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Enabling New Retail Customer Experiences with Big Data - AWS Online Tech Talks

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Learning Objectives:
- Learn how AWS can help retailers realize actual value from their big data
- Learn how to deliver on differentiated retail customer experiences
- Learn how AWS can help you realize actual value from your big data and deliver on the differentiated retail experiences that customers have come to expect

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Enabling New Retail Customer Experiences with Big Data - AWS Online Tech Talks

  1. 1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Enabling new retail experiences with Big Data Toby Knight Manager, Solutions Architecture Amazon Web Services June 2018
  2. 2. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Agenda What’s holding back Big Data in retail? Solving retail data challenges Enabling new retail experiences with Big Data 1 2 3
  3. 3. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. What’s holding back Big Data in retail?
  4. 4. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Retailers want to use Big Data to create differentiated customer experiences... Retailers are planning to use Big Data to build: • Product recommendation engines • Next-gen customer loyalty programs • Real-time order tracking • Interactive voice/chat bots • Dynamically-generated personal offers • Localized product assortments • and more…
  5. 5. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. …but Big Data still challenges most retailers Store AnalyzeIngest 1 4 0 9 5CUSTOMER & OPERATIONAL DATA CUSTOMER & OPERATIONAL INSIGHTS ANALYTICS PIPELINE Rigid data ingest Inability to process new types of retail data Limited analytics platforms Inability to generate new types of insights Data doesn’t “connect” Siloed data prevents single source of truth
  6. 6. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Challenge #1: Data doesn’t connect Line of business E-commerce Data warehouse Your environment Point-of-sale
  7. 7. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Challenge #2: Rigid data ingest Rigid schemas & Rigid formats Systems often unable to ingest new types of retail data In-store video In-store IoT Speech / text Clickstream Social media Schema for retail data is defined on-write… …and locked when data is warehoused SKU # Date Time … SKU # 02-FEB-2018 10:37 AM SKU #
  8. 8. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Challenge #3: Limited analytics platforms Most retail analytics: a series of spreadsheets… • Machine learning • Real-time monitoring • Image / video analysis • Ad-hoc query and more… …limiting ability to use new analytics for innovation:
  9. 9. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Solving retail data challenges
  10. 10. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS helps unlock Big Data for retail in three ways New analytics possibilities – machine learning, natural language processing, Hadoop-as- a-service, and more The right analytics tool for every job Future-proofed for new data types Ingest new retail data for differentiated experiences: image/video, social media, IoT sensors, and more Single source of truth (aka “data lake”) Secure and scalable backbone for all incoming data and all analytics – no more data siloes
  11. 11. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Benefit #1: Single source of truth Today E-commerce Point-of-sale Line-of-business Retail systems feed into their own data stores – information is siloed Tomorrow Individual systems feed into a single data lake – information is cross-referenceable E-commerce Point-of-sale Line-of-business Unified Storage Amazon S3
  12. 12. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Benefit #2: Future-proofed for new data types and many more… Clickstream data Amazon Kinesis Speech-to-text Amazon Transcribe In-store IoT data Amazon IoT suite In-store video Amazon Kinesis Video Tomorrow As your business needs evolve, enrich existing sources with new types of retail data Unified Storage Amazon S3 Today E-commerce Point-of-sale Line-of-business Ingesting ‘standard’ retail data types
  13. 13. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Benefit #3: Right analytic tool for every job Unstructured data Amazon EMR Ad-hoc query Amazon Athena ML as-a-service Amazon ML suite Image/video analysis Amazon Rekognition Tomorrow As business needs evolve, access advanced analytic capabilities on-demand (experiment without risk) and many more… Unified Storage Amazon S3 Customer & operational data Today Cost-effective AWS tools for ‘standard’ analytics Data Warehouse Amazon Redshift Relational Database Amazon Aurora
  14. 14. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Putting it all together… Unified Storage Amazon S3 E-commerce Point-of-sale Line-of-business and many more… Clickstream data Amazon Kinesis Speech-to-text Amazon Transcribe In-store IoT data Amazon IoT suite In-store video Amazon Kinesis Video Data Warehouse Amazon Redshift Relational Database Amazon Aurora Unstructured data Amazon EMR Ad-hoc query Amazon Athena ML as-a-service Amazon ML suite Image/video analysis Amazon Rekognition and many more… Traditional retail data sources Ingest new types of retail data Cost-effective traditional analytics Advanced analytics on- demandSingle source of truth, interoperable with everything
