DailyTemperatureandSales

ofEnergy-usingDurables

JacopoBonan,CristinaCattaneo,Giovannad’Adda,andMassimoTavoni

WorkingPaper23-43

November2023

ResourcesfortheFuture

1

AbouttheAuthors

JacopoBonananaffiliatedscientistattheEuropeanInstituteonEconomicsandtheEnvironment(EIEE)andanassistantprofessorattheSchoolofManagementof

PolitecnicodiMilano.HeobtainedtheMScinEconomicsattheUniversityof

Edinburgh(UK)andthePhDinEconomicsattheUniversityofMilan-Bicocca.HehasbeenaresearchfellowattheCatholicUniversityofMilanandajuniorresearcheratFondazioneEniEnricoMattei(FEEM).Hisresearchfocusesondevelopment

economicswithanemphasisonenergyaccessandefficiency,health,agriculture,andformalandinformalfinancialinstitutions.Heisspecializedinthedesignofimpact

evaluationsandcoordinatedseveralrandomizedcontrolledtrialsinSub-Saharan

AfricaandSouthAsia.

CristinaCattaneoisascientistatEIEE,wheresheisheadoftheresearchareaon

HumanMigration.SheholdsaDPhilinEconomicsfromtheUniversityofSussex-

Brighton(UK),aPhDinEconomicsfromtheUniversitàdegliStudiinMilan(Italy),andaMAinDevelopmentEconomicsfromtheUniversityofSussex.Shehasbeeninvolvedinseveralinternationallyfundedresearchprojects,andshecoordinatedaH2020

projectonPsychological,social,andfinancialbarrierstoenergyefficiency(PENNY).From2007to2018,CristinawasaseniorresearcheratFondazioneEniEnricoMattei(FEEM).CristinaisanadjunctprofessorattheGraduateSchoolinPublicEconomics-DEFAPandpreviouslytaughtinvariousuniversitiesinItalyandabroad,bothat

undergraduateandatgraduatelevel.Hermainresearchinterestsinvolveapplied

econometrics,theeconomicsofmigration,andenergyeconomics.Shehaspublished,amongothers,forInternationalMigrationReview,JournalofDevelopmentEconomics,JournalofInternationalEconomics,JournalofHumanResources,Resourceand

EnergyEconomics,andReviewofEnvironmentalEconomicsandPolicy.

Giovannad’AddaisanassistantprofessorattheUniversityofMilanandascientistatEIEE,wheresheisheadoftheresearchareaonBehavioralScience.Previously,she

workedatMilanPolytechnicandtheUniversityofBirminghamandwasavisiting

researcheratFEEM.SheholdsaPhDinEconomicsfromBocconiUniversity,witha

thesisonnaturalresourceconservationindevelopingcountries,andanMScin

DevelopmentManagementfromLSE.DuringherPhD,GiovannawasaresearchfellowatHarvardKennedySchool,ParisSchoolofEconomicsandUniversityofZurich.

Giovanna’sresearchuseslaboratoryandfieldexperimentstoinvestigatekeydriversofpro-socialbehavior,suchasinstitutions,norms,andleadershipstructures.Her

recentworkhasfocusedontheanalysisoftheimpactofbehavioralenergyefficiencyprograms.

DailyTemperatureandSalesofEnergy-usingDurables

2

MassimoTavoniisthedirectorofEIEE.HeisalsoafullprofessorattheSchoolof

ManagementofPolitecnicodiMilano.HecoordinatedtheClimateChangeMitigationprogramatFondazioneEniEnricoMattei(FEEM)between2015and2018.HehasbeenafellowattheCenterforAdvancedStudiesinBehavioralSciencesatStanford

Universityandapost-doctoralfellowatPrincetonUniversity.Hisresearchisabout

climatechangemitigationpoliciesandhasappearedinmajorscientificjournals.HeisaleadauthoroftheIPCC(5thand6thassessmentreports),co-directsofthe

InternationalEnergyWorkshopandwasdeputyeditorforthejournal‘Climatic

Change’.HeisarecipientofagrantfromtheEuropeanResearchCouncil(ERC).Hehasadvisedseveralinternationalinstitutionsonclimatechangematters,includingtheOECD,theAsianDevelopmentBank,theWorldBank.

