The average policyholder retention rate for personal lines insurance carriers and MGAs in 2017 was 50%. Consumers have more options than ever to change auto insurers along with the capabilities to do so across multiple channels.
Insurers continue to struggle with effective methods to identify customers who are at risk of non-renewal. Typically a manual effort is taken to identify these policyholders that are costly and time-consuming. Coupled with historical data from legacy systems forces insurers into a reactive vs. a proactive mode to retain policyholders and revenue.
With limited resources at hand, property and casualty insurers can leverage AI technologies to quickly identify the policies that need to be reviewed across one or more systems including legacy systems.
The Aureus approach is to implement machine-learning models for policyholder retention that can learn patterns from an insurer's historical data and predict future behavior down to the individual policy level.
Examples of predictive models for personal lines auto policyholder retention that have been developed include models to:
By identifying policyholders that are at risk of not renewing the expiring policies or likely to reinstate their canceled policies, insurers now can proactively communicate with those policyholders, to increase policyholder retention and revenue.
Focused Renewal Campaigns
Campaigns can be developed to contact customers who have been identified as a high risk of not renewing their expiring policies. By proactively contacting these customers, insurers can reduce the number of policyholders who do not renew.
Improved Customer Experience
By proactively contacting customers, the right product can be offered to the right customer at the right time to retain more customers and improve loyalty.