The Relationship Between Internet Marketing, Search Volume, and Product Sales. Honors Research Thesis

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1 TheRelationshipBetweenInternetMarketing,SearchVolume,andProductSales HonorsResearchThesis Presentedinpartialfulfillmentoftherequirementsforgraduationwithhonors researchdistinctionineconomicsintheundergraduatecollegesoftheohiostate University By NicholasLincoln TheOhioStateUniversity June2011 ProjectAdvisor:ProfessorRichardSteckel,DepartmentofEconomics 1

2 Abstract Thispaperdetermineswhetherinternetadvertisement,andaproduct s onlinepopularity,asmeasuredinsearchqueries,canpredictsalesrevenue.totest forcorrelations,thesalesdata,adspending,andgooglekeywordsearchvolumefor Apple sipodandiphonewascollected,anddevelopedintoafinitedistributedlag model. ThemodelfortheiPod ssalesrevenueshowsthatthereisastrongseasonal effectonsales,andneithertheinternetpopularity,asmeasuredbygooglesearches, oradvertisementspendinghasastatisticallysignificanteffect.theiphone ssales revenueisshowntobesignificantlyinfluencedbytheinternetpopularity,andits lag.theiphone srevenueisnotsignificantlyaffectedbyadvertisingorseasonality. Theresultsofthisstudycouldbeusedtodeterminetheeffectivenessof advertisementonconsumerinterestinaproduct,ontheinternet.similarmodels couldbeabletodeterminewhethergooglesearchvolumecanpredictthesales revenuesofotherproducts. 2

3 I.Introduction Internetadvertisementspendinghasbeengrowinginrecentyears,andis projectedtoovertaketelevisionandradioadvertisementswithinadecade.market researchbyexperianhasshownthatover60%ofconsumersofeveryagegroup researchaproductonlinebeforemakingapurchase,andaboutthesamepercentage comparespricesonlinebeforemakingapurchase(experianinc.,2010).many companiesarenowresearchingthemarketpotentialandpredictivepowerofsocial media.hewlett Packard,forexample,hasdemonstratedthatitispossibletopredict movieticketsaleswithdatafromthesocialnetworktwitter(asurandhuberman, 2010).Severalotherstudieshavelookedatinternetmetrics,likethenumberof pagevisits,orclickthroughrates,forimprovingadeffectiveness(canny,chen,and Pavlov,2009;Caruso,Giuffrida,andZarba,2011).Therehavealsobeenstudieson Googlemetricsspecifically,toinvestigatetheeffectivenessofsearchads(Omidvar, Mirabi,andShokri,2011).Surprisinglyhowever,therehavebeennostudiesonthe useoftheactualkeywordsearchvolumeforaproduct,topredictitsrevenue.by understandingtherelationshipbetweensearchvolumeandproductrevenue,ad effectivenesscouldbeimproved. Thispapercontendsthatthesalesrevenueofsomeproductscanbe predictedbykeywordsearchvolume,liketheappleiphone,whilethesalesof others,liketheipod,maynotbe.thepossiblefactorsresponsiblefortheseresults willbediscussed.thisstudyalsosuggeststhat,contrarytomostproducts,thead spendinghasnosignificantimpactonthesalesrevenueforthesetwoproducts. Thelackofcorrelationbetweenadspendingandsalesrevenuewillbeexamined, 3

4 andtheroleofapple suniquemarketingpracticeswillberevealedasthemost probableexplanation.finally,theseasonalityofipodsaleswillbediscussed,and themodelsforalltheseeffectswillbespecifiedandreviewed. II.RelatedWork In2010,Hewlett Packardbuiltamodeltoforecastbox officerevenues,from therateandsentimentoftweetsonthesocialnetworktwitter,andfoundthatthe modelcouldoutperformmarket basedpredictors.thestudyconcludedthat contentonsocialmediawebsites,liketwitter,couldbeusedtopredictrealworld outcomes(asurandhuberman,2010).akeyfindingwasthatthepre release promotionsformovieswereactuallynotpredictiveoftheirperformanceinthebox office.thestudydefinedpre releasepromotionsashyperlinkstotrailersandother promotionalmaterial,andlinkforwardsfromoneuserinthenetworktoothers. Tweetrate,ratherthanpre releasepromotions,wasfoundtobeabetterpredictor ofbox officesuccess.thetweetratewasdefinedas, thenumberoftweets referringtoaparticularmovieperhour (AsurandHuberman,2010).Hightweet ratescorrespondedtomoresuccessfulfilms. IntheHPstudy,thetweetratewasameasureofthepopularityofamovieon thesocialnetworktwitter.adistinctionshouldbebetweenthemeasureof popularityusedinthehpstudy,thetweetrate,andthemeasureofpopularityused inthisstudy,thegooglesearchvolume.thehpstudymeasuredthetweetrate duringacriticalperiod,whichwasdefinedasoneweekbeforethemoviewas released,totwoweeksafter.thereasonthathponlymeasuredthiscriticalperiod 4

