NEW YORK CITY – All parties involved in the life insurance process — carriers, producers and customers — will benefit from using “predictive analytics,” said Robert A. Kerzner here at the opening session of LIMRA’s annual meeting.
Predictive analytics is the obscure-sounding term for an analytic process that is gaining traction in the business community. It refers to using customer data to predict what customers are likely to do in the future, and to identify what stimuli will likely help attract their attention and/or change their behavior.
Some carriers already are using predictive analytics to help identify which customers are most likely to buy, said Kerzner, who is president and chief executive officer of LIMRA, LOMA and LL Global.
Others are using it, or considering using it, to identify cross-selling opportunities, shorten the policy approval process, reduce claim time, identify likely-to-lapse customers, improve policy placement rates and develop hot leads for producers. Predictive analytics also are used in target marketing, policy design, underwriting and several other areas.
Much or all of this data comes from large commercial data warehouses — or “big data,” as consumer database firms have come to be known.
Big data companies collect and store information about consumers. They also analyze the data to identify patterns and correlations about past customer behavior. Predictive analytics occur when the firms apply algorithms to the data to generate predictions about the likelihood that customers — or certain types of customers — will take certain actions or make certain decisions in the future.
Drivers of change
To people unfamiliar with this still-developing discipline, the above description might make predictive analytics sound like some sort of space-age fortune-telling. Data experts say that is not the case. They say their work entails the use of statistical and analytical techniques to uncover trends in data “mined” from various sources. Many industries use this information as a matter of course.
During an interview with InsuranceNewsNet in advance of his address, Kerzner said he
has been studying the field for quite some time. His conclusion is that technology plus predictive analytics will be major drivers of change in the insurance industry.
Some producers may be hesitant to embrace the changes, he allowed. For instance, changes a carrier makes as a result of predictive analytics might alter the processes that producers routinely use to conduct business. However, certain producers may prefer instead to maintain control over the sale and customer contacts as they have done in the past.
Kerzner’s take on that is: “Producers will be smarter to let it happen.”
Data analytics is spawning processes that will free producers to spend more time helping clients say “yes,” he explained. The changes can also relieve producers from involvement with life application routine (e.g., checking blood work, status updates, etc.) and long waits for approval.
Instead, because of pointers gleaned from analytics, producers will be able to see clients who are more likely to buy. They also will be able to see more of these clients and to move on to the next customer more quickly, Kerzner maintained.
By taking advantage of the changes that come with technology and predictive analytics, he said, “producers will be more productive and be able to put attention where they bring real value.”
That’s important because the industry has fewer producers today, and the only way the business will grow is if producers see more people and make more sales, he said.
This does not mean that producers no longer need to provide clients with guidance and advice, he emphasized. LIMRA research shows that the industry always will need producers to help clients make the right decisions. But it does mean that producers won’t have to get in the middle of routine processes, he said.
A look at family man Sam
To illustrate the kind of data that analytics can make available, Kerzner pointed to a fictional character Sam. A big data firm such as Epsilon might show that Sam has a wife and a young daughter, owns a home worth $350,000 and owns a life insurance policy. It also might show that Sam has a college degree, plays an online football game, leases a car, belongs to a local gym, has 401(k) savings and credit card debt, and makes purchases at certain online stores.
(According to Kerzner, Epsilon says it has data on every American household and 1,000 data points on 2.5 million people.)
“Would having this information in hand — before the producer makes a call on Sam — increase the likelihood of making a sale?” Kerzner asked.
“It would,” he said, noting that he is basing that response on his own experience after 30 years in sales. “The data provides producers with a roadmap of the person’s proclivities, and that should materially increase the potential for a sale. You know what they (the customers) like and you know more about them, so you can figure out what subjects to raise with them.”
Analytics data also can provide a roadmap to the prospect’s concerns and the issues they face, he said. “If a life insurance prospect is a 65-year-old man who gives to 16 different charities, for instance, the issues would be different than for a 65-year-old man who is loaded up with debt.”
Specific information of that kind is more helpful to the producer than doing an Internet search on the person, he added.
Analytics data also can provide indicators about who might be a “great prospect” for a mortgage or for life insurance, and about who is most likely to qualify for the loan, he said. In addition, life insurers can pay big data and outside data aggregators to identify newlyweds and others who are experiencing big life events by specific locale — and then send those “hot leads” on to producers in those locations.
Wholesalers can benefit too, Kerzner said. For instance, carriers relay to their wholesalers some analytics-produced information about producers’ preference for types of mutual funds — such as producers who tend to focus on safety or who have a growth orientation. When wholesalers receive information like this, they can think about which mutual funds to show to individual producers “to tickle the person’s fancy,” Kerzner said.
“That gives the wholesalers a huge edge, and it’s a roadmap to success,” he contended.
Many of these things already are being done today by various companies both inside and outside of the life insurance business. Kerzner cited multiple examples, including these two:
· Protective Life has created a Tracker that enables new online customers to see status of their life insurance application and to receive customer alerts. That builds certainty for the customer and helps keep the person engaged, Kerzner said. The Tracker provides notifications for the producer, too. This may help speed up the process, he said, explaining that if a notice indicates a requirement is still not met, the agent can take steps to fix that.
· Pacific Life has developed a term life product that targets younger producers and younger customers (Generations X and Y) consumers via automated processes. Producers enter client information on a highly interactive online portal, which then provides the producer with relevant sales ideas, a video on sales concepts, etc., Kerzner said. It’s all done by computer, including policy delivery. The results so far? “Half of this business is coming from producers that have not done life business with the company in the past,” he said.
Consumer experience is important, the LIMRA chief emphasized. Using predictive analytics will enable the industry to be more effective in this area by simplifying the sales process. That will help improve sales as well as the future of customer experience, he said.
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Source: LIMRA - Fictional family profile developed based on typical consumer data collected by consumer data firms.