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1

Dailytemperatureandsalesofenergy-usingdurables

JacopoBonan1,2,CristinaCattaneo2,Giovannad’Adda3,2,andMassimoTavoni1,2

1PolitecnicodiMilano

2RFF-CMCCEuropeanInstituteonEconomicsandtheEnvironment,Fondazione

CentroEuromediterraneosuiCambiamentiClimatici

3UniversityofMilan

Abstract

Decisionswithsignificantandlong-lastingconsequencescanbeinfluencedbyconditionsatthemomentofchoice,suchasweather.Usingadministrativedatafromanonlineretailer,weexam-inewhethertemperatureandotherweathervariablesaffectthesearchandpurchaseofenergy-usingdurables,namely,airconditioners(ACs)anddryers.WeobservemoresalesofACsonhotdaysandfewersalesofdryersonhot,windydays.Wefindnoimpactforapplianceswhoseusefulnessisnotaffectedbytheweather.ForAC,weather-inducedsearchesandpurchasesareinlower-efficiencyen-ergyclasses.Productsearchdataallowustolookintotheprocessleadinguptopurchase.ProspectiveACbuyerssearchlessintensivelywhenthetemperatureishigher,andtheoppositeholdsforbuyersofdryerswhentemperatureandwindspeedincrease.Modelsofmemoryandattentioncanexplainthesebehavioralpatterns.Understandingthesedynamicsisimportantfordesigningenergy-efficiencypolicies,giventheenergyneedsofcoolingtechnologiesandtheirincreaseddemandandusefulnessinarapidlywarmingworld.

Keywords:Projectionbias;salience;energyappliances

JELclassificationcodes:D91,D12,L81

2

1Introduction

Decisionsthathavefutureconsequencesareubiquitous.Althoughtraditionaleconomicsassumesthatindividualscancorrectlyestimatefuturecostsandbenefits,evidenceshowsthatdecisionswithlargeandlong-lastingconsequencesareheavilyinfluencedbytastes,emotions,andcircumstancesatthemomentof

choice(Busseetal.,

2015;

Simonsohn,

2010).Recentsaliencemodels(Bordaloetal.

(2022),henceforth

BGS)offeraunifyingframeworkforawiderangeofdeviationsfromstandardeconomictheory,suchas

projectionbias(Loewensteinetal.,

2003),referencedependence(Ko?szegiandRabin,

2006;

Kahneman

andTversky,

1979)andframingeffects(Bordaloetal.,

2013)

.Specifically,BGSmodelattentionasinfluenced,bottom-up,bycontrasting,surprising,orprominentstimuli.

Weanalyzeadecisionwithlong-lastingconsequences,thepurchaseofanenergy-usingdurable,andshowhowitisaffectedbydailyweather.Weconsidertwotypesofapplianceswithsignificantimpactsonhouseholdenergyconsumption:airconditioners(ACs)anddryers.UsingdatafromanItalianonlineretailer,weinvestigatetheimpactofweatheronthedecisiontobuyandthesearchprocessleadinguptoit.Wefocusontwomainweatherdimensionsthatmayaffecttheseappliances’perceivedusefulness:averagedailytemperatureandwindspeed.

WefindthathigherdailytemperatureincreasespurchasesofACsanddecreasesthoseofdryers;higherwindspeedreducesdryerpurchasesbuthasnoimpactonACs.Otherdimensionsofweathermeaningfullycorrelatewithpurchaselikelihood:anindexofdiscomfort,capturingperceivedtemperatureandincreasingwithhumidity,affectspurchasesofACsbutnotdryers.Consistentwiththeseresults,highertemperatureleadstoafastersearchprocessforACsandasloweronefordryers.TemperaturealsoimpactstheenergyefficiencyofACspurchasedandviewed,shiftingusers’attentiontowardlower-efficiencyproducts.Wefindnoeffectoftemperatureorwindspeedontheenergyefficiencyofdryersviewedorboughtoronsalesofothertypesofappliances,suchaswashingmachinesordishwashers.Ourresultsarerobusttousingvarioussamples;tocontrollingfortemporalandspatialpatternsofvariationinsales;toconsideringnon-lineareffectsoftemperature.Finally,asurvivalanalysisofthesearchprocessalsoproducesconsistentresults.

