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WebAnalytics:
Functions,KPIsandReportsinSMEs–
AUsageFrameworkandGuidelines
Athesissubmittedforthe
BachelorofScienceinInformationManagement
By
DominikHeller
StudentID:210200205
E-Mail:dheller@uni-koblenz.de
Faculty4:ComputerScience
InstituteofISResearch
UniversityofKoblenz-Landau,Germany
Supervisor:
VerenaHausmann
Prof.Dr.SusanP.Williams
Koblenz,March2016
©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
©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
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
©2016UniversityofKoblenz-Landau,InstituteofISResearch 3
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
4 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
©2016UniversityofKoblenz-Landau,InstituteofISResearch 5
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
6 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
Chapter4reviewsexistingliteratureintheareaofWebAnalytics.Thechapterexplainsthefunctional-
ity,trackingmethodsandreportsofWebAnalytics.
Chapter5givesanoverviewofexistingWebAnalyticsframeworks.Toaccomplishthis,popularWeb
Analyticsframeworkswereanalyzedandtheirstrengthsandweaknessescompared.
Chapter6studiesthespecialcharacteristicsofperformancemeasurementinSMEsandthetheoretical
approachestodevelopmatchingmetricsandkeyperformanceindicators.
Chapter7givesanoverviewofGoogleAnalyticsastheusedWebAnalytictool.Arealworldcaseweb-
siteisusedtointroduceandimplementthebasicfunctionalityofGoogleAnalytics.
Chapter8describesanddevelopsanewWebAnalyticsFrameworkandassistingguidelinesforSMEs,
basedonpreviouslyobtainedfindings.
Chapter9isthesummaryandconclusionchapter.Itsummarizesthefindingsandconcludeswithsug-
gestionsonfurtherresearchpossibilitiesintheWebAnalyticsstudyfield.
ResearchDesign
©2016UniversityofKoblenz-Landau,InstituteofISResearch 7
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
8 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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.
DominikHeller
10 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
©2016UniversityofKoblenz-Landau,InstituteofISResearch 11
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
DominikHeller
12 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
WebsitesandWebsiteClassification
©2016UniversityofKoblenz-Landau,InstituteofISResearch 13
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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14 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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.
WebsitesandWebsiteClassification
©2016UniversityofKoblenz-Landau,InstituteofISResearch 15
Figure3:ExampleWebpage,whichfollowstheWebDesignPrinciples(YCombinator,2016)
DominikHeller
16 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
©2016UniversityofKoblenz-Landau,InstituteofISResearch 17
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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18 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
©2016UniversityofKoblenz-Landau,InstituteofISResearch 19
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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20 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
©2016UniversityofKoblenz-Landau,InstituteofISResearch 21
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.
DominikHeller
22 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
©2016UniversityofKoblenz-Landau,InstituteofISResearch 23
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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24 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
©2016UniversityofKoblenz-Landau,InstituteofISResearch 25
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
26 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
©2016UniversityofKoblenz-Landau,InstituteofISResearch 27
§ AchievingUseableandActionableResults
§ EvaluationofActions
EachofthefivestepsisoutlinedinmoredetailandwithaconsistentcolourstructureinFigure7.
DominikHeller
28 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
Figure7:TheWAProcessFramework(Hausmann&Williams&Schubert,2012)
FrameworksforWebAnalytics
©2016UniversityofKoblenz-Landau,InstituteofISResearch 29
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
30 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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.
FrameworksforWebAnalytics
©2016UniversityofKoblenz-Landau,InstituteofISResearch 31
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
32 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
FrameworksforWebAnalytics
©2016UniversityofKoblenz-Landau,InstituteofISResearch 33
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
34 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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.
FrameworksforWebAnalytics
©2016UniversityofKoblenz-Landau,InstituteofISResearch 35
Table1:ComparisonofWebAnalyticsFrameworks
Due to the different background and temporal connections every framework has diverse strengths
andweaknessesintheaspectofWebAnalytics.TheseareshowninTable2.
