Post on 22-Apr-2022
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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
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Density
affJAvg affC affWJ affJSum affU
ScoreType
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0.05
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MeanScoreDiffe
renceBe
tweenGrou
ps