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

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. © 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
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What’s holding back Big Data in retail?

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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…

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…but Big Data still challenges most retailers
Store AnalyzeIngest
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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

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Challenge #1: Data doesn’t connect
Line of business
E-commerce Data warehouse
Your environment
Point-of-sale

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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 #

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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:

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Solving retail data challenges

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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

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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

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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

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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

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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

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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

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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. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Enabling new retail experiences

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“Work Backwards” from target experience
Store AnalyzeIngest
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OPERATIONAL
DATA
CUSTOMER &
OPERATIONAL
INSIGHTS
BIG DATA PIPELINE
START HERE

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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)

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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

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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

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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

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Example: localized product assortment
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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]

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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/