How to break down insurance customer data
Insurance agents can break down data and its opportunities with this easy framework...
Data presents exciting opportunities in the insurance industry.
There is so much information already flowing through an agency that can help its team understand what’s happening throughout the business, make predictions about what might happen in the future, and ultimately act on that knowledge to positively impact success.
Integrating artificial intelligence that uses management system data to fuel automated workflows allows agencies to focus on higher-impact work such as servicing accounts and developing new business.
A lot goes into making data valuable and consumable, and taking the leap into it can be intimidating. Agents may find themselves asking:
- How do I find the right data/?
- How do I access it?
- How do I make sure it’s trustworthy?
- How do I make it work for my business?
The good news is that agents can break down data and its opportunities with this easy framework: Know. Recommend. Assist.
Know what’s happening.
The first step is knowing what has happened and what is happening inside the agency. Looking at past and current data can give valuable operational insights into things such as staff productivity, sales momentum, profitability and carrier relations. This makes data actionable and easier to report — meaning agencies can get answers from their data faster so they can spend more time being trusted advisors to their customers instead of data gatherers.
Make data-driven recommendations
With the data foundation laid to help agencies know what’s happening in their business, we can now expand to understanding how data helps to predict behaviors and outcomes. The Recommend phase of the framework looks at how an agency can leverage data to better equip agents to meaningfully impact their agency’s KPIs. Essentially, let your data make recommendations to improve your business.
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.
For example, using integrated, generative AI tools can assist agents in crafting concise and engaging communications in a fraction of the time it would take to draft manually. This allows agents to easily catch the attention of their current and prospective clients while freeing up time to do more strategic work.
Know. Recommend. Assist.
Infusing the Know. Recommend. Assist. framework into workflows takes the complexity out of data, opening opportunities for agencies to take advantage of the information already flowing through their business to drive growth and efficiency.
Anupam Gupta is chief product officer at Applied Systems Inc. He is responsible for the company’s product vision and product management teams. Formerly CPO at 4C Insights, a sophisticated Data & Analytics SaaS provider to the AdTech/MarTech industries, which was acquired by Mediaocean, the mission-critical platform for omnichannel advertising with more than $200 billion in annualized media spend managed through its software, connecting the ecosystem of agencies, brands, media, technology, and data. As CPO of the combined companies, he spearheaded their product transformation to the cloud, adding new products fueled by data and intelligence infused in the core workflow. Previously, he’s led product organizations for several tech companies, including at Vubiquity, Mixpo, and Microsoft among others.
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