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BigWinswithSmallData:
PredictionIOinEcommerce
byDavidJones(@d_jones)-TechnicalDirector-ResolveDigital

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Comic by @d_jones. Inspired by xkcd.

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Machinelearning&Ecommerce
better.together.

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PredictiveAPIs
makeitfeellikeyouhaveyourown
teamofMLexperts

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Personalised
recommendations

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Abandonedcartemails

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Leadscoring

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Emailmarketing

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BenefitsofMLinEcommerce
Morevisitorswithlongersessionduration
Improvedretention
Higherconversionrates
Largerordersize
Bottomline,morerevenue.

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machinelearninginecommerce
deeplyenhancescustomerexperiences

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CustomersexpectMLfeatures
Eveniftheydon’tknowwhatMLis

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Realworldexample:
UnitedCellars

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Goal:Increaserevenue
Solution:Productrecommendations
Implementation:PredictiveAPIs

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16kproductviews
60korders
3kproductratings

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79000rowsSmalldata

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Anydatathatlinks
customerstoproducts

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Optimisedatacollection
Initiallyhadnodataforloggedoutusers

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PredictionIO
Opensourcemachinelearningserver
Ecommercetemplates
Evaluationmetrics
Productionready

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WhyPredictionIO?
ActiveOpenSourceproject
Productivityfocused
Built-insupportforApacheSparkMLlib
OpinionatedDASEarchitecture:
DataSourceandPreparator,Algorithm,Serving,Evaluator

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Storefront<->ProcessingQueue<->PredictionIO

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PAPIProductionTips
Monitorperformance
Cachewhererealtimeisnotneeded
Deriveddataislowrisk

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Bricks&mortarretail
inspiresanecommerceexperiencewithML

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Withwine
tastepreferencesarediverse
peopleusuallypreferredorwhite

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A/BTESTwithGoogleAnalytics

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FilterPredictionsAvoidanythingthatlessensrelevance
Don’tshowoutofstockproducts

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A/BTestResults
45%longeraveragesession
22%increaseinconversionrate
37%increaseinaverageordersize

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71%MoreRevenue

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Wheretonext?
Predictivefeaturesinmorechannels
Introducenewpredictivefeatures

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Conclusion

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1
Smalldatacanbeenough
Qualityiskey

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2
PredictiveAPIs
areawesome

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3
MachineLearning
ishighlyeffectivewith
Ecommerce

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resolve.digital/papis2015
DavidJones,@d_jones