5 wastheshorttimespanthatmovieswereshownintheaters.incontrasttothehp research,thisstudymeasuresgooglesearchvolumeovera6 yeartimeperiod,in whichtheproductsbeingobservedsoldcontinuously.nevertheless,theconceptof popularityaffectingsalesrevenueisthesameinbothstudies. III.Data ThedataforthisstudywascollectedfromApple sannualform10 Kreports, andgoogleinsightsforsearch,forthe6 yeartimeperiodbetweenjanuary2004 anddecember2010.allofthedataismeasuredquarterly.googlesearchdatawas usedbecause,atthetimeofthisstudy,googlewasthehighesttraffickedsearch engineontheweb,soitwouldlikelyprovidethebestrepresentationofinternet popularitybysearchvolume(wornack,2011).appleproductswerechosentobe studiedbecauseoftheirpopularityandbrandawarenesswithconsumers.the AppleiPodwasreleasedin2001,soitssearchandrevenuedatawasmeasurable fromthebeginningofthe6 yearperiod.theiphonewasreleasedin2007,soits searchandrevenuedatawaszeroatthebeginningofthe6 yearperiod.however, itsstatisticalmodelwasadjustedforthis.sincetheiphonewasreleasedwithinthe measuredtimeperiod,itwasagoodindicatorofhowpre releasesearchvolume wasrelatedtothesalesrevenuethattheproductgenerates. Toacquirethemostaccuraterepresentationofpopularityontheinternet, GoogleAdWordswasusedtofindthetoptwentykeywordalternativesto ipod and iphone. Thesehighestsearchedkeywordalternativeswereincorporatedintothe searchvolumedata,alongwiththemodelnamesofeachipodandiphonereleased 5

6 duringthe6 yeartimeinterval.duplicatekeywordsthatappearedinthetop keywordalternativeslist,suchas newiphone and iphonenew, wereonly includedonce. Weeklysearchvolumedatawascollectedonthesetsofkeywordsforboth products.theweeklydatawasaveragedtocreatequarterlydata.productsthat werenotreleaseduntilthemiddleofthestudyhadvaluesofzeroforthesearch volumeuntiltheirreleases.topreventthisfromskewingthedata,theseproducts wereonlyincludedinthedataoncethefirstnon zerovaluewasmeasured.they wereincludedinthedatafromthatpointafter,eveniftheirsearchvolumeswent backtozero. GoogleInsightsforSearchprovidedscaleddata,sothesearchvolumeswere notmeasuredinabsoluteterms.thescaleputavalueof100ontheweekthatthe highestsearchvolumewasrecorded,andeveryothervaluewasinproportionto thatnumber.forexample,avalueof50wouldindicatethatduringthatweek, peoplewerehalfaslikelytosearchforthekeywordoritsderivatives,thanduring theweekinwhichthevaluewas100.theadvantagetousingthescaleddatawas thatthepopularityofproductswithverydifferentsearchvolumes,couldbestillbe compared.scalingthedataputitintoproportions,soasearchvalueof23forboth theipodandiphone,forexample,indicatesthatduringthatweek,bothproducts hadthesamerelativepopularity.theuseofscaleddataallowstheimpactof popularityonsalesrevenuetobemeasuredinrelativeterms,irrespectiveof absolutesearchvolume.aproductwithahighabsolutesearchvolumemighthave 6

