All About ClearAI®: Machine Learning’s Impact On Customer Lead Qualification

Leveraging Lead Qualification

Insurance agents know building a robust book of business relies on selling policies to high-value customers, particularly those who retain with their carrier during policy renewal periods. As policy seekers search for both price and value in a hyper-competitive and crowded marketplace, it can be difficult for agents to spot useful information that may indicate an applicant's misalignment with their target clientele.

“Lead qualification,” says Seth Henderson, Clearcover’s AVP of Insurance Product (Revenue), “is used to determine whether a lead fits your target customer profile. In the insurance space, we do this based on the limited information customers provide on comparative shopping platforms or auction sites to better understand whether we want to market our product to a particular customer.”

Proof of prior insurance, homeownership status, claims history—these are only a few of the hundreds of data points applicants and third parties supply when requesting a rate. But evaluating all of that information in real time to inform marketing decisions about lead pursuit is no easy task.

Meet Machine Learning

According to Clearcover’s Director of Product Management and data science expert, Jerry Claghorn, PhD, machine learning may be the answer. He claims, “Insurance has always been a data-driven industry, and the opportunity for disruption comes from shifting our focus from population-level to individualized predictions of risk.”

Machine learning is a form of artificial intelligence that uses algorithmic data to continuously learn about a subject for purposes of making accurate predictions. From Spotify’s playlist recommendations to Google Translate, machine learning is quickly transforming industries with access to previously unwieldy amounts of consumer data.

What sets machine learning apart from other data prediction tools is its ability to learn patterns which are not apparent to humans and dynamically adapt to new information. Legacy information systems rely on humans to identify and encode patterns into static software. Those patterns can be used to roughly classify customer behavior, risk, and other attributes. Machine learning, on the other hand, uses historical data to generate algorithms that, in some cases, can be thought of as a complex system of dynamic rules—tens of thousands of them—so as the data changes, the rules change with it. This enhances the accuracy and specificity of predictions.

Elevating Lead Qualification With ClearAI®

Clearcover’s proprietary machine learning platform, ClearAI®, drives artificially intelligent operational advantages for agents quoting with the carrier. ClearAI® allows Clearcover to quickly deploy learning models over prospective customers and uses those models to produce real-time business decisions about customer viability.

According to Dr. Claghorn, “ClearAI® is responsible for observing the quality of policies we have sold in the past and using those observations to predict the quality of applicants that we know a lot less about. In lead qualification, we have a suite of machine learning models to predict if an individual customer is a good fit for our products when we've just ‘met’ them. We combine those model outputs to determine if we have a product that's a fit for that individual and make the decision whether or not to market our product to them.”

Future Forward

If ClearAI® sounds like something from the future, that’s because it is.

“Our machine learning is all enabled by state-of-the-art data streaming technologies that weren't available fifteen years ago. Because we got to build natively with them, we have a bunch of interoperability advantages that other carriers might not have,” Dr. Claghorn says.

According to Henderson, those advantages extend to Clearcover’s partners. He states, “ClearAI® allows for real-time decision making on whether to market our product on comparative shopping platforms and auction sites. In doing so, Clearcover is able to acquire target customers more effectively and efficiently—a benefit to not only Clearcover, but also our partners.”

As Clearcover aims to bind customers that fit the company’s target profile, ClearAI® sorts which applicants are marketed Clearcover rates based on their available data. Isolating target candidates ensures both Clearcover and agent books are filled with target-specific customers. Those customers are often more likely to renew or expand their policies, directly benefiting agents through commissions.

Henderson says, “If we’re able to put those types of customers on our book of business and the agents are able to do that, they’re building a book of business that’s going to be higher retaining, higher lifetime value, and will only help them in generating future revenue.”

Dr. Claghorn shares his coworker’s enthusiasm, adding, “It's the fact that right at the top of the funnel, before anybody's invested their time, you get an answer about whether or not it's going to be a good use of agents’ time to quote with us.”

Up and to the Right

Perhaps the most exciting thing about ClearAI’s® impact on lead qualification is that artificial intelligence will only continue to improve. As ClearAI® processes more and more customer data, significantly refined models will evolve, optimizing predictions for target customer groups.

ClearAI®, by way of operating, has agents’ best interests in mind. Clearcover depends on partners and producers to grow its business, which is why the company is invested in their successes. For agents, the power of ClearAI® means they can benefit from a greater likelihood of renewing long-term clients at competitive rates that fit their targeted customer mix. And, of course, enjoy the improved books of business that come along the way.

If you’re interested in learning more about ClearAI®, visit clearcover.com/AI.