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WebAnalytics:

Functions,KPIsandReportsinSMEs–

AUsageFrameworkandGuidelines

Athesissubmittedforthe

BachelorofScienceinInformationManagement

By

DominikHeller

StudentID:210200205

E-Mail:[email protected]

Faculty4:ComputerScience

InstituteofISResearch

UniversityofKoblenz-Landau,Germany

Supervisor:

VerenaHausmann

Prof.Dr.SusanP.Williams

Koblenz,March2016

ii ©2016UniversityofKoblenz-Landau,InstituteofISResearch

©2016UniversityofKoblenz-Landau,InstituteofISResearch iii

Declaration/Erklärung

Ideclarethat:

Thisthesispresentsworkcarriedoutbymyselfanddoesnot incorporatewithoutacknowledgement

any material previously submitted for a degree or diploma in any university. To the best of my

knowledge, itdoesnotconstituteanypreviousworkpublishedorwrittenbyanotherpersonexcept

whereduereferenceismadeinthetext.

_____________________

Ichversichere,

dass ichdievorliegendeArbeit selbständigverfasstundkeineanderenalsdieangegebenenQuellen

undHilfsmittelbenutzthabe.

MitderEinstellungdieserArbeit indieBibliothekbin icheinverstanden.DerVeröffentlichungdieser

ArbeitimInternetstimmeichzu.

DominikHeller

Koblenz,March2016

iv ©2016UniversityofKoblenz-Landau,InstituteofISResearch

©2016UniversityofKoblenz-Landau,InstituteofISResearch v

Abstract

Weareenteringthe26thyearfromthetimetheWorldWideWeb(WWW)becamereality.Sincethe

birth of theWWW in 1990, the Internet and therewithwebsites have changed theway businesses

compete,shiftingproducts,servicesandevenentiremarkets.

Therewith,gatheringandanalysingvisitor trafficonwebsites canprovidecrucial information toun-

derstandcustomerbehaviorandnumerousotheraspects.

WebAnalytics (WA) toolsofferaquantityofdiverse functionality,whichcalls for complexdecision-

makingininformationmanagement.WebsiteoperatorsimplementWebAnalytictoolssuchasGoogle

Analyticstoanalysetheirwebsiteforthepurposeofidentifyingwebusagetooptimisewebsitedesign

andmanagement. Thegathereddata leads toemergent knowledge,whichprovidesnewmarketing

opportunitiesandcanbeusedto improvebusinessprocessesandunderstandcustomerbehaviorto

increase profit. Moreover,Web Analytics plays a significant role to measure performance and has

thereforebecomeanimportantcomponentinweb-basedenvironmentstomakebusinessdecisions.

However,manysmallandmedium–sizedenterprisestrytokeepupwiththewebbusinesscompeti-

tion,butdonothavetheequivalentresourcesinmanpowerandknowledgetostandthepace,there-

foresomeevenresignentirelyonWebAnalytics.

This researchproject aims todevelopaWebAnalytics framework toassist small andmedium-sized

enterprisesinmakingbetteruseofWebAnalytics.ByidentifyingbusinessrequirementsofSMEsand

connectingthemtothefunctionalityofGoogleAnalytics,aWebAnalyticsframeworkwithattending

guidelines isdeveloped,whichguidesSMEsonhowtoproceed inusingGoogleAnalytics toachieve

actionableoutcomes.

vi ©2016UniversityofKoblenz-Landau,InstituteofISResearch

MitdemBeginndes26.JahresnachderEntwicklungdesWorldWideWeb(WWW)kannfestgehalten

werden,dassdasInternetseitseinerGründungimJahre1990gängigeWirtschaftsprozessenachhaltig

veränderthat,indemesdenWettbewerbzwischendenAkteureninneuedigitaleMärkteverlagerte.

IndiesemKontextbietetWebAnalyticseineVielzahl vonChancen fürWebsitebetreiber,die jedoch

neuen kompliziertenHerausforderungen gegenüber stehen.UmdiesenAnforderungen entgegen zu

treten,wirdWebAnalyticsSoftwarezurOptimierungderWeb-Ressourceneingesetzt.

DasgewonneneWissenüberNutzerderWebsitebietetneueVermarktungsmöglichkeitenundkann

gewinnbringendzurVerbesserungvonWirtschaftsabläufenundanKundenverhaltenorientiertenStra-

tegieneingesetztwerden.DeshalbspieltWebAnalyticseineentschiedeneRollebeiderInformations-

gewinnungundistzueinerwichtigenKomponentederEntscheidungsfindunginwebbasiertenUmge-

bungengeworden.

UnabhängigvonihrerGrößeversuchenkleineundmittelständischeUnternehmen(KMU)mitihreroft

größeren,digitalenKonkurrenzmitzuhalten,dochverfügensiewederüberdiebenötigtenRessourcen

inFormvonSpezialistenundKnow-how,nochüberdiezeitlichenundfinanziellenMittel.AlsFolgere-

signierenmanchevonihnenundverzichtengänzlichaufdenEinsatzvonWebAnalyticsSoftware.

DiesesForschungsvorhabenzieltaufdieEntwicklungeinesWebAnalyticsFrameworkundzugehörigen

Anleitungen,umkleineundmittelständischeUnternehmenbeiderVerwendungvonGoogleAnalytics,

alsWebAnalyticsSoftware,zuunterstützen.

DasErgebnisdieserwissenschaftlichenArbeitisteinganzheitlichesFrameworkmitzugehörigenAnlei-

tungen,dieKMUsbeiHandhabungundImplementationvonGoogleAnalyticsassistieren.

TableofContents

©2016UniversityofKoblenz-Landau,InstituteofISResearch vii

TableofContents

Declaration/Erklärung......................................................................................................................iii

Abstract............................................................................................................................................v

TableofContents............................................................................................................................vii

ListofFigures....................................................................................................................................x

ListofTables....................................................................................................................................xi

1 Introduction................................................................................................................................1

1.1 MotivationandProblemStatement.....................................................................................1

1.2 AimoftheStudy..................................................................................................................3

1.3 ResearchObjectivesandQuestions......................................................................................3

1.4 OutcomesoftheThesis........................................................................................................5

1.5 OutlineofthisThesis............................................................................................................5

2 ResearchDesign..........................................................................................................................7

2.1 ResearchSteps.....................................................................................................................7

2.2 Limitations.........................................................................................................................10

3 WebsitesandWebsiteClassification.........................................................................................11

3.1 Websites............................................................................................................................11

3.2 WebsiteClassification........................................................................................................12

3.3 WebsiteDesignPrinciples..................................................................................................13

4 WebAnalytics...........................................................................................................................16

4.1 TheEvolutionofWebAnalytics..........................................................................................16

4.2 WebAnalyticsFunctionality...............................................................................................18

4.3 WebAnalyticsDataCollection............................................................................................19

4.4 WebAnalyticsTrackingMethods.......................................................................................20

4.4.1 Logfiles(Server-sidedata).................................................................................................21

4.4.2 Pagetagging(Client-sidedata)...........................................................................................22

4.4.3 Applicationlevellogging.....................................................................................................24

4.5 WebAnalyticsAnalysesandReports..................................................................................24

viii ©2016UniversityofKoblenz-Landau,InstituteofISResearch

5 FrameworksforWebAnalytics.................................................................................................26

5.1 FrameworkforWebAnalyticsbyHausmann(2012)...........................................................26

5.2 MajorComponentsofanOnlineMarketingSystem(Tonkinetal,2012)............................29

5.3 TrinityApproachbyKaushik(2010)....................................................................................31

5.4 FrameworkofRelationsbetweenWebandE-BusinessMetrics(BaselandZumstein,2009)32

5.5 StartupMetricsFrameworkbyDavidMcClure(2005)........................................................33

5.6 ComparisonbetweenWebAnalyticsFrameworks..............................................................34

6 WebAnalyticsPerformanceMeasurementofSMEs..................................................................38

6.1 PerformanceMeasurement...............................................................................................38

6.2 SmallandMedium-sizedEnterprises..................................................................................38

6.3 IdentifyingKeyPerformanceIndicatorsandMetricsforWebAnalytics..............................39

6.3.1 MeasureandMetrics..........................................................................................................40

6.3.2 KeyPerformanceIndicators................................................................................................40

6.3.3 CreatingObjectiveKeyResultsandKPIs.............................................................................41

6.3.4 KPICharacteristicGuideline................................................................................................44

6.3.5 TheKPIDesignTemplate....................................................................................................45

6.3.6 KPIsrelatedtoaWebsiteType...........................................................................................46

7 GoogleAnalyticsImplementation.............................................................................................50

7.1 CaseWebsite.....................................................................................................................50

7.2 GoogleAnalyticsStandardvs.Premium.............................................................................52

7.3 InformationEthics,SecurityandPrivacy............................................................................54

7.4 KeyFeatures......................................................................................................................55

7.5 Installation.........................................................................................................................58

7.6 GoogleAnalyticsInterface.................................................................................................59

7.7 Reports..............................................................................................................................61

7.8 Dashboards........................................................................................................................62

7.9 KeyPerformanceIndicatorImplementation.......................................................................62

8 DevelopingtheGoogleAnalyticsFrameworkandGuidelinesforSMEs.....................................66

8.1 IssueIdentification.............................................................................................................66

TableofContents

©2016UniversityofKoblenz-Landau,InstituteofISResearch ix

8.2 DevelopinganEffectiveGoogleAnalyticsFramework........................................................68

8.3 DevelopingWebAnalyticsFrameworkGuidelines..............................................................71

8.4 FrameworkLimitations......................................................................................................79

9 SummaryandConclusion..........................................................................................................80

9.1 ResearchQuestions............................................................................................................80

9.2 KeyContributionsandFutureWork...................................................................................81

References......................................................................................................................................83

AppendixA–FrameworkandGuidelineWebsite............................................................................87

AppendixB–KPImeetsMetrics:DashboardandCustomisedReports.............................................90

AppendixC–GoogleAnalyticsDimensionsandMetricsOverview..................................................92

AppendixD–GoogleAnalyticsNavigationMap..............................................................................95

x ©2016UniversityofKoblenz-Landau,InstituteofISResearch

ListofFigures

Figue1:OverviewofResearchProject......................................................................................................5

Figure2:ResearchSteps...........................................................................................................................9

Figure3:ExampleWebpage,whichfollowstheWebDesignPrinciples(YCombinator,2016).............15

Figure4:AtimelineoftheWebAnalyticprogress(blog.clicktale.com,2010).......................................17

Figure5:WebPageTagging....................................................................................................................23

Figure6:HighLevelWAProcessCycle(Hausmann&Williams&Schubert,2012)................................26

Figure7:TheWAProcessFramework(Hausmann&Williams&Schubert,2012).................................28

Figure8:MajorComponentsofanOnlineMarketingSystem(adaptedfromTonkinetal,2012).........30

Figure9:TrinityApproach(Kaushik,2010).............................................................................................31

Figure10:FrameworkofRelationsbetweenWebandE-BusinessMetrics (BaselandZumstein,

2009)................................................................................................................................32

Figure11:StartupMetricsFramework(DavidMcClure,2005)...............................................................33

Figure11:CaseWebsitewww.businessculture.org(2015).....................................................................51

Figure12:UniversalTrackingID..............................................................................................................59

Figure13:GoogleAnalyticsInterface.....................................................................................................60

Figure15:WidgetCreation.....................................................................................................................64

Figure16:WebAnalyticFrameworkbasedonHausmann,WilliamsandSchubert(2012)....................69

Figure17:ThenewGoogleAnalyticsFramework...................................................................................70

Figure18:GuidelinesforSMEstoGoogleAnalytics...............................................................................73

Figure19:SMEWebAnalyticsPath........................................................................................................74

Figure20:DesignRecommendations......................................................................................................75

Figure21:KPIcharacteristics..................................................................................................................76

Figure22:WebAnalyticsDefintions.......................................................................................................77

Figure23:WebsiteCategories/KPIs......................................................................................................78

TableofContents

©2016UniversityofKoblenz-Landau,InstituteofISResearch xi

ListofTables

Table1:WebAnalyticsFrameworkComparsion....................................................................................35

Table2:StrengthandWeaknessesofWebAnalyticFrameworks..........................................................36

Table3:SMEComparsion(UN-ECE(1996),NBSC(2003),IFM(2007))..................................................39

Table4:StakeholderandmatchingKPIs(Clifton,2010).........................................................................42

Table5:OKRPlanningTemplate.............................................................................................................43

Table6:OKRPlanningExampleTemplate..............................................................................................44

Table7:KPIDesignTemplate..................................................................................................................46

Table8:ThefourtypesofWebsitesandexamplesofassociatedKPIs(McFadden,2005)....................47

Table9:RecommendedKPIsforSMEsbyFieldofApplication...............................................................49

Table10:ComparsionofGoogleAnalyticsFreeandPremium(Lunametrics.com,2015)......................54

Table11:GoogleAnalyticsFunctionalityOverview................................................................................56

Table11:GoogleAnalyticsReportNavigationMap...............................................................................61

Table12:GoogleAnalyticsDimensionsandMetricsOverviewExcerpt.................................................63

Table13:KPI-GoogleAnalyticsMapping...............................................................................................64

Table14:WebFrameworkRequirementComparsion............................................................................67

xii ©2016UniversityofKoblenz-Landau,InstituteofISResearch

Introduction

©2016UniversityofKoblenz-Landau,InstituteofISResearch 1

1 Introduction

AccordingtoAvinashKaushik(2010),thebeginningofWebAnalyticscanbefoundin1995whenDr.

StephenTurnerdevelopedoneofthefirstknownlogfileanalysisprograms.Thisfirstchapterprovides

ashortintroductiontothereasonsforusingWebAnalyticssoftwaretodayinsmallandmedium-sized

Enterprises (SME).Therebythechapterstarts inSection1.1withthemotivationandproblemstate-

mentfortheresearchproject.Moreover,itdescribestheresearchaiminSection1.2andleadstothe

researchobjectivesandresearchquestionsinSection1.3.Thechapterendsbypresentingtheoutline

ofthethesisinSection1.4.

1.1 MotivationandProblemStatement

WebAnalytics is a technologicalmethod to collect,measure, report and analyzewebsites andweb

application usage data and information (Burby and Brown, 2007). Within the last years, the im-

portance ofWeb Analytics grew and is still growing. TheWeb Analytics USmarket is projected to

reach3.09billiondollarsin2019withanannualgrowthrateof18.3%(ResearchandMarkets,2014).

Due to case studies by Phippen (2004), affords inWebAnalytics are essential for enterprises using

webtechnologies.Enterprisesoftenreleasefreeservicesandproductssuchaswebapplications like

GoogleMaps for the purpose of collecting analytic data. This collected information can be used to

placeadseffectivelyortoimprovetheproductorservice.SomeproductslikeGoogleNow,anintelli-

gentpersonalassistant,aredependentonanalyticdatabecausethegatheredusagedataleadstoim-

provedalgorithms,whicharerequiredforthebasicsearchfunctionalityoftheproduct.Besidescom-

paniessuchasAmazonwiththeirKindleproductscanbenefitfromthecollectedanalyticdatatoim-

provetheuserexperience.Forthisreason,webplatforms,whichprovidethemostvaluableanalytic

informationandincorporatethesedataintoanimprovedproduct,beatthecompetition.Forexample,

Netflixisonecompany,whichsuccessfullyregeneratescustomerwebanalyticdataintoaproduct.The

Netflixmovierecommendationenginecollectsdatatoidentifytheuser’sintereststosuggestmovies.

ThisrecommendationsystemisonereasonforNetflix´sworldwidesuccess.

Onthecontrary,WebAnalyticsarenotonlynecessaryforbigenterprises,butalso74%ofsmallbusi-

nesseshaveawebsite(Clutch,2015)andcouldthereforepotentiallybenefitbycollectingwebdata.

Moreover, small andmedium-sized enterprises play a critical role in our economy. For example, in

Germany,SMEsrepresent99,7%ofallturnovertaxbusinessesandprovide65,9%ofallemployments

(InstitutfürMittelstandsforschungBonn,2010).

The importantroleofSMEs inoureconomiescausesa lotofresearchfocusesontheirperformance

andthereforemeasurementfactorsliketheirkeyperformanceindicators,whicharealsofundamental

forWebAnalytics.

TheCommonwealth Scientific Industrial ResearchOrganizationhas examinedperformancemanage-

mentsystemsforSMEs(Barnes,1998).

DominikHeller

2 ©2016UniversityofKoblenz-Landau,InstituteofISResearch

Studies such as “The evolution of management accounting” (Kaplan, 1984) have traditionally used

managementaccountingtocreateperformancebenchmarks.

By1990,thesestudieshaveshownthatfinancialdataisnotenoughtosatisfytheperformancemeas-

urement in the economy because of the increasing complexity of organizations and themarkets in

whichcompaniescompete(KennerleyandNeely,2002).Thisisrelatedtofinancialreports,whichare

nowlessindicativeofshareholdervalue.

EmphasizedbyFreeman(2011),sustainableshareholdervalue is insteaddrivenbynon-financial fac-

torssuchascustomerloyalty,customerandemployeesatisfaction,internalprocessesandinnovation

management.WebAnalyticsgeneratesdatatotransformandimprovethesefactors.

TheoutcomeofthisisanewincreasingroleofperformancemeasurementrelatedtoWebAnalytics.

The Internet provides newmarketing opportunities, butmanagers of SMEs are facing the problem,

that they need ways to enhance and measure performance from the enterprises interface to the

WorldWideWeb,theirwebsiteorwebapplications.Asaresult,onlinemarketingandwebdesigngain

moreandmoreimportanceandlargeenterprisesshiftmorebudgettotheirwebanalyticactivities.

There is a broad rangeof different tools available to collect this valuablewebanalytic information.

