0.90

CRMatKobo:Useemailstopromotethingslike:• Newbooks• Sales• Releasesbyspecificauthors

Theproject:Exploreapproachesfortargetingandpersonalizingpromotionalemails.Buildasystemwhich:

1) Generatesalistofthemostapplicableusersforagivenmarketingcampaign.

2) Foreachrecipient,providestheoptimalorderingofpromotedbooks.

JakeStolee(Kobo)JaredEccles(Kobo),DariusBraziunas (Kobo),NathanTaback (UofT)

Rakuten Kobo,135LibertySt.Suite101,TorontoON,M6K1A7

Data-DrivenCRMOptimizationforeReadingMarketingEmailTargetingandPersonalization

Introduction

ExploringScoringMethods

• Item-itemsimilarityscorescanbeaggregatedtoprovidethesimilaritybetweenapromotedbooklist(“P”) andauser’slibrary(“L”)– thisisreferredtoasauser’s“affinity”toP.

• Cutoffatathreshold,ortaketop“U”mostapplicableusers.

ProposedScoringMethods:ItemJaccard:Thenumberofuserswhopurchasedbothitemsoverthenumberofuserswhopurchasedatleast oneoftheitems.

Aggregateoverallitemsinauser’slibraryandallitemsinthepromotedbooklist.

• Modeltheprobabilitythatauserwillmakeapurchasefromaspecificemailmarketingcampaign.

• Trainadiscriminativemodelthatisabletopredict𝑝(𝑃𝑢𝑟𝑐ℎ𝑎𝑠𝑒|x),givensomevectoroffeatures, x,thatcapturesinformationaboutboththeuserandthecampaign.

FeatureGeneration:

4Userspurchasedonly “TheGirlontheTrain”

6 Userspurchasedonly “TheHungerGames”

10userspurchased

both

J(x, y) = -./.= 0.5

J(x, y)= 𝐱∩𝐲𝐱∪𝐲

𝐱 ∈ L, 𝐲 ∈ P

affC= cos 𝜃 = 𝐱>?𝐲>𝐱> 𝒚A

affWJ =Jw(𝐱>,𝒚A )= ∑ CDE(FG,IG)KGLM

∑ CNF(FG,IG)KGLM

EmailTargetingUsing“AffinityScores”

affJSum = ∑ ∑ J(x, y)�𝐱∈P

�𝒚∈Q , affJAvg =

affJSumQ P

EmailPersonalizationUsing“AffinityScores”

UserAccountUserPurchaseBehaviourUserEmailPurchaseBehaviourUserReadingBehaviourAffinity ScoresCampaignInfoLabel:Converted {0,1}

Hadoop

𝐃𝐚𝐭𝐚𝐒𝐞𝐭

CurrentlyInProgress/ToBeCompleted:• Algorithmevaluation(logisticregression&neuralnetworkclassifiers,among

others).• Modelselection/evaluation:A/Btestingagainstcurrentaffinity-basedapproach.

• SumJ(x, y) betweenagivenpromotedbookandeverybookinauser’slibrary.• Theresultingscoreforeachpromotedbookcanbeusedtoorderthebooklist

foreveryuser.

Software/ToolsUsedandmore…

UserA’sLibrary Promoted List UserB’sLibrary

0.97

0.20

0.95

0.80

0.60

EmailLogs(HDFS)

TrackingMessages(HDFS)

SQL

WeightedJaccard (betweenmeanitemvectors):Computethemeanitemvectorsforagivenlistanduser’slibrary,calculatethegeneralized“weighted”Jaccard similaritybetweenthetwomeanitemvectors.

affJAvg= 0.391 affJAvg= 0.150✓ ✘

Resulting OrderBasedOn UserA’sLibrary

1.57 0.00

Cosine(betweenmeanitemvectors):Calculatethecosine oftheanglebetweenmeanitemvectorsforthelistanduserlibrary.

“User”Jaccard:Treatcustomerlibrariesandpromotedlistsasbitvectors,whereanelementindicateswhetheraspecificbookispresentornot.Foragivenuserlibraryvector,𝐮,andthelistvector,𝓵,compute:

AMachineLearningApproachtoTargeting

1.95

1 2 3

affU= J(u,𝓵)

• Treatitemsasbitvectors(ofsize𝑁)- everyelementindicateswhetheraspecificuserpurchasedtheitemornot.

• Thesevectorscanbeusedtocomputeitem-itemvector“similarityscores”.

Example(“50BookPledge”)Campaign

NormalizedScore(affC)0.0 0.2 0.4 0.6 0.8 1.0

0

1

2

3

4

5

6

Density

affJAvg affC affWJ affJSum affU

ScoreType

0.00

0.05

0.10

0.15

0.20

MeanScoreDiffe

renceBe

tweenGrou

ps