), hi d-pa y au oma ion se ices a e hose o e ed by ex e nal
endo s ha allow independen so wa e in eg a ion o hei so wa e as a se ice wi hin a
wo kplace, whe eby his so wa e can hen be ins alled by any cus ome o deli e alue au oma ion
and p o ide so wa e and ools o business o au oma e hei ma ke ing ope a ions. Thi d-pa y MA
se ices ha e been shown o p o ide nume ous bene i s o businesses including inc eased
e iciency, cos sa ings and imp o ed cus ome engagemen . Acco ding o s udy by Ma ke o,
businesses ha use hi d-pa y MA so wa e can see 14.5% inc ease in sales p oduc i i y and a
12.2% educ ion in ma ke ing o e head cos s (Ma ke o, 2017). One o he main bene i s o using
hi d-pa y ma ke ing au oma ion ools o PPC managemen is imp o ed e iciency. Se e al
s udies ha e ound ha hese ools can help businesses sa e ime and educe he esou ces
equi ed o manage PPC campaigns. Fo example, a s udy by Kenshoo ound ha businesses ha
36
used hei ma ke ing au oma ion ool could sa e up o 30% o hei ime on PPC managemen
asks (Kenshoo, 2020). Ano he s udy by Wo dS eam ound ha businesses ha used hei
ma ke ing au oma ion ool we e able o sa e up o 20 hou s pe week on PPC managemen asks
(Wo dS eam, 2020).
Ano he s udy by Abe deen G oup ound ha businesses using ma ke ing au oma ion so wa e
expe ienced a 451% inc ease in quali ied leads and a 53% highe con e sion a e han hose who
did no use au oma ion ools (Abe deen G oup, 2014). Thi d-pa y ma ke ing au oma ion ools can
also help businesses imp o e hei PPC campaigns' e u n on in es men (ROI). Se e al s udies ha e
ound ha businesses ha used hese ools achie ed highe con e sion a es, lowe cos -pe -click
(CPC), and highe click- h ough a es (CTR). Fo example, a s udy by AdEsp esso ound ha
businesses ha used hei ma ke ing au oma ion ool we e able o achie e a 62% lowe CPC and a
42% highe CTR han businesses ha did no use hei ool (AdEsp esso, 2020). Ano he s udy by
Kenshoo ound ha businesses ha used hei ma ke ing au oma ion ool we e able o achie e a
9% highe con e sion a e and a 30% lowe CPA han businesses ha did no use hei ool (Kenshoo,
2020).
Thi d-pa y ma ke ing au oma ion can ha e a signi ican impac on businesses, especially in
e ms o ma ke ing e iciency, cus ome engagemen , and e enue g ow h. Acco ding o a s udy by
Sales o ce, businesses ha use ma ke ing au oma ion so wa e can see a 34% inc ease in lead
gene a ion, a 42% inc ease in quali ied leads, and a 25% inc ease in e enue g ow h (Sales o ce,
2017). While hi d-pa y ma ke ing au oma ion can p o ide many bene i s, he e a e also some
disad an ages. One o he main challenges wi h using hi d-pa y au oma ion ools is ha hey may
no be ully in eg a ed wi hin a business's exis ing sys ems, which can lead o da a inconsis encies
and e o s. Addi ionally, hi d-pa y au oma ion ools may be cos ly, especially o small businesses,
and may equi e a signi ican in es men in aining and suppo o be used e ec i ely). Howe e ,
i is essen ial o no e ha he e ec i eness o hi d-pa y au oma ion ools depends on he quali y
o he so wa e, he business's indus y, and he business's ma ke ing goals. Thi d-pa y ma ke ing
au oma ion se ices o e many bene i s o businesses, including inc eased e iciency, cos sa ings,
and imp o ed cus ome engagemen .
37
Howe e , he e a e also some challenges and conside a ions o conside when using hi d-
pa y au oma ion ools. Businesses should ca e ully e alua e hese ools' e ec i eness and
in eg a ion wi h exis ing sys ems be o e in es ing in ma ke ing au oma ion so wa e. One o he
mos signi ican challenges o hi d-pa y ma ke ing au oma ion in PPC managemen is he lack o
campaign con ol. As Balak ishnan and V ooman (2016) iden i ied, when ou sou cing PPC
managemen o hi d-pa y p o ide s, he e is a isk o losing con ol o e he campaign's budge ,
messaging, and o e all s a egy. Addi ionally, hi d-pa y au oma ion ools may no op imize
campaigns as e icien ly as in-house eams. These challenges can lead o subop imal esul s and
educe he e u n on in es men . Ano he challenge o hi d-pa y ma ke ing au oma ion in PPC
managemen is he isk o inc eased cos s.
