Machine Learning & Ecommerce - by David Jones - PAPIs Connect

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Machine Learning & Ecommerce by David Jones

Données & analyses

Machinelearning&Ecommerce
better.together.

PredictiveAPIs
makeitfeellikeyouhaveyourown
teamofMLexperts

BenefitsofMLinEcommerce
Morevisitorswithlongersessionduration
Improvedretention
Higherconversionrates
Largerordersize
Bottomline,morerevenue.

machinelearninginecommerce
deeplyenhancescustomerexperiences

CustomersexpectMLfeatures
Eveniftheydon’tknowwhatMLis

Goal:Increaserevenue
Solution:Productrecommendations
Implementation:PredictiveAPIs

16kproductviews
60korders
3kproductratings

Optimisedatacollection
Initiallyhadnodataforloggedoutusers

PredictionIO
Opensourcemachinelearningserver
Ecommercetemplates
Usesestablishedimplementationofalgorithms
Evaluationmetrics
Productionready

Storefront<->ProcessingQueue<->PredictionIO

PAPIProductionTips
Monitorperformance
Cachewhererealtimeisnotneeded
Deriveddataislowrisk

Bricks&mortarretail
inspiresanecommerceexperiencewithML

Withwine
tastepreferencesarediverse
peopleusuallypreferredorwhite

FilterPredictionsAvoidanythingthatlessensrelevance
Don’tshowoutofstockproducts

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

Wheretonext?
Predictivefeaturesinmorechannels
Introducenewpredictivefeatures

3
MachineLearning
ishighlyeffectivewith
Ecommerce

BusinessLeaders
Startaproject
UsePredictiveAPIs
Thetimingisperfect

resolve.digital/papis
DavidJones,@d_jones

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Machine Learning & Ecommerce - by David Jones - PAPIs Connect

1. MachineLearning & Ecommerce byDavidJones-TechnicalDirector-ResolveDigital

3. Machinelearning&Ecommerce better.together.

6. PredictiveAPIs makeitfeellikeyouhaveyourown teamofMLexperts

7. Personalised recommendations

8. Abandonedcartemails

9. Customisedcontent

10. Leadscoring

11. Emailmarketing

12.

13. BenefitsofMLinEcommerce Morevisitorswithlongersessionduration Improvedretention Higherconversionrates Largerordersize Bottomline,morerevenue.

14. machinelearninginecommerce deeplyenhancescustomerexperiences

15. CustomersexpectMLfeatures Eveniftheydon’tknowwhatMLis

16.

17. Realworldexample: UnitedCellars

18. Goal:Increaserevenue Solution:Productrecommendations Implementation:PredictiveAPIs

19. 16kproductviews 60korders 3kproductratings

20. 79000rowsSmalldata

21. Anydatathatlinks customerstoproducts

22. Optimisedatacollection Initiallyhadnodataforloggedoutusers

23. PredictionIO Opensourcemachinelearningserver Ecommercetemplates Usesestablishedimplementationofalgorithms Evaluationmetrics Productionready

24. Storefront<->ProcessingQueue<->PredictionIO

25. PAPIProductionTips Monitorperformance Cachewhererealtimeisnotneeded Deriveddataislowrisk

26. Bricks&mortarretail inspiresanecommerceexperiencewithML

27. Withwine tastepreferencesarediverse peopleusuallypreferredorwhite

28. A/BTESTwithGoogleAnalytics

29.

30. FilterPredictionsAvoidanythingthatlessensrelevance Don’tshowoutofstockproducts

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

32. 71%MoreRevenue

33. Wheretonext? Predictivefeaturesinmorechannels Introducenewpredictivefeatures

34. Conclusion

35. 1 Smalldatacanbeenough Qualityiskey

36. 2 PredictiveAPIs areawesome

37. 3 MachineLearning ishighlyeffectivewith Ecommerce

38. BusinessLeaders Startaproject UsePredictiveAPIs Thetimingisperfect

39. resolve.digital/papis DavidJones,@d_jones