(TrustRadius,2014).AccordingtoW3Tech.com(2014),GoogleAnalyticsisthemostusedwebsitesta-

tisticserviceintheworld.Itisawebanalyticstoolthatallowstheusertoanalyzenotonlywebsites,

but although search engines, emailMarketing, social networks,mobile, conversion and advertising

campaigns.Todayincreasingthenumberofwebsitevisitorsisanessentialfactortoexpandthescope

ofcustomers.Therefore,aframedSearchEngineOptimizationasapartofWebAnalyticsisindispen-

sable,butdifficulttorealizeforuninitiatedcompaniesandtheiremployees.Majorcompaniesemploy

owndepartmentstoidentifytheirbusinessrequirementscontinuouslyandanalyzetheirwebsitesto

capturedataefficientlyandinstructively.Consequently,thesecompaniesareenhancingtheirperfor-

mancelevels,andWebAnalyticsgivesthemacompetitiveedge.Smallandmedium-sizedenterprises

trytouseWebAnalytictoolsinthesameway,butoftendonothavetheequivalentresourcesinman-

powerandknowledgetostandthepace(Dlodlo&Dhurup,2010).Forthisreason,someevenresign

entirelyonwebanalytics(Zumstein&Züger&Meier,2011).Analogtobigenterprises,SMEsneedto

createcustomreports,segmentaudiencesandtweakimplementationtomeasureandassesstheor-

ganization´sparticularneeds.

ForthepurposeofhelpingSMEstogatheractionabledigitalmarketingdatawithGoogleAnalyticsand

utilizingitineffectivereports,thisthesisseekstocombinethebusinessneedsandresearchliterature

todevelopaWebAnalyticframeworkthatcanguideSMEstoexecutetheirwebanalyticrequirements

inanefficientway.

Introduction


1.2 AimoftheStudy

ThisthesisaimstodevelopaWebAnalyticsframeworktoassistsmallandmedium-sizedenterprises

tomakebetteruseofGoogleAnalytics.Therefore relevantKeyPerformance IndicatorsofSMEsare

identified,analyzed,classifiedandmappedtotheGoogleAnalyticsfunctionalities.Particularattention

is therebygiventoreports.TheframeworkseekstoassistSMEswithchoosingwhatmetricsare im-

portanttomonitorandhowrelatedreportsaregenerated.TheresultingWebAnalyticsdatacanbe

interpretedandreportedtobusinessstakeholderssuchasthemarketingdepartment.

1.3 ResearchObjectivesandQuestions

For thepurposeofdevelopingaWebAnalytics framework,whichstrives toassistSMEswithguide-

linestoimplementaneffectiveGoogleAnalyticssetup,thestudyconcernsfourdifferentresearchob-

jectives.

First,tounderstandthestatusquoofWebAnalytics,primarilyGoogleAnalyticsandreceiveageneral

conspectus,existingwebframeworkswillbeconducted.

InasecondstepbusinessrequirementswillbeidentifiedandWebAnalyticskeyperformanceindica-

tors for small andmedium-sized enterprises ascertained to discover the needs in the performance

measurementofSMEs.

ThethirdstepexaminesGoogleAnalyticstoestimatethescopeofprovidedfunctionalitiesandthein-

formationhandlingcapabilitiesforcaptureddata.

The fourth objective brings together and compares the research results from the previous steps to

createmeaningfulreportsanddevelopaframeworkthatcanguideSMEstoexecutetheirWebAnalyt-

icsrequirementsbymappingtheKPIstoGoogleAnalyticsfunctionality.

Inanutshell theprimaryresearchobjectives,whichhavetobe investigatedtoachievethespecified

goalsare:

1. DistinguishandanalyseexistingWebAnalyticsframeworks

2. IdentifybusinessrequirementsandkeyperformanceindicatorsforSMEs

3. ExamineGoogleAnalyticstoestimatethescopeofprovidedfunctionalities

4. Compare and combine research results RO1, RO2, RO3 to develop a framework that is able to

guideSMEstoexecutetheirwebanalyticrequirements

Toreachtheaforementionedobjectivesthefollowingresearchquestionwasdesigned:

How can a small ormedium-sized businesswith limited capabilitiesmake better use ofGoogle

Analytics?

Duetothecomplexityofthesubject,theover-archingresearchquestionwasspecifiedmoreprecisely

inthefollowingsubsidiaryquestions,whichguidedthecompleteresearchproject.

DominikHeller


Q1a:HowdoexistingframeworksclassifyWebAnalytics?

Q1b:Whichsimilaritiesdotheframeworkscontain?

Q1c:What framework ismostappropriatedtocovertheneedsofSMEsandthe integrationofWeb

Analytics?

Q1d:WhatarethedifferencesforWebAnalyticsbetweensmall,mediumandbigsizedcompanies?

Q2a:Whatarekeyperformanceindicatorsforsmallandmedium-sizedenterprises?

Q2b:WhatarethedifferencesintheWebAnalyticsrequirementsduetotheindustrysector?

Q3a:WhatfunctionalitiesdoesGoogleAnalyticsoffer?

Q3b:HowdoesGoogleAnalyticstoredataandwhichpossibilitiesdousershavetointeractwiththis

information?

Q3c:WhatkindofspecializationdoesaGoogleAnalyticframeworkneed?

Q3d:WhatadditionalfeaturesdoesGoogleAnalyticsPremiumofferforsmallandmedium-sizedcom-

panies?

Q4a:WhatdoesaWebAnalyticsframeworkneedtofitthebusinessrequirementsofSMEs?

Q4b:What is thebestmethodand form todesign theWebAnalytics frameworkandguidelines for

GoogleAnalytics?

Introduction


Figue1:OverviewofResearchProject

1.4 OutcomesoftheThesis

A framework,which delivers guidelines for SMEs towork efficientlywith Google Analytics, identify

businessrequirementsandachievetherelatedWebAnalyticinformationthroughreportswillbethe

essentialoutcome.Additionally,guidelinestosupporttheWebAnalyticsintegrationprocessaccording

to the research objectives are created.All outcomes are designed The resultswill be captured and

presentedonawebsitetomakethefindingseasilyaccessibletoSMEs.

1.5 OutlineofthisThesis

Thethesisispresentedinsevenchapters.

Chapter1coversthebackground,approachandtheresearchobjectivesofthisstudy.

Chapter2 explains the research steps to classify this thesis. Furthermore it introduces the research

stepsandindicatesthelimitations.

Chapter 3 clarifies the concepts of websites. In additionwebsite classifications andwebsite design

principlesareidentified.

DominikHeller


Chapter4reviewsexistingliteratureintheareaofWebAnalytics.Thechapterexplainsthefunctional-

ity,trackingmethodsandreportsofWebAnalytics.

Chapter5givesanoverviewofexistingWebAnalyticsframeworks.Toaccomplishthis,popularWeb

Analyticsframeworkswereanalyzedandtheirstrengthsandweaknessescompared.

Chapter6studiesthespecialcharacteristicsofperformancemeasurementinSMEsandthetheoretical

approachestodevelopmatchingmetricsandkeyperformanceindicators.

Chapter7givesanoverviewofGoogleAnalyticsastheusedWebAnalytictool.Arealworldcaseweb-

siteisusedtointroduceandimplementthebasicfunctionalityofGoogleAnalytics.

Chapter8describesanddevelopsanewWebAnalyticsFrameworkandassistingguidelinesforSMEs,

basedonpreviouslyobtainedfindings.

Chapter9isthesummaryandconclusionchapter.Itsummarizesthefindingsandconcludeswithsug-

gestionsonfurtherresearchpossibilitiesintheWebAnalyticsstudyfield.

ResearchDesign


2 ResearchDesign

This chapter explains the research steps of this thesis in Section 2.1. In addition a short discussion

aboutthelimitationsofthisresearchprojectispresentedinSection2.2.

2.1 ResearchSteps

Theresearchprojectisdividedintofourresearchsteps,showninFigure2andstartedwithStep1“Ini-

tialisation”andtheproblemdefinition.Duetotheproblemdefinition,aresearchaimandspecificre-

searchobjectiveswereformulatedandcomplementedwithresearchquestions.

Inaddition,inStep2“LiteratureResearch(ongoing)andDataAnalysis”anintenseliteratureresearch

wasundertaken,whichdefinesthefoundationoftheseveralresearchsteps.This literatureresearch

wascontinuouslydoneduringtheaccomplishmentofall researchsteps.Atthebeginning,anexten-

siveliteratureanalysiswasconducted.ReviewingWebAnalyticstheories,performancemeasurement

inSMEs,GoogleAnalyticsandotherWebAnalyticssubjects,leadingtothreemainsubjects:WebAna-

lyticFrameworksforSMEs,KPIsforSMEsandGoogleAnalytics.TheanalysisstartedwithWebAnalyt-

icsframeworkanalysis.WithinthegainedinformationwasusedtoanalyseexistingWAframeworksto

figureoutthestrengthandweaknessesofeachframework.InadditionGoogleAnalyticsfunctionality

wasanalysedandkeyperformanceindicatorsforSMEswereidentified.TheGoogleAnalyticsanalysis

wasaccomplishedbytheuseofthecasewebsitebusinessculture.org.Inordertoreceivethisessential

information and start the analysis, the literature sources had to be chosen. The primary literature

sources in this thesis are books and publications fromGoogle Scholar, ACMDigital Library, Spring-

erLink and IEEE Xplore. All publicationswere retrieved into a set, and additional publicationswere

added.After the final setof literaturehadbeen selected, thepublicationswere reviewed,analyzed

andfinallypresented.Theliteraturethatisusedinchapter3tochapter7wascollectedandspecified

asaresultofaniterativeprocess.Thisrefinementprocessisaccomplishedbyrefiningasamplebased

onthetitle,abstractandfulltextofthepublication.Furthermorecontributingpublicationswerese-

lectedtoanswerRO1,RO2andRO3.Forwardandbackwardcitationswerealsoincludedinthesam-

plesetduringtheiteration.Forexample,inordertogathertheinitialsetofresearchliteratureforthe

identification of Key Performance Indicators in Web Analytics, the following keywords were used

“WebAnalyticsKPIs”,“Smallandmedium-sizedenterprisesKPI”,“WebAnalyticsPerformanceMeas-

urement”,“WebAnalyticsmetrics”,“Webmetrics”,WebAnalyticsDataAnalysis”and“KPIWebAna-

lytics Implementation”. Duetothetimelimitation,thescopeofthisstudydoesnotclaimtoreview

theentire literature in theareaof interest.Moreover, this thesis is trying todefine the stateofart

basedonsampleswitha focuson themain subjectsWebAnalytics inSMEs.Asmentioned, thede-

scribed literature reviewand literature analysiswasperformedand continuedduring all further re-

searchstepstoachieverequiredknowledgeinthefurthercourseofaction.

DominikHeller


Aftertheliteratureresearchanddataanalysis,Step3“Visualisation”startedtodeveloptheWebAna-

lytics framework and guidelines. Therefore, a KPI Design Template and KPI Recommendation Table

weredesignedtosimplifytheKPIdevelopmentforSMEs.BesidesdifferentGoogleAnalyticsfunction-

alitywasresearchedtocreateaKPI–Metrics–GoogleAnalytics–Mapping-Template.Basedonthisre-

sultsandtheframeworkanalysisanewWebAnalyticsFrameworkwasdeveloped.Tosimplifyandex-

tendthe framework forSMEsdifferentguidelineswerecreated,bycombining theresultsofStep1,

Step2,andStep3.

In the lastResearchStep4: Synthesis, theprevious stepswere reviewedand suggestions for future

workissued.

ResearchDesign


Figure2:ResearchSteps

DominikHeller


2.2 Limitations

Thisresearchprojecthassomelimitations.EvenifWebAnalyticsandparticularlyGoogleAnalyticshas

a significant importance for the development ofwebsites andmobile applications, the topic is still

younganddiversifying(Clifton,2012).Academicliteratureandpapersarewidespread,butoftencur-

sorilyandmoreoverwithoutafocusonSMEs.Asaresultandinadditiontothetimelimitationsthe

researchercouldnotexhaustivelyreviewthetopic.SometechnicaldetailsaboutWebAnalyticalgo-

rithmsoradvancedGoogleAnalyticsfunctionalitywerenotaddresseddeeply.

Another limitationhas tobemadedue to theevaluationof thenewWebAnalytics frameworkand

guidelines.Theyweredevelopedusingtheoretical(LiteratureResearch)andpractical(CaseWebsite)

input, but theywerenot yet tested andevaluated in a real-world scenario to receive scientific evi-

dence.

WebsitesandWebsiteClassification


3 WebsitesandWebsiteClassification

ThefirstsectiongivesanoverviewoftheInternetcreationandtheevolutionofwebsites.Furthermore

the first sectionexplainshowwebsites canbe classified. Following the growthanddevelopmentof

WebAnalyticsareoutlinedinsection3.2.Finally,section3.3explainsprinciplesforeffectiveandaes-

theticwebsitedesign.

3.1 Websites

“Thesystemweneed is likeadiagramofcirclesandarrows,wherecirclesandarrowscanstandfor

anything.Wecancallthecirclesnodes,andthearrowslinks.Supposeeachnodeis likeasmallnote,

summary,articleorcomment. […]Thesystemmustallowanysortof informationtobeentered.An-

other personmust be able to the information, sometimeswithout knowingwhat he is looking for.”

(Berners-Lee,1989).

TheWorldWideWeb, often named as “theWeb”, is an enormous collection of electronic records.

TheseelectronicdocumentsconsistoflinkedsetsofpageswrittenintheHyperTextMarkupLanguage

(HTML).HTMListhestandardmarkuplanguage,whichisusedtocreatewebpages.Everydocumentis

stored infilesonserversaroundtheglobal Internet.ThebasicconceptoftheWorldWideWebwas

conceivedin1989byTimBerners-Lee,evenifhewasthefirstscientistwiththeideaofalinkednode

network.Berners-LeewasworkingattheEuropeanParticlePhysicsLaboratory(CERN)duringthistime

whenhispaper“InformationManagement:AProposal”gotintothehandsofMikeSendall.Thepaper

discussestheproblemof informationloss incomplexsystemsanddeliversasolutionbasedonahy-

pertext system (Berners-Lee, 1989). After reading the proposal, Sendall assigned TimBerner-Lee to

programaprototype.Berners-LeeimplementedacommunicationbetweenaserverandaHyperText

Transfer Protocol client via the Internet.More andmoreuser accessed theweb server info.cern.ch

overthefirstfivemonthsandthewebgrewandspreadaccelerated.In1994CERNandtheMassachu-

settsInstituteofTechnology(MIT)establishedaconsortiumwhoseaimwastodeveloptheweband

standardisetheprotocolsassociatedwithit(Carpenter,2013).

AnothersubstantialcontributionfortheWWWdevelopmentcamefromtheNationalCenter forSu-

percomputingApplications(NCSA),whichdevelopedMOSAICthefirstinteractivewebbrowserwitha

graphicaluserinterface(GUI).

Browsingfor informationisoneofthemostbasicandessential interactions intheWorldWideWeb

today(Ross,2009).Informationisprovidedonthewebserveratasite,whichoftenintegratesawhole

setofoneormoreregardingwebpages.EveryWebpageinonesetcontainslinkstootherwebpages.

They can be stored on the identical server or any other server connected to the Internet. Asmen-

tionedbefore, everywebpage is typicallywritten inHTML,whichprovidesand structuresall infor-

mationtodisplaythecontentoftext,imagesandvideosonthescreenoftheclient.Thebrowserac-

cesses,locatesandfetcheseveryrequestedwebpagebyinterpretingtheformattingcommandsthat

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eachpagecontains.Whentheuserclicksonalink,thepagebehindthelinkisaccessedandinterpret-

edwith the sameprocess.A link is termedhyperlink and the complete linkage comprises theused

nameoftheschemeorapplicationprotocol,usuallythehypertexttransferprotocol (HTTP)andalso

thedomaininwhichthewebpageissaved(Halsall,2005).

EverywebpageisaccessedandtransferredthroughaTCPconnectionusingtheHTTPapplication-level

protocol.When thebrowser startsanHTTP interaction,each request implicatesanASCII stringand

theresponsefromtheserverintheRFC822formatwithMIMEextensions.Ifthelinkedwebpagesare

distributedoverdifferentservers,aseparateTCPconnectionbetweenclientandserverwillbeestab-

lished forevery interactionandafter the response is received the regardingTCP connectionwill be

cleared. Additionally to HTTP or HTTPS, which is used for encrypted secure communication, the

browser can use a lot of different application protocols like the file transfer protocol (FTP), gopher

protocolornetworknewstransferprotocol(NNTP)toaccesswebcontent(Ross,2009).

Overtime,theexclusiveuseofHTMLtocreatewebpagesstumbledrelatingtointegratenewfeatures

intowebpages.Tovanquishtheserestrictions,appletsweredevelopedto implementcode intothe

page. This code is loaded independent of theHTML interpreter and gets identified and interpreted

fromthebrowserbytags.

Aseveryappletisaself-containedprogram,theimplementationadvantageisthatthepartsofapage

thatcontaincodethatisusedtochangeintheformofappletsdonothavetobeincorporatedintothe

browser.OneprogramminglanguagethatisusedtoembedcodeintoanHTMLpagedirectlyisJavaS-

cript(Halsall,2005).

3.2 WebsiteClassification

DuetotheInternetwebsiteLiveStats(Jan2016),thereareabout980.000.000websitesontheInter-

netin2016.Over75%ofthiswebsitesareinactiveandonlyusedasparkeddomains.Theother25%

consistofmanydifferentkindsofwebsites.Fourdifferenttypesofwebsitesareexemplifiedcloserin

thefollowing(Booth&Jansen,2009).

E-CommerceWebsites

Thegoalof themoste-commercewebsite is togenerateprofitbydrivingvisitors topurchaseprod-

ucts,servicesandsubscriptionsthroughthewebsite.

LeadGenerationWebsites

Thesewebsitesprovide informationabout anorganisation,productor a service to attractpotential

customers.Asdistinguished frome-commercialwebsites, a LeadGenerationwebsitedoesnotoffer

directonlinesales,oftenduetotheunique,complexandcustomisedservicestheyprovide.Theyare

similartoContentwebsitesbutareexecutedbyanorganisationalbody.

CustomerServiceWebsites



ThepurposesofCustomerServicewebsitesarepartlybasedoncommercialwebsites.Thesewebsites

reduceexpensesandimprovecustomerexperiencebyshiftingcommoncustomerservicestoaweb-

site.Theirgoalistoprocesscustomerquestionsandproblems.

ContentInformationWebsites

Nearlyeverywebsiteprovides contentand information.A contentwebsitepresents informationon

diversetopics.Someofthesewebsitesoffertheircontentforfree,whileothersderiverevenuefrom

advertisementoradvertisingsupport.