Acco ding o S ewa d (2019), hi d-pa y ools can be expensi e and may equi e addi ional
s a o manage he campaigns e ec i ely. Fu he mo e, hi d-pa y ools may no always in eg a e
well wi h exis ing ma ke ing sys ems, leading o addi ional expenses. The lack o anspa ency and
isibili y is also a challenge o hi d-pa y ma ke ing au oma ion in PPC managemen . As Zei z (2021)
iden i ied, hi d-pa y ools may no p o ide de ailed pe o mance epo s, making i challenging o
unde s and he campaigns' e ec i eness ully. This can hinde he op imiza ion p ocess and lead o
subop imal esul s.
Finally, da a p i acy and secu i y a e signi ican conce ns wi h hi d-pa y ma ke ing
au oma ion in PPC managemen . Wi z e al. (2016) no ed ha ou sou cing PPC managemen o
hi d-pa y p o ide s can pose a isk o sensi i e cus ome da a. The e o e, o ganiza ions mus
ensu e ha hi d-pa y p o ide s comply wi h da a p i acy egula ions and ha e app op ia e
secu i y measu es.
2.10 TENSCORE AS A THIRD-PARTY MA
One o he essen ial ac o s o conside when selec ing a hi d-pa y ma ke ing au oma ion ool
o PPC managemen is he ea u e se , and businesses should look o ools ha p o ide a
comp ehensi e se o ea u es, including bid managemen , campaign au oma ion, and epo ing
(Kim e al., 2019). This ac is alida ed because obus MA ea u es can help businesses s eamline

38
hei PPC managemen asks, educe cos s, and imp o e campaign pe o mance (Kim e al, 2019).
Ano he c i ical ac o o conside when selec ing a ma ke ing au oma ion ool o PPC managemen
is i s in eg a ion capabili ies.
As Lu e al. (2021) no ed, businesses need o choose ools ha can in eg a e wi h hei exis ing
ad e ising pla o ms, such as Google Ads o Facebook Ads. This is because in eg a ion capabili ies
can help businesses s eamline hei wo k low, educe e o s, and imp o e he accu acy o hei
da a. The ease o use o a ma ke ing au oma ion ool is ano he c i ical ac o ha businesses need
o conside . As no ed by Hsu e al. (2021), businesses should look o ools ha a e easy o use and
equi e minimal aining. This is because ease o use can help businesses o imp o e hei e iciency,
educe e o s, and inc ease use adop ion.
The cos o a ma ke ing au oma ion ool is ano he essen ial ac o o conside . Kim e al. (2019)
no ed ha businesses should look o ools ha p o ide a cos -e ec i e solu ion ha i s hei
budge . Howe e , i is essen ial o balance cos agains he ool's ea u e se , as choosing a cheape
ool wi h ewe ea u es may esul in educed campaign pe o mance and inc eased cos s in
he long un.
Finally, cus ome suppo is ano he c i ical ac o o conside when selec ing a ma ke ing
au oma ion ool o PPC managemen . Acco ding o Lu e al. (2021), businesses should choose
eliable cus ome suppo ools, including phone and email suppo , online documen a ion, and a
knowledge base. This is because eliable cus ome suppo can help businesses esol e issues
quickly, educe down ime, and imp o e hei o e all expe ience wi h he ool. Thi d-pa y ma ke ing
au oma ion ools such as Tensco e claim o imp o e ad quali y sco es wi h ela i e p icing as low as
$25/mon h o one Google Ads accoun . I also has a good cus ome suppo mechanism and easily
in eg a es wi h Google Ads wi hou he need o much expe ise inpu ; based on hese poin s, his
li e a u e e iew explo es he impac o Tensco e as a hi d-pa y ma ke ing au oma ion ool on
Google Ad quali y sco e and he a ious aspec o Tensco e as a ma ke ing au oma ion ool, including
i s ea u es, bene i s, and limi a ions.
Tensco e is a hi d-pa y ma ke ing au oma ion ool ha allows businesses o au oma e hei
ma ke ing ac i i ies. I is a comp ehensi e ma ke ing au oma ion ool ha p o ides businesses wi h
39
a ious ea u es o enhance hei ma ke ing ac i i ies. I s key ea u es include lead nu u ing, lead
gene a ion, email ma ke ing, social media ma ke ing, and cus ome segmen a ion (Tensco e, n.d).
The ool also p o ides businesses wi h de ailed analy ics and epo ing capabili ies o help hem
measu e he e ec i eness o hei ma ke ing campaigns. Tensco e o e s se e al bene i s o PPC
ad e ise s using he ool o ma ke ing. Fi s ly, he ool helps PPC manage s sa e ime and esou ces
by au oma ing hei ma ke ing ope a ions, allowing hem o ocus on o he aspec s o hei
campaigns. Secondly, Tensco e helps PPC manage s imp o e hei lead gene a ion and lead
nu u ing esul ing in inc eased sales and e enue o B2C, Thi dly, Tensco e p o ides Google Ads PPC
pla o ms wi h de ailed analy ics and epo ing capabili ies, enabling hem o measu e he
e ec i eness o hei ma ke ing campaigns and make da a-d i en decisions.
