Data Driven Churn Management in Retail Banking

Client acquisition costs account for a significant part of distribution costs. Most banks therefore have implemented systematic client retention management processes. However, these interventions often take place at a very late stage. The identification of early warning indicators across the entire client population could help to design effective early intervention measures.

Systematic analysis of client‘s transaction datasets to identify triggers and dynamically assess to right moments to actively manage the client relationship to avoid churn. In addition, an estimation of client lifetime value could complement this analysis to provide reliable data points how much to invest into client retention.

Modulos AutoML allows client teams to effectively analyse client datasets and to extract key triggers to initiate effective measures. Additionally, we combine our platform with dedicated AI consulting and data science experience