A life insurance company was seeking better direct marketing response/close rates for their traditional-term life insurance product, which focused on low-cost, affordable protection. They were using behavioral modelling to identify prospects, but response rates were dropping.
Twenty-Ten identified a custom segment of prospect consumers predisposed to the company’s life insurance product, with high propensity to try. Using a Targeting Algorithm, Twenty-Ten targeted individual consumers who best fit the target segment and delivered direct mail pieces designed to drive response. When tested on a the client’s internal file, Twenty-Ten’s Targeting Algorithm picked up high-value prospects originally missed by the client’s preexisting behavioral model, as well as filtered out lower-response candidates that had been originally selected by the client’s behavioral model.
Twenty-Ten’s custom attitudinal filter generated a 19% improvement in response and a 21% improvement in conversion, leading to a 25% reduction in the cost-per-converted policy for the client.