MarketResearchCourse-QuantitativePart英文.ppt

OverviewofResearchMethodologiesQUANTITATIVERESEARCHMETHODS TableofContents PartI IntroductionToBasicQuantitativeMethodsPartII CaseStudy BrandTrackingStudyDemonstrationUsingSPSS14 0SampleSizeCalculationOverviewofDataComposition Scale Typesofvariablesetc ExploratoryDataAnalysisBasicStatisticalTestsToDetectDifferencesBetweengroupsofdoctorsBetweenbrandsStatisticalTestsToDetectRelationshipsStatisticalHypothesisTestingPartIII IntroductionToAdvancedQuantitativeMethodsFactorAnalysisPerceptualMappingSegmentationAnalysis PARTIINTRODUCTIONTOBASICQUANTITATIVEMETHODS DefinitionofQuantitativeResearch Quantitativeresearchisnumericallyorientedandofteninvolvesstatisticalanalysis Themainrulewithquantitativeresearchisthateveryrespondentisaskedthesameseriesofquestions Thisapproachisverystructuredandnormallyinvolveslargenumbersofinterviews TypesofData Numericaldatagenerallycomeintwokinds MEASUREMENTDATA sometimescalledquantitativedata Onanaveragehowmanypatientswithdiabetesdidyoutreatinamonth patientsCATEGORICALDATA alsoknownasfrequencydataorcountdata Whatisyourspecialty Pleasetickone1 Cardiologists 2 Endocrinologists3 Nephrologists4 InternalMedicine5 GeneralPractitioner ScalesofMeasurement Measurementisfrequentlydefinedastheassignmentofnumberstoobjects INTERVALSCALEinwhichwecanspeakofdifferencesbetweenscalepointsHowmanyyearsyouhavebeeninpractice yearsORDINALSCALEwhichorderspeople objectsoreventsalongsomecontinuumHowsatisfiedyourarewithbrandA 1 Notatallsatisfied2 Notsatisfied3 Neutral4 Satisfied5 VerysatisfiedNOMINALSCALEisnotreallyascaleastheydonotscaleitemsalonganydimensionWhatisyourgender Pleasetickone1 Male2 FemaleInwhichstatedoyounormallypractice Pleasetickone1 ACT2 NSW3 NT4 QLD5 SA6 TAS7 VIC8 WA CompositionofData Adatatypicallyconsistsof VARIABLESarepropertiesorcharacteristicsofastudyortherapytypeetc e g attitudinalstatements productcharacteristics functionalandemotionalattributes patientflowetc CASEStypicallycorrespondtorespondentse g Doctors Patients Pharmacistsetc RESPONSESarevaluesassignedtoeachvariablee g 1 2or3etc TypesofVariables Thevariablesfallundertwotypesdependingondifferentvaluesassigned DISCRETEVARIABLESinwhichthevariablecantakeononlyoneofarelativelyfewpossiblevaluese g Gender Location Specialtyetc CONTINUOUSVARIABLESinwhichthevariablecouldassumeanyvaluebetweenthelowestandhighestpointsonthescalee g Numberofpatientstreated DiagnosticparameterslikeBP LDL C HDL C HbA1cetc Wecanalsodistinguishbetweendifferenttypesofvariablesinanadditionalway DEPENDENTVARIABLEisthevariableofinterestorwhosevalueswewishtopredicte g OverallbrandsatisfactionINDEPENDENTVARIABLES alsoreferredasexplanatoryvariablesorpredictorvariables arethevariableswhichinfluencesoraffectsdependentvariable e g Importancestatements productcharacteristics INFERENTIALInferentialstatisticsareconcernedwithusingSAMPLEStoinfersomethingaboutPOPULATIONS Populationcanrangefromarelativelysmallsetofnumbers Whichiseasilycollected toaninfinitelylargesetofnumbers whichcanneverbecompletelycollected HenceweareforcedtodrawonlyaSAMPLEofobservationsfromthatPOPULATIONRangeofstatisticaltestsareavailabletochoosefromwhichdependsontheobjectivesofthestudy TypesofDataAnalysis EXPLORATORYExploratorytechniquesareusedforpresentingdatainvisuallymeaningfulways Also thesetechniquesareusedforinitialdatascreeningpurposes FrequencydistributionsHistogramsBoxandWhiskerplotsStemandLeafplotsMissingvaluesOutliers DESCRIPTIVEDescriptivestatisticaltestsareusedtodescribeasetofdata MeanMedianModeSumMeasuresofdispersionMinimumMaximumRangeVarianceStandarddeviationStandarderrorDistributionKurtosisSkewness