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A Newly Single Consumer is 2-3x More Likely to Buy Life Insurance

Important life events often trigger the purchase of expensive products and services.

When it comes to insurance, being newly single is an important trigger event.

As you can see from the data in the chart below, newly single consumers have a term life policy purchase index of 263. In other words, they are two to three times more likely to purchase term life insurance vs. non-divorced consumers.

Data-driven marketers use this trigger event to significantly increase their response and purchase rates.

In addition to this trigger, marketers use existing propensity models to boost their results. In this case, our Term Life Propensity Model adds even more lift to their response and purchase rates. This is incredibly important for marketers to take seriously, since these events are among the most important factors when determining a target audience.

Trigger_Event_Data_for_Term_Life_Policy_-_Newly_Single.png



How You Can Take Action Today >>

Life insurance marketers use our Propensity Model data to identify consumers who are most likely to purchase whole life insurance, term life insurance, and/or burial insurance (final expense). Our data is used to generate high-performance lists for direct mail and telemarketing. They are also used to create custom audiences for online and digital marketing via Facebook, Google search and display marketing.

Insurance marketers also use our Propensity Model data to profile their current customers to improve their life insurance cross-selling and up-selling results. In both cases, the use of our Propensity Model lists and data results in improved response rates, conversion rates, overall marketing performance and increased return on marketing investment.

Contact us today to start getting better marketing results tomorrow.

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David W. Wilson
David W. Wilson
David W. Wilson

David Wilson has over 25 years of experience helping leading companies improve their marketing results using digital marketing, direct marketing, database marketing, consumer data, predictive analytics and marketing research.