Industry and Services
+30%
Increased detection of customers at risk of churn
The Challenge
Our client, a health insurance company with more than 3 million customers, is facing the need to reduce the churn rate at the end of each policy term.
| The Outcome
Our predictive analytics model anticipated the risk of policyholder churn and helped our client retain them through targeted renewal campaigns.
How we did it
- Implementation of a predictive analytics model which analyzes historical customer, transaction and churn data.
- Detection of customers with a high probability of abandonment in the following 3 months.
- Integration of the model in a web application that allows retraining and obtaining statistical and inferential information from the updated data set.
- Operating on different data sources: policies, benefits, receipts and withholdings.
- Model development, training, validation and testing are implemented.
- Threshold, statistics, inferences and dropout scoring system are set.
- Built on H2O.ai, R and Shiny architecture.
Shall We Talk?
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