Next-Gen Personalization Methodology for Life Insurance

Dror Katzav, Co-Founder & CEO of Atidot

min read

Next-Gen Personalization Methodology for Life Insurance

Consumers have come to expect personalized, multichannel user experiences from companies that they interact with. Most industries are now able to offer their customers products that match their immediate and long-term needs wrapped in tailored messaging that speaks their language and caters to their lifestyle, behavior, attitude, and preferences. This is the basis for the data-driven, ‘People Like You’ marketing strategy that is commonly used throughout B2C campaigns.

Most life insurers currently use traditional segmentation tools such as Tapestry (Esri), Mosaic (Experian), and even Facebook, as the basis for their marketing strategy. This allows insurers to personalize their marketing activities and products in the life insurance vertical. In short, Tapestry, and the like, classify people into over 60 groups and types based mainly on zip code data. This cohort is creating unique lifestyle segments relating to demographics and socioeconomic characteristics. Tapestry, for example, describes US neighborhoods in easy-to-visualize terms, ranging from “American Royalty” to “Heartland Communities”.

But what if you could add a totally new dimension to traditional classification in the form of recurring behavioral patterns to create hundreds of thousands of additional granular segmentation, providing a full and complete insight into your book of business?

Atidot leverages these powerful segments with its AI and machine learning capabilities and augments a behavioral pattern to the data. We use external information from public databases as well as internal sources such as CRM systems. This information will enrich your database and

provide additional insight into your book of business or create a platform for new marketing strategies that are more accurate, potentially enabling marketing campaigns based on real-time customer data. Occupation, the proximity of hospitals, day of premium payment, investment patterns etc., can impact the Lifetime Value of your policyholders and can help create additional revenue sources.

This is the next-generation platform for product personalization and product offering for tailored marketing campaigns, new risk modeling, lapse strategy, and more. Moreover, machine learning technology can keep learning as policyholders’ actions are recorded to create more accurate and additional profiles, thus detecting the most profitable potential customers. This is the basis of our nano-segmentation methodology.

For example, one behavioral pattern is the client’s payment date. Different dates may have different meanings. For instance, if someone is repeatedly late in their premium payments, the machine can find a pattern such as “these people, if their age is between 40-50, tend to lapse within 5 years”. This behavioral pattern could have a totally different meaning if they are between the ages of 20-30. Here, it might suggest that they are busy successful people who don’t have enough time to attend to their finances. Yet they may match the most profitable policyholder groups that insurers have.

Another example of a behavioral pattern relates to sensitivity to financial market trends. For instance, when the bond index is on the rise, some people tend to invest more in their own pension funds. AI and ML technology tie such behavior patterns into different groups, such as the trendsetters segment (a traditional segmented group) that tends to invest when the bond index is up (based on the correlation between two sets of behaviors) indicating that they have a financial orientation and can be treated in two different ways. If the client owns a policy with a 4% guaranteed premium that was issued years ago, they should be encouraged not to lapse that policy. When the bond index is down, insurers might consider selling more protections to that segment.

On a strategic level, if you are catering to this specific group you might launch a marketing campaign in channels that cater to this specific group of trendsetters with a financial orientation. For example, bloggers associated with style can receive targeted ads in fashion publications, direct email, etc. The possibilities are endless.

So, since AI and ML capabilities can be trained to translate real-time events into real-time data, this newfound segmentation becomes the platform for tailored market targeting that can factor any real-time relevant data. For instance, changes in the COVID 19 geographic spread can impact the demand for life insurance, generating real-time targeting on a weekly or monthly basis, through traditional channels such as email campaigns, social networks etc.

Key Benefits:

  • Up to millions of new segmentations
  • A dynamic software platform vs. static commonly used tools
  • Life insurance-specific software that caters to the life insurance segmentation
  • An accurate platform for new marketing strategies
  • Behavioral-based insights that provide a competitive advantage

With newfound segmentation, you can gain a deep understanding of your customer base and full optimization of your book of business, as well as new revenue streams generated from a specific target audience. This next-generation approach will set the basis for marching your company into the challenges of the next decade that are all about customer experience and profitability.

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