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AWS Solutions Library

Guidance for Subscriber Churn Prediction and Retention on AWS

Overview

This Guidance leverages Machine Learning (ML) techniques to build churn prediction models that identify subscribers who are high risk to churn and their key drivers. This can help Communication Service Providers to personalize offerings and retain subscribers.

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

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.

Telecom data is used to identify the churn propensity of a telecom subscriber. This aligns with business objective. A custom machine learning (ML) model is trained in the cloud on customer data to determine churn. Results of the model and feature importance is visualized in QuickSight to help business analysts identify trends to provide decision support of who to approach with a customer retention offer. 

Read the Operational Excellence whitepaper

All data is encrypted both in motion and at rest. Encrypted Amazon S3 buckets store data and SageMaker can only access that data by using the VPC (and not the internet). Training is done in secure containers and the results are stored in encrypted S3 buckets. 

Read the Security whitepaper

SageMaker hosting is used to server the trained model, which takes advantage of multiple Availability Zones and elastic Scaling groups. 

Read the Reliability whitepaper

Serverless technology is used where possible. SageMaker Endpoints can scale up and down as needed to ensure the minimum number of instances needed are running. 

Read the Performance Efficiency whitepaper

SageMaker endpoints can scale up and down as needed to ensure the minimum number of instances needed are running. Instance sizes are measured by using SageMaker Instance Recommender to make sure costs are minimized. 

Read the Cost Optimization whitepaper

By extensively using managed services and dynamic scaling, we minimize the environmental impact of the backend services. All compute instances are sized to provide maximum utility.

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.

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