ProbabilitySamplingMethodsThismethodgivesallmembersofthepopulationaknownchanceofbeingselectedforinclusioninthesample MethodsofSampling Non probabilitySamplingMethodsThequotas quotasampling withinsubgroupsaresetbeforehandusuallyproportionsaresettomatchknownpopulationdistributions Interviewersthenselectrespondentsaccordingtothesecriteriaratherthanatrandom Thesubjectivenatureofthisselectionmeansthatonlyaboutaproportionofthepopulationhasachanceofbeingselectedinatypicalquotasamplingstrategy SampleSizeCalculationGeneralRules Whatmarginoferrorcanyouaccept Themarginoferroristheamountoferrorthatyoucantolerate Lowermarginoferrorrequiresalargersamplesize5 isacommonchoiceWhatconfidenceleveldoyouneed Theconfidencelevelistheamountofuncertaintyyoucantolerate Higherconfidencelevelrequiresalargersamplesize Typicalchoicesare90 95 or99 Whatisthepopulationsize Howmanypeoplearetheretochooseyourrandomsamplefrom Whatistheresponsedistribution Foreachquestion whatdoyouexpecttheresultswillbe Ifthesampleisskewedhighlytooneend thepopulationprobablyis too Ifyoudon tknow use50 Thisgivesthelargestsamplesize Themostconservativechoiceis50 SampleSizeCalculationInfinitevs FinitePopulation INFINITEPOPULATIONThesamplesizedoesnotchangemuchforpopulationslargerthan20 000FINITEPOPULATIONInpharmaceuticalmarketresearchstudieswecommonlyencounterasmallpopulation i e finitepopulation incertaintherapyareassuchasOncology Nephrology Neurologyetc InsuchsituationsthesamplesizecalculationmustincorporateFINITEPOPULATIONCORRECTION FPC Withsmallpopulations wemayhavetointerviewasignificantproportionofthepopulationtoachievestableestimatesHowever onceapopulationreachesabout5000ormore wecangenerallyignorethefinitepopulationcorrectionfactor asithasverysmallimpactonsamplesizedecisions SamplingDesignSourcesofError BIAS occurswhenflawedsamplingprocedureisused Biascannotbemeasured andtheselectionofalargersampledoesnotcorrectforthefactthatthesampleisbiasedSAMPLINGERROR occurswhensamplesofrespondentsdeviatefromtheunderlyingpopulation Ifwehaveusedrandomsamplingmethod anysamplingerrorisduetoachance Withrandomsampling wereducesamplingerrorbyincreasingthesamplesize MEASUREMENTERROR arisesfrommanysourcesdatacollectionmethodsinterviewersqualityandexperiencerespondentsunderstandingofthequestionnairetypeofscalequestionnairedesigncontextofdatacollection StatisticalTestingParametricvs Non ParametricTests Theselectionbetweenaparametricornon parametrictestsdependonwhetherthedatabeinganalysedaresampledfromapopulationthatareNORMALLYDISTRIBUTED alsoreferredasGaussiandistribution ornotPARAMETRICSTATISTICALTESTS areusedwhennormalityismetNON PARAMETRICSTATISTICALTESTS ordistributionfreetests areusedwhennormalityisnotmetAnormaldistributionisasymmetric unimodaldistribution and bellshaped StatisticalTestingPropertiesofNormalDistribution 68 2 oftheresponsesfallwithinastandarddeviationof 1Themean medianandmodeareequalGenerallythedistributiontendtobecomenormalforasamplesizeofgreaterthan30 Mostmarketingquestions i e statistical fallroughlyintooneoftwooverlappingcategories DIFFERENCESIsthereanydifferencebetweenendocrinologistsandGPsintermsofoverallsatisfactionofdiabetestreatment Isthereanydifferencebetweenbrands RELATIONSHIPSIsthereanyrelationshipbetweenimportanceofproductattributesandoverallperformanceofagivenbrand StatisticalTestingDifferencesvs Relationships StatisticalHypothesisTesting Ahypothesistestusuallyderivesfromapriorresearchhypothesis usuallyQUALITATIVEstage statingthatarelationshipexistsbetweentwoormorevariables ortwoormoregroupswillbedifferentonsomecharacteristics Forexample Hypothesis Amongdoctors orthopaedicsbelieve Inpost menopausalwomenwithosteoporosis