LSM551 Transcripts - Amazon Simple Storage Service (S3) · Transcript: Attributes and Levels Many products and services can be thought of as bundles of attributes. An example is the

LSM551:MeasuringCustomerPreferencesSamuelCurtisJohnsonGraduateSchoolofManagement,CornellUniversity

© 2015 eCornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners.

1

LSM551 Transcripts

Transcript: Course Introduction

Successfulbusinessesdeliverproductsandservicesthatmeetrealcustomerneeds.Thekeytosuchbusinessesisthattheyhaveadeepunderstandingofcustomerpreferences.Thiscourseintroducesyoutoconjointanalysis,whichisatimetestedandversatiletechniquetoincorporatecustomerpreferencesintobusinessdecisions.Eachyear,conjointanalysisisusedinthousandsofbusinesssituationsaroundtheworld.

Thecoursebeginswithadiscussionofalternativeapproachestomeasurecustomerpreferences.Wethentalkabouthowconjointanalysisworks,howitcompareswithothertechniques,theprosandconsofdifferenttypesofconjointanalysismethods,thekindsofdecisionsconjointanalysisishelpfulfor,andlimitationsofthetechnique.Wealsogothroughdetailedexamplesofhowdatagatheredviaconjointanalysisareanalyzedandinterpreted,andhowthesedatainformbusinessdecisions.

Thecoursewillgiveyouacomprehensiveunderstandingofawidelyusedmarketingresearchtechnique.Aftertakingthiscourse,youcanidentifysituationsinwhichconjointanalysisisrelevanttoyourbusiness,aswellaseffectivelycommissionandbuymarketresearchfromvendors.Withtheadventofbigdata,itisnowpossibleformanybusinessestotestthefindingsofsmall-scaleconjointexperimentsbyrunningreal-worldexperiments,fieldexperiments,quickly.Thisdevelopmenthasfurtherincreasedtheapplicabilityofconjointanalysis.

Transcript: Author Welcome IamSachinGupta,aprofessorofmarketingattheSamuelCurtisJohnsonGraduateSchoolofManagementatCornellUniversity.

Thiscourseisaboutmarketresearchapproachesforlearningwhatcustomersreallywant.Thecourseintroducesyoutoconjointanalysis,atime-testedandversatiletechniquetoincorporatecustomerpreferencesintobusinessdecisions.Wediscussprosandconsofconjoint



2

relativetootherapproaches.Wealsodelvedeepintohowtoconductconjointanalysis,howtogatherandanalyzedata,andhowtousethedatatomakebetterdecisions.Iwillalsointroduceyoutoindustryexpertswhowilltalkabouthowtheyhavesuccessfullyusedconjointanalysistosolvereal-worldproblemsthattheyfaced.

Transcript: Consumer Preference Matters

Solet'stalkfirstaboutwhyitisimportanttomeasureconsumerpreferences.

Onereasontodothisisthatitallowsthefirmtoofferproductsandservicesthatmeettheneedsofconsumersbetterthancompetitorscanmeetthoseneeds,therebyallowingthefirmtogethighersales,marketshareandprofits.Inparticular,inmanyindustriesfirmshavetoconstantlyinnovateandoffernewproductsandservices.Andsotheyneedtohaveagoodunderstandingofconsumerspreferences.

Thereareseveralreasonswhyfirmsmightfinditadvantageoustoinnovatecontinuously.Onereasoniswhatthiscalledpioneeringadvantage.Theideaofpioneeringadvantageisthatithasbeenfoundthatthefirmthatentersthemarketfirsttheearlyentrytothemarket,hassustainedmarketshareadvantagesrelativetolaterentrancetothemarket.Insomecasesthisadvantageextendstoprofitsaswell.

Asecondreasonforfirmstoinnovateistheconceptofproductlifecyclewhichisthefollowingidea.Ithasbeenfoundthatifyoulookatthesalesofmanyproductcategories,fromthetimewhenthecategorywasfirstintroduced,thenyoufindthatthereisadistinctpatterninthosesalesovertime.Initiallythereisa,afterintroductionthereisaphaseofgrowth,atsomepointthegrowthslowsdownandthemarketreachesmaturity,andthenthemarketgoesintodecline.Sowhenafirmfindsthatitsproductsareeitherinthematurityoratthedeclinephaseoftheproductlifecycle,ithastoworryaboutreplenishingitssalesandprofitsthroughanewproductintroductions.

Athirdreasonisinthecaseofmarketsthathavepatents.Withthepatentthefirmhasprotectionavailabletoitandisabletoreachsalesandmarketshares,butwhenthepatentcomestoanend,thennewproductsenterintothemarketwhichareessentiallycopycatsofthisoriginalproductandthosecopycatserodethemarketshareandprofitsoftheinitialentrance.Anexampleofthisisprescriptiondrugmarketswherefirmsthatinventnewmoleculeshavepatentprotectionforacertainperiodoftime,butwhenthatpatentcomesto



3

anend,geneticdrugsenterthemarketatlowerprices,whichthenreducesthemarketshareandprofitsoftheoriginalentrance.

Sofirmshavetofacetwoimportantstrategyquestionswithrespecttonewproductdevelopment.Oneiswhatiscalledthe"goorthenogo"decision.Thefirmhasdevelopedanewproductorserviceandnowneedstodecidewhethertointroducetheproductinthemarket.Andsoitneedstoforecastwhattheperformanceofthenewproductislikelytobe.Asecondtypeofquestionisadesignquestionwhichis:Howcanwedesignorre-designproductsandservicessoastoimprovethelikelihoodofmarketsuccess?

Letmenowtalkaboutacoupleofexamplesofproductdesignquestions.Thefirstoneisinthecontextofafirmthatsaleslaptops,oneofthekeyfeaturesofalaptopistheweightofthelaptopandtheformofinterestandunderstandingconsumerpreferencesaroundtheweightofthelaptop.Forinstancehowmuchisthecustomerwillingtopayforanultralightlaptop,whichisalaptopthatweightstwotofourpounds,asagainstalightlaptopwhichweightsfivetosevenpounds.Ortakethecaseofafirmthatoffersacreditcard,andwantstounderstandhowconsumerstradeofffeaturesofthecreditcard,suchasthebrandnameofthecreditcard,theinterestrate,andthecreditlimit.

Buttherearetwobroadwaysforanalyzingconsumerpreferencesandforforecastingnewproductperformancefromthemarketresearchpointofview.Oneiscalledconcepttestandthesecondiscalledconjointanalysis.Andwe'llspendalotoftimeinthiscourseonconjointanalysis.Letmestartwiththeconcepttest.

Sowhatistheconcepttest?Inaconcepttest,consumersinamarketresearchstudyarepresentedwiththeproductorserviceidea,whichistheconcept.Andtheyaredirectlyaskedfortheirreactiontothisconcept.Thereactionisusuallymeasuredintheformofapurchaseintentonthescale,andthescalemightbeafivepointorasevenpointscale.Aconcepttestisusefulbecauseitprovidesarelativelyquickreadonthemarket'sacceptanceofaproduct.Therearetwodifferentwaystopresenttheconcept.

Onewayiswhatiscalledthecoreideainwhichtheconsumerinthemarketresearchstudyisinformedaboutthekeybenefitthattheproductorservicewouldoffertotheconsumerandthecostatwhichthisbenefitwilloccur.

Thesecondwaytodothisiswhatiscalledthepositioningconcept,whereinadditiontothecoreideatheconsumerisinformedaboutsomeofthemarketingelementssurroundingtheproduct.Suchasthebrandname,thepackaging,theadvertisingmessageandsoon.Inotherwordstheproductconceptisshowninapersuasiveformwhentheconsumers'reactionisobtainedtoit.



4

Transcript: Limitations of Concept Tests

Soconcepttestsareveryuseful,buttheyhaveakeylimitationandthatisthatiftheconceptisratedpoorly,wedon'tlearnalotabouthowtoimprovetheconcepttoincreaseconsumeracceptance.Inotherwords,thereisn'talotofdiagnosticinformationthatwegetfromaconcepttest.

Conjointanalysis,whichisthealternative,wasdesignedtoovercomethisfundamentallimitationofconcepttests.Inconjointanalysis,were-orientourselvesnottothinkaboutacceptanceoftheproductconcept,buttogaininsightsintotheunderlyingconsumerpreferencesunderlyingtheconcept.Soconjointanalysisprovidesnumericalmeasuresofconsumers'valuesystems(whichisreallyadescriptionofhowmuchconsumersvaluedifferentattributesandthelevelsofthoseattributes).Sothatoncewehavethesenumericalmeasureswecanpredicttheacceptanceofalternativenewproductconcepts,notjustoneconcept.

Transcript: Attributes and Levels

Manyproductsandservicescanbethoughtofasbundlesofattributes.Anexampleisthecomputerwhichhasmanyattributesandletmetalkaboutthreeoftheseattributes,ifI'mrecognizingthattherearemanyothersandthreeoftheseattributesarememory,sometimescalledRAM,theharddiskortheharddisksize,andthepriceofthecomputer.Now,foreachoftheseattributesorfeatures,wecanthinkaboutlevelsoftheseattributes.Let'stakememoryasanexample.Now,theattributememorycouldbeatlevelsforinstance2gigabytes,4gigabytes,or8gigabytes.Harddisksizecouldbeat500gigabytes,750gigabytes,or1000gigabytes.