I is de ined as a SaaS ool ha specializes in helping ad e ise s op imize hei me ics and
egula e click cos on hei PPC campaigns (Zayas, 2021). The ool pe o ms simple bu powe ul
unc ions o moni o ing he indi idual quali y sco es o Google ads campaigns once i is linked o he
Google ads in e ace (D elle , 2011). The ool has been desc ibed as a low-cos PPC solu ion ha
helps imp o e ad e quali y sco e and help educe was e o ad-spend while imp o ing anking on
he sea ch engine esul s, he eby boos ing ROI especially in SMES (Digi al school o ma ke ing,
2020). Acco ding o Bena d (2022), Tensco e se es as a b and moni o ing PPC ool ha o e s
ma ke e s ecommenda ions ega ding op imizing hei campaigns. The ool has been ound e y
use ul and can help PPC ad e ise s o keep ack o hei campaign pe o mance and make he bes
decisions on how o dis ibu e hei campaign budge o mo e p o i able keywo ds he eby educing
was e (Bena d, 2022).
While Tensco e o e s se e al bene i s o businesses, i also has limi a ions. Fi s ly, he ool can
be expensi e o small businesses making i di icul o hem o a o d. Secondly, Tensco e equi e
businesses o ha e signi ican amoun o da a o be e ec i e, which can be a challenge o small
businesses. Thi dly, Tensco e’s use in e ace can be o e whelming o some use s, making i di icul
o hem o na iga e he ool wi hou any in-house expe ise (Ma ke ing au oma ion inside , 2019).
2.11 PROJECT MODEL AND HYPOTHESIS
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This e iew explo e ROAS as he dependen a iable de ined ma hema ically acco ding o Se h
(2023) as ROAS = [( AD REVENUE / AD COST) x 100] . This means ha achie ing mo e Con e sion
alues wi h less Ad spend will esul o highe ROAS. ROAS depend on many ac o s in PPC ad e ising
and p ospec i e online ma ke ing s a egy is e alua ed based on i s inc emen al e u n on ad spend
(Chen & Au, 2022). E alua ing ROAS in digi al ad e ising has been a undamen al p oblem in
ma ke ing (Chen e al., 2018). In he Google ads pla o m, he me ic o ROAS is gi en as con e sion
alue di ided by he cos o ad e ising. Many pas s udies ha e ied o ind he connec ion be ween
ROAS and he exis ing PPC s a egy. Chen and Au (2022) de ined iROAS as inc emen al e u n on ad
spend in ela ion o esponse o he PPC s a egy.
Many s udies ha e ca ied ou esea ch o de e mine he impac o inc ease on Ad budge
on ROAS as a dependen a iable (Chan & Pe y, 2017). O he s udies ocus on e alua ing he impac
o he Media Mix Model on ROAS. Medial Mix Model in ol es he use o obse a ional da a such as
p ice, sales, Ad spend and economic ac o s o Fo ecas he impac on ROAS (Chen e al., 2018) bu
no much has been dedica ed on expe imen ing o p ac ical alida ion o using MA as a casual e ec
on ROAS.
This e iew iden i ies his esea ch gap ha will be exploi ed and expanded in o a model by
in es iga ing he impac applica ion o ROAS sma bidding s a egy and Google Ads au oma ed
pe o mance Max campaign as a PPC s a egy on ROAS and use i o de e mine he le el o
inc emen al ROAS hence o h e e ed o as iROAS. This li e a u e will e iew and modi y he implici
assump ion model de eloped by Chen e al. (2018) whe e Y s and o Sales alue wi h a casual pa h
a ec ed by O and P, whe e O and P e e o O ganic esul s and Paid esul s in sea ch engines
espec i ely, A s and o auc ion and Q s and o use ’s sea ch que y in he sea ch engine (see ig 2.2
below).
41
Figu e 2.2 A causal Model diag am o sea ch ad a a que y le el.
Adap ed om Chen e al. (2018).
Modi ying his model, IF Y= Y Sales alue om Google ads PPC ad e ising, O= S ep esen ing
S anda d shopping campaign s a egy, A = A ep esen ing Ad spend, Q = G ep esen ing Google
ads PPC channels and P = P ep esen ing Pe o mance Max campaign (au oma ed campaign), hen
his e iew eplica es he abo e implici model as shown below in ig 2.6. This s udy p oposed h ee
hypo heses as ep esen ed below:
H1: Pe o mance max campaign has mo e posi i e impac on con e sions han
s anda d shopping/sea ch campaign
Figu e 2.3 Hypo hesis 1 model diag am showing ela ionship be ween Pmax, Con e sions & SSC
H2 Pe o mance max campaign has mo e signi ican impac on ROAS han s anda d shopping/sea ch
campaign
Figu e 2.4 Hypo hesis 2 model diag am showing ela ionship be ween Pmax, ROAS& SSC
H3: Google Ads’ sma bidding -maximize con e sion alue wi h a ge ROAS s a egy has mo e
signi ican posi i e impac on ROAS han manual bidding s a egy.