Examinethedescriptionabove, itbecomesapparent that thetransitionsbetweendifferent typesof

websitesareseamlessandnotalwaysclear-cut.Thecategorisationoftencorrelateswiththepersonal

perspective.Thereforethedivisionandclassificationofwebsitesintothetypesisnotstringent.Any-

how, the webiste categorisation allows to focusWeb Analytics and is therefore expedient. Beside

websitesarealsocategorisedbytheircontentandfunctionalitysuchasnewswebsites,blogs,wikis,

videostreamingservices,onlinesocialnetworks,massiveopenonlinecourses,searchengines,gaming

site,etc.(Booth&Jansen,2009).

3.3 WebsiteDesignPrinciples

“Beautyisintheeyeofthebeholder.”

(MagaretWolfe,1878)

Effectiveandaestheticwebdesignisoneofthekeyrequirementsforasuccessfulwebsiteandthere-

fore forWebAnalytics. Thewebdesign is judgedby thewebsite visitor, not theowner and should

achieveexcellentusability,formandfunction.AccordingtoasurveybyPatelandJuric(2001)Internet

usersareespeciallyattractedtocontentanduserfriendlinessofawebsite,followedbythegraphics.

Websitesthatarenotwelldesignedtendtoperformworseandreflectthisdownsideinsub-optimal

WebAnalyticsmetrics.NiederstRobbins(2012)statesthatwebdesignersneedknowledgeaboutalot

ofunknownfactors,suchassizeofthescreenorbrowserwindow,visitorconnectionspeed,deskor

mobileuser.Websitecontentshouldbeplacedsemanticand ina logicalorder.This thesisdoesnot

aimtocoverdetailedwebdesignprocesses,neverthelessshouldsomegeneraldesignprinciplesbere-

spectedtosupportSMEsorwebsiteownerstoreceiveabasicunderstandingofwebdesignandcon-

siderthisinfluencefactorinWebAnalytics.Toaddressawayofproceedingwhiledesigningawebsite,

sixsimplequestions,whichguidethewebsiteownertodevelopanengagingandeffectivedesignwere

createdindependenceontheKevinHale(2015)designprinciples.

Whatisyourproduct?

Thewebsitedesignshouldhighlightanddescribeinoneconspicuoussentencetheproductorservice

thewebsiteisoffering.

Whatisitfor?

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Awebsiteshouldexplaintheproductorservicevaluetothecustomer.

Iseverythinglegit?

Inordertolookprofessional,thedesignneedstobethestandoftheartandifachievable,addedwith

qualitylabels.Ifthewebsitecreatorhasnosenseforwebdesign,itisadvisabletobuyanduseade-

signtemplate.

Whatistheprice?

Allproductandservicecostsshouldbepresentedclearlyandunderstandabletotheuser.

Whoisusingit?

Testimonialsandreviewsareusefulindicatorsofcustomerhappinessandcustomer-orientedproducts

orservices.Therefore,theyshouldbepresentedonthewebsite.

TowhomcanItalk?

Manywebsitevisitorsorcustomershavequestions,thatiswhyaquestionsandanswersorcustomer

supportwebpageneedstobeeasilyaccessible.

OnepositiveexampleforawebsitepursuingthisdesignprinciplesisthewebsiteoftheAmericanseed

acceleratorYCombinator(2016),showninFigure3.



Figure3:ExampleWebpage,whichfollowstheWebDesignPrinciples(YCombinator,2016)

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4 WebAnalytics

ThischapterreviewstheliteratureonWebAnalyticsandWebAnalyticsframeworks.Therebyweun-

derstandWebAnalyticsasthetechnologiesandmethodstocollect,measureanalyseandreportdigi-

talusagedata inwebsitesandwebapplications (Burby&Brown2007).WebAnalytics technologies

aretypicallysplitintotwocategories,on-siteandoff-siteWebAnalytics.Adatacollectionofacurrent

Websiteiscalledon-siteWebAnalytics(Kaushik,2009).Thesetoolsareappliedtomeasuremostas-

pectsofdirectuser-websiteinteractions,suchasthevisitortraffic,timeonsite,clickpath,etc.

Ontheotherhand,off-siteWebAnalyticssoftwareisoftenprovidedbythirdpartycompaniessuchas

Swydo(http://swydo.com).TheirWebAnalyticstoolsmeasurethepotentialwebsiteaudiencefroma

macro-view to compareonewebsite to another. Therefore, they collectdata fromsources like sur-

veys,marketreports,publicinformation,etc.(Kaushik,2013).

Thefollowingsection4.1outlinestheevolutionofWebAnalytics.ContinuedbyWebAnalyticsfunc-

tionality,WATrackingmethods,analysesand reports,beforechapter5concludeswithanoverview

anddiscussionofexistingWebAnalyticframeworksandidentificationofconnectionsbetweenthem.

4.1 TheEvolutionofWebAnalytics

Aroundtheyear1993,thefirstanalysisofwebserverlogfilesstartedandinadditiontothatthebirth

ofWebAnalyticswaslaunched(Carpenter,2013).Atypicallogfilestoredarecordofeveryserverre-

questsuchasclientIPaddresses,pagecalls,pagedownloads,bandwidth,operatingsystem,operating

browserandversion.Administratorsusedlogfilesmostlyfortroubleshootingpurposes.Overthetime

websiteownersrecognisedthatanalysinglogfilescanleadtovaluableinformationaboutthewebsite

users.Asanexample,tocountIPaddressesallowstheadministratortoenumeratethenumberofvisi-

torstoawebsite,whichcanbeusedtoidentifyhighlyfrequentedwebsites.Theuncomfortableanaly-

sisoflonglogfilesledtothedevelopmentofsimplifiedWebAnalyticsoftware.WebTrendwasoneof

thefirstWebAnalyticsoftwareandgotcommerciallyavailablein1993.Onlytwoyearslaterin1995

the first entirely free log file analysis programs such as “Analog” were introduced (Kaushik, 2007).

They offeredmore functionality thanWeb Trends, such as improved reportswith structured docu-

mentationandabettervisualgraphiclayout(Schneider,2010).Figure4showsatimelineofimportant

stepsintheevolutionofWebAnalytics.

WebAnalytics


Figure4:AtimelineoftheWebAnalyticprogress(blog.clicktale.com,2010).

In 1996, theWeb counterwas conceived and represented the firstWebAnalytic tool to utilise the

pagetaggingfunction,whichisadatacollectionmethod.Overtimewebpagesbecamemorecomplex

and contained images, flash files and videos. Due to the richer website content, approximately, in

1997webpagenumberhitswerenolongerrepresentativeforthenumberofwebpagerequests(Bot-

tégal, 2016). The counting of number page hits shifted to counting the number of individual users,

whorequestthewebsite.JavaScriptgotusedinthewebandthetaggingofJavaScriptcodewasused

tocollectWebAnalyticsdata.Eventoday,JavaScriptcodetaggingisoneofthemostpopularmethods

toreceiveanalyticinformation(Booth&Jansen,2009).

TheWebAnalyticsAssociation(WAA)wasestablishedintheyear2004.TheWAAaimstostandardise

theWebAnalytics terms,definitionsandbestpracticesusedby the industry.Moreover, itdevelops

and implements training and certification programs and consolidates Web Analytics professionals,

consultantsandend-userstoimproveanalyticforcesandcombinethecommoninterest.In2012,the

WAAchangedtheirnametoDigitalAnalyticsAssociation(DAA)becauseoftheincreasingandincorpo-

rateddiverseactivitiesoutsideWebAnalytics.Thisrenamingabandonstheterm“WebAnalytics”to

advancethereferenceofparticularwebsite-onlydatacollection.Nowadaystherearealotmoresimi-

larorganisationswithstandardisationagendas,whichleadstofurtherdevelopment,researchanded-

ucationinthefieldofWebAnalyticsanddigitaladvertising.TheseorganisationsincludetheJointIn-

dustryCommittee forWebStandards in theUKand Ireland (JICWEBS),AuditBureauofCirculations

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electronic in theUKandEurope (ABCe)or the InteractiveAdvertisingBureauEurope (IAB) (Singal&

Kohli&Sharma,2014).

4.2 WebAnalyticsFunctionality

TheprimaryandessentialfunctionofWebAnalytics istocollectandanalyseusagedata.Nowadays,

WebAnalyticsisspreadwidelyandusedindifferentindustriesforvariouspurposessuchasadvertis-

ing,campaigns,e-commerceimprovement,webdesignanddevelopment,websiteperformanceopti-

misationetc(Kaushik,2009).However,allonallwecanidentifyfourmajorusecasesforWebAnalyt-

ics:(1)websiteandwebapplicationdesignanduserexperienceoptimisation,(2)webapplicationim-

provementandproblemidentification,(3)e-commerceoptimisation(4)userexperience.

1. Thewebischangingfast(Kaushik,2012).Steadyimprovementsareneededtobeuptodate.

Thisimplicatesoptimisationsofthetechnicalandinformationarchitecture,layoutanddesign

to improve theuser interaction.TheWebAnalytics information canalsobeused to change

and add functionality to thewebsite orweb application.Oneexample is the site search in-

tegration,toshowthesearchresultsandsearchqueries,whichenablesto identifywhatthe

usersaresearchingforonthewebsite.

2. WebAnalyticsisabletoaddresstheissueofwebcodeandsitefailures,detecterrorsinweb

applications and games to re-engineer features and functionality.Moreover, one significant

factoriswebsiteperformance.Zoompf,aformerWebAnalyticscompany,studiedtheimpact

ofwebsitespeedonsearchrankings(Zoompf,2013).Theydetecteddirecteffectsoftheback-

endperformanceofawebsiteonthesearchengineranking.Theyadvisedwebsiteownersto

explorewaystotestandimprovetheirTimeToFirstByte(TTFB).TheTTFBisameasurement

method to indicate the responsiveness of web server, web page and network resources.

Achievable throughwebserver,webapplicationandwebsitecodeoptimisationordatabase

queriesimprovements.Besides,thereareotherstudies,whichidentifiedintensecorrelations

betweenpage-loadtimeandthe likelinessofausertoconverttoanotherservice(Walmart,

2012).

3. WebAnalyticsisalsousedtoimprovee-commercebyenhancingcustomerorientation,acqui-

sitionandretention.Understanding thecustomerbehaviorandneeds throughusagedata is

importanttoattractattentionandbindthecustomertothee-commerceplatform.Thispro-

cesshasadirectinfluenceontherevenue.Therearemultiplecompanygoalslikereducingthe

bounce rate to approach, that every visitor ends his platform usagewith a targeted action

(Clifton,2012).

4. WebAnalyticsneedstodistinguishbetweendifferentvisitortypes,trafficsourcesanddistri-

butionandmarketingchannelstodeliverpreciseusagedata.Itisusedtodetectandanalyse

diverse trackedtraffic separately fromvarioussourcesand furthermoretoanalysecorrelati-

ons between for example a newsletter and a social media advertising campaign (Clifton,

2012).

WebAnalytics


4.3 WebAnalyticsDataCollection

ElementaryWebAnalyticsaimstoanalysecollectedwebtraffictoidentifyusagepatterns.XiaohuaHu

andNickCercone(2004)createdadimensionalmodeltoinspectthisdata.Themodeldividesdatainto

two different data types named “measurement data” and “dimensional data”. Measurement data

usuallydescribestheusagecountandtimeasapageview,whichisonerequestforawebpage,ora

mouseclick.Withthehelpofmetricsanddimensionsit ispossibletocalculatemetrics.Dimensional

data comprises client information (browser typeandversion,operating systemetc.), time, location,

contentandsessions.

The diverseWebAnalytics data sources can be assorted into four categories (Zheng& Peltsverger,

2014):

§ DirectHTTPrequestdata

§ ApplicationdatasentwithHTTPrequests

§ NetworkandservergenerateddatarelatedtoHTTPrequests

§ Externaldata

DirectHTTPrequestdataisgainedfromtheHTTPrequestmessage.ThewebclientsendsanHTTPre-

questtothewebservertorequestawebpageoranyotherwebresource.Normallywebtrafficmeas-

urementconsistsoftherequestedpageviews.Everyrequesthasdifferentdimensionssuchaspage,

technology,visitor,etc.AnHTTPrequestcontainsarequest lineandtheHTTPheaders.Therequest

lineincludesthemethodtoken,whichindicatesthemethodtobeperformedontheresourceinspect-

ed by the Request-URI (united resource identifier), including host domain, IP and directory path.

MoreovertherequestlineconsistofthementionedRequest-URI,aprotocolversionandthecarriage

return(CR) linefeed(LF).Generally,theunitedresourceidentifier isthebasic informationtoenable

pagecounting(Fielding&Gettys,1999).

AnotherimportantpartoftheHTTPrequestistheHTTPheader.Therequest-headerallowsclientsto

passadditionalinformationabouttherequesttoitselfandtheserver.Thoserequestheaderfieldsare

mostlyusedtodeliverdimensionalWebAnalyticsdataandcontainrequestandclientcharacteristics.

SomeimportantHTTPheaderfieldsfortrackingare:(Zheng&Peltsverger,2014):

UserAgentfield:containsclientinformationsuchasbrowserversionandtype,operatingsystemetc.

Thisinformationisusefultocreatetechnologyclientprofiles.

Referee:savesthelastvisitedURLthatleadstothecurrentURL.Thisinformationcanbeusedtoana-

lysetheuserswebmovement,namedclickstream,byconnectingpreviousrequeststogenerateavis-

itingpathortoilluminatemetricslikeentryrateorexitrate.

AcceptLanguage: isdeterminedby theusersdefaultOperatingSystem language locale forexample

“de”(German)toreceiveWebAnalyticinformationoftheuser’slanguageandcountry.

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Cookie: containsclientsideapplication informationsuchaskeyboardandmouseactions,whichcan

beusedtocreateanalysisasWebAnalyticsheatmaps.Aheatmapdeliversavisualmethodtoshow

visitorusagefeedbackbyhighlightingareasorelementsonthewebpage,thatusersengagewith.

AnotherimportantinformationsourceisApplicationleveldata,whichiscreatedbyapplicationlevel

programssuchasPHPorJavaScript.

Sessiondata isusedtocalculatemetricssuchasfrequencyofvisits,sitetimelength,pageviewsper

visitetc.Importanttogainthisinformationisthesessionreconstruction.Thereisatime-orientedand

anavigation-orientedapproachtoidentifysessions.Time-orientedsessionreconstructionsearchesfor

aperiodof inactivitybyauser. Iftheinstantoftimeisreachedasecondsessionfortheuserbegins

oncehereturns.SessiondataisnormallytransmittedthroughtheURLparametersorsessioncookies.

Referral data is a coded value,which acts for diverse sources leading to the currentweb resource.

Thereforethedatacanbeusedtoanalyseexpectedandunexpectedtrafficlevelsorevaluatechannel

effectivenessinadvertisementtracking.

Useractiondatacontainsprimarilyuserinputdata(keyboardandmouseactions,forexamplesearch

termsorcursorcoordinates)andspecialdatasuchasaudioorvideofileplaying,bookmarkingetc.

Client/Browsersidedataisusercomputerstatusinformationlikedisplayresolutionorcolordepth.

Application leveldata is included in threediffersoptionswithin theHTTPrequest.The firstplace is

withintheURLrequestasan,oftenspecificallycreated,URLparameter,whichcanbeparsedbythe

serversideprogram.HTTPcookieheaderoffersthesecondoption.AnHTTPcookieheaderisasmall

pieceofdatathatisgenerallyusedtostoreapplicationandprofiledata.Finally,thethirdpossibilityis

touseanHTTP-methodwithintheHTTPrequestbodytodelivertheapplicationleveldata(Tappenden

&Miller,2009).

NetworkleveldataisneededtoresolveanHTTPrequesttransmissionsuccessfully.Forexample,the

Webserver logs thecomplete IPaddressandportnumbertoreturnaresponseat theTCP/IP level.

Therewillalsobeserver-generateddatastoredintothelogfilestostoreinformationsuchasfilesize,

processingtime,requestdataetc.

Externaldataisanalysedtointerpretwebusage.Therefore,ithastobecombinedwithon-sitedatato

linkinformationsuchasanIPaddresswithgeographicdata.Additionallyexternaldatacontainsinfor-

mationthatwasminedinaseparateprocessorsearchtermsandadvertisementkeywords.IfaWeb

pagecontainsrevenueorprofit,informationitcanalsobeclassifiedasexternaldata.

4.4 WebAnalyticsTrackingMethods

ThisSectionexplainsthemainWebAnalyticsdatacollectionmethodsandrevealstheadvantagesand

disadvantagesofthedifferentWebAnalyticstrackingmethods.

WebAnalytics


TherearemainlyfivedifferentmethodstocollectWebAnalyticdata.Everycollectionmethoddiffers

fromthedataqualityandcomplexityofcollection.Therefore,thecollectionmethodhastobechosen

wisely.MostcurrentWebanalyticcompaniesuseacombinationof twoormoremethodstocollect

dataforWebAnalytics.,

Thesixdifferentcollectionmethodsare(Hassler2010,S.28ff):

§ Logfiles(Server-sidedata)

§ Pagetagging(Client-sidedata)

§ Applicationlevellogging

§ A/Bmultivariatedataanalysis

§ Survey

§ Interviews

Throughout the followingsections, thethreemostusedWebAnalyticsmethodswillbediscussed in

detail.

4.4.1 Logfiles(Server-sidedata)

The log fileanalysis isoneof theoldestWebAnalyticsmeasurementmethodsandwasusedbefore

thecommercialapplicationoftheInternettocollectWebAnalyticdata.Thewebserversstoreevery

file,whichisneededtorunawebsite.These,primarilyHTMLfiles,describethestructureandcontent

ofthewebsite.Iftheuseropensawebsiteinhisbrowser,thebrowserdisplaystheHTMLfilesofthe

webserver.

Thismovementonthewebsiteisstoredinlogfiles.Everyuserinteractioncanbeusedtoreconstructa

WebAnalyticpathduringasession.Allrequestsandactionsarecollectedonthewebserver.Asare-

sultthelogfileanalysisisfocusingonthesever-sidedatacollection(Hassler,2009,S.45ff).

Typically theweb servers save information like the IP address, browser typeandversion,operating

system,requestedcontent, time,statenumberandthesizeof thedelivered information.TheNCSA

CommonLogexampleshowsatypicalaccesslogentrybyanApacheWebServer,whichrecordsallre-

questsprocessedbythewebserver(ApacheServerDocumentation,2016).

