Predict Customer Churn
Customer acquisition is much more expensive than customer retention.
Customer churn is an important metric to keep track of in insurance companies.
Clients exhibit different behaviour, making it difficult to accurately predict churn without using advanced analytics.
Designed and built a Machine Learning model to predict the probability of churning for customers on a monthly basis.
Created KPIs to capture each client’s service frequency and churn risk over time.
Divided customers into three churn segments to better customize interactions.
Increased customer retention by identifying high-risk clients and putting retention policies in place.
Improved customer experience by customizing interaction with different churn segments depending on the risk.
Improved understanding of optional times to call clients within their subscription cycle.