What does this AWS Solutions Implementation do?

This solution combines Amazon Pinpoint with Amazon SageMaker to help automate the  process of collecting customer data, predicting customer churn using ML, and maintaining a tailored audience segment for messaging.

This solution includes an example dataset you can use as a reference to develop your own custom ML models with your own data.  

AWS Solutions Implementation overview

The diagram below presents the architecture you can automatically deploy using the solution's implementation guide and accompanying AWS CloudFormation template.

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

Predictive Segmentation Using Amazon Pinpoint and Amazon SageMaker architecture

The AWS CloudFormation template 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 Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose. Amazon SageMaker performs batch transform requests to predict customer churn based on a trained machine learning (ML) model.

By default, this solution is configured to process data from the example dataset. To use your own dataset, you must modify the solution.

Predictive Segmentation Using Amazon Pinpoint and Amazon SageMaker

Version 1.0.1
Last updated: 01/2020
Author: AWS

Estimated deployment time: 10 min

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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 solution includes an example dataset you can use to train the included ML model. But, you can modify the solution to use your own dataset.
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