NCSACommonLogexample:

111.111.123.123-dheller[10/Oct/2015:21:01:12+0500]“GET/index.htmlHTTP/1.0”2001043

Log analysis software such as Analog can be applied to extract and analyse the log files. One ad-

vantageofthelogfileanalysis isthesimplesetupundstructure.Gathereddataisstoredlocallyand

coherent “oneview,one log” (Aden,2009,S.33ff). This simple structure isanadvantageandat the

sametimeadisadvantage.Whenthewebsiteuserpushestherefreshbutton, thewebserver isnot

abletodifferbetweenuserswhoarenewtothewebsiteandthosewhoalreadyvisitedthewebsite.

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Therefore, thewebservercreatesanewentry in the log file,which leadstoa largescaleofentries

thatarecomplicatedtoanalyseandinterpret.Toaddressthisproblem,itispossibletousecookies.A

cookieprovidestheuserwithasessionIDandisstoredlocallyontheuser’scomputer.Duetotheses-

sionIDthewebserverisabletorecogniseinformationlikedateandtimeofaspecificwebsitevisit.As

a result, thewebservercan identifya recurringwebsitevisitorandcreatesessionsbringingsmaller

andmorepertinentdata,duetothepossibilityofidentifyingiftheuserrequestedthewebsiterepeat-

edlyduringadefinitetimeperiod.Nevertheless,cookiesarenouniversalremedy,becauseusersare

abletodeletethelocalsetcookiesordisablethemcompletely.

Anotherproblemoflogfileanalysisistheincapabilitytomeasureinputactionlikemouseclickseffec-

tively.Thewebserverisonlyabletocollectdata,whichisgeneratedthroughtodifferentwebsitere-

quests.AccordinglytheinteractioninAdobeFlashcannotbetrackedindetailandall informationin-

side the application is stored in one file, leading to one single entry in the log file (Reese, 2008,

S.228ff).

4.4.2 Pagetagging(Client-sidedata)

Sincetheadventof theWeb2.0themorerecentmethodforWebAnalytics tracking isusingclient-

sideprograms, for instanceembeddedscripts,browseradd-onsandplugins.Even if theuseof flash

contentinwebsitesisdecreasing,thereareotheradvantagesofpagetaggingsuchasthepreciseinput

recognitionanddetectionoffollowinguserinteractions.Furthermore,pagetaggingofferscompatibil-

itytoawidevarietyofprogramsandplugins.

Insteadoftheserver-sidedatacollection,the information isgatheredontheclient-sideasshownin

figure5.

WebAnalytics


Figure5:WebPageTagging

Thismethodisusingthewebserveronlytostorefiles,whichareneededforthewebsitearchitecture.

Inadditionanexternaltrackingservergathersalluserrequestsandinputsinpagetagfilestoanalyse

them(Hassler2009).

WebAnalyticsdata is collected in thebrowser tomeasure informationas theusers’ interactionbe-

tweentwowebsiterequests.Thistechniqueispossiblebyimplementingatrackingcodeintotheweb-

site’s source code. Typically this integrated JavaScript code tracks the entire userwebsite activities

andinteractionsandsavestheinformationintoacookie.Thelocallystoredcookieinformationissent

tothetrackingserver(Reese,2008).

ThetrackingserverisusuallylocatedattheWebAnalytictoolproviderandnotinthewebsitehosting

company.Companies specialised in Web Analytics such as Google or WebTrend are running these

tracking servers to gather, store and analyse thedata. To explain the functionality of page tagging,

GoogleAnalyticsisusedasanexample.ThespecifictrackingcodeisavailableattheGoogleAnalytics

website(GoogleAnalytics,2015):

<script>

(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){

(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*newDate();a=s.createElement(o),

m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)

})(window,document,'script','//www.google-analytics.com/analytics.js','ga');

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ga('create','UA-27744900-1','auto');

ga('send','pageview');

</script>

ThistrackingcodeisusedtomeasuretheinteractiononthewebsiteandsenttotheGoogleAnalytics

trackingserver.Oncetheuserrequestsawebsite,whichisprovidedwithatrackingcode,acookieis

generatedontheuser’scomputerandthedatacollectionstarts.

Thetrackingcoderequestsa1x1sizedpixelimage,namedTrackingBugorWebBug,fromthetracking

servertostartthedatatransmittothetrackingserver.InadditiontotheWebBug,thetrackingJavaS-

criptcodeisresponsibleforthedatacollection.Allinformationisobtaineddirectlyfromthewebsite

visitorsbrowser.Page tagging isable tocollectmuchmoredata thanLog files suchasmouseclicks

andmovement,cursorposition,keyboard interaction,screenresolution, language,country, installed

plug-ins,etc.(Hassler,2009).

Asmentionedbefore, the trackingmethodsolves the significantproblemofmouseclickandmove-

mentrecognition,evensocachingandproxyproblemsarestillextant.

WebAnalyticcompaniesarecontinuouslyoptimisingandupdatingtheirWebAnalytictoolstoensure

broadsoftwarecompatibility.Nevertheless,theusageoffirewallsandthedeactivationofJavaScriptin

the users browser can prevent the data collection. Furthermore, the page taggingmethod has the

same log file cookiedisadvantage,becausecookiesareused for thewebsitevisitor identificationas

well.

4.4.3 Applicationlevellogging

Anotherdatacollectionmethodisapplicationlevellogging,whichiscloselytiedtoanapplication.Ap-

plication level logging shifts the focus fromcollectingdata throughHTTP requestsanduser interac-

tionstotheapplicationuniqueusagedata.Webapplicationexamplesareaforum,anonlinepicture

editor,ane-mailservice,awordprocessororasocialnetworkservice.Thegathereddataiscollected

bytheapplication itselforanadditional functionalmodule likeSharePointsparticularWebAnalytics

serviceframework.

4.5 WebAnalyticsAnalysesandReports

Toanalysethewebtrafficefficiently,itisnecessarytodefinerelevantandmeasurablemetricsandre-

latethemtothecompanybusinessgoals.

DuetoKaushik(2010,S.55ff)themostcommonmetricsusedinWebAnalyticsare:

Visitcount:numberofpageviewsanduniquevisitors

Visitduration:measuredtimeonwebsite

Bouncerateandexitrate

WebAnalytics


Themostimportantanalysesareexplainedsubsequently(Zheng&Peltsverger,2014):

Dimensionalanalysisintegratesmeasuresanddimensionsondifferentlevelstogenerateanalysesand

reports.

Trendanalysisisfocusingondataregardingtothetimedimensiontoshowachronologicalvariationof

theusedmetrics.Asanexample, thegathered informationcan reveal if the request frequencyofa

gendergrouphaschangedduringthepastsixmonths.

Distributionanalysisevaluatesmetricvalues,whichareoftenacalculatedpartitionofthetotaldimen-

sions.Themainfunctionistostudyclientandvisitorprofiles.Onefieldofapplicationisthemeasure-

mentofdifferentoperatingsystemsinthelasttwoyearstoreceiveinformationabouttheclientdiver-

sity. Inadditionotheroftenuseddimensionsare location,browser typeandversion,display resolu-

tion,colordepth,supportedtechnologiesandtrafficsources.

User activity or behaviour analysis attempts tomeasure information about user interaction with a

Websitesuchasclickstreamanalysis,in-pageanalysisandengagementanalysis.

Engagement analysis is aboutmeasuring the visitors involvement intowebsite and is thereforeone

themostusedanalysisinthemarket.Toreceivethisinformation,Engagementanalysisfocusesonun-

finishedconversionsandtriestocreateengagementcalculationstodistinguishbetweendifferentuser

visits.

Clickstreamanalysisisusedtocreateaclickpathandanalyseshowthevisitorisnavigatingthrougha

website.Thisclickstreamdataconsistsofalistthatincludesallvisitedwebsitesinchronologicalorder

duringasession.Analysisresultinnavigation,informationarchitectureandstructureimprovementsto

optimisethevisitor´swebmovement(Kaushik,2012).

Visitorinterestanalysismeasuresuserattentiononawebsite.Thismeasureddatacanbereceivedby

usingthepagetaggingapproachtocollectinputinformationsuchasmousepointermovementtocre-

ateforexampleheatmapsinGoogleAnalyticsortomeasuremousescrolling,whichisimportantfor

modern one-siteweb pages. The break down helps to develop and improvewebsite structure and

contentplacementtofindforexamplethebestpositionforadbannersorotheradvertisements.

Conversion analysis is among themost applied analyses in the e-commerce sector. The conversion

rate expresses a percentage that defines the outcomes dividedby unique visitors or visits. Submis-

sionsofordersonane-commercewebsiteareusuallycalledoutcomes.ExemplarilyGoogleAnalytics

offersMulti-ChannelFunnelsconversionreportstorevealwhichcampaignsorchannelshavecontrib-

utedtoawebsitevisitorsconversion(Kaushik,2012).

Performanceanalysisisusedtofind,evaluateandsolveperformanceproblemsorerrorsofwebsites.

For example, old and newwebsites can be analyses and verified by software such as Google Pag-

eSpeedtoolstomonitorwebserver,webapplicationperformancebytestingaccesstimesoftheweb-

siteorrunanadditionaltestlikeHTTPheaders,Nsrecordslookups,PortScantests.

DominikHeller


5 FrameworksforWebAnalytics

Toovercometheinadequaciesofanalysingwebdata,theimplementationandtheusageofWebAna-

lyticssoftware,variousdivergentframeworksweredeveloped.Thecommonapproachineachofthe

frameworksechoestheidentificationoftheparticipants,theirinterplayandconnectiontoWebAna-

lyticsprocesses.Inordertoreceiveanoverviewandtochooseaframeworkforthefurtherresearch

approach, fivedifferentWebAnalytics frameworkswere selecteddue to the literatureanalysis and

describedindetail.

5.1 FrameworkforWebAnalyticsbyHausmann(2012)

The aim of the Web Analytics framework by Hausmann, Williams and Schubert is to guide SMEs

throughthewholeprocessofWebAnalytics.TheydividetheirWebAnalyticsframeworkintotwopic-

tures.Figure6showsthefivestepswithintheHighLevelWAProcessCycle.

Figure6:HighLevelWAProcessCycle(Hausmann&Williams&Schubert,2012)

ThefivephasesofaWebAnalyticsprojectsare:

§ BusinessRequirements

§ PlanningforWebAnalytics

§ DevelopingaDataCollectionCapability

FrameworksforWebAnalytics


§ AchievingUseableandActionableResults

§ EvaluationofActions

EachofthefivestepsisoutlinedinmoredetailandwithaconsistentcolourstructureinFigure7.

DominikHeller


Figure7:TheWAProcessFramework(Hausmann&Williams&Schubert,2012)



TheprocesscyclestartswiththeBusinessRequirements,whichemphasisestheidentificationofneeds

andthearchitectureofthewebsite.ThisphaseincludestwostagesnamedInformationNeedsAnalysis

andInformationArchitectureAudit.InformationNeedsAnalysisaimstoidentifytheobjectivesofthe

websiteownerandpotentialvisitorsregardingtothewebsite.InthesecondstageInformationArchi-

tectureAuditthewebsiteisanalysedduetocontent,structure,designandusagedataleadingtoes-

sentialknowledgeaboutthewebsiteitself.

Moreover, thesecondphasePlanning forWebAnalyticsextends thebusiness requirementsbycon-

ducting a detailed analysis of Web Analytic possibilities such as data collection methods, metrics,

tools,privacyandlegalissues.Additionallytheidentificationoforganisationneedsobservesmeaning-

fulfacetsoftheorganisationswebsiteitselfbeforetheWebAnalyticstoolsselectionstarts.

In addition,Phase3,DevelopingaDataCollectionCapability is all about the implementationof the

WebAnalytics tool and the data processing. The first activity Implementation of theWebAnalytics

tool instructs to setup the selected toolandadapt theprocesses tomeasure the requiredmetrics.

FollowingDataProcessingcontainsthedatacollection,preprocessingandpatterndiscovery,whichis

doneautomaticallybythemostWebAnalyticstools.

Phase4,AchievingUseableandActionableresultsaddapatternanalysis,identificationandtheaction

takingtoanalysegathered informationandconvert them intoanoptimisedWebAnalyticsproceed-

ings.

Concludinginphase5,EvaluationofActions,whichreviewstheWebAnalyticsprocessesandtakenac-

tionstoquestioniftheimprovementshaveledtopositiveoutcomes.Thisfinalstageconductsphase3

to5.

Therearedifferentneedsforactionovertime.Dataprocessingshouldbedonecontinuously,despite

thepatternanalysisandidentification,whichshouldbeaccomplishedweekly.Furthermore,thedata

needstobecheckedandthelinkedtoolreadjustedifnewdatatypesareneeded.Onahigherscale,

monthlyactionaimtowebsitefinetuning,updatingordebugging.Finally,two-yearlyactioninvolves

fundamentallywebsitereviewtoforexamplere-designorrepositionthewebsite.

TheWebAnalyticsframeworkbyHausmann,WilliamsandSchubert(2012)buildsaperfectfoundation

for thedevelopmentof thenewWebAnalytics frameworkandguidelines for SMEs,becauseof the

phased,well-structuredintegrationprocessandWAfocusonSMEs.

5.2 MajorComponentsofanOnlineMarketingSystem(Tonkinetal,2012)

Tonkinetal.(2012)developedtheframeworkof“majorcomponentsofanonlinemarketingsystem”

showninFigure8.

DominikHeller


Figure8:MajorComponentsofanOnlineMarketingSystem(adaptedfromTonkinetal,2012)

Thismodel is basedon the idea that amodernwebsite is part of an interconnected system that is

neededtoimprovethecompany´sbusinessrequirements.TheapproachindicatesWebAnalyticstobe

amethodtomeasureandanalysee-commercecomponentstosupporttheachievementofthebusi-

nessobjectives.

Tonkindividestheframeworkintotwomajorparts.Theleftpartcontainstheeightinboundmarketing

channels,whichrepresentpossibilitiestoengagewithwebsitevisitorsandpotentialnewwebsitevisi-

tors,whicharelinkedtoacquisitionchannels.WebAnalyticsisusedtoanalyserelationsbetweenall

thesechannelsandtoevaluatethevalueofeachchannelbymeasurementssuchasreturnoninvest-

ment.Additionallytherightsideoftheframeworkcontainsthecustomerecosystemcycle, including

theessential conversionprocess and thepost-alesprocess.A conversionprocess covers conversion

goalachievementofawebsiteregardingtothebusinessobjectives.Besidesthepost-salesprocessin-

cludesactionstoeffectoptimisationsinrepeatbusiness.DuetoTonkin,theexpansionofthecustom-

erecosystembyincreasingthenumberofrepeatbuyersandtheconversionrateofvisitorsisthees-

sentialpurposeofWebAnalytics.

TheMajorComponentsofanOnlineMarketingSystemframeworkbyTokin(2012)expressestheim-

portanceoftheconnectionbetweenthewebsiteandthebusinessrequirements,whichneedstobe

respectedinthecreationofanewWebAnalyticsframework.



5.3 TrinityApproachbyKaushik(2010)

OnewidelyusedWebAnalyticsframeworkistheTrinityapproachbyKaushik(2010).Itaimstogener-

ateWebAnalyticsactionableinsightsandmetrics.Theoverallapproachtendstoachievegenuineun-

derstandingaboutwebsitevisitorsthatleadstosustainablecompetitivetranscendence.

Figure9:TrinityApproach(Kaushik,2010)

The frameworks built on the three components: behaviour, experience and outcomes. Behaviour

analysisconsistsofclickstreamdata,whichisusedforclickdensityanalysis,visitorsegmentationand

identifyingkeymetricstogatherwebsitevisitorbehaviourinformation.Outcomeanalysisisthemoni-

toringcomponentandtriestogaindataaboutthegoalachievement.Everywebsiteownershouldde-

fineclearwebsiteobjectives.Forexample,onlineretailerscanmeasuresalesrevenuesorpurchases

per visit, whereas informationwebsites can look for new unique visitors, average visit time or the

amountof shared informationbymeasuringconversions. Finally, the thirdpartof the framework is

Experience Analysis, which aims to identify the website visitor action and behaviour information.

Gaineddatadeliversinformationaboutwhatvisitorsdoonthewebsite,notwhytheybrowseoruse

thewebsite.Duetotheframeworkexperienceanalysistoolscancontainvisitorsurveys,A/Btesting

methodologyandlabusabilitytesting(Kaushik,2010).InthecomparisonofbothTonkinetal.(2012)

andKaushik(2007)frameworksitisobviousthattheyarerelatingwebsiteusagewithwebsiteconver-

sion.Analysingvisitorbehaviourachievesincreasingconversion.Furthermore,financialmeasuresare

DominikHeller


notneededtoreachconversiongoals,whichcanthereforeincludeengagementgoalstocauseinter-

estandawareness.

KaushiksTrinityApproachframeworkhasastrongfocusonwebsitevisitorbehavior,whichneedsto

be integrated into thenewWebAnalytics frameworks and guidelines for SMEs to guarantee a cus-

tomerorientation.

5.4 FrameworkofRelationsbetweenWebandE-BusinessMetrics(BaselandZumstein,2009)

BaselandZumstein(2009)createdtheframeworkoftherelationsbetweenwebande-businessmet-

rics. This approach ismainly focusing on online retailers, although an application to other types of

websitesarepossible.

Figure10:FrameworkofRelationsbetweenWebandE-BusinessMetrics(BaselandZumstein,2009)

The framework start is concentrating on the diverse traffic sources that lead to an entry page of a

website.ThesetrafficsourcesarelinkedwiththeinboundmarketingchannelsofTonkinetal.(2012).

Efficiency can be proofed by analysing each traffic source. Following the website visitor browses

throughthewebpagesuntilheleavesfromanexitpage.Iftheentryandexitpageareidenticalthe

usagecanbetrackedbythebouncerate.SimilartoKaushiksframeworkdifferentmetricscanbecre-

ateddue to thevisitorbehaviour, including forexamplenumberofpageviews, visit frequency, the

lengthofvisitanddepthofvisit.Onthebasisofthismetricsdiverserationscanbecalculatedsuchas

theconversionrate.InadditiontheframeworkrecommendsafewmetricandrationKPIs.Thesepa-

ratedwebande-businessmetricscanbemergedintoKPIstolinkwebsiteobjectivesintobusinessob-

jectives.ThisobjectivefocusisalsosimilartotheoutcomeanalysisbyKaushik.MergedKPIsareonline

revenuepervisitandonline revenueperuniquevisitor.The lastpartof the framework illustratesa

product purchase, beginningwith product site, leading to the basket and closing at the orderweb



page.BaselandZumsteindeclaredifferentratiostospecifythevisitorsexitpointoutofthepurchase

process.

