Healthcare │ Artificial Intelligence

Churn Prediction Models Improve Health Insurance Customer Retention

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.

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“We had never worked with such accurate models.
Machine Learning and predictive analytics is the way of the future.”

Identifying opportunities together

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.

Setting a goal for success

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.

Our challenges and learnings

  • Obtain optimal classification results when working with imbalanced datasets resulting from a much lower number of disaffiliations compared to existing customers.
  • Employ business acumen to balance a dataset by engineering new features without changing the real-life outcomes described by the original dataset.

The Wovenware Approach

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