Ourfindingsareconsistentwithamodelofsalience,adaptedfromBGS,thatwepropose.Weatheraffectstheperceivedusefulnessoftheapplianceand,throughit,thelikelihoodofpurchase.Astheappliance’sattributesrelatedtoitsusefulnessbecomemoresalient,otherattributes,suchasitsenergyefficiency,loseprominence.Thesemechanismshaveimplicationsforthesearchprocess,whichbecomesfasterandmoresuperficialwhenweathermakestheperceivedusefulnessofanappliancemoresalient.

Weexaminepotentialalternativeexplanationsforourresults.Rationalbehaviormayexplainusers’be-haviorinseveralways.First,customerscouldbealreadysetonbuyingtheapplianceandweatherjustinfluencesthetimingofthepurchase,inducingthemto"pullthetrigger"onit.Inthiscase,wewould

3

observeaweather-inducedintertemporalsubstitutionofsalesinourdata.Second,weathermayleadcus-tomerstousetheapplianceandrealizethatitisbroken.Underthisscenario,ourresultswouldbedrivenbyappliancereplacements.Third,weathermayallowuserstoacquirenewknowledgeconnectedtotheusefulnessoftheappliance.Fourth,thepurchasemayaddresstheneedtourgentlydealwiththeweatherconditions.Ourfindingscouldalsobeexplainedbyrationallearningaboutfuturetemperaturesorbyageneraltendencytoremainhome,search,andbuyunderparticularweatherconditions.Finally,selec-tionissuesandtheimpactofheatoncognitiveperformancecouldalsobepotentialdriversoftheresults.Throughaseriesofancillaryexercisesandanalyses,weprovideevidencethatourdataarenotconsistentwithanyofthesemechanismsandinsteadfurthersupportsalienceasthekeychannelbehindtheimpactofweatheronactualinvestmentdecisions.

Ouranalysishaslimitations.First,wecanonlyobserveusersontheretailer’swebsite.Thissettingallowsustoobservebehaviorunderminimaldemandeffects,butourresultscanonlyspeakoftheeffectofdailyweatheronsalesonthewebsite,ratherthanonoverallpurchasesonthemarket.Second,theretailerdidnotprovideuswithdataonprices.Weaddressthislimitationintwoways.Weretrievepricesforasubsetofproductsinoursamplethroughwebscrapingandpricetrackingservices.Inaddition,wehavedataonthepromotionsactiveonproductsandalwayscontrolforthemintheanalysis.Third,ourdatadoesnotincludeanyinformationonthesupplyside.Wedonotknowhowthestockofappliancesindifferentenergyclasseschangedwiththeweather,norwhethertheretailerplacedcertainproductsonsalemoreprominentlydependingontheweather.Wetestforthepresenceofweather-relatedstrategicbehavioronthepartoftheretailer,butourabilitytoexplorethischannelislimited.Finally,wehavenoproxyforpurchases’welfareimpact,suchastheprobabilityofreturns,asin

Conlinetal.

(2007)

.

Ourresultsareimportantfromapolicyperspective.ACsanddryersareexpensive,withimpactsonresidentialenergyuselastingupto15years.Spacecoolingandclothesdryingareresponsiblefornearly

20and6percentofresidentialbuildings’energyconsumption,respectively(IEA,

2018;

Bendt,

2010)

.ACgeneratesadditionalimpactsonglobalwarmingduetoitsemissionsofgreenhousegasesbutisalso

acriticaltoolinadaptingtorisingglobaltemperatures,whichexplainsitsgrowingadoptionrates(Colelli

etal.,

2023).Understandingwhetherdecisionswithsuchlargeandlong-lastingenvironmentalimpacts

aresubjecttobiasisimportanttodesigningpoliciesthatmaximizeconsumerandsocialwelfare.Effectivepolicyresponse,however,requiresidentifyingwhichbiasisatwork.OurresultssuggestthateffortstomaketheinformationonACenergyefficiencysalientshouldbehighestonhotdayswhenweatherincreasessalesofthistypeofgood.Policymakersshouldalsoprotectconsumersfrommarketingstrategiesexploitingsaliencebiases.

Thispaper’sresultscontributetotheliteratureonprojectionandsaliencebiasinconsumerpurchases.

