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PredictingtheSuccessofKickstarterCampaigns

Peter(Haochen)Zhou

STAT157FinalProject

ProfessorAldous

December5th,2017

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1. Introduction

Overthepastdecade,wehavewitnessedanincreasingpopularityincrowdfunding.

Crowdfundingisthepracticeoffundingaprojectorventurebyraisingmanysmall

amountsofmoneyfromalargenumberofpeople,typicallyviatheInternet.Duetoitseasy

accessibilityandbroadexposuretothepublic,entrepreneursandaspiringcreatorsseekto

utilizetheplatformtoraisemoneyfortheirprojects.Accordingtocrowdfundingresearch

firmMassolution,crowdfundinggrew167percentin2014.Toputthatintodollars,

crowdfundingplatformsraised$16.2billionin2014,escalatingto$34.4billionin2015.

For2016,crowdfundingtrendsarepredictedtopassVCfundingforthefirsttime.

Kickstarter,launchedin2009,isoneofthemajorcrowdfundingplatformsalongwith

Indiegogo,RocketHubandGoFundMe.Withamissiontohelpbringcreativeprojectstolife,

Kickstarterhasattractedover$13millionbackerstosuccessfullyfund134,767projects

withatotalamountexceeding$3.4billiontodate.InKickstarter,projectsarelistedinto15

categories:Art,Comics,Crafts,Dance,Design,Fashion,FilmandVideo,Food,Games,

Journalism,Music,Photography,Publishing,TechnologyandTheater.InKickstarter,

“Creators”arepeoplebehindtheprojectswhoareseekingfunding.“Backers”arepeople

whopledgemoneyforprojectstheybelieveinand“pledges”aremonetarycontributions

towardstheprojects.

Kickstarterhasaunique“All-or-Nothing”model,meaningunlessaprojectreachesits

fundinggoal,nobackerwillbechargedanypledgetowardsaproject.Ontheonehand,itis

lessriskyforbackersbecauseprojectsthatarenotfullyfundedhavelesslikelihoodof

completion.Ontheotherhand,itaddsmoreincentivesforcreatorstoconnectwiththe

publicandtofinishtheirprojects.AnotheruniquefeatureofKickstarteristhatbackerswill

onlyberewardedinexperienceorcreativeproductsinsteadofequity.Inaddition,

Kickstarterclaimsnoequityincreators’projectsandcreatorsmaintainfullownershipof

theirwork.

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AlthoughnumerousprojectsenjoyedtremendoussuccessonKickstarter,theaggregated

averagesuccessrateisdecliningovertheyears.Datahaveshownthattheaggregated

averagesuccessrateofKickstartercampaignswas43.70%in2011,43.56%in2013and

35.82%atMay2017.Inthispaper,Iseektoexplorefactorsthataffectthesuccessof

Kickstartercampaigns.Ibelievethatsuccessratediffersacrossdifferentcategoriesand

priorresearcheshaveshownthatFilm&VideoandMusicarethelargestcategoriesand

haveraisedthemostamountofmoney.Film&Video,MusicandGamescombinedaccount

foroverhalfofthemoneyraised.MyprojectattemptstopredictthesuccessofKickstarter

campaignsbyanalyzingthesignificanceoffactorsidentifiedinthedata.Ihypothesizethat

amongallfactorslisted,thecreator’spriorsuccess,goal,numbersofperks,creator’s

Facebookfriends,totalwordcountinthedescriptionaremoresignificantthanother

factors.

2. DataandMethodsIcollectedmydatafromKaggle.com,theopenonlinedatabase.Thedatacontain4000

most-backedprojectsand4000liveprojectswithlimitedinformationsuchasthepledged

amount,category,goal,location,blurbandnumberofbackers.Anotherdatasetcontains

3652Kickstarterprojectsin2017withcomprehensiveinformationsuchasaproject’s

creator,goal,maincategory,duration,numberofcomments,numberofupdatesand

creator’sFacebookfriends.Thereare51uniquecharacteristicsforeachprojectandI

intendtousethisasmymainsourceofdata.Hereisasnapshotofmydataset:

Sincethedataispre-processed,IwillfirstcleanthedatainRtoconvertthemintoa“ready-

to-use”format.Secondly,IwillperformlinearregressioninStatatoexplorethe

significanceofkeyvariablestotestmyoriginalhypothesisthatthecreator’spriorsuccess,

goal,numbersofperks,creator’sFacebookfriendsandtotalwordcountinthedescription

aremoresignificantthanotherfactors.Inadditiontolinearregression,Iwillgenerateplots

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tovisualizethedataandfurtherexploretherelationshipbetweensuccessrateandkey

factors.DuetoKickstarter’s“All-or-Nothing”model,Iwilllimitthescopeofmyprojectand

onlyanalyzeprojectswithsuccess=1(“success”isadummyvariableandtakesthevalueof

1whentheprojectmeetsitsfundinggoaland0otherwise).

GiventhedecliningaggregatedaveragesuccessrateofKickstartercampaigns,Ihopethis

papercanuncoverthemythsbehindthesuccessandserveasapredictionofsuccessfor

creatorsandbackers.

