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Real-Time Personalized Customer Experiences at Bonobos (RET203) - AWS re:Invent 2018

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In this session, learn how Bonobos, an online retailer for men's clothing and accessories, powers their personalized customer experiences on top of AWS. We start by exploring the foundational elements required to build an effective retail data platform as well as the building blocks provided by AWS to deliver these experiences. Learn how Bonobos leverages Segment in their architecture, and hear from Bonobos and Segment on the objectives, challenges, and outcomes realized by Bonobos through their journey in constructing and deploying their personalization platform.

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Real-Time Personalized Customer Experiences at Bonobos (RET203) - AWS re:Invent 2018

  1. 1. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Real-time personalized customer experiences at Bonobos Aniket Deosthali Director Data Science & Insights Bonobos R E T 2 0 3 Calvin French-Owen CTO Segment James Jory Partner Solutions Architect AWS
  2. 2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda Foundational elements of a retail data platform Building blocks for creating personalized experiences on AWS Introduction to Segment’s customer data infrastructure Overview of personalization architecture at Bonobos Q&A with Bonobos and Segment
  3. 3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  4. 4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Realizing customer and operational insight from your data requires a robust retail data platform
  5. 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  6. 6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. What are personalized customer experiences? Personalized customer experiences are about delivering truly unique digital experiences to each customer
  7. 7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Common approaches for recommender systems
  8. 8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Explicit and implicit user feedback/behavior
  9. 9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Recommender systems on AWS three ways
  10. 10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Recommendations based on relationships
  11. 11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Collaborative filter Gremlin query gremlin> g.V().has("customer","customer_id","c1").as("targetCustomer"). in(”purchased").where(neq("targetCustomer")).dedup(). group().by().by(out(”purchased"). where(within("self")).count()).as("g"). out(”purchased").aggregate("self"). select(values). order(local).by(decr).limit(local, 1).as("m"). select("g").unfold(). where(select(values).as("m")).select(keys). out(”purchased").where(without("self")). groupCount(). order(local).by(values, decr).by(select(keys).values("name")). unfold().select(keys).values("name") ==>p5 ==>p3 ==>p1
  12. 12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Collaborative filtering architecture using Amazon Neptune https://github.com/aws-samples/amazon-neptune-samples/tree/master/gremlin/collaborative-filtering
  13. 13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Collaborative filtering using matrix factorization M VT
  14. 14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Collaborative filtering using matrix factorization M U VT
  15. 15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Collaborative filtering using Apache Spark on Amazon EMR https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin/ https://spark.apache.org/docs/latest/ml-collaborative-filtering.html
  16. 16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Collaborative filtering architecture using Apache Spark on Amazon EMR
  17. 17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon SageMaker Deploy Build Train
  18. 18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon SageMaker examples for personalization Recommender System Uses neural network embeddings for non-linear matrix factorization to predict user movie ratings on Amazon digital reviews (MXNet Gluon) Targeted direct marketing Predicts customers most likely to convert based on customer and aggregate metrics (XGBoost) Predicting customer churn Uses customer interaction and service usage data to find those most likely to churn, and then walks through the cost/benefit trade-offs of providing retention incentives (XGBoost) Time-series forecasting Generates a forecast for topline product demand (Linear Learner) https://github.com/awslabs/amazon-sagemaker-examples/tree/master/introduction_to_applying_machine_learning
  19. 19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon SageMaker architecture Helper code Inference code Model hosting (EC2) Interface endpoint Helper code Training code Model training (EC2)
  20. 20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  21. 21. Segment by the numbers - 300 billion monthly events - 400,000 HTTP rps - 16,000 containers - 250 microservices - hundreds of endpoints Background
  22. 22. Javascript iOS ETL Segment Amazon Redshift Kinesis Salesforce Lambda
  23. 23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  24. 24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Analytics Bonobos DSI team (Data Science & Insights) Statistics Structured problem solving Spreadsheets Build resilient systems Databases Business strategy/tactics Engineering One-off analyses Business rules Looker & Machine Learning DSI Mission We use data, technology, and good judgement to solve business and customers’ problems.
  25. 25. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Understand the customer journey in real-time Business Intelligence (i.e. self-service reports) & Advanced Analytics (i.e. attribution modeling) & Real-time Analytics (intra day/hour decisions) Consumer facing experiences across Web and Guideshops Segment generates the customer journey - GS events (PoS) - Site + app events - Marketing events
  26. 26. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Anatomy of a service: Propensity
  27. 27. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Anatomy of a service: Personalization Redis (Cache) Bonobos.com (Chelsea) Alfred service Amazon Kinesis Contracts facilitate workflows: Product-Eng team implement experiences across platforms while DSI team async builds data infra and models ‘Classic’ 24 hour batch processing for web analytics Python application Data Warehouse Looker Daily Real-time personalization
  28. 28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Example: Personalization
  29. 29. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  30. 30. Thank you! © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Aniket Deosthali Director, Analytics Bonobos Calvin French-Owen CTO Segment James Jory Partner Solutions Architect AWS
  31. 31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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