Figu e 2.5 Hypo hesis 3 model diag am showing ela ionship be ween sma bidding, ROAS & manual
bidding.
The h ee hypo hesis p oposes a ela ionship be ween pe o mance max campaign,
s anda d shopping/sea ch campaign, sma bidding s a egy ( a ge ROAS), manual bidding
s a egy and hei impac on con e sions and ROAS in Google ads PPC channels, using hese 3
hypo heses wi h he model adap ed om Chen e al. (2018) which es ablished a ela ionship on
ROAS and cos o ad spend, a holis ic model is hen p oposed o demons a e he h ee hypo hesis
48
Figu e 3.1 50% SPLIT: Con ol & T ea men G oups
Figu e 3.2 Pmax and SSC as T ea men & Con ol G oups Respec i ely
Figu e 3.3 Tensco e in e ace showing campaign s uc u e sco e on Google ads
3.7 PMAX VS S anda d Sea ch Campaign
The di e ence be ween he s anda d shopping and sea ch campaigns is ha shopping
campaigns a e o p oduc ma ke ing while sea ch ads a e o bo h p oduc ma ke ing and se ice-

49
based. To inc ease he eliabili y o he da a used in he p ojec analysis, he au ho es ed he Pmax
campaign o di e en B2C online businesses. Hence, a spli expe imen was c ea ed using Pmax as
a ea men g oup and sea ch ads as a con ol g oup, bu his ime, he online business being
a ge ed is a se ice based whe e leads gene a ion is he goal o he campaign ins ead o
pu chases/sales. The dependen a iable is CONVERSION, while expe imen is in es iga ing he
causal e ec s o Pmax and Sea ch ads as independen a iables on con e sions The expe imen was
spli in o 50%, indica ing equal a ic and budge alloca ion as was done when ROAS is he
dependen a iable. Fig 3.3 and Fig 3.4 illus a e as explained.
3.8 INTEGRATING TENSCORE WITH GOOGLE ADS PLATFORM
The Tensco e so wa e was in eg a ed in o he Google Ads in e ace by linking he speci ic
Google ads IDs by he au ho as he adminis a o o he PPC in e ace. A e connec ing he
Tensco e, he exis ing campaigns o be used as con ol g oups we e selec ed and es uc u ed using
he Tensco e es uc u ing in e ace. The Tensco e so wa e immedia ely sugges ed au oma ed
changes o be made o he exis ing campaign o imp o ed pe o mance s a ing om he campaign
Ad g ouping s uc u es.
A new se ies o ad g oups was c ea ed om he exis ing campaign and au oma ically
published in he Google Ads in e ace. All he newly c ea ed ad g oups a e di e en ia ed om he
exis ing Ad g oups wi h a TS P e- ix o no e hose ad g oups being c ea ed using he Tensco e ool.
A e he new ad g oups we e c ea ed, a spli campaign was se up as an A/B es expe imen as
seen in Fig 3.3. The con ol g oup campaign con ains Ad g oups manually c ea ed wi hou he
Tensco e so wa e, while he ea men campaign con ains only l he ad g oups au oma ically
c ea ed using he Tensco e hi d-pa y ools wi h a TS p e ix. All o he pa ame e s in he campaign
a e made o be he same as he spli g oups equally ecei ed 50% o a ic and budge sha e du ing
he pe iods o he expe imen a ions. The p ocess is epea ed in bo h se ice-based businesses and
ecomme ce-based campaigns in B2C online businesses.
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3.9 EXPERIMENT DURATION AND KPI SAMPLE SIZES
The o al du a ion o he expe imen span om Ma ch 20 h, 2023, o June 15 h, 2023,
which in ol ed app oxima ely 90 days. Ideally, he expe imen was se o e mina e on June 20 h
bu he da a o he esul s was ex ac ed un il June 15 h.
The numbe o clicks ep esen s he subjec sizes in he expe imen as i shows a numbe
o people who saw he expe imen and clicked on i . O he impo an da a which consis o he
sample sizes a e he imp essions and CTR, which indica ed he a e a which people clicked on he
ads du ing he expe imen pe iods. These sample sizes a e below o bo h he E-comme ce
pla o m and leads campaign.
