Predictive Segmentation Using Amazon Pinpoint and Amazon SageMaker

Predictive Segmentation Using Amazon Pinpoint and Amazon SageMaker combines Amazon Pinpoint with Amazon SageMaker to help automate the process of collecting customer data and creating Amazon Pinpoint segments identified by Machine Learning (ML) for tailored audience messaging. These segments can include users predicted to churn, users predicted to make a purchase, and other predicted user behaviors relevant to your business needs.

This Guidance includes a sample dataset that you can use as a reference to develop your own custom ML models using your own data.

Deploying Digital User Engagement Events Database is a prerequisite to deploying this Guidance.

Overview

The diagram below presents the architecture you can build using the example code on GitHub.

Predictive Segmentation Using Amazon Pinpoint and Amazon SageMaker | Architecture Diagram
 Click to enlarge

Predictive Segmentation Using Amazon Pinpoint and Amazon SageMaker architecture

The code deploys a daily batch process orchestrated by AWS Step Functions. The process begins when an Amazon CloudWatch time-based event triggers a series of AWS Lambda functions that use an Amazon Athena query to query customer data stored in Amazon Simple Storage Service (Amazon S3). The data is crawled daily by AWS Glue.

The customer data includes endpoints exported from Amazon Pinpoint and end-user engagement data streamed from Amazon Pinpoint using the Digital User Engagement Events Database solution. Amazon Sagemaker performs batch transform requests to predict customer churn based on a trained machine learning (ML) model.

By default, Predictive Segmentation Using Amazon Pinpoint and Amazon SageMaker is configured to process data from the sample dataset. To use your own dataset, you must customize the Guidance.

Predictive Segmentation Using Amazon Pinpoint and Amazon SageMaker

Version 1.1.0
Last updated: 12/2020
Author: AWS

Additional resources

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Features

Automation

Build an architecture that automates the collection of customer data, predicts customer churn using ML, and maintains a tailored audience segment for messaging.

Customization

This Guidance includes an example dataset you can use to train the included ML model. But you can modify the code to use your own dataset.
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Solving with AWS Solutions: Predictive Segmentation using Amazon Pinpoint and Amazon SageMaker
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