Policyholder Loyalty Modeling: 6-wk Implementation.

Insurance coverage can terminate for several reasons such as cancellation due to the policyholder obtaining coverage elsewhere or allowing the coverage to lapse due to non-payment of the premiums. It is no secret that policyholder loyalty is a top priority for many companies; acquiring new customers can be 5 times more expensive than retaining existing ones.

How can we use data science to uncover the key indicators driving policyholder lapse behavior, estimate the lapse risk associated with individual customers, and deploy a data-driven retention strategy? How can you gain an in-depth understanding of your policyholder behavior and lifetime value to meet ambitious business targets around reducing earnings volatility, lapses, driving cross sell/upsell and improving their marketing capabilities?

Our response to both of those questions: with our Policyholder Loyalty solution. By prioritizing and targeting customers for retention campaigns based on their likelihood to lapse, you can reduce the cost of marketing campaigns, significantly reduce lapse rates, increase profitability, and improve customer experience. More specifically, you can

1. Create a customer analytics record with 300+ features unifying 100+ policyholder, insureds, policy, product/underwriting, claims and service transaction variables
2. Define “lapse” types: renewal lapse, mid-term lapse, etc.
3. Segment policyholders by tenure or lifetime value segment groups
4. Build lapse propensity models across segments creating a lapse risk score along with identifying top lapse triggers
5. Create 5-6 key uplift scenarios and quantifying their impact
6. Formulate a treatment campaign to prevent policyholder churn (or policy lapses) for high value segments

How quickly? The solution we deliver on Microsoft Azure allows you to get a complete setup ready in eight (8) weeks. For example, Azure Data Lake enables us to capture seriatim (policy level) data, accounting for millions of in-force and lapsed policies on the books). With Azure Data Factory (ADF) we significantly cut down on creating data-driven workflows for orchestrating and automating data movement and data transformation. Azure SQL DB - relational database managed service – helps us create a highly-available and high-performance data storage layer for the applications and solutions in Microsoft Azure cloud. Finally, a lot of time savings come from Azure ML Services that streamlines the building, training, and deployment of ML models that comprise the solution, and from an amazing PowerBI that supports a pre-built dashboard with rich visualization.

Here are typical results

• 8 weeks from whiteboard to results with 80%+ model predictive accuracy • 50% reduction in the upfront data preparation time required • 50% lapse reduction for high value customers over a 1-year campaign period, 40% reduction in campaign costs • Quantified potential retention savings of $10M+ across the general insurance business • 12 insights (6 new and actionable for the business) • 300 features available in one week allowed for additional 5 ML models • Availability of lapse propensity scores and lifetime value segments on CRM for easy targeting in outbound campaigns