VitaminDinadequacyiswidespreadandneedstobecorrected thanGPsTotestthisresearchhypothesisstatistically wewouldformulatetwostatisticalhypothesis Nullhypothesis themeanfororthopaedicsontheabovestatementisequaltoorlessthanthemeanforGPs Expressedinsymbols Ho ortho GPAlternativehypothesis themeanfororthopaedicswillbegreaterthanthemeanforGPs Expressedsymbolically H1 ortho GPThetwohypothesesaremutuallyexclusive Theycannotbothbetrue Asaresearcherperformingastatisticaltest wemustultimatelymakeoneoftwodecisionsrelativetothenullhypothesisChoosingastatisticaltesttotesttheresearchhypothesisOne tailedtest isusedifweknowthedirectionofthehypothesiseddifferencei e OrthopedicsbelievethanGPsTwo tailedtest isusedifwedonotknowthedirectionofthatdifference ChoosingaBasicStatisticalTest DIFFERENCES RELATIONSHIP NOTE Theseanalyseswillbedemonstratedwiththehelpofacasestudy PARTIICaseStudy BrandTrackingStudy DemonstrationUsingSPSS14 0SampleSizeCalculationOverviewofDataComposition Scale TypesofvariablesExploratoryDataAnalysisBasicStatisticalTestsToDetectDifferencesBetweengroupsofdoctorsBetweenbrandsStatisticalTestsToDetectRelationshipsHypothesisTesting PARTIIIINTRODUCTIONTOADVANCEDQUANTITATIVEMETHODS MULTIVARIATETECHNIQUES CaseStudyFactorAnalysisPerceptualMappingSegmentationAnalysis MultivariateTechniques Multivariatetechniquesarebroadlygroupedintotwocategories INTERDEPENDENTtechniquesareusedtoeithergroupingsimilarcustomersorvariablesToidentifytargetcustomersegments groupingcustomers Toidentifyunderlyingattitudinal emotional themes or factors groupingvariables DEPENDENTtechniquesareusedtostudytheeffectofanumberofvariablesononeormorevariablesToidentifydriversofbrandsatisfaction satisfactionvs product attitudinal emotionalattributes ChoosingaMultivariateTechniqueInterdependentTechniques NOTE Theseanalyseswillbediscussedindetail ChoosingaMultivariateTechniqueDependentTechniques FactorAnalysis FactoranalysisisanexploratorydataanalysistechniqueoftenusedasadatareductionmethodFactoranalysistransformsasetofcorrelatedvariablesintofewerconceptualdimensionsknownas factors Factornamesareprovidedbyanalyst basedondominantorcommonattributeswithinthebundleofattributes Samplesizeconsiderations Thecommonruleistohaveatleast10 15responsesperattribute FactorAnalysisCaseStudy MARKETINGAPPLICATIONAmarketerhavemeasured30differentPERCEPTIONATTRIBUTESthatareconsideredimportanttoaspecifictherapyareaThemarketerwantedtoidentifyunderlyingthemesorfactorswithinthose30attributesratherthanevaluatingindividualattributes TOOLBasedonintercorrelationsamongthese30ATTRIBUTES factoranalysisreducedthemto7THEMESORFACTORSThishashelpedthemarketertofocusanddevelopmarketingstrategyaccordinglyThishasalsohelpedthemarketresearchertoreducethenumberofattributestobeincorporatedinthequestionnaireforfuturestudies FactorAnalysisCaseStudy QUESTIONNAIREPleaseindicateyourlevelofagreementwitheachofthefollowingstatementsastheyapplytothetreatmentofa specifictherapyarea Usingthe5 pointscaleshown where 1 means stronglydisagree 5 means stronglyagree pleasecrossthemostappropriateresponse Attribute1Attribute2 Attribute30 FactorAnalysisIdentificationofUnderlyingThemes The30perceptionstatementsratedbyrespondentsaresimplifiedinto7keythemesorfactorsusingfactoranalysis FactorAnalysis FactorAnalysis The30perceptionstatementsratedbyrespondentsaresimplifiedinto7keyfactorsorcomponentsusingfactoranalysis FactorAnalysisIdentificationofUnderlyingThemes PerceptualMapping PerceptualmapisavisualrepresentationofTWOdatasetssuchasdifferentbrandsandproductattributesdifferentbrandsanddemographictraitsdifferentcorporationsandkeyimageattributesetc PerceptualmapisproducedbyusingTWOdifferenttechniques