DominikHeller
36 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
-Doesnot provide executionor
implementationsteps
Relationsbetween
webande-
businessmetrics
-Strongfocusone-commerceperspective
-PracticalKPIandmetricthinking
-Perspectiveframework
-Doesnot provide executionor
implementationsteps
Startupmetrics
-Strongfocusonthewebsitevisitor
-Practicallyrelevantinvestmentstrategy
-Testseachvisitorsneedsandopportunities
-Contributiontoquickimplementation
-Focus on results, action taking and perfor-
manceimprovement
-Doesnot provide executionor
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
FrameworksforWebAnalytics
©2016UniversityofKoblenz-Landau,InstituteofISResearch 37
frameworkstructurestheWAimplementationprocessmorepreciselyandphased,whileprovidinga
simplisticapplication.ThereforethefurtherresearchwillconcentrateprimarilyontheWebAnalytics
frameworkbyHausmann,WilliamsandSchubertanduseitasthefoundationforthenewWebAnalyt-
icsframeworkandguidelines.Howevertheframeworkhasnofocusorprocesssectionsespeciallyfor
GoolgeAnalytics and someof the phases are only examining the implementation superficially. This
aggravatestheapplicationforSMEswithlimitedknowledgeandincreasestherequisitionforsupport-
ingWebAnalyticsandGoogleAnalyticsguidelines.
DominikHeller
38 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
©2016UniversityofKoblenz-Landau,InstituteofISResearch 39
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
40 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
WebAnalyticsPerformanceMeasurementofSMEs
©2016UniversityofKoblenz-Landau,InstituteofISResearch 41
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
42 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
WebAnalyticsPerformanceMeasurementofSMEs
©2016UniversityofKoblenz-Landau,InstituteofISResearch 43
enabled.Thetemplateuserdoesnothavetosticktothelayout,anOKRcanhavearandomamountof
KPIsandasaconsequenceaKPIscanhaveanynumberofmatchingmetricsandgoals.
WebAnalyticsGuidelineYear:2016
ObjectiveKeyResult
KPI
Metric/Goal
ObjectiveKeyResult1
KPI1 KPI2 KPI3 KPIn
Metric1 Goal1 Metric1 Goal1 Metric1 Goal1 Metric1 Goal1
Metric2 Goal2 Metric2 Goal2 Metric2 Goal2 Metric2 Goal2
Metricn Goaln Metricn Goaln Metricn Goaln Goaln Goaln
ObjectiveKeyResult2
KPI1 KPI2 KPI3 KPIn
Metric1 Goal1 Metric1 Goal1 Metric1 Goal1 Metric1 Goal1
Metric2 Goal2 Metric2 Goal2 Metric2 Goal2 Metric2 Goal2
Metricn Goaln Metricn Goaln Metricn Goaln Goaln Goaln
ObjectiveKeyResult3
KPI1 KPI2 KPI3 KPIn
Metric1 Goal1 Metric1 Goal1 Metric1 Goal1 Metric1 Goal1
Metric2 Goal2 Metric2 Goal2 Metric2 Goal2 Metric2 Goal2
Metricn Goaln Metricn Goaln Metricn Goaln Goaln Goaln
Table5:OKRPlanningTemplate
AccordingtoClinton(2010)anorganisationisgoodadvisedtodefineamaximumoftenKPIs.Incon-
trasttoClinton,Peterson(2006,p.13)recommendsamodelthatadvocatesadifferentview:
§ Seniorstrategists,suchastheCEOofaretailwebsiteshouldget2-5KPIs
§ Mid-tierstrategists,suchastheVicePresidentofMarketingshouldget5-7KPIs
DominikHeller
44 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
§ 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.
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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
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•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.
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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.
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50 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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.
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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
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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
DominikHeller
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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
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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
56 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
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©2016UniversityofKoblenz-Landau,InstituteofISResearch 57
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
58 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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.
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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
60 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
Figure13:GoogleAnalyticsInterface
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©2016UniversityofKoblenz-Landau,InstituteofISResearch 61
Table11:GoogleAnalyticsReportNavigationMap
7.7 Reports
The report view enables the analyst to present data in different views. The source/medium report
highlightsthewaysvisitorsconnecttothewebsite.Customisedreportsareanessentialelementtore-
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62 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
GoogleAnalyticsImplementation
©2016UniversityofKoblenz-Landau,InstituteofISResearch 63
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.
