SAS - Hortonworks: Creating the Omnichannel Experience in Retail webinar march 16-2017-v2

Crea%nganOmnichannelCustomerJourneyinRetailEricThorsen,GMRetail/CPG,HortonworksDanMitchell,DirectorGlobalRetailandCPG,SAS

Copyright © SAS Ins1tute Inc. Al l r ights reserved.

Eric Thorsen General Manager

Retail/CPG Hortonworks

Speakers

Dan Mitchell Director, Global Retail and CPG

SAS

3 ©HortonworksInc.2011–2016.AllRightsReserved

Millennials vs. Baby Boomers

Millennials will be 50% of workforce by 2020, 75% of workforce by 2030

Grew up alongside technology, tend to be more optimistic and community-minded

Boomers 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 2013 Millennial and Boomer purchasing trends conducted by Radius Global Market Research (Radius GMR)


Understanding the New Consumer


Amazon Impact

•  Growthof“everythingstore”erodingtradiFonalshoppingpaGerns,basicsandstaples,andreducingstandardpurchasesoftradiFonalitemsattradiFonalretailers

•  Anecdotalimpactof“Showrooming”whereconsumerswillresearchproductsin-store,andulFmatepurchaseonlinethroughAmazon

•  Amazonhasadvancedsupplychaintodeliverinlessthantwodaysinlargemarkets,someFmessameday,takingE-CommerceoutofreachoftradiFonalretailersandforcingdeepdiscountstoremaincompeFFve

Forbesar)cleshowinghowAmazoncangrowto$3Tcompany!


“We need to be more agile in responding to our customers needs online and in store”

CEO of Major SoQlines Retail Group

“We need to enhance our in-store experience, offering state-of-the -art digital tools”

Treasurer, Apparel & Accessories Retailer

“Improving technology to grow the linkage between the internet & the store to enhance the customer experience is our top goal”

CEO, Department Store Retailer

PrioriFesoftheRetailCEO


Traditional systems under pressure

Challenges •  Constrains data to app •  Can’t manage new data •  Costly to Scale

BusinessValue

Clickstream

GeolocaFon

WebData

InternetofThings

Docs,emails

Serverlogs

20122.8ZeKabytes

202040ZeKabytes

1

2 New Data

ERP CRM SCM

New

Tradi%onal

*Mul)plesofBytesKilobyteMegabyteGigabyteTerabytePetabyteExabyteZeFabyteYoFabyte

1,000,000,000,000,000,000,000

Muchofthenewdataexistsin-flightbetweensystemsanddevicesaspartoftheInternetofAnything


HortonworksConnectedDataPlaOormsandSolu%ons

HortonworksConnecFon

HortonworksSolu%onsEnterpriseData

WarehouseOpFmizaFon

CyberSecurityandThreatManagement

InternetofThingsandStreamingAnalyFcs

HortonworksConnec%onSubscripFonSupport

SmartSense

PremierSupport

EducaFonalServices

ProfessionalServices

CommunityConnecFon

CloudHortonworksDataCloudAWS HDInsight

DataCenterHortonworksDataSuite

HDFHDP


Holis%cCustomerInterac%onModel

HDPandHDFSubscrip%on

Opera%onalServices

Applica%ons

Support/”BreakFix”

ProfessionalServicesandPartnerSI’s

Configure,ManageandUpgrade

ComponentsIncluded

CustomerProposalComponents

10Transformation

--- Maturity Stages à Optimization Exploration Awareness

Marketing

Merchandising

IT Ops

---

Mat

urit

y St

ages

à

PeerCompe%%veScale

Standardamongpeergroup

Commonamongpeergroup

Strategicamongpeergroup

NewInnovaFons

Digital

Store Operations

No UseCaseName

1a SingleViewofCustomer1b SingleViewofCustomer2 BasketAnalysis3 SocialListening4 EnrichedBasketAnalysis5 ClickstreamAnalysis6 RecommendaFonEngine7 PriceOpFmizaFon

8 Beacon/SensorMonitoringandIngest

9 StoreCommunicaFons10 EmailManagement11 EDWEnhancement12 InventoryOpFmizaFon13 PathtoPurchase14 SupplyChainTelemetry15 CustomerServiceAnalysis16 PreventaFveMaintenance17 MachineLearning/AI

