According to the latest Big I Agency Universe Study, independent agents and brokers consistently rank customer renewal and retention to be more critical to their long-term success than finding new clients or cross-selling new services.
Indeed, according to recent Vertafore research, a modest 3% increase in customer retention, achieved consistently over a five-year period, can produce a revenue increase of 15%. So improving client retention is a essential growth strategy.
Achieving those retention goals, however, can be a challenge. Identifying at-risk accounts — and spotting them in time to take action — isn’t easy.
An analysis of current policy retention, compiled by Vertafore from 3,700 agencies and over 150 million policies shows that the median policy retention rate among independent agencies is just 82%.
Routine policy renewals aren’t enough to ensure long-term success. Moreover, many agencies don’t know their true retention rate. In most agencies, 15% of total policies are at high risk of non-renewal, and another 10% are at somewhat elevated risk. If not for the hard work of landing new accounts and/or expanding existing accounts into new product lines, most agencies would experience extremely small gains year-over-year — or none at all.
So how can brokers and agents know when a customer is thinking of cancelling or choosing not to renew, and why?
Predictive data analytics may have the answer for the independent agencies.
Data Science for You
Predictive analytics isn’t new in the insurance distribution channel. For example, large carriers with data science teams have been using predictive analytics techniques for some time. But until recently, the unlocking the power of these analytics to truly inform key business decisions and outcomes has been limited to those with the time and resources to invest in a dedicated program.