Table 3.1 Sample size o he expe imen
3.10 MATHEMATICAL MODEL FOR EXPECTED RESULTS VALIDATION
Bea ing in mind he hypo hesis o his s udy is u he lis ed below as ollows:
a) H1 - Pe o mance max campaign has a mo e posi i e impac on con e sions han
he s anda d shopping/sea ch campaign;
b) H2 - Pe o mance max campaign has a mo e signi ican impac on ROAS han he
s anda d shopping/sea ch campaign;
c) H3 - Google Ads’ sma bidding -maximize con e sion alue wi h a ge
ROAS s a egy has a mo e signi ican posi i e impac on ROAS han manual
bidding s a egy.
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3.10.1 Fo Hypo hesis 1 – Impac On Con e sion
The change in he numbe o con e sions is deno ed by ∆CN whe e:
• CN is he numbe o con e sions.
• PMAXNC is he numbe o con e sions om Pe o mance Max campaign.
• SCCNC is he numbe o con e sions eco ded by s anda d sea ch/shopping campaigns.
𝑓𝑜𝑟 𝑡ℎ𝑖𝑠 𝑠𝑡𝑢𝑑𝑦, ∆CN which is change in he numbe o con e sions which is he di e ence in he
numbe o con e sions be ween he ea men g oup and he con ol g oup in he expe imen s
which a e Pmax and SSC espec i ely. Hence, Hypo hesis 1 is TRUE IF PMAXNC > SCCNC which
implies ha ∆CN >0, whe e ∆CN = PMAXNC – SCCNC
3.10.2 Fo Hypo hesis 2 -Impac on ROAS
Fo his s udy, he change in ROAS is deno ed by ∆R meaning he di e ence be ween
ROASPMAX And ROASSSC. Whe e ROASPMAX is he e u n on ROAS om Pmax campaign and
ROASSSC. is he e u n on ad spend om s anda d shopping/sea ch campaigns.
The e o e, ∆R = ROASPMAX - ROASSSC.
IF ROASPMAX > ROASSSC. hen ∆R will be posi i e which implies ha hypo hesis- H2 is
sus ained o be TRUE else Hypo hesis 1 is FALSE. The e o e, H2 is TRUE only when ∆R = ROASPMAX
- ROASSSC leads o inc emen al ROAS (iROAS) as de ined by (Chen e al, 2018)
3.10.3 Fo hypo hesis 3 -Impac o sma bidding s a egy ( a ge ROAS) on ROAS
To de e mine he impac o sma bidding s a egy on ROAS, his s udy conside ed he
change in ROAS o s anda d shopping campaign be o e and a e he implemen a ion sma bidding
s a egy. The e o e, ∆R is he change ROAS be ween manual and sma bidding calcula ed o e equal
pe iod o ime which is 45 days each o he o al o 90 days o he spli expe imen . ROASMD
ep esen he ROAS om manual bidding while ROASSM he e o e iROAS which is inc emen in ROAS
esul ing om i ROASSTB -ROASMD
3.11 INTEGRATING RELIABILITY IN THE EXPERIMENTATION PROCESS
Reliabili y as a ac o o quan i a i e esea ch measu emen de ined as an indica o o he
52
s abili y o he measu ed alues ob ained in epea ed measu emen s unde he same ci cums ances
using he same measu ing ins umen (Maslaka & Su ucu, 2020). To ensu e he eliabili y in he
me ics being analyzed in his p ojec , he me hodology been adap ed ensu ed ha he ollowing
measu es we e aken, iz.
The expe imen was ca ied ou in 3 h ee di e en B2C businesses which included women
appa els online s o e in USA, ac ical gea online s o e based in Canada, Cus om ashion online s o e
based in Aus alia, Colonics he apy online se ices based in Aus alia and Luck smi h se ices based
in USA. The spli in pa ame e s used in he expe imen whe e sha ed equally in he a io o 50% so
ha bo h he con ol g oups and ea men g oups ecei ed he same a ics and he same budge
alloca ion wi hin he ime o he expe imen s. The a ge loca ions o bo h he con ol g oups and
he ea men g oups a e made o be he same o a oid any undue ad an age acc uing o ei he he
con ol o ea men g oup. Bo h he con ol and ea men g oups a e made o ha e he same ad
scheduling, his allows all he ads o un a he same pe iods o he day. The landing pages o bo h
he con ol and ea men g oups a e same, his allows o uni o m exposu e o he same useabili y
a io in ela ion o landing page expe ience.
Fo he ecomme ce p oduc s campaigns, bo h he ea men and con ol g oups a e
connec ed o he same me chan cen e accoun meaning ha same p oduc s a e being ad e ised
ac oss he wo g oups.
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4. RESULTS
The heo e ical concep o his p ojec unde sco es he e ec i eness o MA on he ollowing
concep s iz is con e sions, ROAS, and o he PPC pe o mance me ics in Google ads ad e ising
channels. The hi d-pa y au oma ion which is ano he leading concep unde sco es he easy o
ca ying ou epe i i e asks agains he human manipula ions. The esul s o his s udy a e explo ed
and displayed wi h a en ion o connec ing he esul s o hese he si ical concep s.