TheframeworkbyBaselandZumsteinwasselectedandanalysedtoobserveaWebAnalytic frame-

workthatfocusonobjectives,whichevolvebymixingmetricstoconnectwebsiteobjectivesintobusi-

nessobjectives.

5.5 StartupMetricsFrameworkbyDavidMcClure(2005)

TheStartupMetricsframeworkwasdevelopedbyDaveMcClure(2005)andisbasedonfiveelements,

whichshouldleadtoasuccessfulbusiness.

Figure11:StartupMetricsFramework(DavidMcClure,2005)

These five elements are furthermoremetrics and named acquisition, activation, retention, revenue

andreferral.Onedistinctiveness incomparisontoallotherframeworks isthattheelementsarenot

followingastrictorder.Therefore,thefocusoftheframeworkisprimaryondevelopingimprovement

throughoneofthemetrics.TheAcquisitionisusedtogenerateattentionthroughavarietyoforganic

andinorganicmeansandincludesmetricssuchastraffic,mentions,costperclickandsearchresults.

FollowingActivation wants to turn the resulting drive-by visitors into enrolled visitors. Appropriate

DominikHeller


metricsareenrolments,signups,completedonboardingprocessandsubscriptions.Retentionisneed-

edtoconvinceuserstocomebackandbindthemtothewebsite,whichleadstometricssuchasen-

gagement,timesincethelastvisitordailyandmonthlyactiveuse.ThefourthelementRevenuecon-

tainsthebusinessoutcomes,whichvariesbytheuniquebusinessmodel.Regardingmetricsarecus-

tomerlifetimevalues,conversionratesorclickthroughrevenues.Finally,thelastelementistheRefer-

ral. Referral is the viral recommendation of the website to other potential visitors, which can be

measuredbymetricssuchasinvitessent,viralcycletimeorviralcoefficient.

DavidMcClure(2005)createdaWebAnalyticsframeworkwithastrongfocusonSMEsandtheirre-

source allocation and investment. This perspectiveneeds to be attended in thenewWebAnalytics

frameworkforSMEs.

5.6 ComparisonbetweenWebAnalyticsFrameworks

Toprovideabetteroverviewaboutallanalysedframeworks,twotablesweredesignedtomakethe

comparisonoftheframeworkseasier.Table1illustratesthecharacteristics,premises,relativestrate-

gy, relationship, functions, historical background, basic structure and evolution of each discussed

framework.



Table1:ComparisonofWebAnalyticsFrameworks

Due to the different background and temporal connections every framework has diverse strengths

andweaknessesintheaspectofWebAnalytics.TheseareshowninTable2.

DominikHeller


Strengths Weaknesses

WebAnalytics

Framework

- Overcomes the shortcoming of

standardWebAnalyticframeworks

- Strong and well structured imple-

mentationsteps

- The phases containing tasks and

questions help to pass through the

integrationeasily

- Focus on results, action taking and

performanceimprovement

- Considersnearlyeveryimplementa-

tionstep

-Only focuses on the vaguely

processes

-Missing the detailed guidance

forSMEs

Majorcompo-

nentsofanonline

marketingsystem

-Detailedinboundchannels

-Doesnot provide executionor

implementationsteps

-Nearly no Web Analytic pro-

cessesareexplained

-Short of Web Analytic logic

amongeveryedge

Trinityapproach

-StrongfocusonKPIsandMetrics

-Attempt to understand user, instead of

trackingoftenvaluelessmetrics

-Customerbehaviouranalysis


implementationsteps

Relationsbetween

webande-

businessmetrics

-Strongfocusone-commerceperspective

-PracticalKPIandmetricthinking

-Perspectiveframework


implementationsteps

Startupmetrics

-Strongfocusonthewebsitevisitor

-Practicallyrelevantinvestmentstrategy

-Testseachvisitorsneedsandopportunities

-Contributiontoquickimplementation

-Focus on results, action taking and perfor-

manceimprovement


implementationsteps

Table2:StrengthandWeaknessesofWebAnalyticFrameworks

AfterthecomparisonofthedifferentWebAnalyticsframeworksabove,itbecomesapparentthatthe

WebAnalytics frameworkbyHausmann,WilliamsandSchubert (2012) combines themost compre-

hensive Web Analytics integration approach for SMEs of all frameworks. No other Web Analytics



frameworkstructurestheWAimplementationprocessmorepreciselyandphased,whileprovidinga

simplisticapplication.ThereforethefurtherresearchwillconcentrateprimarilyontheWebAnalytics

frameworkbyHausmann,WilliamsandSchubertanduseitasthefoundationforthenewWebAnalyt-

icsframeworkandguidelines.Howevertheframeworkhasnofocusorprocesssectionsespeciallyfor

GoolgeAnalytics and someof the phases are only examining the implementation superficially. This

aggravatestheapplicationforSMEswithlimitedknowledgeandincreasestherequisitionforsupport-

ingWebAnalyticsandGoogleAnalyticsguidelines.

DominikHeller


6 WebAnalyticsPerformanceMeasurementofSMEs

ThischapterdeliversthefundamentaltheoreticalbackgroundtomeasureWebAnalyticsinSMEsand

todevelopaWebAnalyticsframeworkandguidelinesforWebAnalyticsusageinSMEs.ThereforeSec-

tion6.1ofthischaptergivesanoverviewofPerformanceMeasurement.InSection6.2smallandme-

dium-sizedEnterprisesarequestionedandtheir importance foreconomies isdetermined.Following

this, Section6.3explains the conceptofObjectKeyResults, KeyPerformance Indicator andmetrics

developmentinrelationtoWebAnalytics.

6.1 PerformanceMeasurement

Performancemeasurementisawidelyusedconceptindiverseareas.Usually,performanceisameas-

ure of howwell amechanism or process achieves its purpose. In enterprisemanagement,Moullin

(2003)statesanorganisationsperformanceas“howwelltheorganisationismanaged”and“thevalue

theorganisationdeliversforcustomersandotherstakeholders.”Forthisresearch,‘performance’isre-

latedtoachievingstockholder/investorinterests.

Measuringperformance isamulti-dimensional concept.Effectivenessandefficiencyare the twoes-

sentialdimensionsofperformance.This isemphasizedbyNeely,Adamsetal. (2002):“Effectiveness

referstotheextenttowhichstakeholderrequirementsaremet,whileefficiencyisameasureofhow

economically the firm’s resources are utilizedwhen providing a given level of stakeholder satisfac-

tion”. To attain superior relative performance, an organizationmust achieve its expected objective

withgreaterefficiencyandeffectivenessthanitscompetitors(Neely1998).Toillustrateefficiency,ef-

fectiveness,andthevaluedelivered,multi-measuresshouldbeused.Thesemeasuresareessentialfor

SMEs tounderstand thedevelopment and conditionof thebusiness.Moreoverperformancemeas-

urement allows to induce estimated behavior, when goals are defined and feedback is obtained

(McFadden,2005).Toanalysetrafficonawebsite,thereisawiderangeofWebAnalyticstoolssuchas

“GoogleAnalytics”andsomeareevenfreeofcharge.ThoughthereexistsabarrierforSMEstousing

WebAnalyticsefficiently,whichisespeciallylinkedtoliteratureandtherewithinonhowtoimplement

theuseofWebAnalytics.AuthorssuchasHassler(2010)orParmenter(2010)recommendthatabusi-

nessshoulddevelopKeyPerformanceIndicators,whileprovidingrarelyadviceonhowSMEscanbegin

toinitiateperformancemeasurementinWebAnalytics.

6.2 SmallandMedium-sizedEnterprises

SmallandMedium-sizedEnterprisesare thebackboneofmosteconomies in theworld today.SMEs

are crucial to growth and success of economies and account between50 and 70%of jobs inOECD

countries.ExceptionsareJapanandItalywithlargersharesthanusualandtheUnitedStateswithrela-

tivelysmallshares(OECD,2009).AtypicalexampleoftheimpactofSMEsistheUK,withover4.8mil-

lionSMEs,whichaccountmorethan50%ofemploymentandcompanyturnover(Treasury,2010).

WebAnalyticsPerformanceMeasurementofSMEs


Thissignificantimpactisalsoappreciableinlowandmiddle-incomecountriessuchasNigeria.70%of

industrialemploymentand60%of laborforceareaccountedbySMEs(Lawal,2007).Small firmsare

knowntobeinnovative,adaptive,flexibleorvitalandtherebyplayacrucialroleforthestrengthofan

economy.Sadly,duetotheirscaleofoperationsandfinancialcircumstancesmostdonothaveade-

quateimplementationsofWebAnalytics(Clutch,2015).AlotofSMEsarefacingfinancialdifficulties

becauseoftheiryoungexistence,inexperiencedandinformationalopaque(Ekpu,2016).Accordingto

IBIResearch(2011)thekeyfactorsfornotusingWebAnalyticsaretime(51%),know-how(40%)and

cost(30%).

There are noworldwide-accepteddefinitions of small andmedium-sized enterprises (SMEs). There-

fore,thedefinitiondiffersbetweencountriesandisoftenrelatedtothetotalsizeoftheeconomyas

seen in Table 3. Typical definitions are groundedon categories such as numberof staff and annual

turnover. InGermanythe"InstitutfürMittelstandsforschung"(IfM)definesSMEsascompanieswith

staffingbetween1-499,dividedinsmallbusinesseswith1-9employeesandturnoverproceedsunder

1millioneuroandmediumcompanieswith10-499employeesandturnoverproceedsunder50million

euro.Inthisstudy,theGermandefinitionisusedandtheWebAnalyticframeworkandguidelinestry

toalignpre-eminentlytheneedsofsmallenterpriseswithoutanalyticspecialists.

Micro

Small

Medium

SME

Large

GermanyNo.ofStaff

Turnover

1-4

1-9

<=€1mil

10-499

<=€50mil

1-499

>=500

EUNo.ofStaff

Turnover

<10

<2€mil

<50

<€10mil

<250

<€50mil

1-250

>=250

OECDNo.ofStaff

Turnover

1-9

10-49

50-499

1-499

>=500

ChinaNo.ofStaff

Turnover

<300

300-2000

1-2000

>=2000

Table3:SMEComparison(UN-ECE(1996),NBSC(2003),IFM(2007))

6.3 IdentifyingKeyPerformanceIndicatorsandMetricsforWebAnalytics

Oneessentialelementofthisthesis isthe identificationofKPIsandmetricsofSMEs.TheseKPIsare

used to discover business requirements and will be implemented afterwards in theWeb Analytics

softwareGoogleAnalytics.ThisSectionseekstoexplainandaddressthewayofproceeding.

DominikHeller


6.3.1 MeasureandMetrics

Measureisthedefinitionofhowsuccessinreachingabusinessobjectiveisdetermined.Definingspe-

cificmeasuresprohibitsambiguity(Neely,2002).

TwobusinesspartnersaretakingabouttheirWebAnalyticsplans.Botharetrackingwebsitevisitorsto

collectinformation.Eachonehasanownunderstandingofthetermvisitor.Thereforeoneistracking

theoverallamountofvisitors,whiletheotheroneistrackinguniquevisitors.Asaresultonewillde-

terminenewandoldwebsitevisitors,whereastheotheronlymeasuresnewuniquevisitors,excluding

knownvisitors.Regardingtothisprofounddifference,itisessentialtodefineasingleandclearmeas-

ure.

Metricsarethebasicinformationforanalysingwebtrafficandareusedtooptimiseawebsitetomeet

thegoals.DuetotheWebAnalyticsAssociation(2008)therearethreetypesofwebmetrics:counts,

rationsandKPIs.Thebasicunitofameasureiscalledcountsuchasthetotalnumberofvisitors.Ratios

arecountsdividedbyothercountsforexampletheclick-throughratio,whichcontainsthenumberof

click-throughsforalinkdividedbythenumberthelinkwasviewed.InadditionaKPIcanbeacountor

a ratio.Basic counts canbeappliedbyallwebsite types.However aKPI is connected toabusiness

strategy.Thereisanindividualelementofadimensionthatcanbemeasuredasasumorratio.Fur-

thermoreadimensionisasourceofdatathatcanbeappliedtodefinediversetypesofsegmentsor

countsandstandforadimensionofvisitorbehaviororsitedynamics.Ametricismeasuredacrossa

dimension.TheWebAnalyticsAssociationStandardscommittee identifiedthethreemost important

metricstobe:UniqueVisitors,Visits/Sessions,PageViews(WebAnalyticsAssociation,2008).

6.3.2 KeyPerformanceIndicators

Due toBrianClifton (2010, p.55) Key Performance Indicators “represent the key factors, specific to

your organization, thatmeasure success”.Moreover, the selected KPIs should reflect the organisa-

tion’sgoalsandmustbemeasurable.Therearealmost infinite characteristics regarding toKeyPer-

formanceIndicators.AnimportantdifferencebetweenKeyPerformanceIndicatorsandPerformance

IndicatorsisthatmetricsarenotalwaysclassifiableasKeyPerformanceIndicator.PerformanceIndica-

torstorevealthedifferenceinmoredetailsixpossibleKPIsarechosen:

§ Conversionrate

§ Bouncerate

§ Navigationpatterns

§ Averageordervalue

§ Averagevisitvalue

§ Customerloyalty



ThesesixKPIsareallconnectedtospecificbusinessgoals.IfacompanydeclaresKPIs,itisnevertheless

possiblethattheintentionoftheKPIisconstrueddifferentlybetweentheemployees.Awebdesigner

forexamplecouldlookforeveryKPI,butconcentrateonlyonthedatathatheisabletoactonsuchas

thenavigationpatterns.OwingtothisthewebdesignerreceivesreportsaboutsixKPIs,buthandles

onlyoneasaKPIandfiveasPIs.

TodevelopKPIs it isnecessary todefinestrategicbusinessgoals.ThereforeClifton (2010)showssix

fundamentalpointstoprepareKPIs:

§ SetObjectivesandKeyResults

§ TranslatetheKeyResultsintoKPIs

§ ProofKPIstobeactionableandaccountable

§ CreatehierarchicalKPIreports

§ DefinepartialKPIs

§ Consolidate

ThesepointscanbeappliedasaguidelinetohandletheprocessofconstructinganddevelopingKPIs.

6.3.3 CreatingObjectiveKeyResultsandKPIs

Objective Key Results (OKRs) are providing information about business goals. They have to be pre-

paredbeforethespecificKPIsarechosen.

The definition of OKRs can be handled in four steps. At first the stakeholders have to bemapped.

Stakeholdersareinternalorexternalorganisationalunitssuchasamarketingagency,contentcreator

oraCEOwhoisaffectedbythecreatedWebAnalyticreports.Theyshouldprovidetheirperspective

onhowtheanalyticdatafitstotheirdepartment.Inthesecondstepthestakeholdersdeterminere-

quirementsandexpectationbydiscussingthecurrentstatus,whatdatacanbecollected,theaccuracy

andlimitationsofWebAnalyticsinformation.

Following the third step defines or sets theOKRs. Therefore thewebsites objectives fromdifferent

pointsofviewshavetobeselected.Objectivescanleadtomorerevenueaswellasaddinsights.One

exampleforacontentwebsiteistheidentificationandremovingoftheleastpopulararticle,whichis

theresult,bymeasuringthewebpagetraffic,whichistherelatedobjective.Aftertheidentificationof

theOKRs,allobjectiveshavetobelinkedtometricsthatfunctionasaKPI.Noteworthy,unlikemetrics,

whichrepresentnumericaldata,KPIsbelongtoabusinessstrategy.

ThenextTable4showssomematchingexamplesforOKRsandKPIs:

StakeholderOKR SuggestedKPIs

Toseemoretrafficfromsearchengines Percentageofvisitsfromsearchengines

DominikHeller


Percentageofconversionsfromsearchengine

visitors

Tosellmoreproducts

Percentageofvisitsthataddtoshoppingcart

Ratioofvisitsthatcompletetheshoppingcart

overthenumberthatstarted

Percentage of visits inwhich shopping cart is

abandonedatpositionXintheprocess

To see visitors engagingwith ourweb-

sitemore

Percentage of visits that leave a comment,

clickalovebutton(FacebookLike,TwitterFol-

low,Google+1,etc.)ordownloadabrochure

Percentage of visits that complete a Contact

Usformorclickamailtolink

Averagetimeonsitepervisit

Averagepagedepthpervisit

Tocross-sellmoreproducts toourcus-

tomers

Averageordervalue

Averagenumberofitemspertransaction

Toimprovethecustomerexperience

Percentage of visits that bounce (single-page

visits)

Percentageof internal site searches thatpro-

ducezeroresults

Percentage of visits that result in a support

ticketbeingsubmitted

Table4:StakeholderandmatchingKPIs(Clifton,2010)

All developedKPIsneed tobeactionable andaccountable.Due toCliftonmeasurement changesof

KPIs,whichdonotleadtotakeactionarenotwelldefined.ViceversaefficientKPIscreateexpectation

anddriveactionbyhelpingtheorganisationtoquicklyanalyseandunderstandvisitordata.Further-

moreimportantistocreatehierarchicalKPIreports,whichcontainonlyrelevantinformationtofocus

theattentionoftheobserver.

ThenextstepistodefinepartialKPIs,alsonamed“microconversion”.OneexampleforapartialKPIis

thenavigationtothedownloadarea,whentheconversionisapurchaseprocess.PartialKPIsdeliver

additionalinformationtothemeasurement.InthelaststepallKPIshavetobeevaluated.Somecon-

structed KPIs may gain important metrics, others have to be consolidated or deleted in order to

achievethebestgoals.

Basedontheknowledgeandinsightsofthepreviouschaptersandsections,atemplatewasdeveloped

whichcanbeusedtodefineKPIs(seeTable5).ThetemplatestructureallowssearchingforOKR,which

willbedividedintodifferentKPIs.Matchingmetricsandgoalscanbedefinedtolinkthemtothespe-

cificKPI.Withthehelpofthistemplateaclearstructureandprocess-orienteddevelopmentofKPIsis



enabled.Thetemplateuserdoesnothavetosticktothelayout,anOKRcanhavearandomamountof

KPIsandasaconsequenceaKPIscanhaveanynumberofmatchingmetricsandgoals.

