What does this AWS Solutions Implementation do?
This solution 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 solution includes a sample dataset that you can use as a reference to develop your own custom ML models using your own data.
Deploying the Digital User Engagement Events Database solution is a prerequisite to deploying this solution.
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
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 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, this solution is configured to process data from the sample dataset. To use your own dataset, you must customize the solution.
Predictive Segmentation Using Amazon Pinpoint and Amazon SageMaker
Version 1.1.0
Last updated: 12/2020
Author: AWS
Estimated deployment time: 10 min
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Features
Automation
Customization
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