3. LiteratureReviewPriorresearchershavestudiedsimilartopicsandprovidedinsightfulfindingstothe

topic.Thereisevenawebsite(http://sidekick.epfl.ch)thatshowsreal-timepredictionof

thesuccessofKickstartercampaigns.Inthissection,Iwillconductliteraturereviewoftwo

academicpapers“PredictingtheSuccessofKickstarterCampaigns”(Hussain,Kamel&

Radhakrishna)and“LaunchHardorGoHome”(Etter,Grossglauser&Thiran)inorderto

gainmoreinsightsformyownanalysis.

In“PredictingtheSuccessofKickstarterCampaigns”,theauthorsusedamixtureof

simpleandcomplexfeaturestoperformtheirpredictivetask.Simplefeaturesinclude:time

features,locationfeatures,categoryfeatures,textfeatures,goal,staffpickedandnumberof

rewardlevels.Thecomplexfeaturesinclude:previousprojectsbycreator,averagemoney

perrewardlevel,preparednessanddistancetomeanblurblength.Afteridentifyingthe

features,theauthorsusedk-nearestneighbors,logisticregression,supportvector

classificationandrandomforesttocarryoutthemodel.K-nearestNeighborsproducedthe

resultthatonly4ofthesimplefeatureshavepredictivevalue:thegoalamount,lengthof

theprojectdescription,thenumberofrewardlevelsandtheaveragedollaramountper

rewardlevel.Inaddition,RandomForestisprovedtobethebestclassifieryieldingthe

accuracyof80.37%.Itauthorsproposedabettermodeltopredictthesuccessandfailureof

Kickstartercampaigns.

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In“LaunchHardorGoHome”,theauthorsstudiedthepredictionofsuccessontwo

features:thetime-seriesofmoneypledgedandsocialattributes.Theyshowedthat

predictorsthatusetime-seriesfeaturesreachahighpredictionaccuracyof85%.Thesocial

predictorsreachaloweraccuracybutthetwofeaturescombinedyieldhighprediction

accuracy.Thetwopapersarehighlysignificantandlaidthefoundationforfuture

researchesonsimilartopics.

4. Analysis 4.1AnalysisonKickstarterprojectstatistics

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FromthedataontheKickstarterwebsite,weobservethatacross15categories,the5

categoriesthathavethehighestsuccessratesare:Dance,Theater,Comics,MusicandArt.

The5categoriesthathavethelowestsuccessratesare:Technology,Journalism,Crafts,

FashionandPublishing.Itisintuitivethatsuccessratevariesacrossdifferentcategories

duetotheuniquenatureofeachcategory.

However,thedataitselfisnotconvincingenoughbecausethenumberoflaunchprojects

differssignificantlyacrosscategories.“Dance”onlyhas3,765launchprojectswhereas

“Film&Video”has64,773projects.Thesmallprojectsizemaynotberepresentativeofthe

wholepictureandthusweneedtoconductmorein-depthanalysis.

4.2LinearRegressiononkeyvariablesNext,Iwillconductlinearregressiontoexplorethesignificanceofkeyvariables.Inmy

regression,thedependentvariableisnaturallogoftotalpledges:ln(totalPledge)andIwill

addimportantindependentvariablesoneatatimetoexplorethebestfittedmodel.

Intuitively,Ihypothesizethatcreator’sinitialgoal,numberofcommentsundertheprojects,

numberofFacebookfriendsthecreatorhas,creator’spastsuccessrate,numberof

competitors,numberofperksthecreatorprovidesforbackers,totalwordcountinthe

blurb,numberofimagesinthedescriptionsandnumberofupdatesareimportantfactors

forsuccess.Hereattachedaregressiontablewithdifferentindependentvariables.Intheregression

output,adjustedR-squaredmeasureshowwellourindependentvariablesjointlyexplain

thevariationindependentvariable.WenoticethattheadjustedR-squaredhasabigjump

(0.3938to0.4538)fromregression(5)to(6).Itshowsthatpastsuccessrateisan

importantindependentvariableinourmodel.

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regression (1) (2) (3) (4) (5) (6) (7)

dependent lnpledge lnpledge lnpledge lnpledge lnpledge lnpledge lnpledgeintercept 8.2597

(0.0403)

p=0.000

8.2309

(0.0389)

p=0.000

8.1947

(0.0497)

p=0.000

7.8686

(0.0927)

p=0.000

7.6155

(0.9492)

p=0.000

7.0257

(0.1011)

p=0.000

6.8416

(0.0994)

p=0.000

goal 0.000043

(1.66e-

06)

p=0.000

0.00004

(1.63e-

06)

p=0.000

0.00004

(1.64e-

06)

p=0.000

0.00004

(1.63e-

06)

p=0.000

0.00004

(1.60e-

06)

p=0.000

0.00003

(1.58e-

06)

p=0.000

0.00003

(1.56e-

06)

p=0.000

comments -- 0.001426

(0.0001)

p=0.000

0.00142

(0.0001)

p=0.000

0.00139

(0.0001)

p=0.000

0.00129

(0.0001)

p=0.000

0.00134

(0.0001)

p=0.000

0.00124

(0.0001)

p=0.000

friends -- -- 0.08287

(0.0709)

