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Understanding Agency Sentiment

In our previous blog article, “Using AI for Increasing Agent Productivity,” we discussed how many insurance companies can only analyze agent productivity based on the premiums written and the loss ratio of their network of independent agencies. In part 2 of our series of articles on “The Top 3 Emerging Trends for Agent/Advisor Analytics Using AI”,  we will focus on the benefits of understanding agency sentiment for insurance companies that utilize a network of independent agencies.

Customer Sentiment vs. Agency Sentiment

Today everyone is worried about customer sentiment, but not agency sentiment. We believe that agency sentiment is equally, if not more important:

One unhappy policyholder = one policy at risk

One unhappy agency = N policies at risk

The risk for carriers with one unhappy agency is N times higher!

Agency Churn

Sentiment analytics can play a significant role in identifying an agency that is at risk of taking their book of business elsewhere. Insurance carriers can benefit by understanding what the sentiment is at any given time of an individual agency. Identifying an unhappy agency sooner than later that writes a large amount of premium can lower the risk of lost premiums for insurance carriers.

In addition to knowing the sentiment of an individual agency, the sentiment of multiple agencies across a defined geographic region or by individual products can be analyzed on a real-time basis. For example, if the sentiment of 15 agencies who sell BOP policies in the northeast starts to decline, the risk is now N x 15 policies at risk.

Agency Sentiment has proven to be a leading indicator of Agency Churn.

Agency Sentiment to Drive Agency Engagement Strategy

To prevent agency churn, insurance companies can also use the sentiment of an individual agency as a way to improve and monitor their relationship. As an example, the sentiment of a single agency could be related directly to its commission structure, claims experiences of their policyholders, or how easy it is for the agency to sell the insurance carrier’s products.

If it becomes more difficult for this agency to sell policies for any reason, their sentiment may decline. The agency may or may not make the insurance carrier aware that they are experiencing a problem and, as a result, offering their customers alternative products from other carriers.

Unfortunately, the carrier is almost operating in the dark or blind faith. In this example, the carrier could lose a policyholder but not the agent.

Agency Sentiment as a Means to Understand Policyholder Sentiment

Insurance carriers have access to policy and claims data but do not have the best access to customer interaction data. As a result, the best way for carriers to understand their customers better is to understand their independent agents better.

On paper, an individual policyholder may look like a happy customer. Their premiums are paid on time, and there have been minimal or no claims activity. But if the agency of this policyholder becomes an unhappy agency, the carrier is at risk of losing the policyholder to another carrier used by the independent agent.

Conclusion

Sentiment and predictive analytics can provide insurers a better understanding of how their entire independent agent network and individual agencies feel about their company and products.

In addition to customer sentiment, we believe that agency sentiment is equally, if not more important. Agency churn frequently results in otherwise happy policyholders leaving their insurance company based on the agency’s relationship with the carrier.

Sentiment analytics can play a significant role by proactively identifying agencies that are at risk of taking their book of business elsewhere.

By the nature of their direct relationship with policyholders, independent agencies have more customer interaction data in their AMS, CRM, and email systems. If carriers want to understand their policyholders better, they simply need to understand their network of independent agencies better.Having access to such information proves to be a significant competitive advantage for any carrier.

Interested in learning how Aureus can help you leverage machine learning to predict your customer's behavior? Click on the link below to get more information.

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Anurag Shah
Anurag Shah
Anurag Shah is CEO and co-founder of Aureus Analytics. He was the founding member and CEO for EdVenture prior to joining the leadership team at Omnitech, where he served as the COO and Head of Global Operations.

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