Customer churn is a dynamic and important problem to solve for finance, specifically in asset management. The acquisition cost for each customer, combined with cross-product churn and the potential loss of assets in case of a churn, drives the need to proactively predict and prevent churn. Churn prevention is more beneficial than constant acquisition of new customers to replace the churners.

In this paper, we outline how TAZI’s Customer Retention Solution works. This solution is based on TAZI’s Continuous and Explainable, No-Code, Automated Machine Learning (AutoML) platform. We describe how continuous learning helps discover new evolving churn micro-segments. We also describe how business units, such as customer outreach teams, can take churn prevention actions easily and quickly using Explainable AI.

The resources at the end of the paper to see how much you could save with a tool like this and how you could create a customized churn prevention solution.