Create a Churn Prevention Solution in Google Cloud

In this blog post, we develop a reference churn analytics pipeline that helps to evaluate the churn risk for individual users and recommends personalized churn treatment plans that can be executed by marketing teams.

The ability to identify and interpret churn patterns and prescribe the right treatment is as important as achieving churn prediction accuracy. In this article, we discuss how to build a solution that helps quantify, investigate, and fight customer churn, complaints, and any other issues related to customer dissatisfaction.

In this article, we describe the design of the Next Best Action model that we commonly use in practice and elaborate on the methodology for offline efficiency evaluation.