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ABD307_Deep Analytics for Global AWS Marketing Organization

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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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ABD307_Deep Analytics for Global AWS Marketing Organization

  1. 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  2. 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amit Prakash Sr. Manager, Advertising, Analytics, and Global Marketing Operations AWS Marketing Neelesh Gattani Sr. Manager, Data Science AWS Marketing Speaker introduction
  3. 3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  4. 4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Era of big data
  5. 5. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  6. 6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Analytics Journey at AWS Marketing
  7. 7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Three distinct phases of our analytics journey Data in silos Data integrity issues Limited visibility across different data sources No reporting No analyses 2012
  8. 8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  9. 9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  10. 10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Single data repository Web Analytics Billing Marketing CRM Social Advertising Amazon Redshift
  11. 11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Business applications Targeting Machine Learning Marketing ROI KPI Alerts Metrics Reporting Analytical Marketing Programs Amazon Redshift
  12. 12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Example of dashboards on Amazon QuickSight with sample data
  13. 13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  14. 14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  15. 15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  16. 16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  17. 17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Data Science Platform
  18. 18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • 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
  19. 19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • 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
  20. 20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  21. 21. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  22. 22. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • 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
  23. 23. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  24. 24. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  25. 25. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. User-interface for on-demand requests
  26. 26. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  27. 27. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  28. 28. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Sample email: Econometric Analysis
  29. 29. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Web-based Zeppelin notebooks for building analytical and ML PySpark scripts on dev environment How the data science team works
  30. 30. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  31. 31. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  32. 32. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Real-time streaming Event-based triggers Data Lake Where are we going next? AmazonKinesisStreams AWS Lambda
  33. 33. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  34. 34. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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?
  35. 35. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Thank you!

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