Similarly,pricecouldbeatthreedifferentlevels,couldbeatmanylevelsbutthreeofeachcouldbe$800,$1000,or$1200.Nowonceweorientourselvestostartthinkingaboutproductsandservicesasbundlesofattributesandeachattributeaspotentiallybeingatseverallevels,wecanthenthinkaboutproductsasbeingcombinationsofonelevelofeachattribute.So,whatyouseehereisanexampleofacomputerwhichisdefinedonthreeattributes.Ithas2gigabytesofmemory,thereis500gigabytesofharddisksize,anditisatapriceof$1000andso,wehavebeenabletoconstructthehypotheticalcomputerbyjustpickingonelevelfromeachattribute.

So,whatdoesconjointanalysisgiveusonceagain?Itisintendedtogiveuscostumer'svaluesystemswhichisinotherwordstwodifferentmeasures.Oneis,whatistherelativeimportanceofeachattributetothecostumer?Sogoingbacktothecomputerexample,conjointanalysisis



5

totellus-isintendedtotellus,whatisthepercentageimportanceofmemoryrelativetoharddiskforagivenconsumer?Ifaconsumerforinstancerunscomputerapplicationsinwhichtheyhavetoloadverylargefileswiththememory,theymayplacehighimportanceonmemoryrelativetoharddisksize.

Thesecondmeasurethatwewantfromconjointanalysisisthevalueprovidedtothecostumerbyeachattributelevel.So,let'sthinkabouttheattributeharddisksize.Wehadthreelevelsofthat;500gigabytes,750gigabytes,and1000gigabytes.Now,wewouldexpectforanyconsumerthatthevalueassociatedwouldgoupaswegofrom500to750to1000gigabytes,butitmaybethecasethatthevaluegoesupsignificantlywhenwegofrom500to750gigabytes,buttheconsumerdoesnotcarealotbetween750and1000gigabyte.Thatistheutilityisaboutthesame.Theconsumerthinksthat750gigabytesisaboutalltheywouldeverneed.So,conjointanalysisisreallyintendedtogiveusthesetwoanswerstothesetwoquestions.Therelativeimportanceoftheattributesandthevalueassociatedwitheachlevelofeachattribute.

Transcript: Ask The Expert: Marco Vriens on Using Conjoint Analysis

Canyoudescribeatimewhenyouusedconjointanalysistosolveabusinessproblem?

I'veactuallydealtwithmanybusinessproblems,butIthoughtaboutitthismorning.ThiswasawhilebackforSonywhenIwasstillbackintheNetherlands,andProfessorDickWittinkwasactuallymythesisadviseratthattime.AndwegotengagedonaprojectwithSony,andtheproductwascarstereos.

AndthebusinessproblemthatthemarketingpeopleatSonyintheNetherlandshadwastwofold.Onewas,theywantedto--theyusuallygotbasicallyabunchofcarstereosshippedfromJapan,andtheyhadtosellthosecarstereosintheNetherlands.SothatwasalittlebitfrustratingforthembecausetheywantedtoreallyofferasetofproductsintheNetherlandsthatwouldappealtotheDutchaudience,andthecarstereosthatcameoverfromJapanwerenotalwaystherightonesfortheDutchmarket.

Thesecondproblemthattheyhadwasthattheywantedtobemorescientificindeterminingwhatthebestandoptimalfeatureswere,thatthecarstereoshouldhaveinthefirstplace.Andthat'showwegottoapplyingconjointtothatparticularbusinessproblem.



6

Couldyouexpandonthatexample?

Soifyouthinkaboutproductslikecarstereosandoftentimesproductsthataretechnologyproductswhichhaveoftentimes--thinkaboutcameras,carstereos,computers,etceteraandevenforotherproductsaswell.Oftentimesproductscanbedefinedonalargenumberofattributesandlevels,andsoifyouthinkaboutcarstereo,youcanthinkaboutwhattypeofcontroldoesacarstereohave,isittouchcontrolversusrotarycontrol,whatisthesoundquality,isitaverageorisittopnotchqualityanddoesithaveaCDindexyesorno,doesitgiveyouautomatictrafficupdates,whatisthepricelevel,itcouldbe$50,or$100,$200,whatisthebrandandwhatisthedesignofthecarstereo.

Ifyoulookatallthesefeaturesandalltheseattributes,thereareliterallyhundredsifnotthousandsofpotentialproductsthatyoucancreatefromtheseattributes,that'sasituation,ifyoureallythinkaboutit,thatyoucannotevaluateortestinaconceptvalueapproachbecauseitsjusttoomanyalternatives.Whatyoudowithconjointisyoubasicallyselectinasmartwayasmallnumberofprofilesthatyoucaneitheraskrespondentstoevaluatethatsmallsetofprofiles-theseprofilesthatarehypotheticalproducts.Youcaneitheraskrespondentstoevaluatethoseprofilesonebyoneoritcouldputtheminachartandaskpeoplelikeifyou-ifthesewouldbetheoptionsthatwouldbeavailablewhichwouldyouchoose.

Butbydoingthatbyaskingrespondentstorespondtothatsmallsetofprofilesandthenapplyingmodelingtogetdata,Iamabletoactuallygivemanygoodinformationaboutallthepotentialproductsthattheycanbuildwiththatattributesandlevels.AssoeventhoughIamonlyaskinglet'ssay,evaluationsonlet'ssay16hypotheticalproducts,Iamgoingtobeabletogivemanagementinsideintohowappealingthousandsofpotentialproductscouldbeandthat'sreallythekeybenefitofdoingconjoint.

Whywasconjointpowerfulinthatinstance?

Thereasonwhythiswassopowerfulwas,obviouslynumberone:becauseofthekeybenefit,asmallnumberofresponsesfromthesampleofrespondentsallowsyoutogetinsightintothethousandsofpotentialproductsthatSonycouldproducebasedontheattributesandlevels.That'sthekeybenefitnumberone.

Butspecifictothesituationandtootherbenefits,onewasbecauseweusedcomputer-aideddesigntechniquetocreatethehypotheticalcarstereos,peoplegotaveryrealisticviewonhowthecarstereoswouldactuallylooklike,andthatenabledSonytolookathowthedesignofthecarstereoactuallyinteractedwithsomeofthefeatures,andobviouslynowadaysthereisalotoftalkabouthowimportantthedesignofaproductis.Thismethodologyreallyallowedustocombinegettinginsightintothesofterelementslikedesign,andcombineitwiththeharderelementsofthefeaturesthattheproducthas.Sothatwasverypowerful.



7

ThethirdbenefitwasthatthesmallmarketingteamintheNetherlandshadtonegotiatewiththepowerfulmanagementteaminJapan,andunlesstheywerereallyabletocomeupwithsomeharddata,andsomevalidatedinsights,basicallytheyhadnoleverage,butwiththeseinsightstheJapanmanagementteamwasactuallyverysurprisedandveryimpressed,becausetheDutchteamwasabletoactuallycreateproductsthattheJapanmanagementteamhadnoteventhoughtof.

Whatcriticalinformationdidconjointyieldinthatexample?

Wellonceyougatherthisdataandyouestimateyourmodel,nowkeepinmindthatyouhave,wehadhundredsofrespondents.Andasmanypeopleknowinmarketing,noteverybodyhasthesamepreferences,somepeoplelikethisandsomepeoplelikesomethingelse.Andsoitbecomesalittlebitofacomputationalproblem.Butconjointallowsyoutodothat.

Onceyouhaveyourmodelestimated,Icancreateasimulationmodel.Andthatsimulationmodelallowsmetosay,okay,pickacrossthisgroupofconsumers,pickthefour,fiveprofilesandcarstereosthatwouldbemaximally,optimallyappealingtoyourtargetaudience.Sothatisahugebenefit.

Asecondbenefitisalsothatyoucangetsomeinsightintoiftherearemaybesomepotentialdiscretemarketsegmentsthatreallyhaveverydifferenttastesfromeachother,somaybeyouneedtotakethatintoaccount,anditcouldevenbethatifthosesegmentshavedifferentdemographics,thatcouldactuallymeanthatyoumayneedadifferentmarketingcampaignforthat.

Canyoudescribesomecommonpitfallstoavoidinconjointanalysis?

Thereareacoupleofcommonpitfallsandacoupleofmistakesthatpeoplewanttoavoid.Makesurethatyourexperimentaldesigncanactuallyestimatethemodelthatyouneed.NowadaysthereisalotofstandardsoftwareavailablefromSPSStoassortedsoftware.Thatsoftwareisingeneralverygood,butyoustillhavetobecarefulbecausetherearesituationswherethesoftwarecannothandleyourparticularproblemifyoudon'tstillgoaheadandcomeupwithastandarddesign,thatdesignmightnotbethecorrectdesignforyourproblem.

Ifyoudostartmodeling,thinkaboutsubstitutionandcannibalization,andifyoureallythinkthatthoseeffectsaregoingtotakeplaceinyourmarket,youneedtomakesurethatwhenyouanalyzethedatathatyouusetherightmodels,sothatyoucanactuallygetinsightintothosekindsofdynamics.Yeahandjustfollowthebestpracticesasmuchaspossible.Ithinkthisisreallyanareawhereyouwanttohirepeoplewhodothisasadayjobandwhodothisalotsothatyouknowthatthepeoplewhoaregoingtodothisprojectforyouwilldoitwell.

Whataresomebestpracticesthatyou’drecommend?



8

Ifyoutalkaboutbestpractices,intheareaofconjoint,thatisactuallyabigquestion.Ididalotofthinkingaboutitthismorning,becauseobviouslypeoplehavebeenwritinghundredsofarticlesandbooksaboutthedifferentmethodologies,andthereareIdon'tknowhowmanyvarieties.SoIwantedtobringitdowninacoupleofsimplerecommendationswithoutgoingintotoomuchdetail,becauseifyoureallywantthefullanswerIthinkyouneedtoreadapaperIthink.Butitboilsdowntothreeareas.

