Will Your Insurance Policyholder Churn? The Story Behind the Data

Identifying customers likely to churn and convincing them otherwise are important and distinct challenges for insurance providers.

Auto insurance policyholders typically have annual or bi-annual opportunities to change their plans to the competition. A wide range of life events and behaviors can indicate or trigger customer churn – from big purchases like cars or homes, to important moments like weddings, graduations, children and promotions, to actual online shopping for competitors’ products. These customer details and activities are not captured in application forms or other standard customer touchpoints, but are scattered across many disparate types of data and owners of information.

Leveraging more types of data related to churn triggers can make all the difference in retention targeting and marketing strategies.

Read a case study on enabling a leading US bank to test and onboard more data.

The DemystLabs product helped one large US carrier expand the data pool from 1 to 8 external sources to better identify churn triggers and retention strategies. Prior to DemystLabs, this carrier primarily using internal data to make those assessments.

Using additional data enabled the provider to uncover key insights, including:

1. Families with a young child are likely to move and therefore likely to change insurance policies.

2. Customers engaging in increased online shopping research close to renewal are demonstrably more likely to drop their policies.

3. Parents of new drivers (around the ages of 16-17) are less likely to churn.

Multi-touch needs assessment are at the core of this analysis. The ability to not only access but also query various data sources across difference points in time to track behavioral changes can go a long way in identifying and reversing patterns of attrition.

In this instance, the carrier was able to forecast US$14 million in annual benefits from applying incremental data and insights to its retention marketing strategies.

Start exploring the types of data you can use to reduce attrition.



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