4.1 RESEARCH RESULTS OF ALL KPI FOR TACTICAL GEAR E-COMMERCE CAMPAIGNS
The esul s below show all majo KPI measu ed agains he wo main campaigns unde s udy
in he spli expe imen o he B2C ac ical gea E-comme ce pla o m. The wo ypes o
campaigns a e Pmax and SSC
Table 4.1 All Ta ge KPI om Pmax and s anda d shopping campaigns a e 90 o spli expe imen
4.2 CONVERSION METRICS RESULTS FROM THE TACTICAL GEAR E-COMMERCE CAMPAIGNS
EXPERIMENTS
The s udy compa es he impac o ull in en o y au oma ed pe o mance campaign agains
s anda d campaign in a ac ical gea supply indus y and he esul s o pe iods o 90 days. F om
Table 1, i shows ha Pmax eco ded a o al o 55.3 con e sions while he S anda d campaigns
eco ded only 14 con e sions he pi-cha and ba cha in ig 4.1below illus a e % p opo ion which
shows ha ou o he o al numbe s o con e sions eco ded du ing he pe iods, Pmax eco ded
79.8% o he con e sions while he SSC eco ded 28.2% o he con e sions.

54
Figu e 4.1 Cha showing Numbe o con e sion esul s om Pmax & SSC
The con e sion ends as shown in ig 4.2 below shows ha Pmax s a ed eco ding
con e sion om Ma ch 21 which is he second day o he expe imen while he S anda d shopping
campaign s a ed eco ding con e sions on Ma ch 29 which is 9 days a e he expe imen s a ed
se ing ads.
Figu e 4.2 Con e sion T ends om Pmax and SSC Expe imen om Ma 20-June 15
Figu e 4.3 Con e sion Ra es om Pmax and SSC Campaign Expe imen s
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4.3 ROAS METRICS RESULTS FROM THE TACTICAL GEAR E-COMMERCE
CAMPAIGNS
F om he Table 1.0 abo e, a su p ise me ics shows ha SSC eco ded a highe ROAS agains
he Pmax campaigns despi e eco ding he highes numbe o con e sions wi h a alue o 4.57 and
4.01 espec i ely. The igu e in Fig4.4 shows he ROAS p opo ions o each o he campaigns
ep esen ed in bo h pi-cha and ba cha simul aneously. F om Fig 4.4, he ROAS. Ano he
in e es ing me ics is he ROAS ends in Fig 4.5 which shows ha S anda d sea ch campaigns
eco ded e y low ROAS un il May 9 h.when he bid s a egy was changed om manual bidding o
au oma ed bidding -Ta ge ROAS
Figu e 4.4 ROAS Sha e be ween PMAX & SSC
Figu e 4.5 ROAS T ends Pmax and ROAS T ends S anda d Shopping
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Figu e 4.6 Con e sion Values and Ad Spend om Pmax & SSC -spl expe imen
4.4 CLICKS AND CLICK RATES METRICS RESULTS FROM THE SPLIT CAMPAIGNS
The click me ics is an impo an me ics ha measu e he willingness o people o in e ac
wi h ads by clicking on i a e imp essions a e es ablished (Gi aldo-Rome o e al., 2021) Acco ding
he Pi- cha in Fig4.7, Pmax ecei ed 71.4% o he o al clicks du ing he pe iods o he campaign
expe imen while SSC ecei ed only 28.4% o he clicks. The 71.4% ep esen a o al o 1,842 clicks
while he 28.5% ecei ed om SSC ep esen a o al o 739 clicks as shown in Fig 4.7 below.
Figu e 4.7 cha showing numbe o con e sions be ween Pmax & SSC Campaign
Ano he impo an esul s he e is he click a e which is he a e a which an ad is being clicked
compa ed o he numbe o imp essions being made (Osmundson, 2022) FIG4.8 indica ed ha Au oma ed
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ML d i en campaign PMAX has a highe CTR compa ed o S anda d shopping campaigns despi e
showcasing he same ypes o p oduc s iews du ing he ime o he campaign.
Figu e 4.8 Clicks & Click Ra es Pmax Vs SSC Campaign Resul s
Fig 4.9 indica ed an in e es ing phenomenon whe e he s anda d shopping/sea ch
campaign eco ded highe imp ession han he au oma ed campaigns. Ou o a o al o 116,092
imp essions eco ding du ing he pe iods o he expe imen , SSC campaign go 65,023 while Pmax
ecei ed 51,069 imp essions as illus a ed in he Fig 4.90 below. A d ill down o he imp essions
shows ha SSC ecei ed mo e imp essions on mobile de ice wi h 52,611 imp essions while Pmax
ecei ed 35,587 imp essions on mobile de ices. The Pmax had mo e imp ession on compu e de ice
han SSC wi h 14.658 while SSC go 11,362 imp essions as seen in ig 4.9 below.