Youhaverecommendationsandbestpracticesintheareaofdatacollection,youhaverecommendationsandbestpracticesintheareaofanalysisandmodeling.Andyouhavebestpracticesintheareaofsimulationsandrecommendations.

Intheareaofdatacollection,therewereacoupleofthings,Ithink,peopleshouldthinkabout.Oneisthatbecauseyouareaskingpeopleabouttheirpreferencesandyouwanttopredictultimatelywhatthey'regoingtodointhemarketplace,Ithinkitisreallyimportantthatyoudesignataskthatisasrealisticandcloseaspossibletowhatpeopledointherealworld.Andthat'soneofthereasonswhyinourSonystudyweusedcomputerdesigningtechniques--toreallycreatethoseprofilesthatreallylookedlikerealcarstereos,becausethecloseryouaretotherealworld,thebetteryou'regoingtopredicttherealworld.

Thesecondbestpracticeisthatyouneedtodoapretesttomakesurethatpeopleunderstandthetaskandthatpeoplecanreallydowhatyou'rewantingthemtodo.Thentryevaluatingsixteenprofiles,thatisnotaneasythingtodo,especiallyifeachprofileisdefinedonbetweenfivetotenattributes,thatcanbeachallengingtask.Andsomeoftheproblemsthatwedealwitharemuchbiggerthanthis.Wegotapproachedbyacarcompanyandtheywantedtotestahundredattributes,soobviouslyyouneedtodo,youneedtodosomethingalittlebitmorespecialized.Butinthosesituations,itbecomesincrediblyimportantthatyouendupwithatask,thatyoureallyknowpeoplecandothis,andpeoplewilldothis.AndI'vebeeninthosesituationsthatIgeneratedataskfortherespondents,andIdidacoupleoftestinterviews,and,loandbehold,halfway,peoplearecheckingout.They'restillgivingyouanswers,butthey'renottheanswersthatyou'relookingfor.Soyouwanttomakesurethattheynotonlycandoit,buttheyalsowilldoit.

Intermsofexperiementaldesign,whichisobviouslyaveryfoundationalpartofcreatingaconjointstudy,thebestpracticeisreallytomakeuseofBayesianmethods.Idon'tknowifProfessorSachinGuptadiscussesBayesianmethodsinhisclass.Butthekeybenefitofthatapproachisthatitreallyallowsyouto,withfewerprofiles,getthesamequalityofinformation.Andthatoftentimesmeansthatyoumayonlyneedhalfoftherespondentsthatyouwouldhaveotherwiseneededinordertogetthesamequalityofinformation,soit'saverypowerful,

verypowerfulapproachtodesigningexperiments.Intheanalysisandmodeling,IwouldsaythecurrentstateoftheartIthinkisoneofhierarchicalbaseestimationmethods.



9

Andit'salso,Iwouldsay,usingnon-standardchoicemodels.Foralongtime,thestandardmodelofanalysiswasthelogitmodel,themultinominallogitmodel,andit'saverypowerfulmodel.Butinaconjointchoicecontext,itdoesnotalwaysworkasyouwantitto.Andoneofthereasonsforthatisthatthatmodelisnotreallyagoodmodeltocapturecannibalizationandsubstitutioneffects,andthoseeffectsareveryreal.Andsothinkabout,ifyouareSony,andyouarethinkingaboutdesigningaproductlineforyourcarstereos,thathasmaybe,Idon'tknow,eightortendifferentcarstereos,youwanttoknowthateachindividualproductthatyouputinyourproductlineishopefullystealingmarketsawayfromyourcompetition--butnotfromtheotherproductsinyourproductline,right?Soifyouwanttogetinsightintothosetypesofdynamics,that'swhereyouneed,Iwouldsay,moreadvancedchoicemodels,likethesource-of-volume-probitmodel.NowthatmaygoalittlebittoodeepforanMBAclass,butIjustwanttocallitoutthatifanybodyeverdoesastudyinthisarea,thattheyareawareofthefactthattherearethingsbeyondthemultinominallogitmodel.

NowthethirdareaIthinkisreallyimportant,andthathastodowithsimulationsandrecommendations,andletmejustmaybefocusonrecommendations.Ifyoudoaconjointstudy,theconjointstudy,theconjointmodelwillallowyoutodevelopamarketsimulation,andI'mprettysurethatProfessorGuptawillshowsomeofthesemarketsimulatorsinhisclass.Andthosearepowerfulintstrumentsbecausetheywillgiveyoupredictedmarketshares,andtheywilltellyouhowsuccessfulaproductcanbe.Butyouneedtobealittlebitcautious,andyouneedtobecautiousforanumberofreasons.

Oneis,intheconjointyouassumethatpeopleinthemarketare100%awareofthealternativesandare100%awareofthenewfeaturesofthenewproducts.Andthat'sobviouslynotalwaysthecase.Soyouneedtoadjustyourpredictionsdownward.

Theotherelementis,peopledon'talwaysdowhattheysaythattheywilldoinasurvey.Noweventhoughthisisnotaresearchthing,Ithinkitisstillveryimportant,becauseIhavebeeninsituationswherepeoplewoulddoaconjoint,theywouldgiveadvicetomanagement,andthenloandbehold,lateronintherealworlditturnedoutthattherealsuccessoftheproductwasnotnearlyasgoodaswhatwethoughtitwouldbe.Andsonow,havingsaidthat,Imustsaythatconjointoftentimespredictsreal-worldperformancereallywell,butyoustillneedtogivethatdisclaimerbecauseofthereasonsIjustmentioned.Iftheawarenessisnotwhereitneedstobe,ifpeopleonthesurveyarenotdoingwhattheysaythey'redoing,then,yeahyoujusthavetobecarefulwiththat,youhavetosettheexpectations,Ithink,inaveryrealisticway.Oratleasthavethatconversationwiththepeopleyou'regivingthatadviceto.

Transcript: How Conjoint Works



10

So,howdoesconjointanalysiswork?Inconjointanalysis,wedonotaskedconsumerstodirectlytellustheimportanceofattributesorthevaluetheyreceivefromeachleveloftheattribute.

Forinstance,wedon'tgototheconsumerandsay,"Howimportantismemorytoyou?"or"Howmuchdoyouvalue4gigabytesofmemoryrelativeto2gigabytesofmemory?"Insteadinconjointanalysis,weaskconsumerstoperformarealistictask:thatofprovidingoverallevaluationsofproductsandthenweusethoseevaluationstoinfertheconsumer'svaluesystem.

Forinstance,wewillshowaconsumeracomputerasawholeandaskthemtoevaluatethecomputer.Fromthoseevaluations,wewouldinfertheimportanceassociatedwiththeunderlyingattributes.Inprovidingthisevaluation,therespondentsmustsimultaneouslyconsiderboththegoodandthebadcharacteristicsoftheproductinmakingthejudgment.Thatiswemustmaketradeoffsandthentheygaveustheevaluationoftheproduct.Thealternativetoconjointisofcoursetoasktheconsumerdirectlytotellushowimportantistheattributeandhowmuchtheyvaluetheleveloftheattribute.

Now,ithasbeenfoundthatthereareseveralconcernswhenweaskconsumersdirectly.Oneconcernisthatconsumersoftentendtorateallattributesasbeingimportant.Sotheysay,"Memoryisimportanttome,harddiskisimportanttomeandthepriceisimportanttome."Andthat'saproblembecausetheyarenotreallymakingtradeoffsbetweenattributes.

Anotherconcernisthataskingconsumersdirectlyisanunrealistictask.Thisisnothowconsumersgoaboutmakingdecisionsabouttheproduct.Theydon'tlookatoneattributeatatimeandthinkabouthowimportantthatattributeis.Instead,theyareusedtoevaluatingproductsinaholisticway.

Thethirdconcernisthatsometimesconsumersareunwillingtotellustheirtruthfulevaluationofunderlyingattributesandhereisanexampleofthatlastconcern.AstudywasdoneatabusinessschoolwherethegoalwastounderstandhowMBAstudentsthinkabouttheattributesofjoboffers.So,ifyouthinkaboutjoboffers,thejobcouldhaveattributeorfuturesuchasthesalaryassociatedwithit,theregionofthecountryinwhichthejobislocated,thefunctionalareainwhichthejobis;ifit'samarketingjoboranaccountingjoborafinancejob,howmuchtraveldoesthejobinvolved,howmuchopportunitythereistoadvanceinthejobandsoonandsoforth.Andtheresearcherswantedtounderstandthepreferencesofthestudentsforthesedifferentfeaturesofjobs.Theydividedthestudentsintotwogroups

randomly.Thefirstgroupwasaskedtodirectlytelltheresearchershowimportanttheattributeswereandtheywereaskedtorankordereightfeaturesofthejobs.Thesecondgroupwasgivendescriptionsofjoboffers.Eachjobwasdescribedinallthefeaturesoftheattributes



11

andthentheresearchersinferredfromthatwhatmustbetheimportanceassociatedwiththefeaturesofthejobs.

So,let'sthinkaboutthedifferenceintheresultsbetweenthesetwogroupsintermsoftheimportanceoffeaturesandlet'sfocusononeparticularfeaturewhichisthesalary.Interestingly,theresearchersfoundinthiscasethatthegroupthatwasaskedtodirectlytelltheresearcherstheimportanceofsalarysaidthatsalaryissixthmostimportant;it'snotreallythatimportant.Theothergroupwhichwasnotaskeddirectly;whentheyinferredtheimportanceofsalary,weinfactfoundthatsalarywasthemostimportantfeature.Andso,thesecondgroupwasreallydoingaconjointtaskandwefindthatwhenweinferredtheimportanceofcertainfeatures,thoseareactuallyquitedifferentfromthedirectlystatedimportanceofthatparticularfeature.