7 highsalesrevenue,butinrelativeterms,thatproductmightnotbeaspopularasa productwithasmallerabsolutesearchvolume. QuarterlysalesrevenueoftheAppleproducts,andadvertisementspending figuresweretakenfromannualform10 Kreports.Thedataforbothofthese variableswasmeasuredinmillionsofusdollars.advertisementspendingwas collectedasawholefigureforallmediums,ratherthanaspecificfigureforonline advertisement.thisallowedforthepossibilitythatconsumerscouldhavelearned abouttheproductfromanyformofadvertisement,andthensearchedforitonline. IV.SettinguptheHypothesisTest Thisexperimentcanbebrokenintotwocomponents:theaffectof advertisingoninternetpopularity,andtheaffectofinternetpopularityonsales revenue.ifbothoftheserelationshipsarefoundtohavecorrelations,thenbythe transitiveproperty,advertisingaffectssalesrevenue,andshouldbeincludedwith popularity,inacombinedmodel.therehasbeenextensiveresearchintohow advertisingaffectssalesrevenue,andithasbeenconcludedthatcurrentadvertising influencescurrentsales,aswellassalesintothefuture(weiss,1995).duetothe possibilityofcurrentadvertisementsinfluencingfuturesales,alaggedtermshould beincludedinthemodeltomeasureitseffect.whilethereisnoresearchonthe influenceofinternetpopularityonsales,itisreasonabletosuspectthatcurrent popularity,likeadvertising,hasbothpresentandfutureeffectsonsales.the inclusionofalaggedtermforpopularitycanmeasurethiseffect. 7

8 Thehypothesistobetestediswhetherinternetpopularityinfluencessales revenue,and,ifadvertisingiscorrelatedwithinternetpopularity,whetherboth advertisingandinternetpopularityinfluencesalesrevenue.thenullisthatthere arenocorrelationsbetweenadvertisementspending,internetpopularity,andsales revenue. V.DevelopingtheModel Duetothecurrentandfutureeffectsofadvertisingandpopularity,alinear finitedistributedlagmodelwasdevelopedtotestthehypothesis.letsstandfor salesrevenueattimet.letprepresentinternetpopularity,andarepresent advertisementspending.finally,letαbeaconstant,andleturepresentallthe unmeasureddeterminantsofsalesrevenue.writingtheequationwithpandaboth laggedonequartergives,. (1) Thisequationcanbemodifiedwithatimetrendtoaccountfortheincreasing tendencyofsalesrevenuefortheipodandtheiphone,whichgives,. (2) Equation(2)canbefurthermodifiedfortheiPod,whichexhibitedstrong seasonalityfromyeartoyear(seefigure1).theinclusionofdummyvariablesfor eachquarter,minusone,leadstothefinalmodelfortheipod,. (3) Equations(2)and(3)aretheregressionequationsusedfortheiPhoneand theipod,respectively.theparameterstothesemodelswereestimatedbyordinary 8

9 leastsquares.asexpected,theinclusionofdummyvariablesincreasedthe goodnessoffitfortheipodmodel,atthecostofthreedegreesoffreedom(seetables 1and2).Topreventtheresultsfrombeingspurious,anaugmentedDickey Fuller testwasperformedtodetermineiftheprocesseswerenon stationary.themodels forboththeipodandtheiphoneweredeterminedtobestationaryprocesses. VI.Results TheresultsoftheiPodregressiongivethefollowingmodelforsalesrevenue: ipod _ Revenue = (popularity t ) (popularity t 1 ) 11.77(ads t ) (ads t 1 ) (Q1) (Q2) (Q3) (4) t + u t Thetimeofyearwastheonlystatisticallysignificantfactorthatinfluenced theipod ssalesrevenue.asfigure3shows,salesinthefourthquarterofeachfiscal yearescalated,andthendroppedoffinthefirstquarterofthenextyear.the regressionoutputshowsthatthethreedummyvariableswereallstatistically significant,andtheirnegativecoefficientsreinforcethefactthatsalesrevenuewas lowerduringthosequartersthaninthefourth(seetable2).thestrongrelationship betweenthefourthfiscalquarterandsalesrevenueisunderstandable,considering thatitcoincideswiththechristmasseason.surprisingly,neithertheinternet popularitynortheadspendinghadastatisticallysignificanteffectonsales.the adjustedr 2 andtheprobabilityofthef statisticimplyastronggoodnessoffitfor themodel. TheresultsoftheiPhoneregressiongiveasimilarmodeltoequation(4),but withoutseasonality: 9