p=0.243

0.0906

(0.0706)

p=0.199

0.0869

(0.0689)

p=0.207

0.1301

(0.0655)

p=0.047

0.1320

(0.0633)

p=0.037

pastsuccess -- -- -- 0.5689

(0.1367)

p=0.000

0.5329

(0.1335)

p=0.000

0.4205

(0.1270)

p=0.001

0.3509

(0.1230)

p=0.004

competitors -- -- -- -- 0.0037

(0.0004)

p=0.000

0.0037

(0.0004)

p=0.000

0.0031

(0.0004)

p=0.000

perks -- -- -- -- -- 0.0709

(0.0055)

p=0.000

0.0558

(0.0055)

p=0.000

wordcount -- -- -- -- -- -- 0.0005

(0.0001)

p=0.000

images -- -- -- -- -- -- --

updates -- -- -- -- -- -- --

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2R 0.3057 0.3560 0.3562 0.3631 0.3938 0.4538 0.4890

regression (8) (9)

dependent lnpledge lnpledge

intercept 6.8957

(0.0971)

p=0.000

6.8548

(0.0963)

p=0.000

goal 0.00003

(1.55e-06)

p=0.000

0.00003

(1.53e-06)

p=0.000

comments 0.0010

(0.0001)

p=0.000

0.0009

(0.0001)

p=0.000

Friends

0.1110

(0.6718)

p=0.072

0.1416

(0.0613)

p=0.021

pastsuccess 0.3920

(0.1200)

p=0.001

0.3967

(0.1187)

p=0001

competitors 0.0021

(0.0004)

p=0.000

0.0017

(0.0004)

p=0.000

perks 0.0472

(0.0055)

p=0.000

0.0409

(0.0055)

p=0.000

wordcount 0.0003

(0.0001)

p=0.000

0.00025

(0.00005)

p=0.000

images 0.0220 0.01987

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(0.0025)

p=0.000

(0.0025)

p=0.000

updates -- 0.0379

(0.0065)

p=0.0002R 0.5144 0.5249

Analyzingtheregressionoutput,regression(9)givesusthebestfitwiththeadjustedR-

squaredof0.5249.Inregression(9),thedependentvariableisln(totalPledge)andthe

independentvariablesare:goal,numComments,noFacebookFriends,pastSuccessRate,

numCompetitors,numPerks,totWordCount,numImagesandnumUpdates.

Thus,myinitialmodelcanbewrittenas:

ln 𝑡𝑜𝑡𝑎𝑙𝑃𝑙𝑒𝑑𝑔𝑒 = b, + b.𝑔𝑜𝑎𝑙 + b/𝑐𝑜𝑚𝑚𝑒𝑛𝑡𝑠 + b4𝑓𝑟𝑖𝑒𝑛𝑑𝑠 + b8𝑝𝑎𝑠𝑡𝑠𝑢𝑐𝑐𝑒𝑠𝑠 +

b;𝑐𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑜𝑟𝑠 + b<𝑝𝑒𝑟𝑘𝑠 + 𝛽?𝑡𝑜𝑡𝑎𝑙𝑤𝑜𝑟𝑑 + 𝛽A𝑖𝑚𝑎𝑔𝑒𝑠 + 𝛽B𝑢𝑝𝑑𝑎𝑡𝑒

Plugginginthevaluesfromtheregressionoutput,mymodelis:

ln 𝑡𝑜𝑡𝑎𝑙𝑃𝑙𝑒𝑑𝑔𝑒

= b, + 0.0000261𝑔𝑜𝑎𝑙 + 0.0008538𝑐𝑜𝑚𝑚𝑒𝑛𝑡𝑠 + 0.1416243𝑓𝑟𝑖𝑒𝑛𝑑𝑠

+ 0.3967113𝑝𝑎𝑠𝑡𝑠𝑢𝑐𝑐𝑒𝑠𝑠 + 0.0017468𝑐𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑜𝑟𝑠 + 0.0408655𝑝𝑒𝑟𝑘𝑠

+ 0.0002534𝑡𝑜𝑡𝑎𝑙𝑤𝑜𝑟𝑑 + 0.0198685𝑖𝑚𝑎𝑔𝑒𝑠 + 0.0379527𝑢𝑝𝑑𝑎𝑡𝑒

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Iuselog-linmodelfortheregressioninordertocorrectskeweddataandprovidemore

intuitiveinterpretationsofthecoefficients.Fromtheregressionresult,wecanobservethat

𝛽., 𝛽/, 𝛽8, 𝛽;, 𝛽<, 𝛽?, 𝛽A𝑎𝑛𝑑𝛽Baresignificantbecausetheirrespectivep-valueislessthan

0.01anditmeansthattheyaresignificantat1%significancelevel.However,weshould

notethat𝛽4hasap-valueof0.021,whichisnotsignificantat1%significancelevelbutis

significantat5%significancelevel.

Firstly,lookingattheregressionresult,wecanobservethatadjustedR-squaredis0.5249.

Itshowsthat52.49%ofchangeinln(totalpledge)canbeexplainedbytheindependent

variablesinthemodel.52.49%isadecentfittobeginwithandtheresultvalidatesmy

choiceofindependentvariables.