Severalstudiesshowtheimpactofdailyweatherorpollutiononconsumers’decisions(Conlinetal.,

4

2007;

Busseetal.,

2015;

AclandandLevy,

2015;

BuchheimandKolaska,

2017;

Changetal.,

2018;

Qinetal.,

2019;

Liao,

2020;

Lamp,

2023)andlifechoiceswithlong-lastingconsequences(Simonsohn,

2010)

.1

Comparedtothesepapers,ourstudyhasuniquedataonthesearchprocessthatallowsustoanalyzedecision-makingfromthestart.Byexploitingthesesearchdataandinformationonapplianceenergy-efficiencyclasses,ourpaperprovidesadditionalevidenceontheprocessbehindsalience-induceddecisions.Moreover,theavailabledataonbothACanddryersallowustotestthebroadersalience-inducedeffectsofweatheronsales.Asin

Busseetal.

(2015),wedocumentthatsalienceandprojectionbiascan

leadtoachangeinthesaleprobabilityinbothdirections.

Heetal.

(2022)specificallyanalyzedtheeffect

ofweatheronthedecisiontopurchaseACsandfoundthatdeviationsfromcomforttemperatureincrease

thelikelihoodofpurchasingEnergyStarmodels.Weimproveonthispaperbyprovidingevidenceonsearch;byusingamorepreciseindicatorofenergyefficiency,i.e.,EUenergyclasses;andbyanalyzingtheimpactofweatheratfinertemporalgranularity(weekly).

Studiestypicallypitsalienceandprojectionbiasastwoalternativemechanisms.However,despiteagree-ingthatbotharebehindtheevidencetheypresent,theycannotultimatelydistinguishempiricallybetweenthem.Recenttheoreticaldevelopmentsinthesalienceliteratureovercomethedistinctionbetweenthesetwomechanismsbymakingprojectionbiasamanifestationofthebroadereffectsofsalientstimulion

attentionandchoice(Bordaloetal.,

2022).Weapplythisframeworktoexplainourfindings.Inaddition,

ouranalysisofsearchprovidesthefirstevidence,tothebestofourknowledge,ofhowbottom-upsalienceaffectshowindividualsseekandattendtoinformation.

Theremainderofthepaperisorganizedasfollows.Section

2

describesthefieldwheretheanalysisisconducted,Section

3

providesatheoreticalframeworkforourfindings,Section

4

describestheempiricalapproachandpresentsresultsforACanddryers,Section

5

discussespossiblealternativemechanismsandSection

6

concludes.

2Settinganddata

WeusedatafromalargeItalianonlineretailer.Wecanaccessconsumers’searchandpurchasingdataandexaminethedecision-makingprocessanditsoutcomes.Althoughonlyabout16percentofappliancesalesoccurredonlineinItalyduring2018,onlinechannelsplayedacrucialroleinpurchasedecisions:74

percentofbuyersoflargeappliancesinitiatedtheirsearchonline(Flaviánetal.,

2020).Restrictionsdueto

theCOVID-19pandemichavecauseda64percentincreaseinonlinesalesinEurope.2

Reopeningphysicalstoreshasnotrestoredonlinesalestotheirprepandemicvalues.Thesefigureshighlighttherelevanceof

1

Projectionbiasisdocumentednotonlyinthefieldbutalsoinexperimentalsettings(AugenblickandRabin,

2019)

.

2Source:Eurostat,availableat

https://ec.europa.eu/eurostat/statistics-explained/index.php?

title=E-commerce_statistics_for_individuals.

5

theonlinesettingnowandinthefuture.

ThepenetrationratesofACsanddryersinItalyarestilllimited,thoughrapidlygrowing.In2021,only48.8and15.2percentofItalianfamiliesownedanACandadryer,respectively

ISTAT

(2022)

.Thesefigureswere29.4and3.3percentin2013

ISTAT

(2014)

.Thelifecycleisestimatedtobearound15yearsforACsand13yearsfordryers.Therefore,weexpectthatinourdata,theseappliancesaremainlypurchasedforthefirsttime.

Ourdatacomprisethefullnavigationhistoryof112,428websiteusersbetweenJune1andOctober16,2018.WeidentifycustomersprimarilythroughtheirregistrationID.Usersmakingapurchasemustbeloggedintothewebsite.Instead,simplynavigatingthewebsitedoesnotrequireuserstoberegisteredorloggedin.Inthesecases,weidentifyusersthroughcookie-basedtracking.Cookiesarelinkedtothecomputer’sIPaddressandbrowser.Thisimpliesthatwecannotidentifyasthesameusersomeonewhovisitsthewebsitefromdifferentcomputersorbrowsersorclearscookies.Wealsocannotdistinguishifmultipleindividualsviewthewebsitefromthesamesharedcomputer.Theselimitationsprimarilyaffectourabilitytofollowusers’fullnavigationhistoryiftheyarenotlogged-intothewebsitewhenbrowsing,

