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Behavioral Economics

The Power of Predictive Moments: How digital distribution of life insurance must evolve

Pick the right moment as much as the right message
moments long

Executive Summary

Background

Digital distribution models typically aim to make it easy for consumers to buy life insurance, focusing on slick design and conversion optimization to compete for market share.

The bigger challenge for the insurance industry’s market growth, however, is capturing consumers’ attention and awakening their recognition of the need for life insurance in the first place.

Marketing life insurance online can be challenging. Not only is every brand vying with a huge array of information for consumers’ attention, psychological research has shown that people may actively avoid messages that force them to consider their own mortality.

We tested the idea that marketers can benefit from positioning products and messages to align with certain times in consumers’ lives when they are likely to be more receptive. Such times, known as predictive moments, occur when:

  • Life events such as marriage, having a child, or buying a first home create or awaken the understanding of a need for life insurance.
  • Immediate context, such as the information they are looking at online, brings that need near to top of mind.
Predictive moments

When life events combine with an immediate context that make risks and responsibilities salient (more likely to be top of mind) in that moment.

Study method

We conducted an experiment that randomly placed 2,852 U.K. participants into hypothetical predictive moment scenarios. We then measured how effectively a predictive moment was created, using a neuroscience testing technique called Implicit Reaction Time, which assesses the conviction behind participants’ answers to attitudinal questions by the time it takes them to respond.

Implicit Reaction Time Test

A test that measures how quickly participants respond to attitudinal questions. Fast reaction times imply stronger associations between mental concepts and hence more strongly held beliefs and higher conviction in participants’ responses to questions.

Findings

  • People who had relevant life events were more receptive to life insurance, even if the event did not change their underlying risk (e.g., experiencing bereavement). Those individuals were also more likely to own a policy and to have made a change to their policy at the time of the life event.
  • The predictive moment simulations influenced people’s receptiveness to life insurance if they had previously experienced the particular predictive context presented. Contexts that reminded people of parenting responsibilities or of bereavements were associated with greater receptiveness to insurance.
  • Even if a predictive moment was created by the context, people still needed to be reminded that life insurance was relevant for them.

Implications for insurance marketers

Marketing that engages consumers at moments when life events and mindsets align could be powerful means for insurers to help people make important choices about financial protection. Such models could include embedding insurance within relevant digital experiences that bring these risks top of mind. Leveraging predictive moments with truly persuasive messages will always be important when it comes to awakening consumer need for life insurance.

Strategies incorporating predictive moments may enable insurers to focus on effectively targeting new customer sets in their markets, rather than competing for existing market share, which may reduce acquisition costs. However, more research is needed to understand exactly which online experiences may provide the most effective predictive moments. Novel audience research techniques that explore participants’ conviction in their responses can be valuable for furthering our understanding of human attitudes and insurance purchasing behavior.


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