Secondly,fromtheregressionresult:numbersofFacebookfriends,numbersofperks,

numbersofupdates,numbersofimagesandpastsuccessrateinfluenceln(totalPledge)the

mostandarethusthemostsignificantindependentvariablesinregression(9).

Totestwhetherthismodelisagoodfit,Iwillperformtwotests:observedvs.predicted

valuestestandhomoscedasticitytest.

Totestforobservedvs.predictedvalue,Iplotln(totalPledge)against

ln(totalPledge)_predictinordertoexplorethelinearrelationship.Weshouldexpecta45-

degreepatterninthedata.Fromtheplot,ifweignoreoutliers,thelinearrelationshipis

relativelycloseto45-degree.Itshowsthatourmodelisdoingagoodjobinpredicting

lntotalPledge.

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Totestforhomoscedasticity,Iuseresidualvsfittedvalueplottostudyifthereareany

patternsoftheresidualsplottedagainstfittedvalues.Iftherearenopatterns,itindicates

thatthemodeldoesnotviolatetheMultipleLinearRegressionassumptionsandourmodel

iswell-fitted.Ignoringtheoutliers,wecannotobserveanypatternsofresidualsvsfitted

valuesandtheerrorsappeartobehomoscedastic.Thus,wecanconcludethatourlinear

modelisagoodfit.

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Iunderstandthattheremightbeomittedvariablesinthemodel.However,giventhemodel

isagoodfitprovedbytwotestsaboveandourindependentvariablescanexplainupto

52.49%ofthechangeinln(totalPledge),Iwillusethismodeltocarryoutmyanalyses.One

potentialimprovementofthispaperistoidentifyandstudyotherindependentvariables

thataresignificantinexplainingln(totalPledge).

4.3 HypothesesFormulationGiventheresultofmyregression,Iwillmodifymyhypotheses.Beforeconductingany

research,myoriginalhypotheseswere:Amongallfactorslisted,thecreator’spriorsuccess,

goal,numbersofperks,numberofFacebookfriends,totalwordcountinthedescriptionare

moresignificantthanotherfactors.Mymodifiedhypothesesare:numbersofFacebook

friends,numbersofperks,numbersofupdates,numbersofimagesandpastsuccessrateare

themostimportantfactorsforacampaign’ssuccess.

ForthenumbersofFacebookfriends,Ihypothesizethatthereisapositiverelationship.

ThemoreFacebookfriendsthecreatorhas,themoresupportheislikelytoreceive.When

thecreatorlaunchesaproject,theirFacebookfriendsaremorelikelytosupportthem

initiallycomparedtostrangers.Inaddition,inthecurrentera,socialmediaismore

powerfulthaneverandFacebookisanidealplatformtoconnectwithsupportersand

garnersupportforcreativeprojects.

Fornumbersofperks,Ihypothesizethatperksareattractivetobackers.Sincebackers

cannotclaimanyequityfromtheproject,perksaretheonlyrewardstheycanreceivefrom

creators.TheperksinKickstartervaryinformsandmonetaryvaluesandserveas

incentivesforbackers.

Therelationshipbetweenthenumberofupdatesandthesuccessofcampaignsisnot

obviousatthebeginning.Onemayarguethateachupdateconveysmoreinformationofthe

projectandprovidesmorevaluableinformationtohelpbackersmaketheirdecisions.

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However,itisdifficulttoquantifythisrelationshipandmoreresearchneedstobe

conductedtofurtherexplorethisrelationship.

ThenumberofimagesincreasestheprobabilityofsuccessofKickstartercampaigns.

Backersaremoreattractedbyvisualimagesandthenumberofimageshelpsthem

understandtheprojectsbetter.Thus,itisimportantforcreatorstouseimagesto

communicatewiththeKickstartercommunityandattractbackers.

Creator’spastsuccessrateisanimportantindicatorofthesuccessoftheircurrentprojects.

Pastsuccessrateindicatesthecreator’sexperienceandconnectionstotheKickstarter

community.Creatorsarelikelytocreatemultipleprojectsinthesamecategoryandifthey

haveconnectedwithbackersfortheirpreviousprojects,itislikelythatthebackerswill

continuetosupporttheircurrentendeavors.However,inthedataset,itisdifficultto

distinguishnewcreatorsandcreatorswithallpriorfailuressincethepriorsuccessrates

areboth0.

4.4 DataVisualizationandSummaryStatisticsMydatasetcontains3652Kickstarterprojectsin2017only.Ofthetotal3652projects,

1503projectsmettheirfundinggoalsandareconsidered“successful”.Forthispaper,Iwill

analyzetheattributesof1503successfulprojects.Asmentionedabove,Kickstarterhasa

unique“All-or-Nothing”modelandevenprojectswithhugeamountsoftotalpledgeswill

failiftheydonotmeettheirfundinggoals.Anotherpotentialimprovementofthispaperis

toanalyzetheattributesof“failed”projectssuchasfundinggoals.First,Iwillvisualizethe

successrateineachofthe15categoriesin2017:

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Forprojectsin2017,thethreecategoriesthathavethehighestsuccessratesare:Comics

(72.92%),Theater(71.88%)andDance(51.43%).Thethreecategoriesthathavethe

lowestsuccessratesare:Crafts(28.71%),Technology(26.25%)andJournalism(17.78%).

