how to architect and omnichannel retail solution to achieve real-time customer insights

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HowtoArchitectanOmnichannelRetailSolutiontoAchieveReal-TimeCustomerInsights


Speakers

EricThorsen,VPRetail,

Hortonworks

JasonGautereau,Sr Manager,Pre-SalesSupport,USRetail,CPG,Comms &

Media,SAS

MarkLochbihler,PrincipalEngineer,

Hortonworks


Agenda

BusinessChallengesinRetailCustomerUseCaseandDemoTechnologyandArchitecture


BusinessChallengesinRetailERICTHORSEN


Millennialsvs.BabyBoomers

Millennials will be 50% of workforce by 2020, 75% of workforce by 2030Grew up alongside technology, tend to be more optimistic and community-mindedBoomers more likely to use traditional media (newspapers vs. magazines)Millennials more likely to be swayed by word of mouth, tends to be digital

PWC “Next Generation Global Study 2013Millennial and Boomer purchasing trends conducted by Radius Global Market Research (Radius GMR)


AmazonImpact• Growthof“everythingstore”

erodingtraditionalshoppingpatterns,basicsandstaples,andreducingstandardpurchasesoftraditionalitemsattraditionalretailers

• Anecdotalimpactof“Showrooming”whereconsumerswillresearchproductsin-store,andultimatepurchaseonlinethroughAmazon

• Amazonhasadvancedsupplychaintodeliverinlessthantwodaysinlargemarkets,sometimessameday,takingE-Commerceoutofreachoftraditionalretailersandforcingdeepdiscountstoremaincompetitive

ForbesarticleshowinghowAmazoncangrowto$3Tcompany!


“MarketofOne”Campaigns vs Promotions• Therelevanceofattributionandsegmentation

Purchase History and Social Sentiment• Relevantpromotionsbasedonpastactivity

Reactive vs. Predictive• PastCausalityvs.trendingindicators

Michael S. Malone, “The Human Face of Big Data”

“Thismarkstheendofthereignofstatistics.For400years,we’vebeenforcedtosamplecomplexsystemsandextrapolate.Now,withbigdata,itispossibletomeasureeverything.”


CustomerUseCaseandDemoJASONGAUTEREAUX


RetailUseCaseHowcanretailersleverageanalyticstosupportthecustomerjourney…ateverystep?

BusinessObjectiveAlignmerchandisingandmarketingtosynchronizeOmnichannelcustomerengagementCreatemorepersonalizedshoppingexperienceacrosschannels

TechnologyLeverageandharmonizecustomerdataacrosschannelsLeverageanalyticsandlocationservices,inrealtime,topredictbehaviorandoptimizeinteractions

BenefitIncreasedcustomerconversionIncreasedsalesandcustomersatisfaction


TechnologyandArchitectureMARKLOCHBIHLER


Parallel&

Serial,Pu

b/S

ub,

WebServices,M

Qs

Source-basedEngines

Microservices

UAA

QueryGen

Folders

CASMgmt

DataSourceMgmt

AnalyticsGUIs

etc…

BIGUIs

EnvMgr

ModelMgmt

Log

Audit

UAAUAA

DataMgmtGUIs

In-MemoryRuntimeEngine

In-Database

In-Hadoop

In-Stream Solutions

APIs

Platform

Analytics

DataManagementFraudandSecurityIntelligence

BusinessVisualizationRiskManagement

!

CustomerIntelligence

CloudAnalyticsServices(CAS)

DataEnablement

ITEnablement

DeploymentEnablementDeveloperEnablement

SolutionsEnablement


SAS® VIYA™ ANDSAS9

&• It’sanAND strategy

• Canco-existonsamehardware(physicalorvirtual)

• Data,models,andcodecanbeaccessedviabridges


HDPasadatasourceforTraditionalAnalyticsSAS/ACCESS® InterfacetoHadoop

§ Hortonworksasadatasource§ BridgesexistingSASsystems§ Push-downlogic

§ ExplicitandimplicitHiveQL§ MapReduce§ Pig


HDPasadatasourceforTraditionalAnalytics

SAS/ACCESS® InterfacetoHadoop


HDPasadatasourceforin-memoryAnalytics

SAS® VisualAnalytics

SAS® VisualStatistics


HDPasadatasourceforSASHighPerformanceAnalyticsSAS/ACCESS® HPSTATandotherSASHPbundles

AndSAS® EnterpriseMiner


HDPasadatasourceforin-memoryAnalyticsSAS“From”Hadoop

EP

§ Massively-parallel,in-memory§ Parallel,asymmetricdataload§ SASEPisYARNREADY

§ Datavisualization§ Descriptiveandpredictiveanalytics§ Machinelearning§ Modeldeployment/scoring§ Modelmanagement


DATAATREST- HDPasanin-memoryanalyticplatformSAS“In”Hadoop

EP

§ Massively-parallel,in-memory§ NodatamovementoutofHDP§ Broadrangeofinterfaces§ Datavisualization

§ Descriptiveandpredictiveanalytics§ Machinelearning§ Modeldeployment/scoring§ Modelmanagement


Goal§ Acquire§ Transform§ Cleanse§ Integrate§ Expand§ Format

NewDerivedColumns

DistancetoStore

SpendLast6Months

CleanAddress

UnderstandingDATAManagementforAnalytics

1000’smore!