WebAnalyticsGuidelineYear:2016

ObjectiveKeyResult

KPI

Metric/Goal

ObjectiveKeyResult1

KPI1 KPI2 KPI3 KPIn

Metric1 Goal1 Metric1 Goal1 Metric1 Goal1 Metric1 Goal1


Metricn Goaln Metricn Goaln Metricn Goaln Goaln Goaln

ObjectiveKeyResult2

KPI1 KPI2 KPI3 KPIn




ObjectiveKeyResult3

KPI1 KPI2 KPI3 KPIn




Table5:OKRPlanningTemplate

AccordingtoClinton(2010)anorganisationisgoodadvisedtodefineamaximumoftenKPIs.Incon-

trasttoClinton,Peterson(2006,p.13)recommendsamodelthatadvocatesadifferentview:

§ Seniorstrategists,suchastheCEOofaretailwebsiteshouldget2-5KPIs

§ Mid-tierstrategists,suchastheVicePresidentofMarketingshouldget5-7KPIs

DominikHeller


§ Tacticalresources,suchastheDirectorofOnlineMarketinggetthesameKPIsastheirmanagers

plusdetailedKPIsonreporting,whichleadsto7-10KPIs

InadditionandconsideringthatthisthesisaimstoaddressSMEs,DaveMcClure(2015)recommends

todefineamaximumof2-5KPIsforSMEs.

AsanexampleTable6presentsafictiveOKRwithKPIsandmatchingmetricsandgoals.

Year2016

ObjectiveKeyRe-

sult KPI

Metric/Goal

IncreaseWidgetSalesby15%

Macroconversions Microconversions SalesProductivity Remarketing

eCom-

merce

Sales +5% InstagramFollows +800 LeadResponseTime -20%

Brand

awareness +10%'

Computer

Orders +25% WebRegistrations +150 AverageDealSize +15% SocialMedia +10%

Voucher

Sales +12% NewsletterSignups +4000 UnitTrans. +10%

Referrals

fromRM +55%

Table6:OKRPlanningExampleTemplate

6.3.4 KPICharacteristicGuideline

Basedontheprevioustheory,it ispossibletodefinecharacteristicsforwell-definedKPIs.Allcharac-

teristicsareinfluencedbytheresearchofParmenter(2010)andClifton(2010).

Relevant:Allperformance indicatorsneedtobematchedtotheorganisationalstrategyandprovide

valuabledata.

Well-defined:KPIsaredefinedclearandwithoutambiguitytodeliverexplicitresults.

Understandableanduseable:Stakeholdersofdifferentdepartmentshave tobeable tounderstand

theequivalentmeaningandknowhowtousetheKPI.

Comparable

Inordertocomparemetricswithotherorganisationsandtofindadvicewhenfacingproblems,theKPI

shouldbechosencomparable.

Testable:ThegainedKPIdataneedstobestoredandmadeavailableforevaluation.

Costeffective:KPIshavetoberesource-conservingtomeasureanddelivermorevaluetotheuser.



AttributableandResponsive:Themeasuredperformanceindicatorshouldbeaffectedbytheuserin

ordertoimprovethewebsite.

Createinnovation:Innovativebusinessprocessesandideasshouldnotbeprohibitedbyconsistencyin

measurements.

Statisticallyvalid:Thedataneedstobevalidtocauserelevantanalytics.

Efficient:

TheKPIneedstobemeasurableinareasonableamountoftime.

Tosimplifyandreduce thesecharacteristicsDoran (1981)definedthe“S-M-A-R-T”characteristicsof

KPIs:

§ Specific

§ Measurable

§ Attainable

§ Realistic

§ Timebound

AllthesecharacteristicsarealsorecoveredinthedetailedtenKPIcharacteristicguidelines,butareas

welluseableasamorebasicandsolidspecificationtoselectrelevantKPIs.

6.3.5 TheKPIDesignTemplate

ConsideringallinformationaboutKPIsandbasedontheOKRPlanningTemplate,anadditionalKPIDe-

signtemplatewasdeveloped.ThisKPIDesigntemplatecontainsallinformationaboutonespecificKPI.

When the SME management unified their strategy on relevant KPIs, supplemental information is

neededtoimplementtheKPIeffectively.Atfirst,theKPInameiswrittenintothetemplate.Further-

more,theresponsiblestakeholdersarepredefinedaswellasthepreviouslydefinedgoaliscopied.

Afterwardsonequestionneedstobedefined,whichdescribestheexactobjectivetargetoftheKPI.In

addition,thetemplateuserneedstosearchfortheidealdatacollectionmethodandtheregardingda-

tasource.ThiscanbeafeatureofaWebAnalyticssoftwareoranothermediumsuchasasurveywith

thededicateddatasource.Optionallyascalecanbechosen,whichdefinesthescaleofthedatacol-

lectionmethod in detail. The targets are associatedwith the goal and define it precisely. All other

templatefieldsareneededtoschedulecollection,reportingfrequencyandtheexpirydate.Whenthe

KPIisimplementedanddataiscollected,thelastpointKPIevaluationcanbeusedtorecordinference

abouttheKPI.

KPIDesign

NetPromoterScore

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KeyStakeholder MarketingTeamPrimaryGoal GrowCustomerSatisfactionAddressedAudience Websitevisitors

KeyPerformanceQuestionTo what extent are our customers satisfied with our

service?KeyPerformanceIndicatorName: NetPromoterScore

DataCollectionMethods:The data will be collected using Google Analytics and

theFanExamplugin.

Scale

Usinga0-10scale (Notatall likely toextremely likely)

participantsanswer:Howlikelywouldyourecommend

Company x to a friend or colleague

NPS = pecentage of Promoters (score 9–10)

Passives(score7-8),Detractors(score0-6)

Targets 50percentbytheendof2020DataSource SurveyofWebsiteVisitorsCollectionFrequency DailyReportingFrequency WeeklyExpiry/RevisionDate 6monthsKPIEvaluation

Table7:KPIDesignTemplate

6.3.6 KPIsrelatedtoaWebsiteType

Smallandmediumenterpriseshavetoworkconservingresources.ThereforethecreationofKPIsdue

tothewebsitetypeisanefficientapproachtobeginwith(seeTable8).BasedonMcFaddenafewKPIs

aredevelopedtodescribethecourseofactioninwebsitetypeKPIcreation.

Commerce

•Conversionrates

•Averageordervalue

•Averagevisitvalue

•Customerloyalty

•Bouncerate

LeadGeneration

•Conversionrates

•Costperlead

•Bouncerate

•Trafficconcentration

Content/Media•Visitdepth

•Returningvisitorratio



•Newvisitorratio

•Pagedepth

Support/Selfservice

•Pagedepth

•Bouncerate

•Customersatisfaction

•Topinternalsearchphrases

Table8:ThefourtypesofWebsitesandexamplesofassociatedKPIs(McFadden,2005)

Thecasewebsiteusedwithinthisthesisbelongstothecontent/mediatypewebsitesandtherefore

thefurtherdescriptionwillbefocusingoncontentwebsites.

Contentwebsitesaregenerallyconcentratingonadvertisingandpromotionwiththemaingoaltoin-

creasewebsitevisitortrafficbykeepingknownvisitorsandgainingnewvisitors.Onesuccessfactoris

the continuously improvement and creation of new site content. Some content websites use their

contentonlytolinkittoothertypesofwebsitestoengagetheusertothewebsite.McFadden(2005)

mentionsfourmainKPIsforcontentwebsites includingreturningvisitorratio,newvisitorratio,visit

andpagedepth.

The visit depth indicator provides themeasurement of the ratio between unique visitors and page

views,meaningthetotalnumberofpagesavisitorrequestseachvisit.Thiscanbeusedtoseehow

many and steps a visitormakes as he passes through thewebsite and how long he is visiting each

page.Normally,visitorswithahighervisitdepthareengagingmorewiththewebsite.Viceversaweb-

sitevisitorwithalowvisitdeptharerequestingfewerwebpagesandcanthereforebeseemtonotbe

interestedinthewebsitecontent.Ifthevisitordepthislow,awaytoincreasethevisitordepthisto

lookfortheKPItargetandaddressthetargetedaudiencewithmoreattractivecontent.Anotherat-

tempt to attract thewebsite visitor is the development ofmore interactivewebsite content to in-

creasetheinvolvement(McFadden,2005).

Tomeasurereturningvisitors,aratiobetweenuniquevisitorsandoverallvisitshastobecalculated.

ThisKPImeasurestheloyaltyofthewebsitevisitor,animportantindicatorofretention,whichreveals

informationabouttheactivityandeffectivitytoreturnvisitors.AhighrationinthisKPIindicatesthat

notmanyvisitorsarereturning,thereforetherationshouldbelow.Alowrationalsoimpliesthatthe

visitor is engaged to the website and its content. Very low ratios could signify problems with the

bouncerateorclickfraud.Personsorscriptsthatproduceshamorsimulatedwebsiterequestswith-

outhavinganyinterestintothesitearecalledclickfraud.AccordingtoClicktale(2013)theaveragera-

tioforreturningvisitorsisabout25%.Anincreasedreturningvisitorratecanbeachievedbyimprov-

ingandcreatingqualitycontent.

Another importantKPI is thenewvisitor ration.This rationmeasuresnewvisitor vs.uniquevisitors

andindicatesifthewebsiteisabletointerestnewvisitors.Aninfluencingfactoristheageoftheweb-

site, cause newwebsites often attract new visitors. Reminding the Startupmetrics framework, the

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customerorientationisanotherfactor.Thewebsitecanfocusonnewvisitorsintheacquisitionphase

ortrytoaddresschannelssuchasretentionandreferraltokeepandengagecustomers.Normallythe

new visitor ration is decreasing over time,while vice versa the returning visitor ratio is increasing.

Gaining new visitors can be achieved by developing and running newmarketing strategies or cam-

paigns.

An essential KPI for contentwebsites is called page depth. This ratiomeasures the pages views of

unique visitors for a particular website.While visit depth is concentrating on the visitor, the page

depthKPIisfocusingontheesteemofawebsite.Visitdepthisusedtoidentifyinwhichwebsitesvisi-

torshavespecialinterestsandthattheseinterestsareconsistentwiththewebsitegoals.Webpages

withahigherthanaveragepagedepthindicateanexceptionallyinterestofthewebsitevisitorstothe

web page.When pageswith high page depth ratios are not part of thewebsitesmajor content, a

switchandreorientationtoothercontentcanbeadvisable.

In order to proof thewebsite typeKPIs ofMcFadden (2005) and to identifymore relevant KPIs for

SMEsanoverviewfromsevendifferentWebAnalyticsspecialists(Marr,McFadden,Sammer,Cohan,

Knight,Szotos,Kaushik)wascreated.Table9showstheresultcomparison.AlllistedKPIsareespecially

suggestedforsmallandmedium-sizedenterprises.



Table9:RecommendedKPIsforSMEsbyFieldofApplication

TheoverviewinTable9highlightsthemostnamedKPIs,whicharealsocoveredbyMcFadden(2005).

Onthesegrounds,theKPIsidentifiedbywebsitetypeinTable8canberecommended.InadditionTa-

ble9addsa fieldofapplicationtoeveryKPI,which is independentof thewebsitetype.Thefieldof

application classifies KPIs into the categories: employees, behavior, outcomes, internal process and

acquisition.Thesecategoriescanbeusedtogetanideaoftheapplicationpossibilities.Moreoverbe-

comesapparent,thattheadvisedKPIsofallanalystsareoftenunrelatedtoeachother.Thisindicates

thedifficultyofsuggestingcommonKPIsforSMEs.Nevertheless,theinformationinTable9isauseful

proposalforSMEstofindmatchingKPIs.

DominikHeller


7 GoogleAnalyticsImplementation

GoogleAnalytics (GA) is themostusedWebAnalytics toolonthemarket.AccordingtotheW3Tech

website,whichprovidesabroadrangeofinformationaboutwebtechnologies,GAisinstalledin40%

ofallknownwebsites(W3Tech,2016).

DevelopedaftertheacquisitionofUrchinin2005GoogleAnalyticswasimprovedrapidlyandproved

popularinthewebindustry.Theimprovementprocessstartedwithanoptimisedreportinginterface

forgreatercustomisationandcollaborationin2007andcontinuedwithadvancedsegmentationand

dashboardmanagement,customreportingandmotioncharts,untiltheintroductionofprofileconver-

sionsin2009.Thisupdateenabledtheusertocreategoalsforsitetime,pageviewsandfurthermore

tosetaltersandnotificationforevents.

Twoyears laterGoogle improvedtheprofilemanagementtoswitchquicklybetweendifferentdash-

boardsandviews.2012wasanimportantturningpointforGoogleAnalyticswiththelaunchofGoogle

Analytics Universal. Universal offers the possibility to track users across diverse platforms. This is

achievedbyreferringauniqueIDtoeverywebsitevisitortotrackthevisitorondifferentdevices. In

additionfeaturessuchasflexibletrackingcodesforeverydomain,organicsearch,referralandsearch

termexclusionwereintroduced.

GoogleAnalyticsispartoftheGooglesoftwarefamilyandtherewithintegratedinotherGooglesoft-

waresuchasGoogleAdWordsorothertrackingsoftware.AlotofthirdpartyWebAnalyticsplatforms

arecompatibletoGoogleAnalyticsoruseittoreceiveanalyticdatafortheirplatform.

7.1 CaseWebsite

Inordertobeabletodevelopauseful,effectiveframeworkandregardingguidelinesbasedonthelit-

erate research, a realwebsite is needed. Therefore the casewebsitewww.businessculture.org (see

Figure11)wasaccessiblefortheresearcherandwasusedfortesting.

GoogleAnalyticsImplementation


Figure11:CaseWebsitewww.businessculture.org(2015)

Businessculture.org contains the project Passport to Trade 2.0. This project aims to develop free

online trainingmaterials to help European SMEs and students towork abroad (businessculture.org,

2016).Theaward-winningwebsiteoffersEuropeanbusinesscultureguidelinesforonlineandface–to-

facecommunication. It includes information suchasup-to-date insights intoEuropeanbusiness cul-

tureforabout31Europeancountriesandisaccessibleinninelanguages.

Thewebsitebelongstothecontentwebsitetypeas it isfocusingonofferingcontenttothewebsite

visitor.Thelandingpagecontainsatopnavigationbarwithsevendropdownmenus.Inadditionalan-

guagebaronthetopisavailableoneverywebpagetoswitchbetweentheninelanguages.Further-

moreconspicuous is thesocialnetwork integrationforTwitter,Facebook,GooglePlusandLinkedIn.

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Theconsistent rightsideof thewebsite includesanadvertisementbanner,a recentpostsselection,

linkagetoprojectpartners,aTwitterandFacebookwidgetandfinallythesearchfield.

Businessculture.orgoffersaresponsivedesignthatiscompatibletomobiledevices.Eventhoughthe

overall look isoutdatedand theunderlyingCSSstylesheet seems topositionandarrange theboxes

imprecisely,GoogleAnalyticsisalreadyintegratedintothewebsite,whereforetheGoogleInstallation

chapterisbasedontheliteraturereview.

7.2 GoogleAnalyticsStandardvs.Premium

ThethesisusesGoogleAnalyticsastheonlyWebAnalyticsoftwareapproach,becauseit isthemost

usedWebAnalyticssoftwareonthemarketduetoW3Techs(2016).Furthermore,GoogleAnalyticsof-

fersa freeusageplan,which is resource-efficientandtherewithsuitable forSMEs.Thewidespread

usagedoesalsofacilitateaknowledgeexchangebetweenSMEs.Howeverevenifthethesisisfocusing

onGoogleAnalytics,itisadvisableforanSMEtocompareallWebAnalyticssoftwareproductsonthe

marketbeforechoosingoneunquestioningly.AnendorsedcompetitorofGoogleAnalyticsistheopen

sourcesoftwarePIWIKasanexample(PIWIK,2016).

IngeneralthemajordifferencebetweenthestandardandpremiumversionofGoogleAnalyticsisthe

amountandsourcesofcollectabledata.GoogleAnalyticsPremiumincludeshighervolumestocollect

dataand furthermoreextraslots forcustomdimensionsandmetrics.Another feature is theRoll-up

properties,whichenablestheanalysttocombinemultipleproperties.

IftheanalystisinneedfortheDoubleClickplatform,whichisanintegratedad-technologyplatform,to

importdatainadditiontoGoogleAdWords,GoogleAnalyticsPremiumhaspossibilitiestoincludethe-

seservices.Theimportantreportingfeaturesarenearlythesame,evenifGoogleAnalyticsPremiumis

offeringcustomfunnelreports,whichenablebetterflowreporting.

Intermsofsampling,GoogleAnalyticsPremiumissupportingmuchmoreprecisesamplesduetothe

highervolumeofcollecteddata.GoogleAnalyticspre-calculatesreportsonrelativelysmallamountsof

datatogeneratequickrequests.ThereforeinGoogleAnalyticsPremiumasamplingislessoftenneed-

ed.Amajordifferenceistheservicesupport,whereGoogleAnalyticsPremiumincludesserviceinter-

actionandsupportincontrasttoGoogleAnalyticsStandard.Table10givesanoverviewofthediffer-

encesbetweenbothGoogleAnalyticsversionsindetail.

GoogleAnalytics Standard Premium

DataCollection

HitsPerMonth 10million 1billion+

CustomDimensions/Metrics 20each 200each

PropertiesPerAccount 50 50+

ViewsPerProperty 25 25+

Roll-upProperties x



DataFreshnessNo timeframe gua-

ranteed 4hoursguaranteed

ImportingAdvertising&OtherData

AdWordsIntegration x x

AdSenseIntegration x x

DoubleClickCampaignManagerIntegration x

DoubleClickBidManagerIntegration x

DoubleClickforPublishersIntegration x

ImportCustomDataSources x x

Query-timeDataImport x

Reporting

StandardReports x x

CustomReports,Dashboards,&Segments x x

CustomFunnelReports x

IntelligenceAlerts x x

Real-timeReports x x

FlowVisualizationReports x x

MCFReports&AttributionModeling x x

Data-drivenAttributionModel x

Sampling

StandardReportsPre-aggregated x x

ReportRowLimitPerDay 50k 75k

Session Threshold for Sampling in

AdHocReportsCustomTables

500kperproperty 50mperview

ReportRowLimit 200k

UnsampledReports x

UnsampledReportRowLimit 3m

APIs

Standards&MCFReportData x x

Real-timeData Beta Beta

ConfigurationManagement x x

ConfigurationManagementWriteAccess Beta Beta

UnsampledReports x

RawSessionDataViaBigQuery x

Service

ServiceLevelAgreements x

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CustomerSupport Included

CustomizedTraining Included

ParticipationinGoogleEvents x

EarlyEnrollementinBetas x

Table10:ComparisonofGoogleAnalyticsFreeandPremium(Lunametrics.com,2015)

AGoogleAnalyticsPremiumPlancostsabout105.000€peryearduetoClifton(2015).Thispricewill

benot reconcilablewith the resourcesofmostSMEs.MoreoverdoesGoogleAnalyticsofferawide

rangeof functionality for freeand thedata limitation isnotabreak toa successfulandbroadWeb

analysis, iftheSMEmanagesthefreedatacollectioncapabilitiesefficiently.Thethesiswilltherefore

beconductingthefreeGoogleAnalyticsversion.