10 iphone _Revenue = (popularity t) (popularity t 1 ) (ads t ) (ads t 1 ) t + u t (5) UnliketheiPod,theiPhonewasfoundtobesignificantlyaffectedbyboththe currentandlaggedinternetpopularity,aswellasthetimetrend(seetable3). Specifically,ifthecurrentpopularityonthewebweretoincreasebyoneunit,as measuredbythegoogleinsightsscale,theiphone ssalesrevenuewouldlikely increaseby$81.45million.ifthelaggedpopularityonthewebweretoincreaseby oneunit,theiphone srevenuewouldlikelyincreaseby$108.27million.aswiththe ipod,theadspendingandlaggedadspendingweresurprisinglyinsignificant.the adjustedr 2 andtheprobabilityofthef statisticimplyastronggoodnessoffitfor thismodeltoo. VII.Discussion Themostsurprisingdiscoverywasthatadvertisinghadnoeffectonsales revenueforeitherproduct.themostprobableexplanationforthisisapple s uniquemarketingstrategy.accordingtoresearchbyadvertisingage,applespent $28milliontoadvertisetheiPodanditsfeatures,whentheproductwasfirst releasedinthefourthquarterof2001.applethenreducedtheadspendingto merely$4.4millionforallof2002(bulik,cuneo,andjohnson,2007).thestrategy wastoallowconsumerstotrytheproduct,andletnewsspreadviawordofmouth. ItwassuccessfulbecauseoftheintensebrandloyaltyofApple scustomers,andthe simpleandaestheticallyappealingpackagingthateasilydistinguishedtheipodfrom itscompetitors.theipodwasmarketedasacoolandhipproduct,andthisideawas 10

11 reinforcedbytheipod slimitedretailoutsideofapplestores,whichgaveit exclusivity(barrile,2006).appleplannedtoincreaseadvertisementonlyifsales startedtofall,andsincefeatureswererepeatedlyaddedovertime,salesremained high,evenastheproductaged(bulik,cuneo,andjohnson,2007).sincetheiphone wasreleasedin2007,ithasbeenmarketedbythesamemethod.although advertisementspendinghasincreasedinabsolutevalueovertime,ithasremained lowinproportiontosalesrevenue.forthisreason,itmakessensethatthead spendingmightnotbeagoodpredictorofsalesrevenue. Anothersurprisingdiscoverywasthattheinternetpopularityhadnoeffect ontheipod ssalesrevenue.themostprobableexplanationforthisisthatby2004, theipodhadalreadybeeninstoresfornearlythreeyears.consumersdidnotneed toresearchtheproduct,orcompareitspricewithcompetitors,becausetheprice remainedrelativelyconstantduringthoseyears,evenasnewfeatureswereadded (Bulik,Cuneo,andJohnson,2007).Googlesearcheswouldlikelyhavebeendoneby consumerswishingtoresearchtheproduct,buytheproduct,orseeknewsstories aboutit.ifthenoveltyoftheipodhadwornoffby2004,searchqueriesmayhave beentriggeredonlybythereleaseofnewmodelsornewsabouttheproduct,rather thanbyconsumers desiretobuyit. TheiPhonewasstronglyinfluencedbyboththecurrentquarter sinternet popularityandthepreviousquarter s.thepopularityoftheiphonereachedapeak aboutayearafteritsrelease,andreacheditshighestpointinlate2010(figure4). Themostlikelyexplanationforthe2010spikewastheannouncementthatanew iphonewouldbereleasedfortheverizonwirelessnetworkserviceprovider.until 11

12 then,at&thadbeentheonlywirelessserviceproviderthattheiphonewas compatiblewith.byhackingtheiphone,itwaspossibleforconsumerstoaccess otherwirelessnetworks.infact,someofthehighestsearchedkeywordsthat GoogleAdwordsreportedfortheiPhoneincludedthephrase iphonehacked, soa VerizoncompatibleiPhonewouldhaveencouragedconsumerstoresearchthenew product.theiphone ssalesreachedhighpointsatthesametimeastheinternet popularity,andtherelationshipwasspecifiedbyequation(5). Oneexplanationfortheinfluencethatthelaggedpopularityhadonthe iphone,isthatconsumersnotonlyresearchedthephone,buttheservicecontractsit hadwithwirelessproviders.duetothesecontracts,whichtypicallylasttwoyears, consumerswouldbemorelikelytoresearchandcomparethepricesoftheiphone anditscompetitors.thiswouldtakemoretimethansimplyresearchingthephone byitself.servicecontractscouldexplainthedifferencebetweenthelagged popularityoftheiphonebeingsignificant,andthelaggedpopularityoftheipod beinginsignificant. VIII.Conclusion ThispaperhasshownthatthesalesrevenueoftheAppleiPhonemaybeable tobepredictedbyitsinternetsearchpopularity,asmeasuredbygooglesearch volume.thenumberofgooglesearchescouldindicatehowinterestedconsumers areintheproduct.salesoftheipodweredeterminedtobeheavilyinfluencedby thetimeofyear,andwerenotpredictablebyinternetpopularity.whilesales spikedduringthechristmasseason,theywerenotcorrelatedwithincreasedgoogle 12