  15. 15. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. A modern data platform is critical for machine learning …and AWS provides ML tools for retailers of all capabilities Provides the data needed for machine learning… Single, cross-referenceable data source to feed ML models Ability to ingest new data types to develop non-intuitive insights Low data science capability High data science capability Amazon Deep Learning AMI Tools for advanced data scientists Amazon SageMaker Machine learning build/train/deploy service Amazon ML as-a-service API-based tools for image/video analysis, sentiment analysis, text-to-speech, and more
  16. 16. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. A modern data platform supports retail ML use cases • Dynamic Pricing • Pricing Optimization • Forecasting & Optimization (Profit, Revenue, Supply Chain, Demand) • Digital Store Assistant • Product Recommendation • Personalization Platform • Loyalty Program Optimization • Support Case Automation • Product Placement • Shopper Intelligence • Store Footprint Selection Common Retail ML Use Cases Queryable ‘single source of truth’ on customer & operations
  17. 17. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Enabling new retail experiences
  18. 18. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. “Work Backwards” from target experience Store AnalyzeIngest 1 4 0 9 5CUSTOMER & OPERATIONAL DATA CUSTOMER & OPERATIONAL INSIGHTS BIG DATA PIPELINE START HERE
  19. 19. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Different retail data lake ‘flavors’ Consumer Insights Data Lake Operations / Inventory Insights Data Lake Post-Sales Insights Data Lake • CEO • CFO • CIO / CTO • Chief Marketing Officer (CMO) / Chief Digital Officer (CDO) • Chief Operating Officer (COO) • Chief Operating Officer (COO)
  20. 20. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Customer Insights Data Lake Retail data type Typical data sources include: Multichannel Campaign Management (MCCM) and CRM systems SAS, SFDC, IBM, Oracle, Epsilon, Axciom, Adobe, SugarCRM, ... Digital Marketing Platforms and CRM systems Marketo, Hubspot, Marin, Criteo, DoubleClick, Kenshoo, CAKE, Adobe, ... Product Catalog / Master Data Management / Product Information / Product Lifecycle Manager / RAMA Infor, Stibo, Informatica, JDA, SAS, SAP, IBM, Oracle, Dassault, Arena, Windchill, ... Point of Sale (POS) / Transactions System Aptos, Square, Lightspeed, NCR, Catapult, RetailPoint, FuturePOS, Fujitsu, … Order Management System (OMS) IBM Sterling, Manhattan, SAP, Oracle, Aptos, Kibo, … Customer Master [OMS systems, POS Systems, Ecom Systems] Start by identifying most important use cases… …and work backwards to the specific data types and sources to feed into data lake Sample use cases Lifetime Customer Value (LTV) insights: • Cross-channel purchase history • Customer segmentation Conversion Ratio (CVR) insights: • Value scoring for marketing actions • Mapping of full customer journey • Churn prediction Propensity to buy insights: • Customized promotions • Selective discounting • Intent signal identification
  21. 21. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Operations / Inventory Insights Data Lake Sample use cases Operations/inventory insights • Real-time view of inventory • Demand forecasting • Optimized allocations and Inventory Management process • Operational efficiency comparison across supply chain stages • Operational efficiency comparison across geos Retail data type Typical data sources include: Product Catalog / Master Data Management / Product Information / Product Lifecycle Manager (PIM / PLM) Infor, Stibo, Informatica, JDA, SAS, SAP, IBM, Oracle, Dassault, Arena, Windchill, … Supply Chain Management (SCM) Manhattan Associates, JDA, Infor, Oracle, SAP, IBM, … Warehouse Management System (WMS) Manhattan Associates, HighJump, Oracle, SAP, Infor, … Order Management System (OMS) IBM Sterling, Manhattan, SAP, Oracle, Aptos, Kibo, … RAMA, Sales and Ops Planning, Inventory Management, Demand Planning, Forecasting, Allocation, Markdown / Discounts Management JDA, Oracle, IBM, SAP, Manhattan, Infor, … Start by identifying most important use cases… …and work backwards to the specific data types and sources to feed into data lake
  22. 22. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Post-Sales Insights Data Lake Sample use cases Post-Sales insights • Predictive / proactive support • Call center automation • Churn prediction • Service / warranties root cause analysis • Cross-sell / up-sell opportunity identification Retail data type Typical data sources include: Multichannel Campaign Management (MCCM) and CRM SAS, SFDC, IBM, Oracle, Epsilon, Axciom, Adobe, … CRM / Customer Care / Call Center ZenDesk, ServiceNow, SFDC, Oracle, SAP, MSFT, Pegasystems, SugarCRM, … Returns / Warranty IBM Sterling, Manhattan, SAP, Oracle, Aptos, Kibo, … Repairs / Spare Parts Inventory RMA + Inventory Management Start by identifying most important use cases… …and work backwards to the specific data types and sources to feed into data lake
  23. 23. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Example: localized product assortment Store AnalyzeIngest 1 4 0 9 5 Store-specific product assortment New experience Unified Storage Amazon S3 E-commerce purchases Weather data Point-of-sale data In-store video feeds Clickstream data Disproportionately purchased items by geo [Hadoop-based analysis] Customer in-store dwell time by product area [video analytics] Prediction of popular SKUs based on local weather forecast [machine learning]
  24. 24. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Where to find: More information Retail customers already using AWS: https://aws.amazon.com/retail/case-studies/ AWS Quick Starts for Big Data and Analytics: https://aws.amazon.com/quickstart/ Create an AWS Account: https://aws.amazon.com/free/ Contact AWS Sales: https://aws.amazon.com/contact-us/aws-sales/

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