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
Aniket Deosthali
Director Data Science
& Insights
Bonobos
R E T 2 0 3
Calvin French-Owen
CTO
Segment
James Jory
Partner Solutions Architect
AWS

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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

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Realizing customer and operational insight from your data
requires a robust retail data platform

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What are personalized customer experiences?
Personalized customer experiences are about
delivering truly unique digital experiences to each
customer

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Common approaches for recommender systems

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Explicit and implicit user feedback/behavior

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Recommender systems on AWS three ways

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Recommendations based on relationships

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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

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Collaborative filtering architecture using Amazon Neptune
https://github.com/aws-samples/amazon-neptune-samples/tree/master/gremlin/collaborative-filtering

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Collaborative filtering using matrix factorization
M
VT

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Collaborative filtering using matrix factorization
M U
VT

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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

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Collaborative filtering architecture using Apache Spark on
Amazon EMR

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Amazon SageMaker
Deploy
Build
Train

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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

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Amazon SageMaker architecture
Helper code
Inference code
Model hosting (EC2)
Interface
endpoint
Helper code
Training code
Model training (EC2)

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24. Segment by the numbers
- 300 billion monthly events
- 400,000 HTTP rps
- 16,000 containers
- 250 microservices
- hundreds of endpoints
Background

26. Javascript
iOS
ETL
Segment
Amazon
Redshift
Kinesis
Salesforce
Lambda

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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.

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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

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Anatomy of a service: Propensity

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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

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Example: Personalization

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34. Thank you!
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Aniket Deosthali
Director, Analytics
Bonobos
Calvin French-Owen
CTO
Segment
James Jory
Partner Solutions Architect
AWS

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