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Do You Even Data

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A Newly Married Consumer is 3-4x More Likely to Buy Life Insurance

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

When it comes to insurance, marriage is an important trigger event that should be noted for any potential marketing campaign.

As you can see from the data in the chart below, newly married consumers have a term life policy purchase index of 365. In other words, they are three to four times more likely to purchase term life insurance vs. non-newlyweds.

Data-driven marketers use this trigger event to significantly increase their response and purchase rates. So, if you haven't already been taking marriage into strong consideration for your target audience development, now is your chance.

In addition to marriage, 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.

Trigger_Event_Data_for_Term_Life_Policy_-_Newly_Married.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.