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64 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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-
GoogleAnalyticsImplementation
©2016UniversityofKoblenz-Landau,InstituteofISResearch 65
sions”metric with 86.83% indicates that the web page is not very attractive for returning visitors.
Nearlyeverywebpageofbusinessanalytics.orghasanewsessionsindicatorofabove80%,whichindi-
catesthatthewebsiteisnotfocusingonorattractingvisitorstocomeback.Thiscouldbeexplained
withthebusinessculturecontent,whichisnormallynotneededtobereviewedmanytimes.
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66 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
©2016UniversityofKoblenz-Landau,InstituteofISResearch 67
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
68 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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.
DevelopingtheGoogleAnalyticsFrameworkandGuidelinesforSMEs
©2016UniversityofKoblenz-Landau,InstituteofISResearch 69
Figure16:WebAnalyticFrameworkbasedonHausmann,WilliamsandSchubert(2012)
DominikHeller
70 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
Figure17:ThenewGoogleAnalyticsFramework
DevelopingtheGoogleAnalyticsFrameworkandGuidelinesforSMEs
©2016UniversityofKoblenz-Landau,InstituteofISResearch 71
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.
DominikHeller
72 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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.
DevelopingtheGoogleAnalyticsFrameworkandGuidelinesforSMEs
©2016UniversityofKoblenz-Landau,InstituteofISResearch 73
Figure18:GuidelinesforSMEstoGoogleAnalytics
DevelopingtheGoogleAnalyticsFrameworkandGuidelinesforSMEs
©2016UniversityofKoblenz-Landau,InstituteofISResearch 75
Figure20:DesignRecommendations
DevelopingtheGoogleAnalyticsFrameworkandGuidelinesforSMEs
©2016UniversityofKoblenz-Landau,InstituteofISResearch 77
Figure22:WebAnalyticsDefintions
DominikHeller
78 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
Figure23:WebsiteCategories/KPIs
DevelopingtheGoogleAnalyticsFrameworkandGuidelinesforSMEs
©2016UniversityofKoblenz-Landau,InstituteofISResearch 79
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.
DominikHeller
80 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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
©2016UniversityofKoblenz-Landau,InstituteofISResearch 81
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
82 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
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.
References
©2016UniversityofKoblenz-Landau,InstituteofISResearch 83
References
Aden,T.,2009.GoogleAnalyticsImplementieren.Interpretieren.Profitieren,Hanser,München.
Akkus, I. E., Chen,R.,Hardt,M., Francis, P., andGehrke, J. ,2012.Non-TrackingWebAnalytics. Pro-
ceedingsofthe19thACMConferenceonComputerandCommunicationsSecurity.
Apache Server Documentation, 2016. Apache HTTP Server Version 2.2. Available at:
http://httpd.apache.org/docs/2.2/logs.html#accesslog[AccessedJanuary28,2016].
Berners-Lee,T.&Fischetti,M.,2000.WeavingTheWeb:ThePast,presentandFutureof theWorld
WideWeb,London:Texere.
Berners-Lee,T,1989.InformationManagement:AProposal,CERN.
Birmingham,A,2014.WebAnalytics-MorethanGoogleAnalytics,FirstEd.2014.
Booth,D.,Jansen,B.,2009.AReviewofMethodologiesforAnalyzingWebsites.
Botégal, B, 2016. Definition and history of Digital analytics. Available at:
http://en.bricebottegal.com/definition-history-web-analytics/[AccessedJanuary14,2016]
Burby, J., & Brown, A., 2007. Web Analytics Definitions - Version 4.0. Available at:
http://www.digitalanalyticsassociation.org/standards[AccessedNovember22,2015].
Businessculture.org,2016.Availableat:http://www.businessculture.org[AccessedJanuary12,2016].
Carpenter,B.,2013.NetworkGeeks:HowTheyBuiltTheInternet,London,Springer-Verlag.
ClickTale,2014.Availableat:http://www.clicktale.com[AccessedOctober02,2015].
Clifton,B.,2010.E-MetricsWhitepaperUnderstandingWebAnalyticsAccuracy,Wiley,NewYork.
Clifton,B.,2012.AdvancedWebMetricswithGoogleAnalytics(3rded.).Indianapolis,IN:JohnWiley&
Sons.
