SAS - Hortonworks: Creating the Omnichannel Experience in Retail webinar march 16-2017-v2
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Only 23% of businesses can integrate customer insights in real-time. Learn how to change that. Join us to hear from industry experts on how to transform your organization’s data into the best omnichannel customer experience. Through this webinar, participants will hear how one retailer, with over 5 million customers and 750 brands, developed precise customer lifetime models using trusted data and delivered personalized promotions at scale. Through a single customer view and customer analytics, the retailer was able to quickly learn what changes needed to be made to improve the customer buying journey, and make those changes rapidly and effectively. Presenters : Dan Mitchell, Director of Global Retail and CPG Practice at SAS, Eric Thorsen, VP Retail at Hortonworks
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SAS - Hortonworks: Creating the Omnichannel Experience in Retail webinar march 16-2017-v2
- 2. Copyright © SAS Ins1tute Inc. All rights reserved. Eric Thorsen General Manager Retail/CPG Hortonworks Speakers Dan Mitchell Director, Global Retail and CPG SAS
- 3. 3 © Hortonworks Inc. 2011 – 2016. All Rights Reserved 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)
- 4. Copyright © SAS Ins1tute Inc. All rights reserved. Understanding the New Consumer
- 5. 5 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Amazon Impact • Growth of “everything store” eroding tradiFonal shopping paGerns, basics and staples, and reducing standard purchases of tradiFonal items at tradiFonal retailers • Anecdotal impact of “Showrooming” where consumers will research products in-store, and ulFmate purchase online through Amazon • Amazon has advanced supply chain to deliver in less than two days in large markets, someFmes same day, taking E-Commerce out of reach of tradiFonal retailers and forcing deep discounts to remain compeFFve Forbes ar)cle showing how Amazon can grow to $3T company!
- 6. Copyright © SAS Ins1tute Inc. All rights reserved. “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 PrioriFes of the Retail CEO
- 7. 7 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Traditional systems under pressure Challenges • Constrains data to app • Can’t manage new data • Costly to Scale Business Value Clickstream GeolocaFon Web Data Internet of Things Docs, emails Server logs 2012 2.8 ZeKabytes 2020 40 ZeKabytes 1 2 New Data ERP CRM SCM New Tradi%onal *Mul)ples of Bytes Kilobyte Megabyte Gigabyte Terabyte Petabyte Exabyte ZeFabyte YoFabyte 1,000,000,000,000,000,000,000 Much of the new data exists in-flight between systems and devices as part of the Internet of Anything
- 10. 10 Transformation --- Maturity Stages à OptimizationExplorationAwareness Marketing Merchandising IT Ops ---MaturityStagesà Peer Compe%%ve Scale Standard among peer group Common among peer group Strategic among peer group New InnovaFons Digital Store Operations No Use Case Name 1a Single View of Customer 1b Single View of Customer 2 Basket Analysis 3 Social Listening 4 Enriched Basket Analysis 5 Clickstream Analysis 6 RecommendaFon Engine 7 Price OpFmizaFon 8 Beacon/Sensor Monitoring and Ingest 9 Store CommunicaFons 10 Email Management 11 EDW Enhancement 12 Inventory OpFmizaFon 13 Path to Purchase 14 Supply Chain Telemetry 15 Customer Service Analysis 16 PreventaFve Maintenance 17 Machine Learning / AI 4 Purchasing & Logistics 10 11 13 1a 1b 12 Use Cases are available at different levels of maturity 15 14 16 17 8 8 7 3 5 2 9
- 11. 11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Retail Use Case: Major Big Box Retailer Web Logging • Track behavior of web visitors. Analyze logs Single View of Customer • Online • In-store • Project Desk • Commercial Counter RecommendaFon Engine • Knowing details of consumer deliver relevant promoFons Price OpFmizaFon • Scan compeFtor pricing on-line • Suggest new price based on margin point and awareness of compeFFve pricing Financial ReporFng • Calculate COGS and insert into ERP system • Replace legacy BI Report that would never complete A True Hadoop “Journey” • Five Use Cases since original project • 18-month lifecycle with HDP Hadoop • In-house talent • Significant annual savings due to lower-cost storage and compute Customers typically “Start Small, Think Big”
- 12. 12 To Legacy Retail Conceptual Architecture API Layer Algorithms, ReporFng and AnalyFcs Access Hortonworks Data Plaform (HDP) Banners, Geos, Stores, Sales & Inventory Syndicated Data, Unstructured Data, Safety, Quality, Warrantee Data Ingest Apps Mobile Digital Self-Service Affinity RFM Tops and Flops Supply Chain Loyalty Customer Profiles Cleansing Staging Storage Syndicated Data Private Label Mfg Feeds to Legacy: • Customer DNA • CRM Systems • ERP Systems • MarkeFng AutomaFon • Digital • Brand AnalyFcs • Loyalty
- 13. Copyright © SAS Ins1tute Inc. All rights reserved. Omnichannel Analy1cs
- 14. Copyright © SAS Ins1tute Inc. All rights reserved.
- 15. Copyright © SAS Ins1tute Inc. All rights reserved. SAS® Omnichannel Analy1cs Powers Retail …at every step of the customer journey
- 16. Copyright © SAS Ins1tute Inc. All rights reserved. What makes up context? Customer Rela1onship Context: Previous purchases Real-Time Context: In-Store & On-line Personal Context: Current Shopping Purpose Relevance 16
- 17. Copyright © SAS Ins1tute Inc. All rights reserved. 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
- 18. Copyright © SAS Ins1tute Inc. All rights reserved. Context: Defined by Customer Profile 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
- 19. Copyright © SAS Ins1tute Inc. All rights reserved. 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
- 20. Copyright © SAS Ins1tute Inc. All rights reserved. Real-Fme CX + Omnichannel AnalyFcs + Customer Journey OpFmizaFon
- 21. Copyright © SAS Ins1tute Inc. All rights reserved. Data and AnalyFcs Aligned with customer journey and business objecFves 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
- 22. 22 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Hadoop for Retail DATA REPOSITORIES ANALYSIS Single view of consumer Targeted promoFons RecommendaFon engines Basket analysis Price opFmizaFon Inventory opFmizaFon Loyalty management Path to purchase Security Opera%ons Governance & Integra%on ° 1 ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° N YARN : Data Opera%ng System Script SQL NoSQL Stream Search Others HDFS (Hadoop Distributed File System) In-Mem ERP EDW RDBMS CRM EMERGING & NON-TRADITIONAL SOURCES SOCIAL MEDIA BEACONS SENSOR RFID CLICKSTREAM IN-STORE WIFI LOGS SERVER LOGS TRADITIONAL SOURCES CRM STORES PRODUCT CATALOG STAFFING PLANS ERP POS TRANSACTIONS INVENTORY WEB TRANSACTIONS