This phase of the framework provides a myriad of opportunities to gain valuable insights. Many of these possibilities are just that — possibilities. However, there's a lot of excitement around how these insights can play a role in renewals and retention, account rounding and upselling, and customer sentiment.

Renewals and retention: One of the most valuable insights data recommendations can provide during the renewal process is which accounts are most at risk of not renewing. The recommendation algorithms analyze upcoming renewals, looking at things like the price increase of the renewal, reasons why the price is increasing, and the overall value of a client. Each renewal is then flagged as high risk versus low risk. This equips the agency with knowledge of where to be more hands-on with clients who need that extra care and attention.
Account rounding and upselling: The next insight data recommendations can provide for each renewal is understanding the client's needs and how that matches up with their current policies. We know that agencies want to be advisors to their clients, offering advice on coverage and policy options. Unfortunately, manually reviewing a client's risk, each of their policies and all their coverages is a long process, leaving agents acting as data gatherers rather than advisors. This is where data comes in. Agencies can leverage algorithms that do the data digging for them, give recommendations of what policies are missing and even which coverages may be missing or underinsured. Agents can provide additional value to their clients while driving profitability and reducing E&O for their business.
Customer sentiment: Another important insight that can intelligently guide an agency's behavior is understanding how happy each client is – or isn't. This level of insight may sound impossible, but with artificial intelligence, nearly anything is possible. Customer sentiment can be measured by analyzing past interactions with an agency's clients. It looks at factors like how often the client is opening or responding to emails, texts, and phone calls, monitoring social media posts, billing, payments, claims, and service requests, among others. All of this comes together in a simple score, 0-100, that becomes incredibly useful. For example, if an agent is interacting with a client that has a lower score, they know to put in additional effort. This is a powerful way to harness data to know where to put extra effort into specific clients, being a trusted advisor to the right clients at the right time.

Get assistance From AI

The Assist phase of the framework is where data intersects with AI-driven automation. AI has the potential to augment agencies' efforts and leverage data to assist agents in completing their work more quickly, more accurately, and more productively. Connected intelligence workflows that are fueled by data and AI can optimize agents' time by having relevant insights when and where they are needed, automating what can be automated, and ultimately, spending time on the highest value work.

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