How To Predict When Your Clients Will Leave

Customer renewal and retention is essential to long-term success. According to Vertaforce, “with just a consistent 3% increase in retention, agency revenue could be up to 15% higher after five years.” That means, improving client retention is necessary for growth. But how do you predict at-risk accounts?

The average client retention rate is just 83% across the industry, according to Vertaforce who aggregated data from 3,700 agencies. That means, firms must replace almost a fifth of their clients every year, just to maintain business at the same level. And the answer to how to do that is in the numbers.

Predictive data analytics have been touted across all industries, from baseball to the market, for years, and the same goes for advisors. In the insurance world, large carriers with data science teams have been using predictive analytics techniques for years. But thanks to the convergence of cloud computing, the accessibility and breadth of data, and artificial intelligence and machine learning technology, predictive data analytics can now target the factors that impact a firm’s growth and success.

By utilizing large amounts of data combined with machine learning, AI, and impressive computing power,  prediction models can analyze an extensive list of variables to help give advisors a better sense of when a client might leave. And give them enough time to do something about it. Simply, by knowing which accounts are at risk, advisors can focus their time and attention on the places that will have the most impact.

Predictive analytics has the potential to radically transform the fortunes of advisors by predicting at-risk policies, accounts, and relationships.


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