Theresultisconsistentwiththeall-timedata.SincethelaunchofKickstarter,the5

categoriesthathavethehighestsuccessratesare:Dance,Theater,Comics,MusicandArt.

Originally,Iexpectedthatthesuccessratebycategoryvariesoveryearduetotheshiftof

trend.Theresultissurprisingandshowsthatthesuccessratebycategoryhardlychanges

overtime.

InKickstarter,aprojectisconsidered“successful”onceitmeetsitsinitialfundinggoal.

However,successrateonKickstarteraloneisnotindicativeoftheproject’ssuccessin

general.Therearemanynuancesbehindthesuccessrate.Afterobservingthesuccessrate

bycategoryin2017,thenextstepistostudytherelationshipbetweenpledgeamountand

category.Itisnaturaltothinkthattotalpledgeamountisrelatedtotheprojectsize,which

differsbycategory.Nowweusedatatofurtherexplorethisrelationship.

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

Successratein2017

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Sincethereareonly8successfulprojectsinthecategory“Journalism”,Iomitthatcategory

fromtheanalysis.Aswecanseefromthegraphabove,across14categories,Technology,

Design,Food,GamesandFashionhavethehighestmeannaturallogoftotalpledges.Crafts,

Theater,Art,DanceandComicshavethelowestmeannaturallogoftotalpledges.Itverifies

ourpreviousassumptionthatpledgeamountisrelatedtoprojectsize,whichdiffersacross

categories.ProjectsinTechnology,GamesandFoodmightrequiremoreResearchand

Developmentcostsandmorecapitalinvestments.Thus,creatorsrequiremorefundingin

ordertocompletetheprojectsinthesecategories.Inaddition,duetothelargesizeofthe

projectsinthesecategories,thecreatorsaremorelikelytoraisehighamountsofpledges.

Naturally,wethinkthattotalamountofpledgesisrelatedtothenumberofbackers.

However,backerswithlargeamountsofpledgesmightaffecttheaccuracyofouranalysis.

Now,Iwillexploretherelationshipbetweennaturallogoftotalpledgesandthetotal

numberofbackersbycategorytostudywhichcategoriesaremorepronetooutliers.I

expectthelargerthetotalnumberofbackers,themorepledgesthereare.

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Fromtheresultabove,wecanconcludethat:Technology,Games,DesignandFashionare

morepronetooutliersthanothercategories.Itmeansthatthesefourcategoriesaremore

likelytoattractbackerswithhugeamountsofpledges.Previously,wefoundoutthat

Technology,Games,DesignandFashionarefouroutofthefivecategoriesthatreceivethe

mostpledgesin2017.Wecanthusconcludethattheyareindeedthemostpopular

categoriesin2017.Inaddition,thecategorieswithmoreoutlierscorrespondtothe

categoriesthatreceivehigheraverageamountofpledges.Wecanconcludethatthelarger

thesizeoftheproject,themorelikelythecreatorwillreceivelargeamountsofpledges.

InKickstarter’s“All-or-Nothing”model,creatorsneedtoreceivethefullamountoftheir

goalswithinacertaintimeperiod,otherwise,theywillreceivenothing.Whilenearlyhalfof

theprojectsfail,therearestillalargenumberprojectsthataresignificantlyoverfunded.

Nowwestudytheaveragepercentageofoverfundedbycategory.Inouranalysis,

overfundingstatusiscalculatedbythetotalpledgedividedbycreator’sgoalanditgivesus

apercentageamount.

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Whenwelookatthesummarystatisticsofoverfundedprojects,wefindthatthemeanis

640.1.Itindicatesthatthemeanamountoftotalpledgesforsuccessfulprojectsis6times

theoriginalgoal.However,thestandarddeviationisextremelylargeat9233.767andit

showsthattherearealotofvariationsintheoverfundingprojects.

Fromthecalculationofmeanpercentagegoal,wefindthatMusichasthehighest

percentagegoalof2724.68.Itshowsthatonaverage,projectsintheMusiccategorycan

achieveover27timesoftheirfundinggoals.Dancehasthelowestpercentagegoalof

115.4907anditshowsthatsuccessfulprojectsintheDancecategorybarelymeettheir

fundinggoalsonaverage.Therankingofallcategoriesaccordingtotheiroverfunding

statusis:Music,Technology,Fashion,Games,Design,Publishing,Comics,Art,Photography,

Journalism,Crafts,Food,Film&Video,TheaterandDance.Therankingprovidescreators

anindicationonhowwelltheirprojectswillperformbasedontheircategoriesaccordingto

thedatain2017.

Thedataonlycoverprojectsin2017anddonotrepresenttheoveralloverfundingstatusof

projectssincetheinceptionofKickstarter.However,Idonotexpectabigdifferenceaswe

2724.68%

602.83%490.52%

115.49% 0.00%

500.00%

1000.00%

1500.00%

2000.00%

2500.00%

3000.00%

Percentagegoalachievedin2017

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foundoutpreviouslythatthesuccessrateacrosscategoriesisconsistentthroughoutthe

years.