Transcript: Ask The Expert: Johannes Gehrke on Big Data

Whatis"bigdata"?

So,Iamgoingtotalkaboutbigdatainthiscourse.So,whatisthisbigdataallabout?Bigdataisdatathatislarge,complexdifficulttoanalyzeusingtraditionaltools,suchasrelationaldatasystemsandtraditionalstatisticaltools.

Let'slookalittlebitintothehistorybecausethatisactuallyquiteinterestinghowthisallcameabout.So,inthe80's,itwasreallythetimewhenrelationaldatabasesystemstookoff.Relationaldatabasesystemsaredatabasesystemswhereyouhavedatalikeyouthinkaboutwhat'sinadatabasesystemintermsofatableandyourrowsandcolumnsinitanditisreallythebackboneoftoday'sinformationtechnologyinfrastructure.Thismaybewheneveryoumakeabanktransfer,wheneveryoumakeapurchaseatthesupermarket,wheneverIsentyouanitem,youmakeentriesonthesedatabasesandtheyhavenicepropertiesofnotlosinganyoftheinformationthatisinthere.Andtraditionally,theykeptthecurrentstateoftheenterprise,yourcurrentcustomers,yourcurrentsales,yourcurrentinventory,yourcurrentprices,andthisisvaluablebecausethedatabaseservedasastructurallayeronwhichyoucouldbuildotherapplications,addanewbaseeitherinternalorexternalcustomers.

Forexample,enterpriseresourceplanning,humanresourceapplications,billing,payment,procurement,andsoonandthesedatabases,theybecameaveryimportantassetand

thereforeitstartedsprawlingallovertheenterprise.Andtheresultwasthatenterprisesendedupnotonlywithonedatabase,butwithlotsofdatabases,oneforthesalesdepartment,



12

anotherforthemarketingdepartment,andthethirdonethathandlessales,inventory,andsoon.

Istherevalueacrossthesedatabases?

So,nowthatwehaveallthesedatabasesacrosstheenterprise,istherevalueacrossthesedatabases?AndIthinkthat'sexactlywhatpeoplestartedtorealize.So,theywantedtocreateanintegratedviewofallofthisdata.

Forexample,havinga360-degreeviewofacustomerthatcreatesalotofvalueandyounowseethecustomerintegratedacrosstheservicedatabaseandthemarketingdatabase.Forexamplenow,whenthecustomercallsandcalls,forexample,aboutaserviceproblem,afterwardsyoucantrytoupsellhim,eitherto,orher,toadifferentlevelofserviceortodifferentversionoftheproduct,orcrosssellhertodifferentproductatall.Andalso,youmayfindjustwithinoneofthesedatabasesyouhavethesamecustomer,threetimes.Forexample,lookatmylastname,youknow,Igetspammailwheremylastnameismisspelledinvariousdifferentvarieties,andthecustomerthatlookssobad,actually,thecustomerappearsthreetimesinthedatabasemaynotbesobadafterall,becausethissumactuallyhavethreeindividualcustomers.Andsothis,eventhoughthesethreedifferentcustomersareslightlydifferent,anddataintegrationcanhelpyoutocleanupyourdataandcorrectyourerrors.Andthereareseveral,alotofvalueintheintegrationofenterprisesthatyouarealsointeractingwith.Forexample,datadirectoryfromthesupplierswherethereareseamlessinstantinformationflowacrossthewholesupplychain.

What'sthehistoricalperspective?

Didn'tthesedatabasesthenalreadygetquitebig?Yes,theydid.Therewasanothercompoundingissue.Manypeoplerealizedthatitisnotonlythecurrentstateofenterprisethatisreallyimportantbutthereishugevalueinlookingatevolutionofthestateoftheenterpriseovertime.

So,especiallyinthe90'sthenthisresultedintothecreationoftheso-calleddatawarehouses,thatdidn'tonlykeepcustomerswiththeircurrentaddresses,butallthepreviousaddressesofthecustomers.So,didn'tonlykeeptherecenttransactionsbutthewholetransactionhistory.

Forexampleagain,forthepriceoftheproduct,itdidn'tonlykeepthecurrentpricebutthewholehistoryofthepricesandhowthepricefluctuatedovertime.Forexamplewithdifferentholidaysorspecialeventsandthisthenallowsyoutogetreallydeepunderstandingthatyoucouldn'tgetjustfromasnatchofthedata.

Let'sjust,forexample,takeacustomer.Acustomermaybetrackedwithloyaltycardstounderstandthelifecycleofthecustomer,forexample,fromcollegetothefirstjob,tothefirst



13

babyandthatthenallowedenterprisestomarkettheirwhole-partportfoliovery,verydifferentandveryspecificallytothiscustomeratdifferentpointsintheirlife.Andthesizeofnowthisintegrateddatabasesthatalsokeptthewholehistoryofeverythingthatwasgoingon–thatarereallynowusedfordecisionsupport,notforthecurrentstateoftheenterprise,theythenmadetraditionalstatisticaltoolsinadequate.

Andtherefore,theyresultedintheemergencealso,thisfirstwaveofbigdatawhichwascalled"datamining"orknowledgediscoveryindatabases,andthisisreallytheprocessofdiscoveringpatternsinthedataandthisincludesactuallymanyofthetechniquesthatarecoveredinthiscourse,buttheyarescaleduptoworkovergigabytesorpetabytesofdata.

Asimpleexamplewouldbethatdataminingwouldallowretailertofindoutwhatitemscustomerswouldpurchasetogetherinasupermarketorinwhatsequencetheywerepurchasingbooksfromabookstore.

Whatarethe"3V's"ofbigdata?

So,bigdataisreallytheculminationofallofthesedevelopmentsthatwejusthavetalkedabout,soit'snotonlylargebutithaswhat'softencalledthethreeV's.So,thethreeV'sarevolume,velocityandvariety.So,letmequicklytalkthrougheachofthem.

So,whatdoesvolumereallymean?Imean,ifyoulookattheamountofdataintheworld,thisisgrowingexponentially.SoImeanonewaytothinkaboutthisexponentialgrowthisthatifyouthinkabouttheamountofdataintheworld,90%ofthathasbeengeneratedjustoverthelasttwoyears,andactuallytheamountofdatathatisgeneratedeveryyearisdoublingabouteveryfortymonths.So,thedriversofallofthisareallaroundus.

Forexample,there'sthisubiquityofdata-generatingmachinery.Forexample,sensorsthatcaptureactivitiesinmanufacturingorforexample,serverlocksfromdatacentersthatcanbeanalyzedforintrusionsorforthehelpofserversorfortheanalysisofoutrageousorsoftwareproblems.Butit'salsoreallydrivenbyallofthegadgetsthatwecarryaroundeverydayandthesocialmediathatweareusing.

Forexample,ifyoujustlookatoneextremeintheperiodbetweenNewYear'sEveatNewYorkState2012-2013.Thereare1.1billionphotosuploadedtoFacebook.IfyoulookatTwitter,it'snowprocessinghalfabilliontweetsperday.Allofthisisreallyagoldmineformarketing.Andreallywhatthisalsomeansisthatifyouthinkaboutpetabytesinterms-insteadofterabytesandsotraditionally,theterabytesinthe90'swasoftencalledthe"terrorbyte"becausepeoplehadalotofterrorinfrontaboutit,butnowit'sreallypetabytes.

Forexample,Wal-Martsupposedlygets2.5petabytesofdatafromitscustomertransactionsperhour.Soifyoucouldanalyzetheimpactofcustomerpropertiesonsales,youcould



14

traditionallybuildamodel,forexample,usingfivevariablesbutnow,youcandothisusing500andsimilarlythisisallthestoragecapacity.Now,itactuallydoescostyouacoupleofhundredofdollarstopurchaseaharddrivethatcanholdalloftheworld'smusic.

Whatdowemeanby"velocity"and"variety"?

So,wejusttalkedaboutthe"3Vs"andwejustcoveredvolumeandtheothertwo"Vs"arevelocityandvariety.

So,velocityisreallythespeedofdataanditmeansthatwithrealtimeinformation,wecanmakemuchmoreaggressivemarketingdecisionsthanwithtraditionalmethods.

Let'sjusttakethisexamplethatI'mindowntownIthaca,it'saboutlunchtime,andnowwhatcouldhappenisthatIcouldgetacoupononmycellphonefromarestaurantthatisnearbythatmatchesmytaste.ThisismuchmoreeffectivethanthecouponinthenewspaperthatisontheweekendandthatIneededforcuttobringalong.Alsobecauseofitsimmediacy,it'ssortofreallycapturesthecontextinthatI'mcurrentlyin.

Anotherexamplewouldbe,youknow,takeashoppingmall.Theymayhavecamerasinstalledthatmeasuretrafficinandoutofstores.Whatthisdoesisthatitallowsyounowtogetanestimateofsalesofthestoresevenbeforetheyhavepublishedanysalesfiguresanditcouldbeveryprofitableforthefinancialindustry.

So,thelast"V"isvarietyandthisisalsoimportantbecauseifyoulooktraditionally,sothatinrelationaldatabases,youknow,youhadyourrowsandcolumnsandyourcustomersandemployeesanddepartmentsandmanagersandnow,thevalueonbigdatacomesupbylookingonlyatonedatasourcebutatthisunionofallthedatasourcesortheintegrateddatasources,andthisnowcomesfromdatafromdiscussionforumsandblogsandGPSdatafrommobiledevicesorevenvideosandphotospostedintweetsandsocialmediaandaccesstowebsites.Sothereisalotofvalueinintegratingthedatathatallofthiswithhugevarietyandcreatingvaluefromit.



