[Retail & CPG Day 2019] Cloud Journey of Traditional Retailers for Digital Innovation - Philip Thompson, AWS 리테일 기술 리더

2. Retail Cloud Journeys for
Innovation -> Transformation
2
Philip Thompson
Worldwide Tech Leader - Retail
pbct@amazon.com

3. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
“We had three big ideas at Amazon that
we have stuck with for 20+ years, and
they are the reason we are successful:
put the customer first, invent, and be
patient.”
Jeff Bezos
Founder and Chief Executive Officer
Amazon.com, Inc.

4. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
“We had three big ideas at Amazon that
we have stuck with for 20+ years, and
they are the reason we are successful:
put the customer first, invent, and be
patient.”
Jeff Bezos
Founder and Chief Executive Officer
Amazon.com, Inc.
FAILING = LEARNING
INNOVATION = PATIENCE

5. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon’s
Growth Flywheel
> Value
> Selection
> Convenience
© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

6. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
1. Value. 2. Selection 3. Convenience

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retail
The Amazon Retail relationship with AWS
[ maturity ]
[ cashier-free technology ]
“Just Walk Out”

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retail
The Amazon Retail relationship with AWS
[ learnings ]

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Technology Approach
• Transforming retailers
engagement = Digital
Customer Engagement
• Reinventing retailer’s
legacy = Unified
Commerce
• Completing retailer’s view
= Big Data
Business Outcome
• Driving sales and margin
• Improved operations
• Innovating store experience
• Digital Transformation
AWS in retail
Born from Retail, Built for Retailers

10. 10© 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved |
© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and
M O R E R E T A I L C U S T O M E R S O N A W S T H A N A N Y W H E R E E L S E
Thousands of AWS Retail customers | Over $1 Trillion in consumer spending | Across every retail segment

11. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
EnablingTechnologies
• Frictionless Experiences
• Voice Everywhere
• Computer Vision
• AR/VR/MR
• Pervasive “Things” - IoT
• Robotics
Tech Modernization
• Serverless Microservices
• Purpose Built Databases
• Machine Learning
• Retail Data Lakes
Enabling retail transformation
Innovating on consumer expectations
Supply Chain
Safety & Loss
Prevention
Facilities
Management
Environmental
Monitoring &
Control
Connected Home
Connected Vehicle
Intelligent Inventory
Management
Operational
Efficiencies
Customer
Engagement

12. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
• Cloud Center of Excellence
• Well Architected Strategy
Retail cloud roadmap trend
REINVENTION
PROJECT FOUNDATION MIGRATION REINVENTION
Cloud Native Innovation
Tech Debt Retirement
ML PREDICTIVE INVENTORY &
ANALYTICS
VOICE & DIGITAL
ASSISTANT
POS & OPERATIONS
AUTOMATION (IOT,
Computer Vision, Voice,
AR/VR/MR)
Value
Time
FRICTIONLESS
EXPERIENCES
PURPOSE BUILT DATABASES
SERVERLESS MICROSERVICES
CAPABILITIES
CRM & SINGLE VIEW OF
THE CUSTOMER
CONTINUED DATA CENTER
EXIT
* RETAIL SYSTEMS MIGRATIONS
* E-COMMERCE /
DIGITAL
SYSTEMS
* RETAIL DATA LAKEDEV / TEST
(experimentation)

13. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
“We’re not competitor obsessed, we’re CUSTOMER OBSESSED. We
start with what the customer needs, and we work backwards.”
- Jeff Bezos
“frictionless experiences”
© 2018 Amazon Web Services, Inc or its Affiliates. All rights reserved.

14. retail
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15. retail
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16. retail
© 2018 Amazon Web Services, Inc or its Affiliates. All rights reserved.