Afterexploringdifferentattributesofsuccessfulprojectsbycategory,Iwillnowuseatable

tosummarizethestatisticsofthe5independentvariablesinmyhypotheses:

Fromthesummary,wecanobservethemin,median,mean,maxandstandarddeviationof

eachindependentvariable.ThenumberofFacebookfriendshasthewidestrange(1to

5000)andlargeststandarddeviationof795.Thesuccessratehasthelowestrange(0to1)

andsmalleststandarddeviationof0.26.Theseindependentvariablesareprovenbythe

linearmodelidentifiedbeforeandtheycollectivelyexplainupto52.49%ofvariationsin

predictednaturallogofpledgeamounts.

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ThenumberofFacebookfriendsisacrucialfactorforthesuccessofprojectssincethe

moreFacebookfriendsacreatorhas,themoreconnectionshehaswiththecommunity.

However,thenumberofFacebookfriendsisalsotheindependentvariablethathasthe

largeststandarderror.SomepeoplemayarguethatthemoreFacebookfriendsacreator

has,thebetter.Iwouldarguethatalthoughacreatorshouldhaveacertainnumberof

Facebookfriends,thequalityoftheirFacebookfriendsisalsoimportant.Fromtheplot

above,weobservethattherelationshipbetweenthenumberofFacebookfriendsandthe

naturallogoftotalpledgeamountisextremelyweak.Intheageofsocialmedia,people

tendtobefriendwithstrangersonsocialmediaplatforms.Inaddition,somepeoplemay

evenpurchase“friends”and“followers”onFacebookorTwittertoappeartobemore

popularthantheyactuallyare.Althoughitmayseemthatthecreatorhasawide

connectiononsocialmediawithalargenumberoffollowers,thetruthisthatthe“fake

friends”willnothavemuchinfluenceonthesuccessofprojects.Ingeneral,whilewe

recognizethesignificanceofthenumberofFacebookfriendsonthesuccessofprojects,we

shouldnotignorethenuancesbehindthenumberofFacebookfriends.

TostudywhetherthenumberofFacebookfriendsvariesbycategory,Igeneratethe

followinggraph:

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Aswecanobservefromtheplotabove,theredoesnotseemtobeastrongrelationshipof

theaveragenumberofFacebookfriendsacrossdifferentcategories.However,Musicand

DancecategoriesstandoutsincecreatorshavemoreFacebookfriendsonaverage

comparedtoothercategories.Therecouldbevariousreasonsbehindthisfinding.For

example,creatorsinMusicandDancecategoriesmaytendtobemoreartisticandspend

moretimeandenergyonsocialmedia.However,thereasonisnotvalidenoughwithout

furtherresearchandIwillexcludethisdetailedpartofanalysisfrommypaper.

Asforthenumbersofperks,onaveragecreatorsprovidebackerswitharound9perksand

therangeisfrom1to74.Fortheproject,Iamusingthenumberoftheperksinmyanalysis

insteadofthevaluesoftheperks.Thenumbersofperksprovideanincentiveforbackers

whentheyconsideraboutbackingtheprojectssincetheycannotclaimanyequityfromthe

projects.Hereisthevisualizationofthenumberofperksandthenaturallogoftotal

pledges.Aswecanobservefromtheplotbelow,thereisapositiverelationshipbetween

thenumberofperksandthenaturallogoftotalpledge.Itshowsthatperksareattractiveto

backersandcaninfluencetheirdecisionsonKickstarter.

Tostudywhetherthenumberofperksvariesbycategory,Igeneratethefollowinggraph.

Fromthegraph,wecanobservethattheaveragenumberofperksaresimilaracross

categoriesexceptforComics.Inourpreviousanalysis,wefoundoutthatprojectsinthe

Comicscategoryhavethehighestsuccessratein2017.Thehighsuccessratecouldbedue

21

tothelargenumberofperkscreatorsprovideinComicscategory.Perksareindeed

importantforthesuccessofKickstartercampaigns.

Anotherimportantindependentvariableinmymodelispastsuccessrate.Themeanpast

successrateis0.5737andthemaxis1.Thedatavisualizationisattachedbelow:

Aswecanobservefromtheplot,themajorityofprojectshaveapastsuccessratebetween

0.5and1andonly2projectshavepastsuccessratebelow0.Wewouldexpectcreators

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whohaveahighpastsuccessratetobemoreexperiencedinKickstarterandhavemore

supportersintheKickstartercommunity.Asaresult,theywillhaveahigherprobabilityof

successfortheirnewprojects.Creatorswhohaveahighpastsuccessratemayalsohave

moremotivationstostartnewprojects.Inaddition,pastsuccessratecanserveasthe

“resume”forcreatorsandmayinfluencethechoiceofbackers.Backerswilllikelytohave

morefaithincreatorswithhighpastsuccessrates.

Asfarasthenumberofsuccessfulcampaigns,creatorswhohaveasuccessfulprojectin

2017have1.993successfulprojectsonaverageinthepastwiththestandarddeviationof

2.89.Itfurthershowsthatnotallcreatorswithcurrentsuccessfulprojectshaveextensive

experiencesonKickstarter.However,moreexperiencesdefinitelyhelpcreatorsin

launchingtheirfuturecampaigns.