Clifton, B., 2015. Should you pay 150.000$ for Google Analytics Premium. Available at:
https://brianclifton.com/blog/2015/10/27/should-you-pay-150000-for-google-analytics-premium/
[AccessedNovember4,2015].
Clutch, 2015. Available at: https://clutch.co/web-designers/resources/web-2015-small-business-
survey[AccessedNovember18,2015].
Cutroni,J.,2015.GoogleAnalytics,FirstEd.2010.
DigitalAnalyticsAssociation,2014.Availableat:http://www.digitalanalyticsassociation.org[Accessed
September05,2015].
Dlodlo,N.,Dhurup,M.,2010.BarriersToE-MarketingAdoptionSmallAndMediumEnterprisesInThe
VaalTriangle.
DominikHeller
84 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
Doran,G.T,1981.There’sS.M.A.R.T.waytowritemanagement’sgoalsandobjectives.
Ezzedin,A,2014.TrackingProductJourneyfromCartingtoPurchasing.
Ekpu,V.,2016.DeterminantsofBankInvolvementwithSMEs.
Fielding, R, Gettys, J., 1999. Hypertext Transfer Protocol. Available at:
http://www.ietf.org/rfc/rfc2616.txt[AccessedatDecember07,2015]
Freeman,M.B.,Hyland,P.,2003AustralianOnlineSupermarketUsability.TechnicalReport,Decision
SystemsLab,UniversityofWollongong.
GoogleAnalytics,2016.Availableat:https://analytics.google.com[AccessedFebruary01,2016].
Hausmann,V,Williams,S.,Schubert,P.,2012.DevelopingaFrameworkforWebAnalytics.
Halsall,F.,2005.ComputerNetworkingandtheInternet(5thed.).England,Addison-Wesley.
Hassler,M.,2009.WebAnalytics:Mektrikenauswerten,Besucherverhaltenverstehen,Websiteopti-
mieren1ed.,mitp.Heidelberg.
Hassler,M.,2010.WebAnalytics:Metrikenauswerten,Besucherverhaltenverstehen,Webseitenop-
timieren2nded.,mitp.Heidelberg.
HMTreasury,2010.CompetitionCommission,2002, volumes1–4 fordetailsof a reportonbanking
servicestoSMEsinUK.
IBIResearch,2011.Availableat:http://www.ibi.de/files/SEPA_in_Germany-a_snapshot.pdf[Accessed
December01,2015].
Internet Live Stats, 2016. Available at: http://www.internetlivestats.com/total-number-of-websites/
[AccessedDecember09,2016].
Kaushik,A.,2007.Webanalytics:anhouraday.Indianapolis,IN:Sybex.
Kaushik,A.,2009.WebAnalytics2.0:TheArtofOnlineAccountabilityandScienceofCustomerCen-
tricity(1sted.).Indianapolis,IN:JohnWiley&Sons.
Kaushik,A.,2012.WebAnalytics2.0:TheArtofOnlineAccountabilityandScienceofCustomerCen-
tricity(2nded.).Indianapolis,IN:JohnWiley&Sons.
Kennerley,M., Neely, A.,2002. "A framework of the factors affecting the evolution of performance
measurementsystems."Internationaljournalofoperations&productionmanagement.
LawalWA, IjaiyaMA, 2007. Small andmedium scale enterprises access to commercial banks,Asian
EconRevJIndianInstEcon.
Leiva, L. A. and Vivó, R. 2013.Web Browsing Behaviour Analysis and Interactive Hypervideo. ACM
TransactionsontheWeb,7,4,article20.
Lovett,J.,2009.USWebAnalyticsForecast,2008To2014.Cambridge,MA:ForresterResearch.
References
©2016UniversityofKoblenz-Landau,InstituteofISResearch 85
Lunametrics.com, 2015. Available at: http://www.lunametrics.com/blog/2015/09/30/comparing-
google-analytics-premium-and-google-analytics/[AccessedOctober10,2015].
Mangold,B,2015.LearningGoogleAdWordsandGoogleAnalytics,FirstEd.2015.
McClure, Dave, 2005. Available at: http://blog.twoodo.com/288/best-intro-guide-to-saas-startup-
metrics/[AccessedOctober22,2015].
