To meet the needs of the global marketing organization, the AWS marketing analytics team built a scalable platform that allows the data science team to deliver custom econometric and machine learning models for end user self-service. To meet data security standards, we use end-to-end data encryption and different AWS services such as Amazon Redshift, Amazon RDS, Amazon S3, Amazon EMR with Apache Spark and Auto Scaling. In this session, you see real examples of how we have scaled and automated critical analysis, such as calculating the impact of marketing programs like re:Invent and prioritizing leads for our sales teams.

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AWS re:Invent
Deep Analytics for Global AWS
Marketing Organization
A B D 3 0 7
N o v e m b e r 2 8 , 2 0 1 7

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Amit Prakash
Sr. Manager, Advertising, Analytics, and Global
Marketing Operations
AWS Marketing
Neelesh Gattani
Sr. Manager, Data Science
AWS Marketing
Speaker introduction

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What to expect from this session
• Analytics journey of AWS Marketing
• Two key problem statements for deep analytics
• AWS architecture to solve those problem statements
• What’s next in this journey

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Era of big data

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Data flywheel
Better
Analytics
Better
Products
More
Users
More
Data
Clickstream
User activity and
Engagement
Generated content
Usage/Purchases
Social
Dashboards
Reporting
Analyses/Insights
Machine Learning
Optimization
Personalization
Acquisition and
Adoption

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Analytics Journey at AWS Marketing

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Three distinct phases of our analytics journey
Data in silos
Data integrity issues
Limited visibility across
different data sources
No reporting
No analyses
2012

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Three distinct phases of our analytics journey
Single data repository
New data sources
Integrated datasets
Dashboards and
reporting
Manual marketing ROI
analyses
2012–2016
Data in silos
Data integrity issues
Limited visibility across
different data sources
No reporting
No analyses
2012

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Three distinct phases of our analytics journey
Automation
Attribution strategy
Machine learning
Personalization
2016–2017
Single data repository
New data sources
Integrated datasets
Dashboards and
reporting
Manual marketing ROI
analyses
2012–2016
Data in silos
Data integrity issues
Limited visibility across
different data sources
No reporting
No analyses
2012

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Single data repository
Web
Analytics
Billing
Marketing CRM
Social
Advertising
Amazon
Redshift

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Business applications
Targeting
Machine
Learning
Marketing
ROI
KPI Alerts
Metrics
Reporting
Analytical
Marketing
Programs
Amazon
Redshift

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Example of dashboards on Amazon QuickSight
with sample data

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Marketing ROI
Objective: Assess campaign performance and
optimize investments
• Multi-stage econometric models
Machine learning models
Objective: Delivering targeted content through
various marketing channels
• Customer business use-case identification
• Customer persona identification
• AWS service recommendations
Deep-dive on the two analytics
applications

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Manual analyses were not scalable
• Latency and prioritization
• Increasing number of analysis requests
• Marketing end users globally
First problem: Marketing ROI
• Multi-stage analyses to
measure marketing impact
• 1–2 weeks of manual effort
Marketing
ROI

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Not feasible without automation
• Significant burst computing need for short time
• Daily batch process
• Integration with downstream systems
Second problem: Machine learning models
• Service recommendation
and personalization
algorithms
• Daily runs
Machine
Learning
Models

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Infrastructure needs and solution
On demand
Agile
Elastic
Secure
Easy to build
Self-serve analytical requests
Reduction in time latency
Peak/Off-Peak computing
IT security compliance
Scarce development resources
Persistent compute capacity
Parallel processing
Auto Scaling
VPC and encryption
Leverage strengths of the team
What? Why? Solution

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Data Science Platform

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• Central data repository
• Integrated datasets from
different data sources
• Distributed processing
• Handles data manipulation
• Auto-scales for peak computing
for on demand data analysis
• Handles various storage needs
• Inputs, scripts, outputs
• Allows versioning of files
Three key building blocks
Amazon
Redshift
Amazon
EMR
Amazon
S3

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• Open-source distributed
processing system
• Natively supports Python, Java,
and Scala
• Tightly integrated libraries for
machine learning among others
Long-running cluster
• Allows processing of on-
demand analytical requests
• Reduced latency versus
spinning up new cluster every
time
• One node cluster
• Auto-scales for peak
computing
Amazon S3 integration
• EMR File System (EMRFS) to
allow Amazon S3 to store data
• At-rest server-side encryption
with AWS KMS
Features of Amazon EMR that we used
number of Amazon EC2 instances = 1
for long-running cluster; acts as both
master and core node for on-demand
needs

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Needs
• Always at the forefront
• Needed to meet internal IT
security compliance
• EMR cluster in private subnet
• At-rest and in-transit data encryption
• Easy to configure
Caution
• Need to know what to do
• Multiple options available
but need to find the right
option for you
Solution
• Available reference and
resources were very helpful
• re:Invent deep-dive
sessions
• Blogs and
documentation
• Reference architectures
Lessons learnt on security

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EMR security
EMR cluster in private subnet within VPC
Private subnet
Public subnet
VPC NAT gateway
VPC endpoint to
S3 S3 Bucket
IAM Policy at VPC Endpoint IAM Policy at S3 Bucket
Access to VPCE or VPC

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• Easy to build and configure
security for EMR cluster
• Pre-defined security
configurations that allows
server-side and client-side
encryptions
• Easily refer to this configuration
when creating the EMR cluster
EMR: Encryption using security configuration

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D a t a s c i e n c e p l a t f o r m : I n p u t s f o r m a r k e t i n g R O I
On-demand
GUI
stage user inputs
Batch Processes on Amazon EC2
Amazon
Redshift
Amazon
DynamoDB
Inputs
Amazon
S3

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D a t a s c i e n c e p l a t f o r m : I n p u t s f o r M L m o d e l s
Batch Processes on Amazon EC2
Amazon
Redshift
Inputs
Amazon
S3

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User-interface for on-demand requests

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D a t a s c i e n c e p l a t f o r m : P r o c e s s i n g
Auto Scaling
zeppelin
Amazon
S3
Amazon
EMR Cluster

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D a t a s c i e n c e p l a t f o r m : O u t p u t
Amazon
S3
Output
Third-party
integrations
Output
Output
Output

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Sample email: Econometric Analysis

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Web-based Zeppelin notebooks
for building analytical and ML
PySpark scripts on dev
environment
How the data science team works

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D a t a s c i e n c e p l a t f o r m
Auto Scaling
On-demand
GUI
zeppelin
Amazon
S3
stage user inputs
Amazon EMR
Cluster
Batch Processes on Amazon EC2
Data science
Team
spark scripts
Third-party
integrations
Output
Inputs
Output
Amazon
Redshift
Amazon
DynamoDB
Output

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Benefits of the platform
500+ • Number of processed ROI
measurement requests since launch
2+ years • Time effort for 1 FTE saved on ROI
measurement
+173%
• Increase in engagement rates from
personalized marketing as measured
by RCTs

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Real-time streaming Event-based triggers Data Lake
Where are we going next?
AmazonKinesisStreams AWS Lambda

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Lessons learnt
• Security
• Wide variety of best practices documents and tools
• Reference materials saved the day!
• Past re:Invent/summit sessions on fundamentals, service deep-dives
• Blogs/reference architectures
• AWS service documentation pages
• It is a journey, still far to go with many planned enhancements

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Summary: What we discussed
• Evolution of our journey from data collection to deep analytical insights
• Two problems on scaling and automation for deep analytics
• AWS infrastructure to solve those problems
• Where are we going next?

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Thank you!