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Just Walk Out Shopping
(Amazon Go store)
1-Click Checkout
(Amazon.com)

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ID by login cookie
(Amazon.com)
ID by license plate
(Pickup at Amazon Fresh store)

19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
REINVENTION
PROJECT FOUNDATION MIGRATION REINVENTION
Cloud Native Innovation
Tech Debt Retirement
DEV / TEST
ML PREDICTIVE INVENTORY &
ANALYTICS
CRM & SINGLE VIEW OF THE
CUSTOMER
CONTINUED DATA CENTER EXITS
Value
Tim
e
[ NIKE | cloud roadmap ]
* RETAIL SYSTEMS MIGRATIONS
* Wearables, Nike+ NikeID, and other “custom digital / cloud native platforms”
FRICTIONLESS
EXPERIENCES
Today
OPERATIONS AUTOMATION (IOT)
Microservices:
OMNICHANNEL
POS, ECOM, OMS, MERCH
* E-COMMERCE / DIGITAL
SYSTEMS

21. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Nike’s Digital Reinvention Journey:
Project Phase:
• Dev/Test
• Nike Wearables: Fitness trackers, etc.
Foundation Phase:
• Retail data lake
• E-Commerce digital systems
Migration Phase:
• Retail systems migration
• CRM and customer single view
Reinvention Phase:
• ML predictive inventory and analytics
• Serverless microservices capabilities
• Example: Nike Search in mobile apps, web sites and
stores
The Impact of AWS
• Direct-to-consumer sales leads to better pricing power.
• Innovation provides unique insights to redefine the
concept of supply chain optimization.
AWS Retail customer profile: Nike
Before AWS After AWS
Software updates 3 months 2.6 per day
Data transforms 100s 0
Regression testing
90 percent
Manual
100 percent
Automated
Edit to Live 3 hours 5 seconds
Add new experiences 6+ months 1 day
Hardware lead time 3 months No lead time
Nike on AWS: Before and After

22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

23. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Cloud Native Innovation
Tech Debt Retirement
Jan 2016
400+
Developers
TU Clothing
(E-Commerce)
Smart Shop
(Digital)
Groceries online
(E-commerce)
Jan 2017 Jan 2018 Jan 2019
Sainsbury’s
Data Lake
DC Closure
Program
(migration)
Migration
Business Case
Argos Web Assets
(E-commerce)
Customer Loyalty
(AI/ML)
Group Data
Lake
Digital
Banking
Platform
CCoE
REINVENTION

24. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Sainsbury’s Digital/DataTransformation
A four-year journey to a new data ecosystem, single
customer view and cultural shift has led to:
• Cloud First: A companywide commitment
• Integration of Applied Data Science and Machine
Learning
• Value: ML-powered inventory system identifies best
store-specific shelf locations for new merchandise
• Value: Data science algorithms automate van
schedules for grocery deliveries to maximize
efficiency
• Interactive Nectar Rewards program – trials led to five-
fold greater engagement
• SmartShop self-scan in 148 supermarkets
• Pay@Browse in 206 Argos stores
GroceriesOnline
Purpose: Move and migrate Sainsbury’s ecommerce
system to AWS from Oracle RAC Rack DB.
• Consistent customer experiences across all platforms
• Improved performance by as much as 80 percent
• Infrastructure reduced by 30 percent
• Now providing multiple updates daily, up from 5-6 per
year
• 7 terabytes of data moved - with zero outages
AWS Retail customer profile: Sainsbury’s
“Data is of increasing
importance toretailers…
Sainsbury’ssupermarketis
developing its data talent
to optimize its
opportunities.”

25. © 2019, Amazon Web Services, Inc. or its Affiliates.
ML for retailer transformation

26. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark
of digital transformation initiatives supported by AI in 2019
40% - IDC 2018

27. Our mission at AWS
Put machine learning in the
hands of every developer

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FRAMEWORKS INTERFACES INFRASTRUCTURE
AI Services
Broadest and deepest set of capabilities
T H E A W S M L S T A C K
VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS
ML Services
ML Frameworks + Infrastructure
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X F O R E C A S TR E K O G N I T I O N
I M A G E
R E K O G N I T I O N
V I D E O
T E X T R A C T P E R S O N A L I Z E
Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment HostingAmazon SageMaker
F P G A SE C 2 P 3
& P 3 D N
E C 2 G 4 E C 2 C 5 I N F E R E N T I AG R E E N G R A S S E L A S T I C
I N F E R E N C E

