Rise of the Analytical Agent: Building Stronger Insurance Agencies with Business Intelligence

Today’s agents are dealing with a pace of innovation that would boggle their predecessors’ minds. This data-centric era and uncertain economic landscape places pressure on agents to adapt to and incorporate the latest technology, with an urgent need to understand both how it works and what it can do for their businesses. But for the busy and fiscally cautious insurance agent, proven ROI is crucial. Data and business intelligence (BI) allow agents to run their businesses more efficiently. Still, skeptics ponder just how crucial data and BI will be for the future of our industry.

What’s the Hype About?

Data analytics and BI are benefiting agents and improving their businesses in four crucial areas: operational efficiency, strategic decision-making, expanding books of business, and enterprise value.

Operational efficiency. This is where agents are seeing the most tangible daily benefit from data analytics and BI tools. Normal tasks such as simple policy updates or renewal processes can be automated, freeing agents to focus on more complex and potentially more profitable areas. Finding ways to automate more perfunctory, non-executive tasks also creates opportunities for agents to focus on the client experience and further tailoring both solutions and outreach to those clients. Time focused on business development is increasingly critical, and creating a more streamlined and automated operation helps to elevate this priority.

Strategic decision-making. BI tools can provide actionable insights informed by a business’s data, removing a good deal of guess work from decision-making. While it would be nice to always mentally maintain every detail and business aspect or consideration involved in daily business decision-making, technology can help store and process data parameters faster and more efficiently than a human, incorporating nearly every relevant agency detail.

Expanding books of business. The insights from business intelligence tools can help agents analyze their books of business, identify areas for cross-selling to existing clients, and pinpoint niches that might be natural extensions of the agency’s current offerings or clientele.

Enterprise value. As data analytics tools and BI help improve agency efficiency and decision-making, they also increase the value and competitive edge of a business. For example, imagine there are two similarly sized insurance agencies preparing to sell their agency; one is a data-driven agency, while the other operates more traditionally. The data-driven agency with the stronger handle on its metrics and KPIs will likely be seen as more efficient and scalable.

More efficient operations, informed decision-making, and more valuable books of business are all dream outcomes for agents. Those independent agents who take a more analytical approach to their data and BI will have the advantage–but only if they are maintained effectively.

Starting at the Source: Data Management

Quality analytics and actionable BI insights depend on clean, well-maintained data for the most impactful results. I’m reminded of an agent who began using the precursor to SIAA’s AgencyIQ nearly five years ago. After experiencing some complications applying the BI tool, the agent was unhappy and ready to cancel the entire implementation. The agent agreed to allow our team to analyze the data. It was through this process we discovered they were inadvertently recording some data points in an inconsistent manner in their management system, leading to irrelevant insights.

We repaired the errors and adjusted the client’s process. In short order, this agent began producing relevant, actionable BI insights. Today, that agent is still one of our users, as well as one of our biggest success stories.

As with most things in life and business, what you put into something is almost always as important as what you get out of it. Maintaining data is one of these areas, and it should be considered in three different stages: data creation and collection, data storage, and data analysis.

Agents should know and control where their data comes from, including documents, email communications, agency management systems (AMS), customer relationship management (CRM), and elsewhere. From there, agents must grasp how their data is being stored for easy access as well as accuracy.

The outcome of this data input and accessibility, when done properly, is actionable insights through business intelligence. With a thorough understanding of these three stages of data, agents can develop a strategy to strengthen the health and accuracy of their data. But it’s not a static process. It is important for agents to recognize their data strategies will evolve along with their businesses.

As an agency adopts new technology, grows its books of business, or begins working with new partners, those data strategies will need to shift and adapt–just like the agent must adapt to new business needs and environments.

Overcoming Misconceptions

One of the biggest hindrances in the adoption of data analytics and business intelligence are misconceptions around deployment, safety, and real-world use. Let’s look at a few common misconceptions around data, technology, and BI.

Data sharing is too risky. When someone is asked to share their data, there is usually some immediate pushback until a proven benefit becomes evident. Social media feeds and online shopping dashboards, for example, become more tailored to users based on their data, making the user experience more convenient. Data sharing can actually be a secure process, and those agents who work to understand how a potential BI tool acquires and stores data may find themselves much more comfortable.

‘The insights from business intelligence tools can help agents analyze their books of business, identify areas for cross-selling to existing clients, and pinpoint niches that might be natural extensions of the agency’s current offerings or clientele.’

Tools will replace people. The biggest argument against AI tools is the belief they will replace humans and take jobs. However, especially in an industry driven by people and relationships like insurance, human connection will always be a crucial asset. And like every industry that has faced technological advancements, people will always drive the process while adapting. As technology grows more efficient and complex, agents can reallocate time to focus on actions that better contribute to bottom-line growth like submissions, renewals, and building stronger relationships with clients, their communities, and other stakeholders.

Bigger, more complex AI models are better. Agents do not need the biggest, most complex AI models and tools to see positive results. In fact, agents should strive to remove complexity to avoid excessive options. ChatGPT tools, as just one example among many, are built to consider several billion parameters in their outputs. While this makes them super powerful for wide-ranging, industry-agnostic applications, they might be overkill when used in conjunction with specific problems within an industry like insurance (as it pertains to independent agencies). There are numerous, business-specific models built for agents without the additional, unnecessary complexities that make these tools harder to understand and/or adapt to for daily use.

Understanding misconceptions that surround discussion and implementation of data and BI tools will help agents better communicate the benefits of the tools and address uncertainty. Addressing employee concerns across the entire implementation process is just as important, if not more so, as getting the initial buy-in among producers and staff. Keeping lines of communication open and addressing concerns across the implementation stage and after is crucial to any agency’s success. These tools are only as impactful as the people using them.

Tech Works Better Together

Some agents may think, “I already have an AMS and a CRM that work well for my agency. Why do I need a BI tool, too?”

BI tools work in tandem with the AMS and CRM to complete the data life cycle. An AMS and CRM are workflow tools rich in data that all too often goes unexplored or employed to the benefit of the agency. A BI tool will take that data and interpret it, sharing analysis, suggestions, and client-specific insights that can have a meaningful impact on the agency’s bottom line.

While the agent and broker industry still has a long way to go to achieve widespread adoption and deployment of data analytics and BI, the agents who invest in such technology today will quickly outpace those relying on traditional strategies alone. Economic pressures and market conditions will continue to encourage change and adaptation, pushing agents to secure every competitive edge they can to retain clients and grow their businesses. By embracing analytics and BI now, agents will be better equipped to navigate the inevitable industry shifts the future has in store.

Shah is chief data officer for SIAA – The Agent Alliance (siaa.com), a network for starting, growing, and evolving independent insurance agencies. The organization has over 5,200 member agencies writing more than $17 billion in total written premium.

Topics Agencies