4

Purchasing & Logistics

10

11

13

1a

1b

12

UseCasesareavailableatdifferentlevelsofmaturity

15

14

16 17

8

8

73

5

29


RetailUseCase:MajorBigBoxRetailer

WebLogging• Trackbehaviorofwebvisitors.Analyzelogs

SingleViewofCustomer• Online• In-store• ProjectDesk• CommercialCounter

RecommendaFonEngine• KnowingdetailsofconsumerdeliverrelevantpromoFons

PriceOpFmizaFon• ScancompeFtorpricingon-line

• SuggestnewpricebasedonmarginpointandawarenessofcompeFFvepricing

FinancialReporFng• CalculateCOGSandinsertintoERPsystem

• ReplacelegacyBIReportthatwouldnevercomplete

ATrueHadoop“Journey”•  FiveUseCasessinceoriginalproject•  18-monthlifecyclewithHDPHadoop•  In-housetalent•  Significantannualsavingsdueto

lower-coststorageandcompute

Customerstypically“StartSmall,ThinkBig”

12

ToLegacy

RetailConceptualArchitecture

APILayer

Algorithms,ReporFngandAnalyFcsAccess

HortonworksDataPlaform(HDP)Banners,Geos,Stores,Sales&Inventory

SyndicatedData,UnstructuredData,Safety,Quality,Warrantee

DataIngest

AppsMobileDigitalSelf-Service

AffinityRFMTopsandFlopsSupplyChainLoyaltyCustomerProfiles

CleansingStagingStorageSyndicatedDataPrivateLabelMfg

FeedstoLegacy:•  CustomerDNA•  CRMSystems•  ERPSystems•  MarkeFngAutomaFon•  Digital•  BrandAnalyFcs•  Loyalty


Omnichannel Analy1cs



SAS® Omnichannel Analy1cs Powers Retail …at every step of the customer journey


Whatmakesupcontext?

Customer Rela1onship Context: Previous purchases

Real-Time Context: In-Store & On-line

Personal Context: Current Shopping Purpose

Relevance

16


The Big Picture

Whether in-store or on-line, Customers ini1ate events that trigger the need for a response

Sense 01 Their full profile and history, apply contextual analy1cs to iden1fy the best ac1on

Understand 02 Engage in a 1mely, convenient and consistent way. Real-1me or right-1me

Act 03


Context:DefinedbyCustomerProfile

Who They Are

What They Buy

How They

Interact

What They

Feel & Say

Where They Are

Now

Who They Know

Their Value &

Poten1al

Their Loyalty


Core Capabili1es Required

Transac1ons Channel Interac1ons Opinions/Sen1ments Listen/Filter/Trigger

Sense 01 First Party, Third Party Online, Offline Structured, Unstructured Match, Merge, Purge Analy1cs:

Descrip1ve Diagnos1c Predic1ve

Understand 02 Outbound Inbound/Real-1me Orchestrated Omni-Channel

Act 03


Real-FmeCX+OmnichannelAnalyFcs+CustomerJourneyOpFmizaFon


DataandAnalyFcsAlignedwithcustomerjourneyandbusinessobjecFves

CUSTOMER JOURNEY STAGE ANALYTICS KEY BUSINESS OBJECTIVE

Discover

Profile customers Segmenta1on

Evaluate prospects Lead scoring

Reach right prospects Acquisi1on models

Explore

Analyze customer response Offer/ contact op1miza1on

Op1mize marke1ng mix Marke1ng mix modeling

Test marke1ng A/B, mul1variate tes1ng

Buy

Predict future behavior Propensity models

Target accurately Segmenta1on, valua1on models

Personalize marke1ng Next best ac1on models, social/ text VOC analysis

Engage

Expand breadth of customer interac1ons Cross-sell/ upsell

Increase depth of customer interac1ons Loyalty models

Incorporate customer feedback Value of customer analysis

Manage customer airi1on/ defec1on Churn/ airi1on models

Maximize customer value Life1me value models


HadoopforRetail

DATAREPOSITORIES

ANALYSISSingleviewofconsumerTargetedpromoFonsRecommendaFonenginesBasketanalysis

PriceopFmizaFonInventoryopFmizaFonLoyaltymanagementPathtopurchase

Security

Ope

ra%o

ns

Governa

nce

&In

tegra%

on

°1 ° ° ° ° ° ° °

° ° ° ° ° ° ° ° °

° ° ° ° ° ° ° ° N

YARN:DataOpera%ngSystem

Script

SQL

NoSQL

Stream

Search

Others

HDFS(HadoopDistributedFileSystem)

In-Mem

ERP

EDW

RDBMS

CRM

EMERGING&NON-TRADITIONALSOURCES

SOCIALMEDIA

BEACONS

SENSORRFID

CLICKSTREAM

IN-STOREWIFILOGS

SERVERLOGS

TRADITIONALSOURCES

CRM STORES PRODUCTCATALOG STAFFINGPLANS

ERP POSTRANSACTIONS INVENTORY WEBTRANSACTIONS


Thankyou!


Q&AAddi%onalRetailInforma%on:Hortonworks.com->Solu%ons(byindustry)->RetailSAS.com->Industries->Retail

SAS - Hortonworks: Creating the Omnichannel Experience in Retail webinar march 16-2017-v2

Technology