5. ConclusionandFutureWorkInmyproject,IexploredkeyfactorsthataffectthesuccessofKickstartercampaigns.Dueto

thedecliningsuccessrateovertheyears,IbelievethatmyprojectisrelevantandIhopeto

uncoverthemythsbehindthesuccessofKickstartercampaigns.

Iidentifiedalinearregressionmodelthatexplainsupto52.49%ofthevariationsinnatural

logoftotalpledge.Inmymodel,thedependentvariableisthenaturallogoftotalpledges

andtheindependentvariablesare:goal,numComments,noFacebookFriends,pasSuccessRate,

numCompetitors,numPerks,totWordCount,numImagesandnumUpdates.Amongthenine

independentvariables,thefivevariablesthathavethelargestcoefficientare:Facebook

friends,numberofperks,numberofupdates,numberofimagesandpastsuccessrate.

23

Inmygraphicanalysis,Ifirstfindoutthatthesuccessrateofcategoriesstaysconstant

throughtime.Inaddition,projectsinTechnology,GameandDesignhavethelargest

averagenumberoftotalmoneyraised.Coincidently,projectsinTechnology,Gameand

Designhavemoreoutliersthanothercategories.Itshowsthatprojectsinthesecategories

havelargersizesonaverageandrequiremorefunding.Moreover,forprojectsin2017,the

topthreeoverfundedcategoriesare:Music,TechnologyandFashion.

TostudydeeplyaboutthenumberofFacebookfriends,Iconcludethattheaveragenumber

ofFacebookfriendsstaysrelativelyconstantacrosscategories.However,creatorstendto

havemoreFacebookfriendsonaverageinMusicandDancecategories.Asforthenumber

ofperks,thereisapositiverelationshipbetweenthenumberofperksandthenaturallogof

totalpledge.Andthemorepastsuccessacreatorhas,themorelikelyhewillsucceedinthe

currentproject.

Ibelievethatthereisstillmuchroomforimprovementsformyproject.Tobeginwith,my

dataonlycoverprojectsin2017.MyconclusionwillbemoreaccurateandpredictiveifI

canobtaintheall-timedataofKickstarter.Inaddition,thereareothercharacteristicsof

theprojectsthatmightbeinterestingtoexploresuchastheduration,locationetc.Lastbut

notleast,Ihopetogainmoreknowledgeaboutmachinelearningandbuildamodelwith

moreaccuracyinpredictingthesuccessofKickstartercampaigns.

Thisprojectdefinitelybroadensmyhorizononstatisticalmethodsandanalysis.Iwould

liketothankProfessorAldousforhisguidanceandsupportontheproject.Ihopetokeep

learninginthefieldofStatisticsandequipmyselfwithmoreknowledgetoconductmore

in-depthanalysisinthefuture.

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6. AppendixStatacommand:

clearall

importexcel"\\Client\H$\Desktop\STAT157\Kickstarter_success.xlsx",sheet("Sheet1")

firstrow

genlntotalPledge=ln(totalPledge)

reglntotalPledgegoal

reglntotalPledgegoalnumComments

reglntotalPledgegoalnumCommentsnoFacebookFriends

reglntotalPledgegoalnumCommentsnoFacebookFriendspastSuccessRate


numCompetitors


numCompetitorsnumPerks


numCompetitorsnumPerkstotWordCount


numCompetitorsnumPerkstotWordCountnumImages


numCompetitorsnumPerkstotWordCountnumImagesnumUpdates

predictlntotalPledge_predict

labelvariablelntotalPledge_predict"lntotalPledge_predict"

scatterlntotalPledgelntotalPledge_predict

rvfplot,yline(0)

Rcode:

```{r}

library(readxl)

Kickstarter_success<-read_excel("~/Desktop/STAT157/Kickstarter_success.xlsx")

head(Kickstarter_success)

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```

```{r}

a<-Kickstarter_success[Kickstarter_success$mainCategory=='Art',]

b<-Kickstarter_success[Kickstarter_success$mainCategory=='Food',]

c<-Kickstarter_success[Kickstarter_success$mainCategory=='Music',]

d<-Kickstarter_success[Kickstarter_success$mainCategory=='Games',]

e<-Kickstarter_success[Kickstarter_success$mainCategory=='Publishing',]

f<-Kickstarter_success[Kickstarter_success$mainCategory=='Photography',]

g<-Kickstarter_success[Kickstarter_success$mainCategory=='Design',]

h<-Kickstarter_success[Kickstarter_success$mainCategory=='Comics',]

i<-Kickstarter_success[Kickstarter_success$mainCategory=='Technology',]

j<-Kickstarter_success[Kickstarter_success$mainCategory=='Fashion',]

k<-Kickstarter_success[Kickstarter_success$mainCategory=='Theater',]

l<-Kickstarter_success[Kickstarter_success$mainCategory=='Journalism',]

m<-Kickstarter_success[Kickstarter_success$mainCategory=='Dance',]

n<-Kickstarter_success[Kickstarter_success$mainCategory=='Film&Video',]

o<-Kickstarter_success[Kickstarter_success$mainCategory=='Crafts',]

```

```{r}

A<-c(121/269,70/232,191/373,196/447,170/420,29/79,158/347,105/144,110/419,

79/254,45/64,8/45,18/35,174/423,29/101)

labels<-

c("Art","Food","Music","Games","Publishing","Photography","Design","Comics","Technolog

y","Fashion","Theater","Journal","Dance","Film&Video","Crafts")

pie(A,labels=labels,radius=1,cex=0.8)

```

```{r}

summary(Kickstarter_success$facebookFriends)

summary(Kickstarter_success$numPerks)

summary(Kickstarter_success$numUpdates)

summary(Kickstarter_success$numImages)