15

Transcript: Ask The Expert: Marco Vriens on Big Data and Conjoint Analysis

Howdobigdatarelatetoconjointanalysis?

Sonowadaysthere'salotoftalkaboutbigdata,andobviouslybigdatahasanincrediblepotentialtoinformmarketingpeople.Butforthesetypesofproblemsbigdataisactuallynotverysuitedbecausebigdataonlyprovidewhat'salreadyintheworldandcangiveyou,likehowdopeoplerespondtowhat'salreadythere.Butconjointactuallyallowsyoutogetinsightintothingsthatdon'tevenexistyet.SoIjustwanttopointthatout,becausethat'sthediscussionfortoday,andIthinkthat'soneofthereasonswhyIthinkeverybodywhopursuesadegreeinmarketingshouldknowaboutdiscretechoiceandconjointmethods.

Willbigdataanalysisreplaceconjointanalysis?Bigdata,Ithink,isanemergingfield,thereareacoupleofmarketingapplications,thatIthinkforwhichbigdataareincrediblyuseful.Itmaynotbepartofthiscourse,butthingslikeamarketingmixanalysis,determininghowefficientyourmarketingactivitiesare,thatisanarea,Ithink,bigdatacanhelp.Eveninproductdevelopmentwhereyou'retryingtounderstandlike,okay,whatarethetrendsandwhatattributesandwhatfeaturespeoplelikeandhowdoesthatevolveovertime,thatscenarioIthinkbigdatacanhelp.Keepinmindthatconjointisasnapshot.I'minterviewingpeopletodayandI'mgettinginsightintowhatpeopleliketoday.Butitdoesn'tnecessarilytellmehowdatawillevolveovertime,andsothat'swhybigdatawouldcomeintoplay.Andsotosomedegreeaconjointapproachandbigdataaresupplementaryifanything,butit'snotlikebigdataaregoingtoreplacewhatyouwoulddoinconjoint,butitcouldsupplementit.

Transcript: Steps in Conjoint Analysis ThefirstimportantstepindesigningaconjointstudyisselectingtheattributesandlevelstobeincludedinthestudyandIwanttotalkaboutsomeoftheconsiderationsthatgointothisstep.Thefirstconsiderationisthatit'softenusefultouseconsumer'sinputtoidentifytheimportantattributestobeincludedinthestudyandthiscanbedoneindifferentways,butoftentheuse



16

offocusgroupsisusedforidentifyingattributes.Attributesintheconjointstudycanbehardattributes.Forinstance,inacomputerstudy,thememorymaybeahardattribute,buttheycouldbemoodsoftattributessuchasthebrandnameofproductswhichcanbeincludedasanattributeinthestudy.Oneusefulwaytothinkaboutidentifyingattributesofproductistoprofilethecurrentsetofproductsavailableinthemarket.Now,theresearcherneedstofindrealisticwaystocommunicatetheattributesandthelevelsofthoseattributestorespondentsinthestudyandthatcanbedoneindifferentways.Audiovisualaidsareoftenuseful.Forinstance,carcompaniesthatusedconjointanalysistohelpthemdesignnewcarswilluseprototypesofcarsandthenhavetherespondentssitintheprototypetobeabletoexperienceattributeslikelegroomorheadroomandtheseareattributesthatarenoteasilycommunicatedthroughdescriptionbutarecommunicatedbetterthroughexperience.Theuseofreferenceproductisalsoanothermechanismtocommunicateattributesandlevels.Now,itisimportanttokeepinmindthattheattributesandlevelsthatareincludedinaconjointstudymustbeactionlevelbythebusinessthat'sdoingthestudy.Otherwise,therewouldnotbe-itwouldnotbeaveryusefulstudy.It'salsoimportanttokeepinmindthatweshouldhaverealisticlevelsofattributesinastudyandexampleisprice.Ifpriceisanattribute,that'sincludedinthestudy,wedon'twanttoincludelevelsofpricethataresolowthatthefirmcannotactuallydelivertheproductatthoseprices.Ifwedoincludeattributelevelsthatunrealistic,thenwe'llfindthattheinferredimportanceoftheattributewillbeinflated.Letmenextuseanexampletoillustratesomeofthesepoints.Now,whatyouhavehereisdataforaprescriptiondrugmarketinwhichaformofstrengtheneddesignatconjointstudyandtherespondentsinthestudyaregoingtobephysicians.Thephysiciansarequitefamiliarwiththismarketandtherearefourexistingproductsinthismarket;alpha,beta,gamma,anddelta.Ifwelookatthelastrowhere,thelastrowshowsthecurrentmarketsharesoftheproductsandwefindthedeltaisthedominantproductinthismarketwhichhasa70%marketshareandasaresultofthat,mostphysiciansarequitefamiliarwiththeseproductdelta.Now,whenweprofiletheseproductsinthemarket,wecomeupwiththenumberofattributesoftheseproducts.Let'stakeoneoftheseattributesasanexample,durationofsideeffectsofthedrugsisanattributeandinthemarketatpresent,thelevelsoftheseattributeareoneday,twodays,orthreedaysandthesegivestheresearcherssomeindicationofwhatthelevelsinthestudyshouldbe.Wealsonoticedthatanattributelikeclinicalcureratehasbeendescribedbyreferencetoaproductthatphysiciansarefamiliarwiththatisincomparisonwithdelta.So,itcouldbe10%belowtheclinicalcurerateofdeltaoritcouldbe10%abovetheclinicalcurerateofdeltaoritcouldbeequaltotheclinicalcurerateofdelta.So,thismakesitabetterwayto



17

communicatethisattributetothephysician.Letmealsopointoutthedrugcostswhichatthemomentinthemarketisattwodifferentlevels,either$44.60or$58.85.Takingthisdata,nowlet'sseetheattributesinlevelsthatwereactuallyusedbythefirminthatconjointstudy.Thesearetheattributelevelsactuallyusedinthedrugstudyandwefindthatthesameattributesthatexistsinthemarkethavebeenused.Attributeshavedifferentnumbersoflevels,sosomeattributesareatfourlevels,forinstance,1and2and3.Thereisanotherattributewhichisat3levels.So,attributescouldbeadifferentnumberoflevels.Letmefocusondrugcost.Wesawbeforethatthecurrentproductsinthemarketwereeitherat$44.60orat$58.85.Inthisconjointstudy,thefirmdecidedtousealevelofdrugcostslightlylowerthanwhatisavailableinthemarketandalsoslightlyhigherthanwhat'savailableinthemarket,buttheydidnotgotoofaroutsidetherangeofcurrentlyavailableproductsinthemarket.Transcript: Collecting Data Thenextstepinthedesignoftheconjointstudyistousetheattributesandlevelsthathavebeenidentifiedtodevelophypotheticalproductsthatwillbeshowntotherespondentandtodecidehowtherespondentwillbeaskedtoevaluatetheseproducts.Now,usingtheattributesandlevelswecancreatethesetofallpossibleproductsthatcanbecreated.Thissetiscalledthefullfactorial.Usuallythefullfactorialistoolarge,inotherwordsithasjusttoomanyproductstoaskthedespondenttoevaluateinamarketresearchsetting.Sowhatwedois,weselectasubsetoftheseproductstoshowtotherespondent,thissubsetiscalledthefractionalfactorial,andwehaveusuallystatisticalsoftwarethatweusetoidentifythesubset.Thesubsetofproductsischosensoastohaveareasonablenumberofproductsforthesurvey,thatisnottoomanyfortherespondenttoevaluateButenoughtobeabletorunstatisticalanalysesoneachrespondentdataseparately.Nowwhatdoweactuallyasktherespondenttodo?Inmetricconjointanalysis,weasktherespondtoeitherratetheproducts,orweasktherespondenttoranktheproducts.Ifthesetofproductstobeshowntotherespondentislarge,thentherankorderingofproductsisdifficultfortherespondentandwemightoftengointhatsituationtoasktherespondenttoratetheproducts.Transcript: Conjoint Surveys



18

Next,let'stalkabouthowconjointdataarecollectedandanalyzed.Conjointdataarecollectedtypicallyfromasampleofrespondentsfromthetargetmarketbymarketresearchfirms.Aconjointsurveyhastwopartstypically.Infirstpart,respondentsareaskedtoprovidetheirjudgmentofhypotheticalproducts;ratingsorrankings.Inthesecondpart,wecollectinformationabouttherespondent'sdemographiccharacteristics,thepurchasinghabitsandbehaviorsandsoforth,sothatwecanlinkpreferencesthatwehavecollectedintheconjointstudywiththecharacteristicsoftherespondents.Howareconjointserviceadministered?Theyaretypicallyadministeredremotelyontheweb,butdependingoncircumstancessuchastheaccessibilityoftherespondentsandthecomplexityofthetaskthattheyarebeingaskedtodo,theymaybeadministeredonthephoneorinperson.Whenwecometothestageofanalyzingconjointdata,inmetricconjoint,weanalyzethedataforeachrespondentseparately.Whatthisdoesisitallowsforcompletelyidiosyncraticpreferencesofeachrespondent.Notworespondentsneedtobe-needtohavethesamepreferencesfortheattributesinthecategory.Thegoalofthisanalysisistoobtainthepart-worthsortheutilitiesthatareassociatedwitheachattributelevelandtheimportanceofeachattribute.ThisistypicallydoneusingregressionanalysisandIillustratetheuseofregressionanalysistoanalyzeconjointdatausingthecarpetcleanerexample.Transcript: Case Study: Carpet Cleaners

Soasanexample,wehaveastudyhereinwhichcarpetcleanerproductswerestartedinthemarket.Asaconjointstudy,fiveattributeswereidentifiedusingfocusgroupsandtheseattributesarepackagedesign,brandname,price,aGoodHousekeepingseal,andamoney-backguarantee,andasyoucanseehere,theseattributeshavebeendefinedonacertainnumberoflevels.PackagedesignisonthreelevelswhicharecalledA,B,andC.BrandnameisinthreelevelsaswellwhicharecalledCarbona,Resolve,andWoolite.Pricesatthreelevels:$4.99,$5.39,and$5.79.GoodHousekeepingsealisyesorno.It'spresentornotpresent.Andamoneybackguaranteeagainisyesorno.so,ifyouthinkaboutthetotalnumberofpossiblecarpetcleaners-hypotheticalcarpetcleanersthatcanbecontractedusingthesefiveattributesandthelevels,thatnumberisgivenbytheproductofthenumberoflevelsherewhichis108.