AnalyticBaseTable


DATAATREST– HDPasadatamanagementplatformSAS® DataLoaderforHadoop


DATAINREST- HDPasadatamanagementplatform

EP

§ Businessuserinterfaces§ Nodatamovement§ YARNintegration§ vAppDeployment

§ Ingestdata§ Profiledata§ Cleansedata§ Transformdata

ApacheAmbari


SAS9.4In-MemoryLASRServer6

28CPUCores384GBMemory16TBDiskStorag


28CPUCores384GBMemory16TBDiskStorag


28CPUCores384GBMemory16TBDiskStorage





WorkerNode9WorkerNode8WorkerNode7

WorkerNode6

WorkerNode5

EXAMPLEPRODUCTIONENVIRONMENT

BusinessUser

Developer

Admin

ProductionCluster

HighSpeedLocalNetwork

Communication,NoDataMoved

- NameNode1- ResourceManager2- JournalKeeper- Zookeeper- SASEmbeddedProcess

MasterNode1

20CPUCores256GBMemory

12x2TBDiskStorage

MasterNode2


12x2TBDiskStorage- ResourceManager1- HiveMetastore2- HiveServer2- WebHCat2- JournalKeeper- SASEmbeddedProcess

MasterNode3


12x2TBDiskStorage- NameNode1- HistoryServer- TimelineServer- JournalKeeper- Zookeeper- SASEmbeddedProcess

MasterNode4


12x2TBDiskStorage

- HiveMetastore1- HiveServer1- WebHCat1- Zookeeper- SASEmbeddedProcess

WorkerNode4

WorkerNode3


- DataNode- NodeManager- OpenSourceR- SASEmbeddedProcess

WorkerNode2

WorkerNode1


- KnoxGateway- HDPClients- RStudio

VirtualMachine2

8CPUvCores32GBMemory

1TBvDiskStorage

- MySQLDatabaseServer

VirtualMachine3


1TBvDiskStorage

- AmbariServer- RangerAuditServer- RangerPolicyServer- Zeppelin

VirtualMachine1


1TBvDiskStorage

SAS9.4LASRServerController




- SASVisualAnalytics- SASIn-MemoryStatistics

SASViyaWorkerNode4


SASViyaWorkerNode3


SASViyaWorkerNode2


SASViyaWorkerNode1


- SASVisualAnalytics(Viyaenabled)- SASVisualStatistics(Viyaenabled)- SASVisualDataMining

andMachineLearning

SASViyaControllerNode


SASViyaMicroserviceNode


- SASMetadataServer- SASWebServer- SASWebApplicationServer- SASWebClients- SASEnvironmentManager- SASDataLoaderForHadoop- SASScoringAcceleratorforHadoop- SASComputeServer- HortonworksHSTServer- HortonworksActivityAnalyzer

- SASDesktopClients

HDPWorkerNodes

SASEP

SAS9.4In-MemoryLASRServers

SASViya ServersandWorkerNodes

SASServices

HDPUtilityNodes

HDPMasterNodes

SASEP

SAS9.4MetadataServerComputeServerMid-TierServer


SAS9.4MetadataServerComputeServerMid-TierServer


SASVirtual

Machines

Contactustodiscussyourspecificneeds!


DatainMotionHortonworksDataFlow

SASEventStreamProcessing


DATAINMOTION– HortonworksDataFlow (HDF)andSASEventStreamProcessing(ESP)ESPprovides“ComplexEventProcessing”toHDFworkflows

D A T A I N M O T I O N

STORAG

ESTORA

GE

GROUP2GROUP1

GROUP4GROUP3

H D P

INTERNETOF

ANYTHING

SASModelsthatwerebuiltonHistoricalDataCanbemovedclosertothe“Edge”ofamoderndataapplication.

E S P

E S P


EdgeAnalytics- OnPremiseandIntheCloud

STORAG

ESTORA

GE

GROUP2GROUP1

GROUP4GROUP3

D A T A A T R E S T

INTERNETOF

ANYTHING

C L O U D

ON P R EM I S E

STORAG

ESTORA

GE

GROUP2GROUP1

GROUP4GROUP3

D A T A A T R E S THDP

HDP

DeepHistoricalAnalysis

MachineLearningE S P

E S P

E S P


NextSteps

855-8HORTON

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