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FRAMEWORKS INTERFACES INFRASTRUCTURE
AI Services
Broadest and deepest set of capabilities
T H E A M A Z O N M L S T A C K
VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS
ML Services
ML Frameworks + Infrastructure
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X F O R E C A S TR E K O G N I T I O N
I M A G E
R E K O G N I T I O N
V I D E O
T E X T R A C T P E R S O N A L I Z E
Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment HostingAmazon SageMaker
F P G A SE C 2 P 3
& P 3 D N
E C 2 G 4 E C 2 C 5 I N F E R E N T I AG R E E N G R A S S E L A S T I C
I N F E R E N C E

30. 30© 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved |
FRAMEWORKS INTERFACES INFRASTRUCTURE
AI Services
Broadest and deepest set of capabilities
T H E A M A Z O N M L S T A C K
VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS
ML Services
ML Frameworks + Infrastructure
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X F O R E C A S TR E K O G N I T I O N
I M A G E
R E K O G N I T I O N
V I D E O
T E X T R A C T P E R S O N A L I Z E
Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment HostingAmazon SageMaker
F P G A SE C 2 P 3
& P 3 D N
E C 2 G 4 E C 2 C 5 I N F E R E N T I AG R E E N G R A S S E L A S T I C
I N F E R E N C E

31. 31© 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved |
FRAMEWORKS INTERFACES INFRASTRUCTURE
AI Services
Broadest and deepest set of capabilities
T H E A M A Z O N M L S T A C K
VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS
ML Services
ML Frameworks + Infrastructure
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X F O R E C A S TR E K O G N I T I O N
I M A G E
R E K O G N I T I O N
V I D E O
T E X T R A C T P E R S O N A L I Z E
Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment HostingAmazon SageMaker
F P G A SE C 2 P 3
& P 3 D N
E C 2 G 4 E C 2 C 5 I N F E R E N T I AG R E E N G R A S S E L A S T I C
I N F E R E N C E

32. ML in retail / commerce
Primary goals for every retailer
• Increase BRAND equity
• Drive top line sales / growth
• Increase Margin
• Reduce operational costs
• Deliver a more consistent, timely experience to delight customers

33. • Dynamic Pricing
• Pricing Optimization
• Digital Store Assistant
• Next Best Action / Offer
• Propensity to Act
• Relevance to Consumer
• Chat & Voice Assistance
• Location Selection
• Forecasting & Optimization (Profit, Rev
enue, Supply Chain, Demand)
• Real Time Personalization (Product rec
ommendations, content / messaging, Li
fetime Value, concierge)
• Pricing Optimization
• Emotion Recognition
• Bot Creator Authoring
• Personalization Platform
• Conversation Outcomes
• Case Automation
• Threat Vector Identification
• AI-enabled Toys & Games
• Product Placement
• Shopper Intelligence
• ML Image Tagging
U s e C a s e s
ML Retail Use Cases

34. • Consumer Insights
• Identity Management
• Order Management and Fulfillment
• Brand Loyalty / Campaign Management
/ Marketing attribution
• Customer Care / Customer Call sentime
nt analysis
• Virtual merchant / markdown cadence /
merchandise buy planning
• CC bad debt, fraud, and optimization
• Counterfeit product detection
• Gray market selling
• Customer quality check (manufacturing
/ receiving / shipping)
• Traffic in store analysis (mood, mercha
ndise hot spotting, planograms, dwell ti
mes, traffic in the stores, etc)
• Loss prevention
• Procure to Pay audit
• ML for IT Ops (notification reduction)
• Returns Optimization
U s e C a s e s
ML Retail Use Cases