7.3 InformationEthics,SecurityandPrivacy

Fromauserperspective,collectingcontrolledanalyticdigitaldataisaunobtrusivemethod.MostWeb

Analyticsmethodsareunobtrusivemethods.Anunobtrusivemethoddoesnotrequiretheresearcher

tointrudetheparticipantanddataisnotadirectelicitationoftheparticipant.Onegreatbenefitfor

the researcher is theobserver effect,which limits the environmental intrusion. Theobserver effect

implies that people react and behave differently, when they are feeling observed. Sociological re-

searchstates that theresearcher´s“interventionshouldnot impair thenon-reactivityof theerosion

andtracemeasuresbypermittingthesubjectstobecomeawareofhis testing” (Webbetal.,2000).

ThisunperceivedapproachofWebAnalytics,especiallytheusertracking,withoutexplicittaggingrises

toethicalconsiderations.

Thethesis intentstouseGoogleAnalyticsastheWebAnalyticssoftwareapproach.GoogleAnalytics

offersfunctionalitysuchasdiversedemographicfeaturesthathasbeenoftencriticisedethically.Web

Analytics tracksthewebsitevisitor’sbehaviouracrosstheborderless Internetandcreates,basedon

thecollecteddata,userprofilestoinferdemographicsandpersonalinterestsofthewebsiteuser.Fur-

thermorewillthisdemographicdata isstoredcentrallyatGoogle’sdatacentersandsoldtowebsite

publishers.

Another intervention into the sphereofpersonalprivacy canbe found inGoogleAnalytics termsof

use. Google describes, that “Google Analytics collects user interaction information anonymously.”

(Google Analytics, 2016). In addition the terms of use reveal, that “Through cookie created infor-

mationaboutyourwebsiteuseistransmitted(includingtheIP-Address)andstoredonGoogleservers

intheUS”(Google,2016).

The IP Address is classed among personal data and is stored by Google completely unexpurgated.

Therefore, theusageandstorageof the IPAddresswouldnormallyneedastatementofagreement

fromeverywebsitevisitor.Thisagreementwillbegatheredthroughasmallpopupwindowonevery

WebAnalyticsusingwebsite,withoutdelivering furtherdetails andexplanation for ingenioususers.

Furthermoreproblematically,theIPaddressincombinationwithadditionalusagedataandisstoredin



aforeignlegalspacefrombyserviceproviderwithdiversecommercialapplicationinterestsandpossi-

bilities.

DuetoextensivecriticismagainstWebAnalyticstheW3CdevelopedtheDo-Not-Trackpolicy(Akkuset

al.2012,p687).Furthermore,acommunityofanalyticspractitionershaspublishedtheWebAnalytics:

CodeofEthicstoaccomplishmoreintegritybyprovidingthefulldisclosureofdatacollectionpractises

(DigitalAnalyticsAssociation).BrowserdeveloperssuchasMozillahavedevelopedfeaturesfortheir

userstopreventthemfrombeingtracked.Forexample,thereisaDoNotTrackfeatureinFireFoxthat

sendsaDoNotTrackHTTPHeadertothewebsitetoopt-outofthird-partytrackingforpurposes in-

cludingbehaviouraladvertising.Moreoversomeresearchersstartedtobuildownanalyticssoftware

onethicalgrounds.Akkusetal.(2012)andChenet.al(2013)describeawebarchitectureforWebAn-

alyticswhichdoesnottrackindividualusers.

AnotherethicanalyticsattemptfromLeivaandVivó(2013,26)isthenon-identifiabledatacollection.

TheirWebAnalyticstoolcollectsdatainputeventswithoutassociatedcharactercodestopreventthe

trackingofrawkeystrokedata,whichallowsto identifyusersandsensitive information.Onamajor

positionthegovernmentofFinlandalreadyadoptedrulesin1999thatrequiretheholderofpersonally

identifiabledatatopublishpersonalinformationregistrynotice(Finnishpersonalinformationlaw).

7.4 KeyFeatures

ThefollowingsectionwillessentiallybebasedonClifton´sbookAdvancedWebMetricswithGoogle

Analytics(2012)andfocusoninterestingfunctionalityforSMEs.

GoogleAnalyticsoffersawiderangeoffunctionality.Table11showsanoverviewaboutthecomplete

functionalityinfeaturegroups.

DominikHeller


Table11:GoogleAnalyticsFunctionalityOverview(GoogleAnalytics,2016)

ThefollowingfunctionalityarestandardfeaturesofGoogleAnalytics,whichincludebasicmetricsthat

mostSMEswillneedtogetaninitialunderstandingoftheirwebsiteperformance(Clifton,2012):

CampaignReporting

Google Analytics is able to track, compare and combine data aboutwebsite visitors frommediums

suchasorganic search,paidads, referrals,newsletters, links, searchenginesetc.which forward the

usertotheGoogleAnalyticsconnectedwebsite.

AdWordsIntegration



CampaignscanbetrackedeasilywithGoogleAnalyticsduetotheAdWordsintegration.Therewithan

import of cost and impression data is possible, if the AdWords Landing pages URLs are tagged in

GoogleAnalytics.WiththehelpofAdSense,reportscanbegeneratedshowingwhichcontentachieves

themostrevenue.

SocialMediaButtons

Socialmediabuttonswhichareabletosharewebsite informationonsocialnetworks,as includedin

thebusinessculture.orgwebsite,aretrackedinGoogleAnalytics.ThereforeGoogleAnalyticsoffersa

sectionofreportstoanalysethisvisitorengagement.

E-commerceReporting

Fore-commercewebsites,GoogleAnalytics is able to trace transactions to campaignsor keywords,

fetch loyalty and latencymetrics and identify revenue scores. The collected information canbeob-

servedper-productorper-category.

GoalConversions(KPI)

Keypageviewsorkeyeventsarenamedgoalconversions.Goalsto identifyvisitorsareforexample

thefulfillmentofaregistration,afiledownload,amovierequest,blogcommentsorsubmittingasur-

veysuchasthenetpromoterscore.Besidesdetermininggoalconversionsaspageviewsandevents,

thresholdscanbesetsuchastimeonsitelongerthan60seconds.

Funnels

GoogleAnalyticsenables theanalyst tocreate funnelsteps.A funnel forexample isane-commerce

purchaseprocessthatcanbedefinedandanalysedinindividualsteps.Thisleadstoinformationabout

thevisitorpath,whichallowstodeterminewhichwebpagesresultinlostconversions.

Dashboard

Thedashboardisaselectionofcreatedreportwidgets,whichreflectyourkeydata.Itispossibletoin-

cludetwelvewidgetsintoonecustomisedreport.Thisfunctionwillbeexplainedindetaillaterinthe

chapter.

In-PageAnalystreports

In-pageanalyticsoffersavisualoverviewtocontrolthepopularityoflinksonthewebpages.Keymet-

ricsaredirectlyonthewebpagelinks.

GeomapOverlayReports

GoogleAnalyticsisabletopresentdataaboutthevisitorlocationaroundtheworldandrevealitinre-

ports.IP-addressdataisusedtoshowkeymetricsoverlaidontheworld,continent,countryorregion-

almapsregardingtothedimension.

Segmentation

DominikHeller


ThroughtosegmentationGoogleAnalyticscanisolatesubsetsofvisitortrafficdatatoanalyseitinde-

tail.Segmentscanbecombinedsidebysidetoisolateanddefinespecificvisitpatterns.Inadditionthe

advancedtablefilteringcanmarkoutparticulartablerowsinareport.

DataExport

Companiesoftenneedto furtheruse thegathereddata.Thereforereportscanbeautomaticallyex-

portedindiversefileformatssuchasCSV,TSVorPDF.Additionallyatimebasedforwardingofthere-

portsthroughE-Mailisimplementable.

InternalSiteSearchReporting

Websiteswithmanywebpagesareofteninneedforstructuralimprovements.Sitesearchinputscan

beusedtoidentifythewebsitevisitorssearchpatterns.Furthermoreispossibletoidentifypagesre-

sultinginsearchrequests,usedsearchphrasesandpostsearchdestinationpages.

CustomisedReports

AnotherimportantfeaturesforSMEsarecustomisedreports,alsooutlinedindetaillaterthischapter.

Customisedreportspresentselectedinformationthroughtometricsanddimensions.

Eventtracking

In-pageactionsarecalledeventsandusedtoidentifyinteractionwithwebsiteelementssuchasHTML

5 embedded videos, Ajax or widgets. Events round up your page view reports with additional im-

portantinformationandcanalsobeusedtofindbrokenlinksorerrorsonwebpages.

7.5 Installation

TheGoogleAnalyticsinstallationisverysimpleandthereforeevenfeasibleforpeoplewithoutspecific

technicalknowledge.

Firstly a Google account has to be created. This account can afterwards be used to login on

www.google.com/analytics.Theaccountcanalsobecreatedontheanalyticswebsite.Aftertheuseris

signedup,thewelcomescreenofGoogleAnalyticsappears.Forthepurposeofthisthesistheinstalla-

tionintoawebsite,notamobileapp,isexplainedfurther.

The fieldwebsiteURLhas to contain theURLof thewebsite suchas “www.businessculture.org”. In

additiontheindustryandtheaccountnamehastobechosen,matchingtothewebsite.Byconfirming

theDataSharingSettingsGooglewillbeabletodeliveryouadditionalinformationofindustrycompet-

itorstocompareyourresults.Byconfirmingthissettingsthewebsiteownerisalsosharinghisdata.

AnewwindowshowsthepossibilitytocreateaTrackingID,whichisunavoidabletoinstallGoogleAn-

alyticsonthewebsite.ThegeneratedUniversalAnalyticsTrackingcode(seeFigure12)hasnowtobe

copiedintotheHTMLcodeofthewebsite.ItisimportanttopastetheTrackingcodeexactlyonevery

singlewebpagebeforetheendingheadtag“</head>”oftheHTMLcode.



Ifthetrackingcodewasinstalledcorrectly,thephrase“Status:ReceivingData”willappearrightnext

tothe“TrackingID”field.

Figure12:UniversalTrackingID

7.6 GoogleAnalyticsInterface

InadditiontoexplaintheGoogleAnalytics Interfaceindetailashortvideowascreatedto introduce

theGoogleAnalyticsuser.

TheGoogleAnalyticsinterface,showninFigure13,isbasedonaconsistentreportingviewstructure.

Theviewcontainsthereportdatathathasbeencollected.Generallytheviewisincludinganaccount

structurewithasingleaccount,asinglepropertyandsingleview.Theviewsarecustomisableandcan

inandexcludespecificdata.Anotherimportantpartoftheinterfaceisthereportnavigation.Theac-

count list canbeused to switcheasilybetweendifferent accounts andviews.A list of all accounts,

propertiesandviewscanbeaccessedthroughtheHometab.TheHometabalsoincludesasummary

ofthesessionnumbers,averagesessionduration,bouncerateandconversionrate.TheReportingtab

includesallpre-builtcorereports.

In order to find single reporting views, an overview containing all view elementswas createdwith

matchingidentifiers(seeTable11).

DominikHeller


Figure13:GoogleAnalyticsInterface



Table11:GoogleAnalyticsReportNavigationMap

7.7 Reports

The report view enables the analyst to present data in different views. The source/medium report

highlightsthewaysvisitorsconnecttothewebsite.Customisedreportsareanessentialelementtore-

DominikHeller


ceiveanalyticinformationregardingtothedefinedKPIs.Byaddinganewreportinthecustomisation

topmenutheanalysthastoenterthefollowingreportcontent:

- Name:anindividualnamedescribingthereportcontentcanbechosen.

- Type:definesifthereportdatashouldbedisplayedintheexplorer,flattableormapoverlay.

- MetricGroups:containseverymetricsthatisneededtogatherthespecificdata.

- Dimension:definesthedimensioninwhichthedatawillbecollected.

- Filter:areusedtoaddoptionalcommandssuchasregularexpressions.Therewithitpossible

tosearchforandmatchparticularelementswithintext

- View:selectstheviewinwhichthereportwillbecreated.

7.8 Dashboards

Thedashboardisusedtoquicklyviewkeyinformationinasinglereport.Thereforethedashboardcan

becustomisedtoshowthereportingrequirementsoftheSME.Anewdashboardcanbecreatedby

navigatingto“Dashboards”andselecting“NewDashboard”.Thenextstepallowstochoosebetween

a blank canvasor starter dashboard.Ablank canvas is needed tobuild a dashboard containing the

specificdata.Thesingledataviewsofthedashboardarecalled“widgets”.Astandardwidgetcontains:

Metric:displaysdiversemetricssuchasthebouncerate.

Timeline:displaysatrendlineinthedashboard.

Geomap:locatesamapreportinthedashboard.

Table:showsatableofinformation.

Pie:includesapiechart.

Bar:includesabarchart.

Inadditionitispossibletocreatereal-timewidgets,containingacounter,timeline,mapandtable.

7.9 KeyPerformanceIndicatorImplementation

Inchapter6.3theeffectivecreationofKPIswasexplained.ThenextstepforanSME,aftertheinstalla-

tion of Google Analytics, is the implementation of these KPIs into Google Analytics. Therefore the

GoogleAnalyticsfunctionalityhastobemappedtothedefinedKPIsmetricsanddimensions.Thissec-

tionexplainsandillustratestheimplementationofKPIsintoacustomizeddashboardwiththeexample

ofthebusinessculture.orgwebsite.

Businessculutre.orgbelongs to the content /mediawebsite type.OnededicatedKPI for example is

“Visits”and“%NewVisitsbyLandingPage”,sortedbythe landingpage.ThisKPIcollectsdata, that

implies information aboutwhich landingpage is requested themost referring to themost sessions

and furthermore thepercentageofnewsessions.Thegained informationcanbeused toseewhich



landingpagesarepopularandduetothesessionrateitcanbeestimatedifthepagecontentinterests

thevisitor.Themappingprocessstartswiththeidentificationofmatchingmetrics.Thereforealistof

allGoogleAnalyticsdimensionsandfeatureswascreated,whichisgroupedbyfeatures(seeTable12).

Table12:GoogleAnalyticsDimensionsandMetricsOverviewExcerpt

The defined KPI needs themetric “Sessions” and “%New Sessions”, and in addition the dimension

landingpage,becauseonlylandingpagesshouldbecombinedwiththemetrics.

Inanextstepthedefinedmetricsanddimensionshavetobeincludedintothereferringfieldsofthe

“+Addwidget”window(seeFigure15).Aftertheinformationistypedinandthedialogueissaved,the

widgetwillappearandpresentthesearcheddata.

DominikHeller


Figure15:WidgetCreation

Inorder tohelpSMEswith thiscourseofaction,amappingtable (seeTable13orAppendixB)was

created,which shows theneededmetrics anddimensions for someKPIs. The tablealsodivides the

KPIsinwebsitecategoriesandisbasedontheGoogleAnalyticsfunctionalitywindowdesign.

Table13:KPI-GoogleAnalyticsMapping

TherewithitissimpletoimplementaKPIintotheGoogleAnalyticsfunctionality.Furthermorecanthis

tablebeusedtocreateanddevelopnewKPIs.

ThisapproachwasalsorealizedforthecustomizedreportfunctionofGoogleAnalytics(seeAppendix

B).SMEscanalsousetheGoogleAnalyticsReportNavigationmaptofindallpre-basedreportsmore

easily(seeAppendixD).

In the example of the businessculture.orgwebsite, the defined KPI leads to data showing that the

page “business-culture/cultural-differences-in-business/” has the most sessions. The “% New Ses-



sions”metric with 86.83% indicates that the web page is not very attractive for returning visitors.

Nearlyeverywebpageofbusinessanalytics.orghasanewsessionsindicatorofabove80%,whichindi-

catesthatthewebsiteisnotfocusingonorattractingvisitorstocomeback.Thiscouldbeexplained

withthebusinessculturecontent,whichisnormallynotneededtobereviewedmanytimes.

DominikHeller


8 DevelopingtheGoogleAnalyticsFrameworkandGuidelinesforSMEs

Thischapterpresents thedevelopmentof thenewGoogleAnalytics framework.The frameworkde-

velopment is based on the previous findings of the thesis. At first the new framework, based on

Hausmanns,WilliamsandSchuberts(2012)WebAnalyticsframeworkisintroduced.Afterthisattend-

ingguidelinesforSMEsareinvented,whichguideSMEstotheoverallWAprocess.

8.1 IssueIdentification

The issueofWebAnalytics inSMEs isanewfieldofresearch.Hausmanndevelopedoneofthefirst

approaches to address this operational area in detail.Only the Startupmetrics framework byDave

McClure contains similar rudiments of guiding SMEs toWebAnalytics. The framework components

containingAcquisition,Activation,Referral,ReferenceandRevenuepursuearemarkablepointofview

andmethodtoWebAnalyticsforSMEs.AmongtheotherWebAnalyticsframeworksisnoframework

thatisconcentratingprimarilyonWebAnalyticsforSMEs.Moreovertheotherframeworksarepaying

most attention on Web Analytics for e-commerce websites, have only superficial consideration of

businessrequirementsorKPIs,donotidentifythepurposeofthewebsiteandcorrespondingWebAn-

alyticsrequirementsandarenotprovidingassistanceinthecomprehensionofWAmeasurement.As

mentioned inChapter1.1,especiallySMEsarestruggling in termsof time,know-howandcostwith

regardstoWebAnalytics.Thereforetheunderlyingattemptofattentiveselectionfromoneormoreof

thefivecomponentstoinvestresourcesissignificantfornearlyeverySMEwithlimitedfinancialand

humanresources.Asopposedtothis,theWebAnalyticsframeworkbyHausmannshowslessconsid-

erationforthedetailedresourcemanagement.Inreturnitisofferingthemostdetaileddesignedim-

plementationprocessofWebAnalytics thatwas foundduringthe literatureresearch.Thereforethe

newframeworkandguidelinesforSMEsshouldobservetheWebAnalyticsFrameworkbyHausmann

byrefininganddevelopingitfurtheronthemissingedges.