McFadden, C.,2005. Optimizing the Online Business Channel with Web Analytics. Available at:
http://www.Webanalyticsassociation.org/en/art/?9[AccessedAugust20,2015].
Neely,A.,1998)."Threemodelsofmeasurement:theoryandpractice.”,InternationalJournalofBusi-
nessPerformanceManagement.
Neely, A., 2002. Business performance measurement: Theory and Practice, Cambridge, University
Press.
OECDpublication,2009.:Availableat:http://www.oecd.org/cfe/smes/2090740.pdf[AccessedOctober
27,2015].
Parmenter, D., 2010. TheNew Thinking on KPIs, part 2 of 4. Available at:www.strategydriven.com
[AccessedNovember12,2015].
Patel,JuricC.,2001.AnApproachtoDevelopingaWebsiteforSME.
Peterson,E.,&Carrabis,J.,2008.MeasuringtheImmeasurable:VisitorsEngagement.WebAnalytics,
Available at:
http://www.webanalyticsdemystified.com/downloads/Web_Analytics_Demystified_and_NextSta
ge_Global_-_Measuring_the_Immeasurable_-_Visitor_Engagement.pdf[AccessedJanuary02,2016].
Phippen, A., 2004. A practical evaluation of Web Analytics, Available at:
http://www.emeraldinsight.com/doi/full/10.1108/10662240410555306[AccessedFebruary17,2016].
PIWIK,2016.Availableat:http://piwik.org[AccessedatDecember15,2015].
Reese,F.,2008.WebAnalyticsDamitausTrafficUmsatzwird:DiebestenToolsundStrategien,Busi-
nessvillage,Göttingen.
Ross,J,2009.Network:Know-How.SanFranciso,NoStarchPress.
Singal,H.,Kohli,S.,Sharma,A.,2014.WebAnalytics:StateOfArt&LiteratureAssessment.
Schneider,A.,2010.UsingWebAnalyticsDatatosupportSocialSoftwareUsers.UniversitätMünchen.
Tappenden,A.,Miller,J.,2009.Cookies:ADeploymentStudyandtheTestingImplications.
Tonkin,S.,Whitmore,C.&CutroniJ.,2010.PerformanceMarketingwithGoogleAnalytics,WileyPub-
lishing,Inc.,NewJersey.WebTrends(2014).Availableat:http://webtrends.com[AccessedSeptember
12,2015].
DominikHeller
86 ©2016UniversityofKoblenz-Landau,InstituteofISResearch
Wu, J. et al., 2009. Using Web-Analytics to Optimize Education Website. ICHL 2009, LNCS 5685,
Springer,pp.140–164.
Wolfe, M, 1878. Available at: http://www.theirishstory.com/2016/01/13/margaret-wolfe-
hungerford/#.VrFQo2eq78s[AccessedJanuary15,2016].
W3Tech.com, 2016. Available at: http://w3techs.com/technologies/details/ta-googleanalytics/all/all
[AccessedJanuary9,2016].
HuX.,CerconeN.,2002.AnOLAMapproachforWebUsageMining,Prod.o2002IEEEFuzzySystems.
YCombinator,2016.Availableat:http://www.ycombinator.com[AccessedJanuary11,2016]
Zheng,G.,Peltsverger,2014.WebAnalyticsOverviews.
Zoompf,2013.Availableat:https://zoompf.com[AccessedSeptember01,2015].
Zumstein,D.,Züger,D.&Meier,A.,2011.WebAnalyticsinUnternehmen,FELDMGmbH
©2016UniversityofKoblenz-Landau,InstituteofISResearch 87
AppendixA–FrameworkandGuidelineWebsite
ThefollowingpagesshowthewebsiteforthenewGoogleAnalyticsFrameworkandGuidelines.This
websitecanbeusedtopresentallresultstointerestedSMEsinordertotaketheresearchfurtherand
receivefeedbackfortheresearchprojectresults.Thewebsiteisbuiltonaresponsivedesigntoensure
simpleaccessfromeverywebbrowserormobilewebbrowser.
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AppendixB–KPImeetsMetrics:DashboardandCustomisedReports
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AppendixC–GoogleAnalyticsDimensionsandMetricsOverview
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