Now,thatofcourseisaverylargenumberandinanystudy,wecan'treallyexpectarespondenttoevaluatethe108productsbecausethatistoomany.So,inthisparticularstudy,18products,asubsortof18productswaschosentoshowtoeachrespondentandtherespondentwasaskedtoevaluateeachofthese18carpetcleaners.Thetaskgiventothemwas



19

torankedorderthe18carpetcleanerswith18beingthemostattractable,themostpreferredcarpetcleanerandonebeingtheleastattractivecarpetcleaneranddatawerecollectedfromeachrespondentinthisfashion.

Transcript: Analyzing Data Using Regression So,inanalyzingthedatathathadbeengatheredfromtherespondents,wewanttobeabletodecomposetheoverallpreferenceforacarpetcleanerintothepreferencesforthefiveattributesthatmakeupthecarpetcleaner.Forinstance,considertwocarpetcleanersthatareidenticalinallaspectsexceptforthebrandnameandonecarpetcleanerthatiscalledResolveisrankedhigherthantheonethatiscalledWoolitebyaparticularrespondent.Then,wecaninferthatthisrespondentprobablyprefersthebrandnameResolvetoWoolite.Andnowifyouanalyzeall18judgmentsofrespondenttogether,wewilluseastatisticaltechniqueorregressionandtodemonstratethat,letmeshowyouinanexcelspreadsheetthedataofonerespondent.So,whatyouseehereisthedatafromonerespondentinwhichwehave18carpetcleanersorprofilesthatwasshowntotherespondent.Eachofthesecarpetcleanerisdescribedonthefiveattributes;packagedesign,brandname,price,GoodHousekeepingseal,andmoney-backguarantee.Andwedeceivetherespondent'sevaluationforeachofthesecarpetcleanerswhere18isbestandsowefindthattheseparticularcarpetcleaner,thelastoneisrankedasthemostattractiveandthecarpetcleanernumber10isconsideredtheleastattractive.Now,wetakethisdataandweconvertthemaswehaveseenintodummyvariables.So,wehavethesamedataherebutthefiveattributesofthecarpetcleanershavebeencodedasdummyvariables.Ourgoalnowistotaketherespondent'sevaluationsofthe18carpetcleanersanddecomposethoseevaluationsintothepreferencesforthefiveunderlyingattributesandtodothatasIsaidwe'llusemultipleregressionstoanalyzethedata.Nowinexcelyoucanrunmultipleregressionbygoinguntothedatatabandlookingforthedataanalysistool.Whenyouclickondataanalysis,yougetanumberofpotentialanalysistoolsandwe'llusetheonecalledregression.So,ifyouclickonregressionandclickokay,you'llgetawindowinwhichyouhavetofirstspecifytheinputbyrangewhichisthiscaseistherespondent'sevaluationsforthe18carpetcleaners.So,I'mgoingtotakethese18evaluationsandspecifytheseasbeingtheinputbyrange.NoticethatIhaveincludedthefirstcellwhichisthelabelrespondentevaluations.I'mthengoingtospecifytheinputx-rangewhichisthedummyvariablescorrespondingtothefive



20

attributesandthesedummyvariablesarespecifiedhere.Andso,thesedatanowaretheXvariables.Sincewedohavelabelsinthedata,I'mgoingtochecktheboxcalledlabels.I'mgoingtospecifyanoutputrangewhichiswheretheoutputfromtheregressionshouldappearandinordertothat,I'mgoingtochooseanewworksheet.Let'ssaywespecifytheoutputappearhereandthat'ssortofallthatweneedtospecify,ifyouclickokay,wegettheoutputfromtheregression.Now,IhavealreadydonethisregressionbeforeandI'mgoingtoconcentrateinthisareawhereIhaveobtainedtheresultsoftheregression.Whatdoweseehere?Wefindthatthisregressionhad18observations.IthasanRsquareof0.98soithasaverygoodfittothedataandthesearethecoefficientofthedifferentdummyvariableswhichrepresentthevaluesystemofthisconsumerwhichisthenumberswe'retotakeandinterpretnowintermsofutilitiesassociatedwiththeattributesunderlyingthecarpetcleaners.Transcript: Interpreting Results

Soasyourecallwehavecollecteddatafromonerespondentinamarketresearchstudy,wheretherespondentwasshown18hypotheticalcarpetcleaners,eachcarpetcleanerwasdescribedinfiveattributes,andtherespondentwasaskedtoprovideajudgmentintheformofrankorderingofthesecarpetcleaners.Wetookthisdatafromtherespondentandrunaregressionanalysisonthedataandthecoefficientinthatregressionarethepartworthsofthisrespondent.Sowearegoingtousethesepartworthstounderstandthiscustomer'spreferences.Solet'stakealookatthepartworths.

WhatIhaveinthespreadsheetherearethefiveattributesonwhichthecarpetcleanersweredescribed.PackageDesign,BrandName,Price,GoodHousekeepingSeal,andMoneyBackGuarantee.AndforeachattributeIhavethelevelsoftheattributes.

Forinstance,forpackagedesignthethreelevelswereABandC.Inthecolumnlabelutilitieswehavetheestimatedpartworthswhicharenothingbutthecoefficientsfromtheregressionanalysis.

Let'sseewhatthesecoefficientsorpartworthstellusaboutthisrespondent'spreferences.Let'stakealookfirstatthepartworthsforpackagedesign.

WeseethatthisrespondentpreferspackagedesignedBthemostfollowedbypackagedesignCfollowedbypackagedesignA.Notethatthisinformationwasnotavailabletousuntilafterwehadcollectedthisrespondent'sdataandruntheregressionanalysis.Therespondentwasnot



21

directlyaskedtostatewhichofthreepackagedesignswasmostpreferred.Similarlylet'slookatthepartworthsforprice.Weseethatasthepricegoesupfrom$4.99to$5.39to$5.79thepartworthorutilitygoesdownwhichmakessense.Therespondentpreferslowerpricestohigherprices.Butweseethatthepriceincreasesresultinutilitydecreasesinaparticularfashion.Whenthepricegoesupbythefirst40centsfrom$4.99to$5.39utilitydropsby2.83units.Whenpricegoesupbythenext40centsfrom$5.39to$5.79utilitydropsbymorethan2.83units,infactitdropsby4.84unitswhichisthedifferencebetween7.67and2.83.Whatwehavejustlearnedisthatpriceincreasesresultinanon-lineardecreaseinutility.Let'sconductthefollowingthoughtexperimenttoseefurtherwhatwelearnedaboutthisrespondentspreferences.Let'sconsideracarpetcleanerwhichisatthepriceoffivedollarsandthirtyninecents,anddoesnothaveamoneybackguaranteeassociatedwithit.Nowlet'saskthefollowingquestion,ifweweretoraisethepricebyfortycents,inotherwordsraisethepricetofiveseventynine.Andgivetherespondentamoneybackguaranteeonthecarpetcleaner.Wouldtherespondentbebetterofforworseoff.Wellwhatweseeisthatwhenthepricegoesupfortycents,theutilitydropsbyfourpointeightfourunits.Whenthemoneybackguaranteeisprovided,utilitygoesupbyfourpointfiveunits.Sincefourpointfiveislessthanfourpointeightfour,weconcludethattothisrespondentthemoneybackguaranteeisnotworthfortycents.Alloftheseareinferencesthatwedrewabouttherespondentspreferenceswithoutdirectlyaskingtherespondent.

Nextlet'slookattheimportancesassociatedwiththedifferentattributes.Tocomputetheimportanceassociatedwithanattribute,welookattherangeofthepartworthswithintheattribute.Forinstance,let'sconsiderpackagedesign.Therangeofthepartworthisthatthelowestpartworthiszero,andthehighestpartworthiseight.Thisgivesusarangeofeight.

Similarlylet'slookatbrandname,thelowestpartworthisminuspointfiveandthehighestpartworthisonepointfive.Thatgivesusarangeoftwo.Whatweseefromthisisthatifthepackagedesignweretobechangedfromitsworstlevel,whichisAinthiscase,toitsbestlevel,whichisBinthiscase.



22

Wecanswingtherespondentsutilitybyeightunitswhichiswhyweconsiderthisnumbereighttobeanapproximatemeasureoftheimportanceofthisattribute.Tocomputeameasure,arelativemeasureofimportance,wesumtheserangestogetatotalrangeoftwentythreepointsixseven.Andwethencomputethefractioneightdividedbytwentythreepointsixseven,whichispointthreefour.Similarly,twodividedbytwentythreepointsixsevenispointzeroeight.Thesenumbersinparenthesesareinterpretedasthemeasureofrelativeimportance.Sowhatweseehereisthatpackagedesignisthemostimportantattributetothisrespondedwithanimportanceofapproximatelythirtyfourpercent.Bycontrast,theleastimportantattributeforthisrespondentisthegoodhousekeepingsealwithanimportanceofonlysixpercent.Transcript: Ask The Expert: Johannes Gehrke on Advanced Marketing Research

Howcanbigdatahelpwithdecisionmakinginmarketing?

