It usually costs more to acquire a customer than it does to retain a customer.
Focusing on customer retention enables companies to maximize customer revenue over their lifetime.
This blog post will show you how to train a model to predict both the risk AND the time of a customer attrition event using XGBoost.
Combined with a production-level end-to-end Machine Learning pipeline like Customer Churn Pipeline on AWS that has time to event churn modeling baked in, this allows for timely interventions to stop customer attrition.
Customer attrition or simply churn is…
Losing customers costs money. Discovering customers likely to leave, and then stopping them from churning, saves money. Wouldn’t it be great if you could hold onto customers longer, maximizing their lifetime revenue? Wouldn’t it be even better if this could be done with machine learning scaleably?
Well guess what — now you can!
In this blog post, you will deploy an End to End Customer Churn Prediction solution using AWS services. You will build automated training and inference pipelines with Amazon SageMaker and AWS Step Functions for Machine Learning churn…
As a consultant and machine learning developer, I help customers accelerate deployments to production. Opinions are my own.