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Figu e 5.3 SSC ROAS T ends Showing a Rise in ROAS a e swi ching o Sma Bidding om May 9 h, 2023.
F om he esul s om he expe imen as shown in Fig 5.1 and 5.2 abo e iROAS = ROASSTB -
ROASMD ROASSTB = 8.23 and ROASMD = 0.86. Hence iROAS which is he inc emen in ROAS is 8-23-0.86
= 7.37 The e o e, ROAS om sma bidding s a egy eco ded iROAS o 7.37 when compa ed o
manual bidding which p o ed H3 o be TRUE.
5.4 RESEARCH MODEL AND HYPOTHESIS ADOPTION
Ou o h ee hypo heses o he s udy, wo has been p o ed o be accep ed while
only one is p o ed o be alse as discussed abo e, his is summa ized below along wi h he model.

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Table 5.2 Hypo hesis adop ion
5.5 PRATICAL CONTRIBUTIONS
5.5.1 INSIGHT TO PPC MANAGERS AND PPC SPECIALIST
Wha his implies o PPC manage s and specialis s is ha combining SSC and Pmax is a e y
g ea way o ha ness a highe ROAS. Ano he impo an s a egic ou come o he s udy is he need
o s a wi h Pmax campaign and ha e i gene a e many numbe s o con e sions be o e c ea ing
shopping campaigns. In his way, i will educe ROAS down ime since i will ake he s anda d
shopping campaign longe pe iod be o e eco ding a signi ican ROAS. Google ads uses accoun
his o y o de e mine a e age a ge cos pe acquisi ion and a e age a ge ROAS. Since Google ads
equi e a minimum o 15 con e sions and a ce ain ROAS his o y be o e one can se up sma
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bidding s a egy on s anda d shopping campaigns, i hen become impe a i e o use Pmax campaign
o gain accele a ed ac ion on accoun his o y o ROAS o enable be e and s a egic campaign
op imiza ion du ing he ime SSC is unning on manual bidding.
5.5.2 MANAGERIAL PLANNING AND CONTROL
This s udy p oposes a manage ial decision and budge a y alloca ion in e ms o campaign
planning in b2c ecomme ce ad e ising. I p oposes alloca ing mo e budge o pe o mance max
campaign a he i s wo mon hs o he campaign be o e di e si ying o s anda d shopping
campaign. This will enable maximum ROAS since he SSC will ha e exi manual bidding be o e ge ing
he equi ed ROAS.
5.5.3 OPPORTUNITY FOR PRODUCT FEATURE UPGRADE
The esul o his s udy shows ha s anda d shopping campaign has a highe ROAS when
ea ed wi h sma bidding s a egy. The p esen echnical de elopmen ea u es o he s anda d
shopping campaign only allow i o un on manual bidding s a egy un il a ce ain numbe o
con e sions has been egis e ed be o e i can swi ch om manual bidding o sma bidding.
Upg ading he SSC o un on sma bidding s a egy du ing he campaign se up s age wi hou he
need o ge a ce ain numbe o con e sions be o e swi ching om manual o sma bidding s a egy
will p esen mo e oppo uni y o ad e ise s o deli e mo e ROI by boos ing he ROAS igh om he
beginning o he campaign.
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6 CONCLUSION
This s udy del ed deeply in o he impac o Ma ke ing au oma ion on Google Ads PPC
managemen and he a endan e ec o Tensco e-Thi d-pa y au oma ion ool. The s udy was able
o es ablish ha ha use o ma ke ing au oma ion is a leading end in PPC ad e ising ha
ha nesses he la ge chunk o da a in ol ed in Google ads PPC and s eamlined hem in mo e
op imized o m o be e pe o mance. The s udy was able o es ablish ha he Pe o mance Max
campaign can educe ime used in c ea ing and se ing up campaigns as i blended he ads asse s
ac oss all he Google ads in en o ies.
The ask and cumbe someness o c ea ing di e en Google ads asse s ha will appea on
all in en o ies o Google such as YouTube, display ne wo k, The s udy ound ou ha Google ads
PPC ad e ising is becoming oo complex o human managemen and he e o e es ablished ha
one o he bes au oma ed ea u es agains manual maipula ion is he use o Google ads au oma ed
bidding s a egy o gain highe e u n on ad spend. ROAS The s udy concludes ha sma bidding
s a egies wi h a ge ROAS has g ea posi i e impac on e u n on ad spen . I was e y e iden in
he s udy ha a ansi ion om manual bidding in s anda d shopping campaign will exi a s anda d
shopping campaign om low ROAS o a highe and posi i e e u n on ad spend. This e ela ion is in
con ac o many o he s udies ha had p edic ed ha Pmax campaign has mo e po en iali y o
gene a ing a highe ROAS han s anda d campaign.