So,inthiscourse,wetalkedalotaboutdecisionmakinginmarketing.So,howcanbigdatahelpwithit?Well,traditionallydecisionswereveryoftenmadebasedonintuitionorpeople'sopinionsorexperienceandoftenthentheHiPPOmadethedecision(wheretheHiPPOistheHighestPaidPerson'sOpinion).Andthere'ssomegoodexamplewhydatamaytellyouthatthisisnottherightthingtodo.

Forexample,afamousexampleisatAmazon,whereassumeyoujusttookanitemandyouaddittoyourshoppingcartatyourwebsite.Now,mostsitesusedtoshowthecartafterwardswiththegoalthatwhileyouputthisitemuntothecartandthenyouwouldgoaheadandcheckoutandpay.AndatAmazon,redlinkinghadtheideaofactuallyyouwouldshowrecommendationsbasedontheitemsinyourcartsothatyouwouldactuallyputmoreitemsintothecart.Theadvantageofthatwouldbethatwhileyoucould,maybesellmoreitems,youwillhaveamuchbiggeraveragebasketsizeafterall.Thedisadvantagewouldbewhileitmaydistractpeople

fromcheckingout,sotheymayactuallyabandontheshoppingcartwhichwouldreduceconversion,andtherefore,reducedonprofits.Andsoatthatpointintime,theHighestPaidPerson'sOpinionwastostoptheproject.Thisisnotworthwhiledoing,butactuallythanjust,averysimpleexperimentwasrun,itwasreallyhighlysuccessfulandtherestisnowhistory.Youknow,actually,whereveryougoandyoushop,Imeanyouaddsomethingtotheshoppingcart,yournearlyallofdecidedpickwillshowyourecommendations.Andthisactuallyisansample



23

wheretheywasaHiPPOofthisandthendataandasimpleexperimentwasusedtoactuallyhelpinthedecisionmaking.

HowcouldyouconductasimpleA/Bexperimentonyourwebsite?

So,inthepreviousclip,wetalkedaboutthisexperiment,whereweactuallyusedaverysmallexperimenttomeasuretheeffectivenessofapossibleaction.So,assumethatyouhaveawebsitenowthroughwhichyousoldyourproducts.Howwouldyouactuallydosuchasanexperiment?Howdoesthiswork?

So,let'sjustlookbackandseehowactuallywhatwouldbeasimplewayofthinkingaboutthisisthatwheneverarequestcomesin,itgoestosomeserverwithintheinfrastructureofthiswebsite,andthenthatservesitoutforasetofservicesintheenterprisetheresultpagethengetsassembledandit'sreturnedtoyou.So,thisisITinfrastructurebehinditthatdoesallofthis.

Now,inwhatiscalledABtesting,youactuallyrandomlysplitthetrafficbetweentwodifferentversionsofthewebsite,forexamplethecurrentwebsiteandthenanotherwebsitewhereyouchangesomethingbutactuallysomethingjustvery,verysmall.

Forexampleyoumayhavechangedthealgorithmthatyouusedtodeterminewhatitemsarerelatedtotheitemthatyou'recurrentlyshowing.Youmayhavechangedtheadsonthepage.Youmaychangetherecommendationsonthepageandsothisistheonlychange,soyoudon'tchangethesitelayoutoranythingelse.Andthenwhatyoualsohaveissomeclearmetricsofwhattomeasure.

Forexample,inyourwebsite,itcouldbeassimpleas,youknow,howmanyitemshaveyousoldandwhatistheprofitmarginonthem.Forexampleingames,itcouldbetheengagement.Youknow,coulditbein-gamepurchases,thetotalminutesplayed,thenumberofadsclicked.Onacarrentalwebsite,itcouldbethetimetocompletionoftherental,theconversionpercentage,thenumberofupgrades,anythingelse.Andnowthatyouhavetraffic,yousplititintotwoparts.OnethatgoestositeAandtheotheronethatgoestotheinternalothersiteBwhereyouarerunningthesmallexperiments.Andnowyougoaheadandmeasure.Andespeciallyifyourproductisdigitalasinthegameexampleaboveorinthewebsite,thenyoucannotonlyshowtwodifferentwaysofpackagingitbutyoucouldshowlotsofdifferentways

ofpackagingitandactuallynowthesearetwodifferentproductsandyouseetheimpactdirectlyoneachofthemonyourcustomers.

Whataresomeofthepitfallswiththeseonlineexperiments?



24

So,whataresomeofthepitfallswiththeseonlineexperiments?Sofirstofall,hereinthisnewonlineworld,it'sbettertohavelotsandlotsofideasthatarecheaptoimplementandthenthrowoutthosethatdon'twork.Andtheimportantthingisthatyouactuallywanttotrytomeasureallofthembecauseeveningeneral,youmaybewrongmanytimes,butiftheseareallexperiments,youareonlygoingtoimplementthosethatweresuccessful.

Soeven,forexample,youtakeahundredexperiments,90ofthemwereunsuccessfuland10ofthemweresuccessful.Now,thevalueoftheoverallpropertyoryourwebsiteanddigitalproducthasincreasedsignificantlybecausetheonlythingsthatyouimplementwerethesuccessfulsteps.Thisisreallydonealsowithinbigcompanies.

Forexample,in2009,Googlerunmorethan12,000APtestsandonlyabout10%oftheseweresuccessful.

What'stheimportanceofvalidatingyourideaswithdata?

So,oneofthe,oneoftheotherpitfallsthatyouhavewithonlineexperimentsisthatyouhavetoknowwhatareyourmetrics.Themetricswilldeterminewhetheryourexperimentwassuccessful.

Forexample,ifyousetanexperimenttocrosssell,thenyoushouldreallymeasurewhatthecustomersactuallypurchasesomethinginyourcategoryratherthanwiththeoverallpurchasesgotorwhether,forexample,itboostssalesinexistingcategories.Andthisisalsoimportantfromapeoplemanagementpointofview.So,productmanagersusedtodecideonthenextsetoffeaturestoimplement.Butnow,youhavetogettothemindsetthat,whileyoudon'treallyknowwhatisgoingtoworkornot,butactuallyitismuchbettertoactuallycomeoutwithlotsofdifferentwaysoftryingthingsout,especiallycheaplyandthenmeasure,andtheneachraidoutthisprocess.

So,especiallyverysuccessfulpartsofthecompanythathaveahistoryofsuccessmaybeveryresistanttothisbecausewhilewe'veallstudiedthisway,we'vebeensuccessfulwiththis,but-sotheleastyoucandointhiscaseisatleasttogoaheadandvalidateyourideaswithdata.Sonow,youhaveanideathatyouimplementedandwiththisyounowgoaheadandmeasurewhethertheintroductionwasright.Thismaynotbethefirststepintogettingintothestrictestdata-drivenmindsetwhereactuallyyouknowhowtodrivethedecisionprocesswithdataand

yougoaheadtrylotsofideasandlotsofcheapways,andthenyoumeasureandthenyoumakeyourfinaldecisionbasedonthesemeasurements.

Whatisadatascientist?



25

So,oneofthetermsyoualsohearalotwhenpeopletalkaboutbigdataisthenotionofadatascientist.Sowhatisadatascientist?Andyouknow,simplyexplainedit'sthepersonwithinanorganizationwhomakesdiscoveriesindata.Andthisreallymeansgoingthroughallofthedifferentdiscoverysteps.

Firstofall,findingtherightdataindataenterpriseorexternaldatathenintegratingit,youknow,itmaynotbeasniceandrelationalasthetraditionaldataintermsofrowsandcolumns.Theremaybelotsofbaddataformatsorbogusdata,youknow.Youmayhavelotsofconfusingdatafieldslikesynonymsanddifferencesandthengoingthroughthewholeextractionprocessofintegratingit.

So,asimpleexample,let'sassumedyouwantstomeasuresentimentabouttheproductbylookingatTwittertweets.Now,thetoolsavailablethatactuallytaketweets,andthentrytoextractnamesofproductsespeciallygivenyourownproductdatabase,buttheyallneedtobeintegratedbetweentheoverallflowofthedatascienceexperimentsthatyouwanttocreate.Andthenneedtobefedwithyourproductnamesandtherealsomaybeotherprofessionalwebsitesinwhichyourproductisdiscussed.

So,youneedtoagain,takethistextandthentrytoanalyzewhetherthisdiscussionispositiveornegativeorwhatthediscussionsareabout.Andnoneofthiscanbedoneinamanualwaybecauseitdoesn'tscale.Andthenofcourseintheend,thedatascientistbuildsmodelsandlotsofexperiments.So,whatthismeansisthatactually,weneedaskillofsomebodywhohassortofacombinationofstatistician,butalsoasortofahackerandalsosomebodywhohasagoodunderstandingofthebusinesssitetoknowwhattherightquestionsareandalsoinsubstanceandintheend,playtherolesofaconsultanttothedecisionmakers.IthinkthisisabigdifferencebecauseifyoulookattheStatisticsDepartmentinbigcompanies,theywereassignedveryspecific,narrowlydefinedtasksandtheyalsonotethatmuchcommunicationbetweenthepeoplewhomakethestrategicdecisionsandthepeoplewhodockintothedata.Buttoday,datascientists,theyareveryhighvaluepeople.Theyaremuchbetterintegrated.Theycommunicatetheirfindingstoproductmanagers,toexecutives,andpeopleinoperations.Andtheyalsohaveagoodunderstandingofthevalueoftheirfindingsonproductsanddecisionsandcan,therefore,drivealloftheanalysisthattheyaredoingthemselves.

