SlideShare a Scribd company logo
BigWinswithSmallData:
PredictionIOinEcommerce
byDavidJones(@d_jones)-TechnicalDirector-ResolveDigital
Comic by @d_jones. Inspired by xkcd.
Machinelearning&Ecommerce
better.together.
PredictiveAPIs
makeitfeellikeyouhaveyourown
teamofMLexperts
Personalised
recommendations
Abandonedcartemails
Leadscoring
Emailmarketing
BenefitsofMLinEcommerce
Morevisitorswithlongersessionduration
Improvedretention
Higherconversionrates
Largerordersize
Bottomline,morerevenue.
machinelearninginecommerce
deeplyenhancescustomerexperiences
CustomersexpectMLfeatures
Eveniftheydon’tknowwhatMLis
Realworldexample:
UnitedCellars
Goal:Increaserevenue
Solution:Productrecommendations
Implementation:PredictiveAPIs
16kproductviews
60korders
3kproductratings
79000rowsSmalldata
Anydatathatlinks
customerstoproducts
Optimisedatacollection
Initiallyhadnodataforloggedoutusers
PredictionIO
Opensourcemachinelearningserver
Ecommercetemplates
Evaluationmetrics
Productionready
WhyPredictionIO?
ActiveOpenSourceproject
Productivityfocused
Built-insupportforApacheSparkMLlib
OpinionatedDASEarchitecture:
DataSourceandPreparator,Algorithm,Serving,Evaluator
Storefront<->ProcessingQueue<->PredictionIO
PAPIProductionTips
Monitorperformance
Cachewhererealtimeisnotneeded
Deriveddataislowrisk
Bricks&mortarretail
inspiresanecommerceexperiencewithML
Withwine
tastepreferencesarediverse
peopleusuallypreferredorwhite
A/BTESTwithGoogleAnalytics
FilterPredictionsAvoidanythingthatlessensrelevance
Don’tshowoutofstockproducts
A/BTestResults
45%longeraveragesession
22%increaseinconversionrate
37%increaseinaverageordersize
71%MoreRevenue
Wheretonext?
Predictivefeaturesinmorechannels
Introducenewpredictivefeatures
Conclusion
1
Smalldatacanbeenough
Qualityiskey
2
PredictiveAPIs
areawesome
3
MachineLearning
ishighlyeffectivewith
Ecommerce
resolve.digital/papis2015
DavidJones,@d_jones

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