sas - hortonworks: creating the omnichannel experience in retail webinar march 16-2017-v2
TRANSCRIPT
Crea%nganOmnichannelCustomerJourneyinRetailEricThorsen,GMRetail/CPG,HortonworksDanMitchell,DirectorGlobalRetailandCPG,SAS
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Eric Thorsen General Manager
Retail/CPG Hortonworks
Speakers
Dan Mitchell Director, Global Retail and CPG
SAS
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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)
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Amazon Impact
• Growthof“everythingstore”erodingtradiFonalshoppingpaGerns,basicsandstaples,andreducingstandardpurchasesoftradiFonalitemsattradiFonalretailers
• Anecdotalimpactof“Showrooming”whereconsumerswillresearchproductsin-store,andulFmatepurchaseonlinethroughAmazon
• Amazonhasadvancedsupplychaintodeliverinlessthantwodaysinlargemarkets,someFmessameday,takingE-CommerceoutofreachoftradiFonalretailersandforcingdeepdiscountstoremaincompeFFve
Forbesar)cleshowinghowAmazoncangrowto$3Tcompany!
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“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
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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
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HortonworksConnectedDataPlaOormsandSolu%ons
HortonworksConnecFon
HortonworksSolu%onsEnterpriseData
WarehouseOpFmizaFon
CyberSecurityandThreatManagement
InternetofThingsandStreamingAnalyFcs
HortonworksConnec%onSubscripFonSupport
SmartSense
PremierSupport
EducaFonalServices
ProfessionalServices
CommunityConnecFon
CloudHortonworksDataCloudAWS HDInsight
DataCenterHortonworksDataSuite
HDFHDP
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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
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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
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SAS® Omnichannel Analy1cs Powers Retail …at every step of the customer journey
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Whatmakesupcontext?
Customer Rela1onship Context: Previous purchases
Real-Time Context: In-Store & On-line
Personal Context: Current Shopping Purpose
Relevance
16
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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
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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
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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
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Real-FmeCX+OmnichannelAnalyFcs+CustomerJourneyOpFmizaFon
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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
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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