Transcript: Simulations Sonext,wewilltalkabouthowtheestimatedpreferences,orpart-worths,ofrespondentsareusedtosimulatewhatmighthappeninthemarket.Weestimatedthepreferenceofeachrespondentinthesampleseparately,andwestoredtheminadatabase.Wecannowusethese



26

part-worthstoanalyzetheimpactofbusinessdecisions,suchasaddinganewproductintothemarket,ordeletinganexistingproductfromthemarket,orchangingthelevelofanattributesuchasthepriceoftheproduct.Thebasicapproachinallthesecasesistosimulatetheoutcomeforeachindividualinthesampleandthenaggregatethepredictedoutcomesacrosstheindividuals.Let'sgobacktothecarpetcleanerexamplethatwehavetalkedaboutpreviously.Inthatcarpetcleanerexample,wehadthepreferencesofonerespondentthatwehadobtainedusingregressionanalysis.Imaginenowthatthemarketconsistsofthreecarpetcleaners,eachofwhichcanbedescribedasabundleofthefiveattributesinthatexample.Thesethreecarpetcleanersmaybeexistingproducts,ortheymaybehypotheticalproductsthatthecompanyisconsideringincludinginthemarket.So,thequestionofinterestis,forthisparticularrespondentwhosepreferenceswehaveobtainedusingconjoint,canwesaysomethingaboutwhichofthethreecarpetcleanersthisindividualwillchoose?Inotherwords,wewouldliketobeabletopredictthisindividual'schoiceusingtheestimatedpreferencesorpart-worths.Let'sgototheExcelfilewherewehavethedata.Nowinthisdatawehavethethreehypotheticalproductsthatareeithercurrentproductsinthemarketornewproductsthatthefirmisconsideringincluding.Let'stakeanexamplewiththefirstproducthere,aspackagedesignB.Ithasbrandname"Resolve."Itisatthepriceof$5.39.ItdoesnothaveaGoodHousekeepingSeal,anditdoesnothaveamoney-backguarantee.Similarly,therearetwootherproductsinthemarket.Nownext,wehavethedummyvariablecodingoftheseattributesoftheproduct,whichissomethingwehaveseenbefore.Recallthatwehadalreadyestimatedthepreferencesofthisonerespondentusingconjointanalysis,andthesearethepartworth'softhisrespondentthatwehaveobtainedpreviously.Now,thequestionofinterestis,foreachofthesethreeproducts,whatisourpredictionoftheutilitythatthisrespondentwillgetfromthatcarpetcleaner.Howdoweobtainthat?Let'stakethefirstcarpetcleanerhere.Theutilitythatthisrespondentwillgetfromthiscarpetcleanerwillbethesumoftheutilityfromeachoftheunderlyingattributes.Forinstance,forpackagedesignB,theutilitywillbe8unitsofutility.ForbrandnameResolve,theutilityis-0.5.Forthepriceof$5.39,theutilityis-2.83.AndnothavingaGoodHousekeepingSealmeansthattheutilityiszero,andnothavingamoney-backguaranteemeansthattheutilityiszero.So,wesumtogether6.5,whichistheintercept,8unitsminus0.5andminus2.83,whichgivesus11.17unitsofutility.So,whatwearesayingisthatfromthisfirstcarpetcleaner,thisrespondentwillget11.17unitsofutility.Similarly,wecancomputetheutilityfromthesecondproductandthethirdproduct,andthoseutilitiesare15.67and9.33units,respectively.Thenextquestionofinterestis,ifthesearetheutilitiestheindividualgetfromthreecarpetcleaners,whichcarpetcleanerwillthisindividualchoose?Theassumptionwemakeisthattheindividualchoosesthecarpetcleanerthatgivesthispersonthehighestutility,whichinthiscaseisthesecondcarpet



27

cleaner.Andthereforethepredictedchoiceisthatthisindividualwillpickthesecondcarpetcleanerinthemarketandwillnotpickthefirstandthethirdcarpetcleaners.Wecandothisexerciseforeveryrespondentinourstudyandthensumuptheirchoicestoobtainthetotalnumberofunitsthatwillbesoldforeachofthethreecarpetcleanersinthismarket.Transcript: Choice-based Conjoint

So,sofar,wehavetalkedaboutoneformofconjointwhichiscalledfullprofilemetricconjoint.Thisformofconjointisattractivebecausewecanestimatethepart-worths,orutilities,separatelyforeachrespondent,therebyallowingeveryrespondent'spreferencestobecompletelydifferent.Thismighthave(doeshave)adrawbackandthedrawbackisthatwhatwecanpredictinthismethodispreferences,notchoices.Andthenwehavetoconvertthepreferencestochoicesinsomekindofsomewhatad-hocmanner.

Analternativeapproachistodirectlycollectfromconsumerschoicesinsteadofratingsorrankings.Thisapproachiscalledchoice-basedconjointandisnowaverypopularformofconjoint.Sohowdoeschoice-basedconjointwork?Inchoice-basedconjoint,therespondentispresentedwithachoiceset.Inthischoiceset,therearealternativeproductsandtherespondentisaskedtoindicateadiscretechoice.Whichoneoftheseproductswouldtherespondentchoose?Thetaskisthenrepeatedwithseveralchoicesets,let'ssay12choicesets,andtherefore,wehave12observationsfromeveryindividualwhichareevaluationsoftheproducts.Theanalysisofchoicedatanowhastobedoneusingachoicemodel.Anexampleofachoicemodelisalogitmodelinsteadofusingregressionanalysis.

So,whatisthebigadvantageofchoice-basedconjoint?Thebigadvantageisthatthisisamorerealisticmarketresearchtask.Choicesishowconsumersexpresstheirpreferencesinthemarketandsothisismorerealistic.Thedisadvantageofchoice-basedconjointisthatitisoftendifficulttoestimatepart-worthsorutilitiesseparatelyforeachrespondent.Thedatahavetobepulledacrossrespondentstoestimatetheconjointmodel.

Letmeshowyouanexampleofachoice-basedconjointtaskfortherespondent.Whatyouseehereisataskthatwasgiventorespondentinachoice-basedsettingwheretherespondentwasshownfourluxurySUVs.EachoftheseSUVsisdescribedonseveralattributesandsowehaveeightattributesonwhicheachoftheseSUVshasbeendescribed.Therespondent'staskistoanswerthequestion:Whichvehiclewouldyoubemostlikelytopurchase?AndtheyexpresstheanswerofthisquestionbypickingoneofthefourluxurySUVsthattheyareshown.OncetheypickanSUV,thetaskiscompletedandtheymoveontothesecondtaskinwhichfour



28

differentluxurySUVsarenowshownandtheymakeadiscretechoiceagain,andsoon.Andinthisfashion,eightchoicesoftherespondentarepicked.

Transcript: Managerial Uses of Conjoint

Conjointanalysisisusedextensivelybymarketingmanagerstomakeimportantbusinessdecisions.Therearefourbroadareasinwhichconjointanalysisresultsgetused.

Oneisobviouslyproductdesign.Conjointisveryusefulfordetermininghowtodesignproductsandservicesthatappealtoconsumers.Asecondapplicationofconjointisinforecastingthemarketshareofnewproductconceptsthathadbeendesignedbythefirm.Athirdapplicationisinsegmentationandtargeting.Thisapplicationisirrelevantbecauseinconjoint,weareabletoobtainpreferencesforindividualconsumersandtherefore,wecangroupconsumersbasedupontheirpreferencesafterconjointdatahavebeenobtained.

Afourthapplicationispricing.Oneoftheimportantoutputsfromconjointisthatwecanobtainthewillingnesstopayfordifferentattributelevelsinthestudyandthereforethisallowsthefirmtodeterminehowtopricenewproductandservices.Becauseofthesewidespreadapplications,wefindthatconjointhasbeenusedinmanyapplications.Thisincludesconsumerdurablessuchasautomobiles,digitalcameras,smartphones,highdefinitiontelevision,andsoon;consumernon-durables,suchassoapsandshampoosandfoodproducts;anumberofindustrialproducts,suchasaircraft,computersoftware,forklifttrucks;serviceslikecarrentals,hotels,andotherapplications,suchasMBAjobchoices,banneradvertisingintheInternetcontext,andsoon.

Let'sthinknowaboutthekindsofproblemsituationsinwhichconjointismostapplicable.Herearesomeconsiderations.Oneisthattheproductthatwearedealingwithmustberealisticallydecomposableintobasicattributes.Thishappensmosteasilyinproductssuchasconsumerelectronicproducts(digitalcameras,laptops,smartphones,andsoon)butitdoeshappeninanumberofotherproductsituations.

Thesecondconsiderationisthattheproductchoiceshouldbeareasoned,highstakesdecision.Inotherwords,conjointisnotaveryusefultechniquewhentheproductisanimpulsepurchasekindofproduct.Athirdconsiderationisthatfactorialcombinationsofbasicattributelevelsshouldmakesenseandifthat'sthecaseintheproductcategory,thenconjointisaveryapplicabletechnique.Anotherconsiderationisthatproductscanberealisticallydescribedverballyorpictorially.Thisisimportantbecauserespondentsareprovidingtheirevaluationsofproductsbasedonthestimulithattheyseeandthestimulineedstoberealistic.Andthefinal



29

considerationisthatdesirablenewproductalternativescanbesynthesizedfromthebasicattributes.Thesearethesituationsinwhichconjointismostapplicable.

Documents

LSM551 Transcripts - Amazon Simple Storage Service (S3) · Transcript: Attributes and Levels Many products and services can be thought of as bundles of attributes. An example is the