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Improving Life Insurance Direct Marketing With Propensity Model Lists

Improve_Your_Life_Insurance_Marketing_With_Propensity_Model_Lists.png

Executive Summary Snapshot

  1. Individual life insurance ownership has declined sharply since the 1960s
  2. Many consumers want more insurance, but costs prevent purchase
  3. Marketers are using propensity model data and lists to improve results



A Challenging Environment for Life Insurance Marketing

As industry data from LIMRA clearly shows, it is a challenging environment to profitably market and sell life insurance. Since the 1960’s, the percentage ownership of individual life insurance has fallen from 59% to 36%, a massive decline of 23%. Today, 30% of households do not own life insurance. And while 58 million — or half of U.S. households — say they need more life insurance, 65% say they won’t buy because they believe it’s too expensive.

Trends_in_Life_Insurance_Purchases_Data_Decisions_Group.png

Source: LIMRA

About two-thirds say their mortgage, groceries and their electric bill are keeping them from buying some or more life insurance coverage. Nearly half say additional expenses like Internet, cable and cell phone service prevent them from getting covered.


How Propensity Model Data and Lists Improve Direct Marketing Results

For these and other reasons, life insurance marketers are turning to consumer data and advanced modeling to improve their life insurance targeting and response rates. Specifically, these marketers rely on data and lists that are built from purchase propensity models.

Today, propensity model data and lists deliver unprecedented insight into the behaviors and propensities of customers to engage, purchase or consume products and services. Life insurance marketers use propensity models to identify consumers who are most likely to purchase whole life insurance, term life insurance, and/or burial insurance (final expense).

Life insurance marketers also use 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 propensity model data and lists yields the following benefits:

  1. Improved response rates
  2. Higher conversion rates
  3. Better overall marketing performance and ROI

Propensity Model Data and Lists for Life Insurance

The following propensity model data and lists are in high demand. Each propensity model is developed, tested and deployed using the industry’s best compiled consumer data combined with proprietary survey responses from millions of U.S. insurance buyers.

Whole Life Insurance Purchaser

This model is optimized to target those who are most responsive to offers for whole life insurance. The market for whole life insurance is very specific — both age and target income may be helpful in conjunction with this model. Target households are financially savvy and have an above average incidence of females present.

Term Life Insurance Purchaser

This model is optimized to target those who are most responsive to offers for term life insurance. Higher target income households with a variety of investment vehicles move to the top ranks of this model. Age selects may also be useful to further target offers.

Burial Insurance Purchaser

This model is optimized to target those who are most responsive to offers for burial insurance, also known as final expense insurance. The model characteristics prove that income levels are an important determining factor in this purchase decision.


Important Facts and Trends About Life Insurance Buyers

Americans continue to see the value in owning life insurance. In fact, six in 10 people own life insurance. And for those who don’t have it, many agree they need it.

Why People Have It

As many as 132 million Americans rely on life insurance to protect their financial security.  Major reasons given for owning life insurance include:

  • Covering burial and other final expenses (51%)
  • Help replace lost income (34%)
  • Pay off the mortgage (26%)

More Coverage Needed

However, 30% of Americans know they need more life insurance. That boils down to more than 70 million people. Twenty-five percent wish their spouse or partner would purchase some or more life insurance.

Percentage_Who_Believe_They_Need_More_Life_Insurance_Data_Decisions_Group.png

Source: LIMRA

Not Many Will Buy 

Sixty-five percent of Americans say they won’t buy because they believe it’s too expensive. About two-thirds say their mortgage, groceries and their electric bill are keeping them from buying some or more life insurance coverage. Nearly half say additional expenses like Internet, cable and cell phone service prevent them from getting covered.

Americans Worry About Their Retirements — and the Economy

Two-thirds of Americans worry about having enough money for retirement. When asked why, half say they are concerned about the overall economy while forty-four percent said they haven’t saved enough for retirement. These very real concerns can make it difficult to see the important role of life insurance in retirement planning. 

People Want to Buy Life Insurance in Person

While 71% Americans say they research online, more than half want to work with a financial professional to purchase life insurance.  About 1 in 4 consumers say they have bought or have tried to buy life insurance online, with younger consumers more likely to shop online than older people.

Source: LIMRA



How You Can Take Action Today >>

Part of DecisionPoints, Data Decision Group's industry-leading compiled data source, our purchase Propensity Models deliver unprecedented insight into the behaviors and propensities of customers to engage, purchase or consume products and services. Life insurance marketers use our Propensity Models to identify consumers who are most likely to purchase whole life insurance, term life insurance, and/or burial insurance (final expense).

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.