ORLANDO -- Predictive analytics won't close sales, but the tool can identify the pool of strongest potential buyers.
In other words, a little math can do the hard work -- and save tons of money in the process.
"You're going to get the most bang for your buck the first time you try this," said Andy Ferris, managing director at Deloitte Consulting.
Ferris presented examples Wednesday on how big data and predictive analytics can help insurers connect consumers with retirement products at the LIMRA 2017 Retirement Conference.
In his example, Ferris described a client with a term life insurance policy who could be a candidate for a second product. The insurer can use technology to collect all sorts of purchasing data, debt history, credit transactions and more.
"You can really get a picture of that person's lifestyle," Ferris said.
With that, data engineers can look at similar profiles to see how many people went for the upsell product. In Ferris' example, the most likely target group purchased the second product 6 percent of the time. A control group of completely random targets purchased 2 percent of the time.
The result is roughly the same number of sales, Ferris said, but the targeted group resulted in a 70 percent reduction in sales costs.
"Instead of approaching all 10,000 people, the algorithm is going to tell me the right people to approach," Ferris said.
If the use of predictive analytics seems like a futuristic idea, it's not. In a survey, insurers were asked how much they use analytics and how that might change in five years. In the sales and marketing area, 41 percent said they use analytics now. Looking five years out, that figure balloons to 94 percent.
"We're not where we want to be," Ferris said. "Most of that work lies ahead."
InsuranceNewsNet Senior Editor John Hilton has covered business and other beats in more than 20 years of daily journalism. John may be reached at firstname.lastname@example.org.
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