henceouranalysisofsearchbehavior.3

Foreachpageviewedbyauserduringthestudyperiod,wehaveinformationonthetypeofproductviewed(AC,dryer,washingmachine,dishwasher,refrigerator,freezer),thetypeofpage(e.g.,product,listing,orcart),andthenumberofsecondsspentonthepage.Wealsoknowwhethertheuserorderedtheproduct.Wematchthenavigationdatawithproductdataobtainedfromtheretailer.Wehaveinformationontheenergyclassforeachmodel,identifiedbyauniqueproductidentifier.TheEUenergylabelisdisplayedoneachproductpageandliststheenergyconsumptioninkWhandtheenergyclass(Figure

A.1)

.En-ergyclassesaretheresultsofengineeringestimatesbasedontheappliance’ssize,energyconsumption,

andotherparametersandrangefromD(leastefficient)toA+++(mostefficient).4

Energyclassesareanimportanttoolforconsumerstogaugeenergyefficiency,giventhecomplexityofenergyconsumptionin-

formationexpressedinkWh(d’Addaetal.,

2022;

Houde,

2018).Labelsbasedonsimilarenergy-efficiency

classesarewidelyusedworldwide,suchasinChina,India,Brazil,andSouthKorea.

Ourdataincludeotherproductinformation.ForACs,weknowwhethertheyareportableorfixed.Forallappliances,wehaveinformationonactivepromotionsonthedayofnavigation,suchasfreedeliveryorzerointerestrateforpaymentsininstallments.Theretailerdatadonotincludeproductprices.WeretrievedthisinformationtothebestofourabilitythroughwebscrapingbetweenJuneandJuly2022.Weusedproductcodestocollectcurrentpricesfromthesameonlineretailerorothermajorretailersforalltheproductsstillonthemarketin2022.Wegatheredpriceinformationfor220ACmodels(outof517in

3See

d’Addaetal.

(2022)formoreinformation.

4ThenewEUenergylabel,introducedin2021,relabeledenergyclassesonascalefromG(leastefficient)toA(mostefficient)withoutchanginghowefficiencyiscalculated.

6

oursample)and168dryermodels(outof282).Thesepricesareusedtocontrolfortherelativepriceofproductsbyenergyclass.Evenifthepricelevelshavechangedintwoyears,therelativepricesofproductsindifferentenergyclassesshouldnotdiffer.Weprovidesuggestivesupportforthisclaimbycollecting2018pricesthroughanonlinetracker.Theyareonlyavailablefor22ACmodels.Finally,forACs,weusewebscrapingtocollectinformationontheirsize,proxiedbythenumberofexternalandinternalunitsassociatedwitheachproductcode,forthesamesampleofmodelsforwhichwecollect2022prices.

AppendixTable

A.1

providestheaveragepricesin2022byenergyclass.Higherenergyefficiencycor-respondstohigherprices,asexpected.ForthelimitedsampleofACs(22)forwhichwehaveboth2022and2018prices,thePearsoncorrelationis0.80.Thesestatisticsvalidatetheuseof2022priceswhenwespecificallyanalyzepurchasesofappliancesinthedifferentenergyclasses.

WegeolocateIPaddressestoidentifyusers’municipalitiesduringbrowsing,whichallowsustomatchuserswithweatherdata.Wecollectmeantemperature,windspeed,andrainfallforeachdayandmu-nicipality.ThesourceformeteorologicaldataistheE-OBSTemperatureandPrecipitationDataSets

(Cornesetal.,

2018),anensembledatasetavailableona0.1-and0.25-degreeresolution.Wedownscale

thegriddeddatatoamunicipallevel,averagingeachmunicipalcentroid’sfournearestgriddedpoints.Inaddition,from

Mistry

(2020),weretrieveinformationonthethermaldiscomfortindex,whichincludes

differentmeteorologicaldriversofdiscomfort,suchastemperatureandhumidity.5

3Theoreticalframework

Recentcontributionstothetheoreticalliteratureonsalienceattempttointerpretwithinthesameframeworkresultspreviouslyexplainedbyseparatemodels,suchasprojectionbias,presentbias,reference-pointde-pendence,andframingeffects.ThecriticalinsightoftheunifiedmodelproposedbyBGSisthatattentionisinfluenced,bottom-up,bysalientenvironmentalstimuli.Contrastwithsurroundings,surpriserelativetopriorexperiences,andprominencewithinthedecisioncontextdeterminesalience.Saliencecandistractdecision-makersfromtheirgoalsorotherrelevantchoiceattributes.Inoursetting,hightemperatureorotherweathervariablesvarythesalienceoftheattributeslinkedtotheappliances’usefulness.WeatherthusaffectsthelikelihoodofsalesofACsanddryersthroughitsimpactontheirperceivedusefulness.