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summary(Kickstarter_success$pastSuccessRate)

```

```{r}

sd(Kickstarter_success$facebookFriends)

sd(Kickstarter_success$numPerks)

sd(Kickstarter_success$numUpdates)

sd(Kickstarter_success$numImages)

sd(Kickstarter_success$pastSuccessRate)

```

```{r}

statistics<-matrix(c(1,574.5,824.1,5000,795.2921,1,8,9.617,74,6.144794,0,4,5.48,44,

5.653509,0,8,14.8,117,16.42112,-0.5,0.5,0.5737,1,0.2567152),ncol=5,byrow=TRUE)

colnames(statistics)<-c("Min","Median","Mean","Max","SD")

rownames(statistics)<-c("FacebookFriends","Perks","Updates","Images","Successrate")

statistics<-as.table(statistics)

statistics

```

```{r}

library(ggplot2)

p1<-ggplot(Kickstarter_success,aes(x=facebookFriends))

p1+geom_histogram()+ggtitle("NumberofFacebookfriendsinsuccessfulprojects")

p2<-ggplot(Kickstarter_success,aes(x=numPerks))

p2+geom_histogram()+ggtitle("Numberofperksinsuccessfulprojects")

p3<-ggplot(Kickstarter_success,aes(x=numUpdates))

p3+geom_histogram()+ggtitle("Numberofupdatesinsuccessfulprojects")

p4<-ggplot(Kickstarter_success,aes(x=numImages))

p4+geom_histogram()+ggtitle("Numberofimagesinsuccessfulprojects")

p5<-ggplot(Kickstarter_success,aes(x=pastSuccessRate))

p5+geom_histogram()+ggtitle("Pastsuccessrateinsuccessfulprojects")

```

27

```{r}

g6<-ggplot(Kickstarter_success,aes(x=facebookFriends,y=log10(totalPledge)))

g6+geom_point()+geom_smooth()

g7<-ggplot(Kickstarter_success,aes(x=numPerks,y=log10(totalPledge)))


g8<-ggplot(Kickstarter_success,aes(x=numUpdates,y=log10(totalPledge)))


g9<-ggplot(Kickstarter_success,aes(x=numImages,y=log10(totalPledge)))


g10<-ggplot(Kickstarter_success,aes(x=pastSuccessRate,y=log10(totalPledge)))


```

```{r}

g11<-ggplot(c,aes(x=facebookFriends,y=log10(totalPledge)))


```

```{r}

Kickstarter_success%>%

group_by(mainCategory)%>%

filter(n()>10)%>%

ggplot()+geom_point(aes(x=mainCategory,y=numPerks),alpha=0.3,color="tomato")+

geom_boxplot(aes(x=mainCategory,y=numPerks),alpha=0)+

ggtitle("NumberofperksbyCategory(n>10)")+xlab("Category")+

theme(axis.text.x=element_text(angle=90,hjust=1))+

ylab("Numberofperks")+coord_flip()

```

```{r}

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Kickstarter_success%>%

group_by(mainCategory)%>%

filter(n()>10)%>%

ggplot()+geom_point(aes(x=mainCategory,y=facebookFriends),alpha=0.3,

color="tomato")+

geom_boxplot(aes(x=mainCategory,y=facebookFriends),alpha=0)+

ggtitle("NumberoffacebookFriendsbyCategory(n>10)")+xlab("Category")+

theme(axis.text.x=element_text(angle=90,hjust=1))+

ylab("NumberoffacebookFriends")+coord_flip()

```

ggplot(Kickstarter_success,aes(totalNumberBackers,lntotalPledge))+geom_point()+geom_s

mooth()+facet_wrap(~mainCategory)

```{r}

summary(Kickstarter_success$percentageGoal)

sd(Kickstarter_success$percentageGoal)

summary(Kickstarter_success$numSuccessfulCampaigns)

sd(Kickstarter_success$numSuccessfulCampaigns)

```

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7. ReferencesChen, K., Jones, B., Kim, I., & Schlamp, B. (n.d.). KickPredict : Predicting Kickstarter Success.

CrowdExpert. (n.d.). Total crowdfunding volume worldwide from 2012 to 2015 (in billion U.S. dollars).

Etter, V., Grossglauser, M., & Thiran, P. (2013). Launch Hard or Go Home! Predicting the Success of Kickstarter Campaigns.

Hussain, N., Kamel, K., & Radhakrishna, A. (n.d.). Predicting the success of Kickstarter campaigns.

Kickstarter. (2017). Kickstarter Stats.

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