Imagine being able to take the lead in thebattle to retain your best customers by predicting the future; andimagine working with your carriers to price risks more accurately.For carriers and independent agents participating in afuture-focused relationship in which they share data and use modernpredictive analytics to jointly target and retain business, thisideal can be a reality.

Predictive analytics can form the basis for a deeperrelationship between agents and carriers as they work together todetermine which risks to target. Shared information and sharedobjectives drive a stronger partnership—and one that is moresuccessful in the long term.

Some agents and carriers already are sharing access to data andpredictive analytics, creating a new level of transparency andefficiency between them around issues like pricing andunderwriting; customer-retention strategies; finding goodprospects; customer segmentation; and cross-selling among variouslines of business.

1. Traditional methods of predictive analyticswere too expensive and too slow for carriers to take on thechallenge of working with agents individually to targetbusiness. However, with modern predictive analytics, based onmachine-learning technology, carriers can analyze a broad base ofdata and get answers more quickly—in some cases within weeks or afew short months. The costs of SaaS-based solutions makes itmuch more realistic to use predictive analytics more broadly andshare that information with brokers and agents.

2. The competition to attract and retain thebest customers is fierce, particularly as customers become moresensitive to price and less loyal to their insurance companies.Carriers that are using modern predictive analytics are better ableto analyze broader bases of data and identifying meaningfulpatterns. Since carriers have access to data from large customerpopulations, sharing insights based on that analysis with theiragents can help them collectively target the best customers.

3. Carriers and agents can also expand theirbase of business with these customers by improving promotionalprograms tied to specific groups of risks. For instance, predictivemodeling can help agents match the best offers for customers ateach lifecycle stage, and better predict which product to promoteat just the right time.

4. Using predictive analytics, agents andcarriers can define better ways to target and attract specializedmarkets, such as small businesses in a particular niche. Andfor customers at risk of leaving, carriers and agents can worktogether through special service programs, outbound calling basedon identified red flags, and other methods. Carriers, working inpartnership with agents, can be alert to early warning signs andcan intervene before a switch is made.

5. A big part of retaining and attracting thebest risks is not just individualized treatment and cross-selling,but also competitive pricing. As the “last mile” to the client,agents need confidence that the price they are quoting is the bestone, matched closely to the risk. Pricing based on creditscores and other traditional rating variables alone doesn’t alwaysachieve that objective. Modern predictive analytics, on the otherhand, takes much of the guesswork out of underwriting. Carriers areable to review a greater number of variables to determine whichpolicies are likely to generate losses over time, enabling theirdistribution partners to sell with confidence knowing the price ismatched correctly to the risk.

When agents understand the criteria that defines profitabilityfor a particular carrier, and the carrier and agent meet todetermine which risks to target, the partnership inherently growsstronger—and a strong relationship between carriers and theiragents aids greatly in achieving sustainable growth.

Predictive analytics can be leveraged to give agents greaterinsight into customers. By arming agents with the best availabletools to more easily and accurately build and grow theirbusinesses, carriers can increase their bottom line, decrease churnrates and reduce risks, even in a soft market.