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AWS Solutions Library

Guidance for Predictive Scores for Member Retention on AWS

Overview

This Guidance demonstrates how nonprofits associations and membership organizations can proactively understand which members are likely to allow their membership to lapse and the reasons for the same, using AWS Data Lake and artificial intelligence/machine learning (AI/ML) services

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram PDF

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

This guidance can be deployed with infrastructure as code and automation for fast iteration and consistent deployments. Use Amazon CloudWatch for application and infrastructure monitoring. Use Amazon SageMaker Model Monitor and Amazon SageMaker Clarify to track bias and model drift.

Read the Operational Excellence whitepaper 

Use AWS Identity and Access Management (AWS IAM) to ensure users and services have least privilege access, especially to sensitive donor or member data in Amazon S3. Use Amazon Macie to identify possible sensitive data, and obfuscate or remove irrelevant data before using in SageMaker. Use Amazon Virtual Private Cloud (Amazon VPC) to enable connectivity to resources from only the services and users that are needed. 

Read the Security whitepaper 

Most services used in the architecture are serverless, and are deployed with high availability by default. Use continuous integration/continuous delivery (CI/CD) practices, and SageMaker Pipelines to automate model development, deployment and management. Collect and automate action on metrics collected in CloudWatch. 

Read the Reliability whitepaper 

Use monitoring to generate alarm-based notifications using CloudWatch, and adjust resources accordingly. Use SageMaker Experiments to optimize algorithms and features.

Read the Performance Efficiency whitepaper 

User SageMaker Studio auto shutdown to avoid paying for unused resources. Start training with small quantities of data. Use Amazon S3 storage classes appropriately, based on data access patterns. 

Read the Cost Optimization whitepaper 

Use managed services when possible, to shift the responsibility of optimizing hardware to AWS. Shut down resources when not in use.

Read the Sustainability whitepaper 

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.