WeadapttheframeworkproposedbyBGStoformallyexplainhowweatherconditionsaffecttheusers’valuationofthetwoappliances.WedenotetheK>1attributesofACsordryersas(a1,...,aK)anddistinguishbetweenthosewhosesalienceisinfluencedbyenvironmentalstimuli,belongingtosubsetP,

andthosenotdirectlyinfluenced,belongingtosubsetI.Theintrinsicvaluationofthegoodis:

5Thedatasetondiscomforthasasmallergeographicalcoveragecomparedtothatoftemperature,wind,andprecipitation.Forsixmunicipalities,wehavemissinginformationondiscomfort.

7

Vp=wkπka+kπka(1)

Thefirsttermcapturesthevaluationofattributes(ak)k=P,thesecondtermcapturesthevaluationoffea-

tures(ak)k=I,andwkandkrepresentthedistortionstodecisionweights(directlyandindirectly)induced

bysalience.arepresentsthedatabaseofnormalattributevaluesfrommemory.Inourcontext,thepromi-

nenceofanattributeistriggeredbyvariationsinoutsidedailyweatherthataffectthedecisionweightattachedtoit(πk

).AcrucialattributethatispartofthesubsetPinthefirsttermofEquation(1)isthe

usefulnessoftheappliance(theabilitytocoolaroomordryclothes).Heatdistortsupwardthevaluation

ofthisattributeforAC,whilecoldtemperatureandlackofwinddistortitupwardfordryers.Therefore,weexpectthathighertemperatureincreasestheprobabilityofpurchasinganACandlowertemperatureandhigherwindspeedincreasetheprobabilityofpurchasingadryer.

Weassumeweightnormalizationasin

Bordaloetal.

(2012,

2013),whichimpliesthattheattentiondevoted

toasalientattributeisdivertedfromnonsalientones.

wkπk+kπk=1(2)

Whenitiswarmer,thehighsalienceoftheusefulnessoftheACsobscurestheirotherattributes,suchasenergyefficiency,thatarepartofthesubset

IinthesecondtermofEquation(1)

.Asimilareffectistriggeredbylowertemperaturesandhigherwindspeedfordryers.Correspondingly,whenweathermakestheusefulnessoftheseapplianceslessprominent,otherattributesbecomerelativelymorerelevantandreceivemoreattention.Theseotherattributesmayconcernprice,energyefficiency,orotherproductdimensions.However,itishardtoidentifyandtestwhichattributes,amongthemanypossibleones,becomemoresalientduetothisprocess.

Accordingtoourmodelandempiricaltests,thepresenceofacorrelationbetweendailyweatherandpur-chasesisevidenceofbiaseddecision-making.Ouranalysiswill,thereforeattempttoruleoutalternativerationalexplanationsforacorrelationbetweenweatherandsales,suchasintertemporalsubstitution,re-placement,learningorurgencyofpurchases.Whatourmodeldoesnotclaimisthatdecision-makingatoptimalweatherisunbiasedandthatwecanquantifythemagnitudeofthebiasasweathermovesawayfromit.

8

4Results

4.1Descriptivestatistics

Thetotalsampleofusersoftheretailer’swebsiteoverthestudyperiodincludes112,428individualswhoviewedatleastoneappliancepage(AC,dryer,washingmachine,dishwasher,refrigerator,freezer)inthestudyperiod.Asubset,48,076,searchedforproductsovermultipledays.About12,984usersviewedanACpageand12,648adryerpageatleastonce.Oftheseviewers,about55percentnavigatedthewebsiteonmorethanonedayintheperiod(Table

1).Weobserve2,250salesofACsand3,424ofdryers,

respectively.1,409and2,090oftheseACanddryersales,respectively,occuraftermo