Risk Selection in a Digital Age

Risk Selection in a Digital Age

Micro targeted digital marketing and integrated data models will lead to fundamental changes in the life insurance industry.

Audience targeting of social media and display marketing allows insurers to target customers with a particular risk profile. The idea of marketing to prospects with a favorable risk profile is not new. For example, carriers have pursued clients via professional organizations whose members display lower risk since the advent of guild-based benevolent societies in ancient Rome. However, recent developments in the scalability and precision of digital marketing technologies and the emergence of startups with integrated marketing and core operations data models has begun a fundamental shift for the insurance industry.

Users of social media who are “relatively young and healthy” have no doubt seen ads promising what appears to be an extraordinary deal on life insurance. Most often, the ad copy reads something like “Run 5 miles a day? Get $1 m in life cover for $x”.

Although small players in the industry, the companies behind these ads are turning the life insurance industry upside down and are rapidly gaining traction. By way of examples, Ethos Life, Ladder Life and Health IQ are backed by investors including Sequoia Capital and Andreessen Horowitz and have raised close to $100 m in capital each. Growth rates quoted by some of these companies include “400% in 4 months”.

Without knowing the details of these private companies’ business models, and observing that some make more use of social media and display marketing than others, the ads made us consider what marketing insurance on Facebook really means.

As any marketer knows, the keys to success on Facebook are engagement and targeting. When executed well, both of these factors drive down customer acquisition costs while increasing the value of customers viewing the ad relative to competing advertisers. What’s less well understood is the value of display marketing audience targeting factors [MG1] in predicting losses. In insurance, carriers create a competitive advantage by gathering data from rating and underwriting systems and correlating these with losses. This “moat” is built up over a long period of time at the cost of paying claims.

Audience targeting factors like age and gender are relatively straightforward and their implications for rating and underwriting are well understood. However, other factors are more opaque. For example, targeting people over the age of 18 that are “interested in health & wellness” on Facebook yields a target audience of approximately 50 million in the United States.

What this audience comprises is determined by Facebook’s algorithm and since its part of their “secret sauce”, we’ll never know what factors determine eligibility for the group. However, the new generation of Insurtech startups are acquiring customers in vast numbers on these platforms and are, in our opinion, building a “data moat” in the process. Once established, they may be hard to compete against. While most of these companies are acting as intermediaries for established carriers, recent trends suggest that the broker business model is often a step on the path to risk participation.

Said differently, these companies are better positioned to understand the influence on future claims and profitability of group that is “interested in health and wellness” . Of course there are regulatory hurdles in terms of what can be used for rating and the credibility of the data, but with the amount of data being generated through these platforms, it may not be long before some of these factors find use. This could be in the form of merely bidding more aggressively on audiences known to be more profitable.

It’s important to understand the blurring of the lines between marketing and product development taking place here. Companies with integrated, end-to-end data models are creating a substantial advantage in terms of understanding how factors that drive both acquisition costs and comprehensive lifetime value interrelate.

That we are living in times of immense change in the insurance industry is an understatement. The integration of new sources of data into product development and marketing models is already a strategic priority for many insurers. However, we believe a change in perspective can reveal that some of the most valuable data is already publicly available or readily obtainable.