InTable14,issuesofWebAnalyticsinSMEsfromdifferentapproachesareaddressed.Toidentifythe

strength andweaknesses of each approach, the typology in Table 14 is used as foundation for the

analysis.BasedonthisoverviewkeyapproachesandrequirementsforanewframeworkforWebAna-

lyticsaredefined.

WebAna-

lytics

Framework

Startup

metrics

Trinity

Approach

Relationsbetween

webande-

businessmetrics

Majorcompo-

nentsofanon-

linemarketing

system

Haveflexibleanddy-

namicadaptability

x x x

DevelopingtheGoogleAnalyticsFrameworkandGuidelinesforSMEs


Developstrategyx x x x x

IdentifyOKRsand

KPIs

x x

Leadtoactiontaking

andperformancere-

sults

x x x x x

Structuretheintegra-

tionandimplementa-

tionintologicalsteps

x x

Considerdifferent

participants

x x

Concentrateonone

WebAnalyticssoft-

ware

Bespecialisedon

SMEs

x x

Supportandguide

SMEsthroughthe

completeWebAnaly-

ticprocesscycle

x

BeapplicableforWeb

Analyticsbeginners

x x x

ExplainWebAnalytics

functionality

Sustainevolutionand

changeprocess

x

Table14:WebFrameworkRequirementComparison

Therequirementcomparisonshowsoncemore,thattheWebAnalyticsframeworkbyHausmann,Wil-

liams and Schubert (2012) delivers a solid foundation for the new Web Analytics framework and

DominikHeller


guidelines.TheWebAnalytics framework fulfills themost requirementsandcombinesauniqueand

effectiveapproachtoimplementWebAnalyticsintoaSME.Besides,itisnotusingapredefinedWeb

AnalyticssoftwaresuchasGoogleAnalytics,themainweaknessesarederivingfromthemoretheoret-

ical, structural process forWebAnalytics implementation. For example, phase 2 “Planning forWeb

Analytics”mentions the identificationof data collectionmethods andmetrics, butdoesnot explain

thecourseofactioninfurtherdetail.Thisgapwillbeaddressedbythenewguidelines.

8.2 DevelopinganEffectiveGoogleAnalyticsFramework

AtfirsttheframeworkofHausmann,WilliamsandSchubert(2012)istakenintoanewflatdesignap-

proach,whichisinformandcontentequaltothecurrentdesign(seeFigure16).

Alloriginalcontentand information isexactly transferred intothenewdesign.Thenewdesign indi-

catesthedifferentphasesofHausmann´s“HighLevelWAProcessCycle”throughacolouredtriangle

attheleftsideofeachsingleframeworkstep.ThecontinuouslyimprovementoftheDataProcessing,

PatternAnalysis&Identification,ActionTakingandEvaluationphaseisexpressedthroughthedoted

lineontherightside,taggingitasacontinuouscycle.

Inanextsteptheframeworkhastobematchedtothenewrequirements(seeFigure17).Therefore

thestepshavetobeadaptedtothenewconditions,regardingtotheuseofGoogleAnalytics.Because

of that theWebAnalyticsPossibilityAnalysisstep is removedandmerged intotheGoogleAnalytics

step. In addition theTool Selection step is also abstracted from theWA framework for focusingon

GoogleAnalyticsastheWAtoolofchoice.ThereforethenewGoogleAnalyticsstepbuildsthebasisof

thenewframework.AWebAnalyticstoolimplementationisrelatedtoknowledgeacquisition.More-

overneeddatacollectionmethodsandmatchingmetricsbe identifiedtofulfill thestep.Within,the

stepindicatesalso,thatGoogleAnalyticscanbeimplementedstandaloneorwithadditionaltoolssuch

asGoogleAdWords.NexttheImplementationstepwaschangedintotheGoogleAnalyticsImplemen-

tationstep.Thecontainedimplementationactivitywasadoptedwithoutchanges.Implementationin-

volvesthesettingupofGoolgeAnalyticsanditscustomisationtocaptureandprocesstherelevantda-

tatypesandmetrics.Alltheothersteps, including“DataProcessing”,“Reporting,PatternAnalysis&

Identification”,“ActionTaking”,“Evaluation”canbecarriedoverwithoutchange,becauseofthesimi-

larWebAnalyticsproceeding.Websitesaresubjecttocontinuousdevelopmentandevaluation,which

isperfectlyreflectedintheframeworksteps.

ThenewGoogleAnalyticsframeworkiscomplementedwithanumberoneachtriangle.Thisnumbers

areusedtomatchthestepsoftheframeworktotheSMEguidelines.



Figure16:WebAnalyticFrameworkbasedonHausmann,WilliamsandSchubert(2012)

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Figure17:ThenewGoogleAnalyticsFramework



8.3 DevelopingWebAnalyticsFrameworkGuidelines

Inconsiderationoftheliteraturereviewandallgainedfindingsandinformationabouttheintegration

ofKPIsintoGoogleAnalytics,aguidelineneedstobedevelopedtofurtherassistSMEs,whicharewill-

ing to implementGoogleAnalyticson theirwebsite.Theprevious sections8.1and8.2analysed the

gap and issue identification, which showed that the Google Analytics framework needs additional

guidelinestoaddressthegapbetweentheoryandpracticetosupporttheSMEwiththeWebAnalytics

implementation. However, the outcome of this is the grounded idea for guidelines, which are de-

signedintheformofacookingrecipetocomplementtheGoolgeAnalyticsframework.Theseguide-

linesshowwhereandhowtobeginwithWebAnalyticsandallowtheSMEtocompletetheguideline

stepbystepinastructuredprocess.Allsignificantpreviousfindingsneedtobeintegratedandeasily

realisable.AccordingtotheserequirementsaGoogleAnalyticsGuideline forSMEswascreated. It is

shown in Figure18anddivides theWebAnalytics implementation in tendifferent steps,whichare

connectedthroughabluemarkednumbertotheGoogleAnalyticsframework.Eachstepbeginswitha

questionrelatedtoWebAnalytics,thattheusercananswerwithyesorno. Ifhealreadyknowsthe

answertothequestion,theusercanskipthestepandstartwiththenextquestion,highlightedbythe

greenarrow.Iftheguidelineuserdoesnotknowtheanswer,hecanfollowtheinstructionsbehindthe

redarrow.Byfollowingthedescendingorderoftheguideline,theuserisintroducedtotheWebAna-

lyticsimplementationstepbystep.EveryinstructionisdesignedfocusingonSMEsandrepresentsthe

literaturefindings.

Followingeverystepandinstructionisoutlinedanddescribed:

DoyouknowyourwebsitecategoryandmatchingKPIs?

ToidentifythewebsitecategoryandmatchingKPIsFigure23wascreated.Withthehelpoftheinfor-

mationprovided inFigure23andchapter6 theguidelineuser isable to identify theSME´swebsite

typeandfindrelated,commonlyusedKPIswiththe“OKRPlanningTable”(seeTable5).

Areyousatisfiedofyourwebsite?

WebsitedesignisanimportantpartoftheWebAnalyticsprocess.Figure20offersrecommendations

foranefficientandaestheticwebdesign.

DoyouknowyourGoalsandKPIs?

OneofthemostimportantWebAnalyticsimplementationstepsistheidentificationofGoalsandKPIs.

ThereforeFigure22showsKPIcharacteristicsinanoverview.ToguidetheSMEastructuredapproach

wasdesigned,resultingintheOKRPlanningTemplate(seeTable5)andtheKPIDesignTemplate(see

Table7).

IsGoogleAnalyticsinstalled?

TheinstallationofGoogleAnalyticsissimpleandexplainedindetailinchapter7.5.Inaddition,togeta

basicunderstandingofsomeimportantWebAnalyticsdefinitions,Figure22wascreated.

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Doyouknowtheinterface?

TogettoknowtheGoogleAnalyticsinterfaceavideowascreated,whichguidestheuserthroughthe

GoogleAnalyticsinterface.

Doyouknowthemetricsyouwanttotrack?

AfterallOKRsandKPIsaredeveloped,matchingmetricshavetobeidentified.Chapter6explainsthe

processindetailandaddstherecommendedKPIsandmetricstable(seeTable9)tofindKPIsandmet-

ricsrelatedtothefieldofapplication.AnoverviewofallmetricsinGoogleAnalyticsisofferedinAp-

pendixCwiththeGoogleAnalyticsDimensionsandMetricsOverview. If theuser identifiedrelevant

metrics,thenextstepistheimplementationinGoogleAnalyticswiththehelpofthedashboardand

customisedreportstable.

Haveyouworkedwiththedashboard?

Userscangetanintroductiontoreportsbyfollowingchapter7.9“KPIImplementation”andusingthe

DashboardtableinAppendixB.

Doyouknowhowtocreatereports?

Thecreationofreportsisexplainedinchapter7.9andisbasedontheGoogleAnalyticsReportsMap

and customised reports table (seeAppendixB),whichdescribes a simplemethod to implement re-

ports.Thesheetsortablesalreadycontainpredefinedreportsettingsandcanbeextendedbytheus-

er.

Doyouranalyticreportsperformaction?

Thereportevaluationispartofchapter6.3“IdentifyingKeyPerformanceIndicatorsandMetrics”and

chapter7.8“Reports”.Smallandmedium-sizedenterprisesdiffer insize,businessrequirementsand

goals,which are linked to theWebAnalytics performance. Therefore every SMEhas differentWeb

Analyticsdemandsandrequirements.TosupporttheSMEsanyhowtheKPIdevelopmenthelpswith

thegoal identification,whileGoogleAnalyticsreportsareexplained inthethesisandextendedwith

theReportNavigationMap(seeAppendixD),DashboardandCustomisedreportstogeneratefitting

andefficientreportsduetothewebsitetypeoftheSME.

Areinterestedinadditionalfunctionality?

The last stephasno instructionsat themomentandwas integrated toallowtheextensionofaddi-

tionalcontent.

InadditionFigure19showstheWebAnalyticspathtoseeanoverviewaboutall tablesandfigures,

whichareusedduringtheWebAnalyticsimplementationprocess.

Finally,tosharetheresearchresultsoftheGoogleAnalyticsframeworkandguidelines,abasicweb-

sitewascreatedshowninAppendixA.



Figure18:GuidelinesforSMEstoGoogleAnalytics

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Figure19:SMEWebAnalyticsPath



Figure20:DesignRecommendations

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Figure21:KPIcharacteristics



Figure22:WebAnalyticsDefintions

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Figure23:WebsiteCategories/KPIs



8.4 FrameworkLimitations

Duetothetimelimit,thenewguidelinesaresubjecttosomerestrictions.Theframeworkandguide-

line illustrate and pave theway for additional content and extensions. Google Analytics provides a

widespectrumof functionality,whichneeds tobeadded into theguidelinesandtherelatedexecu-

tions.Furthermore,theframeworkwasnotevaluatedbyaSMEtogiveevidenceforthe impactand

scope of the development. Another limitationwas given through the restricted access on the case

website.Changesandadvancedimpactscouldnotbeverifiedandadditionalfunctionalitynotbeinte-

gratedintothewebsite.DuetotheusageofthecasewebsiteandWebAnalyticsframeworkbyHaus-

mann,WilliamsandSchubert(2012),aspecialfocuswasplacedoncontentorinformationalwebsites

ofSMEs,eveniftheguidelinesandKPIdevelopmentaddressesallwebsitetypes.

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9 SummaryandConclusion

In this final chapter the researcher summarises the thesisandbrings together themajor conclusion

drawnfromtheresearchfindings.Thereforeallresearchquestionsareaddressedandsuggestionsfor

futureworkaredefined.

9.1 ResearchQuestions

Atthebeginningofthisthesisanoverallresearchquestionwasdefined

RQ:Howcanasmallormedium-sizedbusinesswithlimitedcapabilitiesmakebetteruseofGoogleAn-

alytics?

andasetof12subsidiaryresearchquestions,whichhavebeenaddressedduringthediversestages

ofthethesis.

RQ1a:HowdoexistingframeworksclassifyWebAnalytics?

TheexistingframeworksclassifyWebAnalyticsfromdifferentpointofviews.Everyauthorhasadif-

ferent approach and sets priorities to a different subject ofWebAnalytics. The differences are de-

scribedindetailinChapter5.6.

RQ1b:Whichsimilaritiesdotheframeworkscontain?

AllframeworksareaddressingtheWebAnalyticsubject.Neverthelesshaseveryframeworkadifferent

approach.FurtherdetailsareshowninChapter5.

RQ1c:WhatframeworkismostappropriatedtocovertheneedsofSMEsandtheintegrationofWeb

Analytics?

Withoutproofforevidence,thebestdiscoveredmethodduringtheresearchwasthedevelopmentof

aguidelinewithasimilartorecipestructure.

RQ1d:WhatarethedifferencesforWebAnalyticsbetweensmall,mediumandbigsizedcompanies?

Becauseofthetotallydifferentamountofcollectabledataandtheavailabilityofresources,dothese

companies handle Web Analytics differently. While big enterprises need to gather more data and

thereforeanalyseit indeeperdimensionstoreceivesimilardetailedsamples,aresmallercompanies

inneedofsupportintheinvestmentofresourcesduetothelackinknowledge,timeandcost.

RQ2a:Whatarekeyperformanceindicatorsforsmallandmedium-sizedenterprises?

KPIsforSMEsweredefinedandcomparedinchapter6.

RQ2b:WhatarethedifferencesintheWebAnalyticsrequirementsduetotheindustrysector?

Theindustrysectorisofteninvolvedorattendedbyaspecialtypeofwebsite.Fourdifferenttypesof

websitesweredefinedandallhaveotherrequirementsinWebAnalytics.

SummaryandConclusion


RQ3a:WhatfunctionalitiesdoesGoogleAnalyticsoffer?

TheessentialfunctionalityofGoogleAnalyticsisexplainedinchapter7.4.Inadditiontable11shows

allofferedfunctionalities.

RQ3b:HowdoesGoogleAnalyticstoredataandwhichpossibilitiesdousershavetointeractwiththis

information?

GoogleAnalyticsstoresdatawithoutguaranteeddatafreshness.Furthermoreareonly10millionhits

permoth includedand rowsare limited to50.000.Theuser can interactwith thedata through the

GoogleAnalyticsAPIorexportdataasaCSV,TSVorPDFfile.

RQ3c:WhatkindofspecializationdoesaGoogleAnalyticframeworkneed?

Allrequirementsaredefinedinchapter8.1.

RQ3d: What additional features does Google Analytics Premium offer for small and medium-sized

companies?

Thisquestionsaddressedintable10.GenerallyremainstoascertainthatSMEsarenotinneedofaddi-

tionalfunctionalityandwillnormallynotbeabletofundtherelatedcostsofGoogleAnalyticsPremi-

um.

RQ4a:WhatdoesaWebAnalyticsframeworkneedtofitthebusinessrequirementsofSMEs?

TheframeworkneedstofitthedifferentmetricsandKPIs.

RQ4b:WhatisthebestmethodandformtodesigntheWebAnalyticsframeworkandguidelinesfor

GoogleAnalytics?

Theframeworkneedstobewellstructuredinindividualsteps,whilebeingreducedontheimportant

processes.Furthermoretheframeworkshouldbedesignedattractively.

9.2 KeyContributionsandFutureWork

Byansweringtheaboveoutlinedresearchquestionstheauthoraddressedallpredefinedobjectives.

ThenewGoogleAnalyticsframeworkandguidelinesdeliveranewapproachtoguideandassistSMEs

tomakebetteruseofGoogleAnalytics.Asimpleandclearlyarrangedstructureprovidesanuncompli-

catedintegrationofGoogleAnalyticsintotheSME.Everystepoftheimplementationfromthedevel-

opmentof theKPIs to themapping toGoogleAnalytics functionality isguidedwith templates.Thus

thisresearchprojecthascontributedtothedevelopmentofaholisticconcepttothetheoryandprac-

ticeofWebAnalyticsusageinSMEs.

Nevertheless, this has opened up a number of areas for future research. In addition to qualify the

frameworkandguidelines,ascientificevaluationorcasestudyhastobeconducted.Thiscouldalsobe

extendedwiththeusageofane-commercewebsitetoaddresstheneedsofmanySMEsinmorede-

DominikHeller


tail.FurthermoreananalysisofnecessaryKPIswithmatchingmetricsfordifferentwebsitetypesand

webapplicationscouldrevealstronglyneededinsights.

In Addition, more features and functionality of Google Analytics needs to be classified and trans-

formedintotheconcepttoexpandthecapabilities.

With the use ofGoogle Analytics numerous violations of data security and privacy go along,which

couldbe avoidedbyusingnon-trackableWebAnalytics softwaremethodsoropen source software

suchasPIWIK.

Inorder to simplify theuseofWebAnalytics, thedevelopmentofautomated reportscouldbepro-

moted.

MoreovercouldtheframeworkandguidelinesbeadaptedtotheLeanUXDesignprocess,whichaims

todecreaseprocessballastandincreasecustomerinteraction.

Furtherstudiescouldalsousedifferentcasewebsiteswithoutrestrictionsand integratenewerWeb

Analyticsfieldssuchaswebapplication,mobileapplicationandInternetofThings(IoT)devices.

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AppendixA–FrameworkandGuidelineWebsite

ThefollowingpagesshowthewebsiteforthenewGoogleAnalyticsFrameworkandGuidelines.This

websitecanbeusedtopresentallresultstointerestedSMEsinordertotaketheresearchfurtherand

receivefeedbackfortheresearchprojectresults.Thewebsiteisbuiltonaresponsivedesigntoensure

simpleaccessfromeverywebbrowserormobilewebbrowser.

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Anhang


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AppendixB–KPImeetsMetrics:DashboardandCustomisedReports

Anhang


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AppendixC–GoogleAnalyticsDimensionsandMetricsOverview

Anhang


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AppendixD–GoogleAnalyticsNavigationMap

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