5. Churn Analysis for a large Insurance provider in US
Overview.
Retaining advisors in a competitive insurance market is crucial for long-term profitability. However, a large US insurance provider lacks in-depth insights into why advisors are churning. This lack of understanding makes it difficult to develop effective strategies to retain valuable policyholders.
Historical advisor data, including performance metrics, demographics, and activity logs, is used to train machine learning models. These models can predict the likelihood of an advisor churning, allowing the firm to prioritize retention efforts.
Advisor relationships and interactions are analyzed using graph theory. This helps identify "churn communities" - groups of advisors with a higher risk of leaving due to shared experiences or influences.