The campaign bulk ad g ouping s uc u e and how o o ganize hem in o e icien hemes
ha will blend in o Tensco e as a hi d-pa y au oma ion in his s udy p o ed o be e y e ec i e by
c ea ing an in e ace be ween Google ads campaigns and PPC managemen especially in
es uc u ing a campaign.
6.1 RESEARCH LIMITATION
This esea ch is limi ed by some ac o s which includes he olume and scope o he PPC
me ics in ol ed. These limi a ions ange om he pe iod om which he da a we e ex apola ed o
he amoun o campaign budge and he ype o se ices been a ge ed which in his case a e la gely
B2C indus ies. Da a in PPC managemen a e g ea ly impac ed o e long pe iod o ime and he size
68
o campaign budge unde in es iga ion. Many big sized companies wi h e y la ge amoun o
campaign budge may ha e a ied obse a ions in analysis wi h he au oma ion ool and campaign
ea u es been used.
Fu he s udies should endea o o explo e bigge campaign da a by ex ac ing me ics
om la ge campaign budge om mul i-na ional companies and agencies. Also ha ing access o da a
anging o e 12mon hs o ac i e campaign pe iod will b ing a mo e ull-de ailed s udy ha will be
mo e alid in making da a d i en decisions. The hi d-pa y ools used in he p ojec a e s anda d
subsc ip ion-based model which ha e limi ed unc ionali y compa ed o ull-op ion subsc ip ions
ha ha e all he hi d-pa y au oma ion ea u es. The esea ch only explo es he use o Google ads
PPC as he digi al ma ke ing channels, he e may be alid hypo hesis ha he esul s and conclusion
eached in his esea ch may no be applied in o he digi al ma ke ing channels like paid socials as
his was no conside ed du ing he esea ch p ocess and applica ion.
6.2 SUGESTIONS FOR FURTHER STUDIES
A ibu ion model plays a e y impo an ole in deciding he con e sion me ics as i de ines
he di e en le els o ouch poin s be o e gi ing con e sion c edi s o a gi en campaigns The
di e en ypes o a ibu ions p esen in Google Ads PPC as a he ime o his s udy a e da a-d i en,
las clicks and posi ioned based a ibu ions models (Google Ads Gene al Resou ces, 2023). This
s udy uses las click model, which implies ha only KPI gene a ed h ough las clicks a e coun ed. I
is es ima ed ha his may a ec he pe o mance o a gi en campaign. Resea ch on a ibu ion
model has shown ha many e u ning use s who did no comple e a pu chase on hei i s isi a e
being coun ed as a di ec isi o and he alues o hei ansac ions a e no e en ually been c edi ed
o he campaigns ha ini ially b ough hem o he websi e (Ru z e al., 2009). The e o e, his
esea ch ecommends subjec ing u u e expe imen s in o a ious a ibu ion models as possible o
ensu e ha each campaigns a e gi en he c edi s i dese ed as di e en ypes o campaigns
may pe o m bes in a pa icula a ibu ion model han o he s.
Gene a i e AI should be in eg a ed in he nex s udy o e alua e how his will a ec he a ge
KPIs. Google indica ed du ing he 2023 Google Ma ke ing Li e (GML) held in he mon h o May,
69
2023 ha i would be launching au oma ed asse s c ea ion ha will use a gene a i e AI o w i e
headlines and ex ads using cus ome s sea ch que ies. The implica ion o his is ha he ads ha
will be seen by use s will be w i en by AI in eal ime and in consonan s wi h hei sea ches on he
sea ch engines. Du ing he ime o his esea ch, his ea u e has no been o icially launched. Fu u e
s udies should in eg a e his gene a i e au oma ion echniques wi h a iew o inding hei impac s
in PPC campaign managemen as agains he s a ic campaign asse s c ea ion.
In eg a ing google p oduc s udio Tha allows au oma ed c ea ion o p oduc s images and
linking hem in o he me chan cen e should also be esea ched upon as hese a e he p ojec ed
ma ke ing au oma ion ea u es ha will be seen a he end o 2023 wi h he aim o deepening
au oma ion in Google ads c ea ion and managemen .
Fu he s udies should endea o o in eg a e i s pa y da a in hei Google ads PPC
channels while ca ying ou u he s udies in ela ed opics. This sugges ion is made based on he
expec ed impac ha i s pa y da a a e assumed o ha e on e ining AI d i en campaigns
op imiza ion including s anda d campaigns ool. In eg a ing i s pa y da a may ha e he likelihood
o impac ing on he esul s and conclusion.

70
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