35. …determine which ML Use Case will have the highest value
Complexity
Business Value
personalization
forecast replenishment
churn prediction dynamic pricing
digital store assistants
talent optimization
a/b testing
product recommendation
merchandising hotspot
store footprint selection
spill detection
frictionless shopping
chat bot
sentiment analysis
real-time bidding platforms
predictive maintenance
position matching
new product development
loss prevention
predictive customer care
Image/video recognition
predictive maintenance
programmatic media buying
return analysis
Consumer Insights
Operational & Inventory Insights
Post-sales Insights
service root cause analysis
cross-sell up-sell
margin analysis
assortment planning

37. Amazon Forecast Amazon Personalize
AWS Retail ML | Experiment With Amazon ML Services
Experiment with speed, using AI Services based on the same technology used at Amazon.com

38. ML Roadmap (function and phase)
1. Video Analytics
6. Foresee Survey Text Analysis
10. Data Capture (customer attributes)
2. CC Optimization
3. Demand Forecasting (EU POC)
4. In-stock LIN Merge
9. Size Scaling
Apr-19 May-19 Jun-19
5. ClickStream (Phase 1)
7. Sentiment Analysis
8. Image recognition (Master Product Data)
Corporate
Retail
Digital
Value Chain
POV/Case Study
Tech Feas/POC
Execute
Deploy
1. Video Analysis – status update lorem ipsum
2. CC Optimization – status update lorem ipsum
3. Demand Forecasting – status update lorem ipsum
4. In-stock LIN Merge – status update lorem ipsum
5. Clickstream – status update lorem ipsum
6. PFS Foresee Survey Data (text analysis) – status update lorem ipsum
7. Sentiment Analysis – status update lorem ipsum
8. Image Rekognition – status update lorem ipsum
9. Product / Size scaling – status update lorem ipsum
10. Data Capture – status update lorem ipsum
Executive Summary

39. Personalization
O p t i o n 1 O p t i o n 2 O p t i o n 3
Amazon
Personalize
Amazon
SageMaker
Pre-built notebook
s with Built-in high
-performance algo
rithms
One-click
Training with mo
del Tuning
One-click
Deployment and Fully
managed hosting aut
o-scaling
Amazon
SageMaker
Built on 20 years of operating
Personalization at scale acro
ss segments, geos, and indu
stries

40. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Zalando’s DigitalTransformation
Aspirations
• Provide a data-driven, personalized shopping experience
with smooth payments, customer service and easy returns
• Inspire customers to explore/discover brands, trends, products
Pre-AWS Challenges
• Infrastructure co-located across data centers in Germany
• Scaling that investment was unsustainable for long term
Implementing AWS
• AWS serves customer-facing workloads, mostly on EC2.
• Also utilize AWS Lambda, RDS and Dynamo Kinesis.
• S3 for storing data, with computational layers built on top
Impact
• Engineering teams quickly turn experimental ideas into reality.
• New features that resonate with customers are scaled and
rolled out quickly, efficiently and cost-effectively
The Impact of AWS
Zalando focuses on data and
machine learning to personalize
customer experiences, quickly
implement new ideas and roll out
popular features. Some of the
results include:
• Algorithmic Fashion Companion
-ML and AI recommendations
• ML-based Sizing -
from customer return and 3D-
scanned model data
• Personalized Homepage - based
on browsing behavior,
preferences, purchase history
• Big Data & Personalization -
analytics for advanced
personalization, fashion insights
AWS Retail customer profile: Zalando

41. Founded
2008
€5.4B
2018 Rev
25% YoY
Growth

42. Zalando technology org…

43. Online use cases for ML
• Personalized marketing
• Size recommendations
• Search experiences
• Offer/discount recommendations
• Loyalty/Gift card recommendations
• Stock optimization
• Warehouse automation/optimization
• etc…..

44. Zalando - ML at scale – automation

45. Personalization

46. Machine Learning at scale

47. ML at scale – automation

48. Speaker Name
Contact information
Thank You
Philip Thompson
Worldwide Tech Leader – Retail
pbct@amazon.com
Thank You