Healthcare │ Artificial Intelligence
A Blue Cross Blue Shield provider in Puerto Rico turned to Wovenware to create a model for predicting churn among Platinum Advantage members with claims and demographic data.
In the health insurance industry, it can be extremely challenging to find useful indicators of unhappy customers outside of direct feedback from customer service calls or surveys. Yet, keeping customer churn as low as possible is extremely important, since the cost of acquiring new customers is steeper than retaining existing ones. Any improvement in customer churn has a big impact on revenue.
Identify members with a high probability of changing providers prior to the open enrollment period based on a limited dataset of claims and demographic data.
We used Innovation Sprint methodology to examine the quality of the
data and determine the feasibility of creating a churn prediction model.
Technologies we take advantage of
Initially did not find any meaningful features to train a deep learning model given that all demographic and claims data followed the same distribution.
Wovenware validated the potential impact of a custom model for predicting patient turnover with a proof of concept based on a subset of sample data that had outstanding accuracy.
The custom predictive analytics model identifies members who are most likely to churn, giving the BCBS provider critical information to target specific populations in customer retention strategies. Artificial Intelligence is playing a key role in enabling the organization to transform the patient experience through proactive remediation.