Currently, many insurance carriers can only analyze agent productivity based on the premiums written and the loss ratio of their network of independent agencies. Looking only at past results doesn’t necessarily provide an accurate view of how an insurance carrier can increase agent productivity going forward. By using AI for increasing agency productivity, insurers can now predict the best course of action as opposed to waiting to review past results.
In one of our previous blog articles, “The Top 3 Emerging Trends for Agent/Advisor Analytics Using AI,” we discussed at a high level what we believe are the top three emerging trends where artificial intelligence predictive and sentiment analytics can help insurance companies improve the effectiveness of their independent agent distribution channel.
In this blog article, we will focus on how AI technologies, such as predictive analytics, can provide insurers with the opportunity to engage with agencies to help improve their productivity proactively.
AI technologies combine structured historical data such as premiums written and the loss ratio of an agency over some time with unstructured data to provide a better understanding of their agents. Unstructured data such as written or audio feedback from agencies is information that insurance carriers possess in their internal systems today.
This understanding can use sentiment analytics to provide an overall sentiment score for the agency.
Anticipating the actions an agency may take going forward is challenging and time-consuming. There is no single answer to how an agency may react to a planned change an insurance carrier may implement.
For example, predictive analytics is used to analyze the actions of an agency in the past to help insurance companies anticipate how their agents may react to a future change in pricing strategy. With independent agencies, there is always a risk when a carrier changes their pricing strategy, as agents could write fewer policies or take their book of business to another insurance carrier.
If a carrier has a network of 4,000 agencies, using a BI tool with no AI capabilities is essentially guessing what the reaction to a change in pricing strategy could mean to an insurance carrier.
Identifying the right match between an agency and the products they are good at selling, or not good at selling, is key to increasing productivity. For example, helping agencies identify upsell and cross-sell opportunities, for the products they have a proven track record selling. Identifying potential gaps in coverage an agency may not be aware of can improve productivity.
Predictive analytics can be used to determine what products the carrier offers that sell the best to specific marketing segments such as VIP or high net worth customers, millennials, or women. Then, match those products to the appropriate independent agencies. Carriers can also manage and spread their risk across agencies more effectively by segmenting higher risk policies from lower-risk policies.
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.
Predictive analytics can help insurers proactively address agent productivity as opposed to only measuring productivity based on premiums written and loss ratios. By taking a proactive approach to assist agents, insurers can better understand which products are the best fit for agencies based upon market segments. They can then share this information with agencies that are receptive to improving their overall performance.
In our next article, we will focus on understanding agency sentiment.
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.