13 searches,possiblybecauseofthelengthoftimethattheproducthasbeenonthe market.finally,apple sadvertisementspendingwasshowntobeapoorpredictor ofsalesrevenueforboththeipodandtheiphone,possiblybecauseofapple s uniquemarketingstrategythatpromotesbrandloyalty,andadvertisementviaword ofmouth. Thepurposeofthisresearchwastodeterminewhethertheinternet popularityofaproductcouldpredictitssalesrevenue,andiftheamountof advertisementspendinghadanyeffectonpopularityandsales.theresultswere inconclusive,asoneproduct spopularitywasshowntobecorrelatedwithsales,but theotherproduct spopularitywasnot.similarmodelscouldbedevelopedforother productstotestthishypothesisonalargerscale.ifinternetpopularity,as measuredinkeywordsearchqueryvolume,isdeterminedtobeagoodpredictorof salesrevenue,thenadvertiserscouldimprovetheeffectivenessoftheirads. 13

14 References Asur,Sitaram,andBernardoA.Huberman.PredictingtheFuturewithSocialMedia. Hewlett PackardDevelopmentCompany,L.P.,2010. Barrile,Steve.IngredientsfortheSuccessoftheAppleiPod:Marketing.Melbourne: WarringalPublications,2006. Bulik,BethSnyder,AliceZ.Cuneo,andBradleyJohnson."Apple snotthinking Different."AdvertisingAge78.26(2007):AcademicSearchComplete.EBSCO. Web.5Mar Canny,JohnF.,YeChen,andDmitryPavlov.Large ScaleBehavioralTargeting. Sunnyvale:Yahoo!Labs,2009. Caruso,Fabrizio,GiovanniGiuffrida,andCalogeroZarba.BehavioralOnline Advertising.Ithaca:CornellUniversityPress,2011. ExperianInc.The2010DigitalMarketer:TrendandBenchmarkReport.Experian MarketingServices,February2010. Hanssens,DominiqueM.,andAmitJoshi.AdvertisingSpending,Competitionand StockReturn.UniversityofCentralFlorida,2008. Omidvar,MohammadAmin,VahidRezaMirabi,andNarjesShokry. Analyzingthe ImpactofVisitorsonPageViewswithGoogleAnalytics. International JournalofWeb&SemanticTechnologyVol.2,No.1,January2011. Weiss,DoyleL.DoesTVAdvertisingReallyAffectSales?TheRoleofMeasures, Models,andDataAggregation.JournalofAdvertising.22Sept Wornack,Brian. Google,MicrosoftAddedInternetSearchMarketSharein December. Bloomberg.13Jan

15 Table1 Dependent Variable: ipod Revenue Method: Least Squares Variable Coefficient Std. Error t-statistic Prob. Constant ipod Popularity Lagged ipod Popularity Ad Spending Lagged Ad Spending R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)

16 Table2 Dependent Variable: ipod Revenue Method: Least Squares Included observations: 27 after adjustments Variable Coefficient Std. Error t-statistic Prob. Constant ipod Popularity ipod Lagged Popularity Ad Spending Lagged Ad Spending Q Q Q Time Trend R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)

17 Table3 Dependent Variable: iphone Revenue Method: Least Squares Included observations: 27 after adjustments Variable Coefficient Std. Error t-statistic Prob. Constant iphone Popularity iphone Lagged Pop Ad Spending Lagged Ad Spending Time Trend R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic)

18 Figure1 ipodsalesrevenueregressionoutputcorrespondingtotable1 18

19 Figure2 ipodsalesrevenueregressionoutputcorrespondingtotable2 19

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