CorrespondenceanalysisMultidimensionalscaling PerceptualMappingCaseStudy MARKETINGAPPLICATIONAmarketerwantedtoidentifyhow5DIFFERENTBRANDSareperceivedon8KEYATTRIBUTESasappliedtoaspecifictherapyareaTOOLCorrespondenceanalysisproducedaperceptualmapwhichpositionsbrandsagainstattributesonatwodimensionalmapThishashelpedthemarketertoUNDERSTANDBRANDPOSITIONING PerceptualMappingCaseStudy QUESTIONNAIREBasedonyourknowledgeandexperiencewiththebrand pleaseidentifypresenceorabsenceofbelow8attributesthatabrandmayhaveBrand1Brand2Brand3Brand4Brand5Attribute1Attribute2 Attribute8 PerceptualMapping Brandsvs Attributes BrandE BrandA BrandB BrandD BrandC Worksfast Improvesbonequality Preventshipfractures Safeforlong termuse Convenience Preventsvertebralfractures Welltolerated Therelativeperceivedpositioningofthevarious5brandsvs 8productattributes IncreasesBMD SegmentationAnalysis Thepurposeofsegmentationanalysisistoformgroupsofrespondents doctors patientsetc suchthat withrespecttoclusteringvariable s eachgroupisashomogenousaspossibleandasdifferentfromtheothergroupsaspossible ThereareTWOmainroutestosegmentation Apriorisegmentation referredasOUTSIDE IN ThesegmentsareASSUMEDbefore handsuchasbasedondemographics geographicetc Cluster basedsegmentation INSIDE OUT ThesegmentsareNOTASSUMEDbefore handbutidentifiedbasedonhomogeneityexhibitedbyrespondentsoncertainparameterssuchasbehaviouralattributes perceptionstatements attitudetowardsnewdrugintroductionetc NOTE Thiswillbediscussedindetail VARIABLESELECTIONisthesinglemostcrucialstepinclusteranalysisTheselectionofvariabledependsonthetypeofbusinessobjectivethatwehaveinmind Wemaywanttosegmentrespondentsbasedon benefitsegmentation or need basedsegmentation or attitudinalsegmentation etc ThevariablesthatmeetourbusinessobjectivearechosenforclusteranalysisThesegmentsarethenprofiledtoidentifydistinctdifferencesbetweenthesegmentsThenumberofclusters i e segments dependsonthefollowingcharacteristics Meaningfulness HowsensibleormeaningfulthesegmentsareAccessibility HoweffectivelycanthesegmentbereachedSustainability ThedegreetowhichthesegmentsarelargeorprofitableenoughActionability Thedegreetowhichtheycanhaveprogramsdesignedforthem SegmentationAnalysisGeneralRules SegmentationAnalysisCaseStudy MARKETINGAPPLICATIONPurposeofthesegmentationstudywastoidentifygroupofdoctorswhoaremostlikelytoadopt prescribeaspecificnewdruginorderto DisproportionatelyinvestagainstthelikelyadoptersofthenewdrugatlaunchIdentifywhichsegmentwouldbemostvaluableto CompanyA bothintheshortandlongtermDevelopvaluepropositionsandmessagesthatwillresonatewitheachsegmentUnderstandwhichmarketingmixwouldbemosteffectiveforeachsegment MeaningfulnessandActionabilityCriteria Anoptimalsegmentationshouldbetheonethatismost Meaningful showsstrongestdifferencesinourmeaningfulnesscriteriaActionable segmentsareidentifiable message able andreachablethroughourlevers Meaningfulness Actionability Howwellmultiplesegmentsdifferentiatedagainstthefollowinge g ProductAdoptionPatterns earlyvs lateTestingpreferencesindiagnosingImportanceattributes Arethesegments Message able MessageresonanceandhotbuttonsReachable LearningPreferencesandPatterns SegmentSize Asolutionfocusingonlikelihoodtoprescribethenewdrugrosetothetopofthedatasettoprovideatangiblesegmentationscheme SophisticatedTreater TraditionalTreater SegmentProfile SegmentProfile SegmentIdentification Thefirststepofimplementationrequiresustodevelopatypingtooltocategorisephysiciansintooursegments Weareprimarilyconcernedwithhowaccuratelywecancategorizethesesegments Development Adoption Howaccuratelycanwedetermineaphysiciansegment ofquestionsintypingtool Whatisthebestwaytoinco