A majority of employers focus on employee demographics to evaluate voluntary benefit offerings, but benefit advisers using data analytics and analysis to more specifically target their clients employee groups and individuals could find better success in creating an appropriate voluntary package.
Fifty-four percent of employers look solely at demographics such as age group and gender to evaluate voluntary benefit options, according to a Towers Watson survey. But, while that information may be valuable, it doesnt completely show benefit advisers the information they need to know to personalize voluntary benefit options and employee engagement, says Lori Black, national voluntary benefits practice leader at Buck Consultants.
Using an advanced analytical approach through employer profile analysis, employee segmentation and predictive modeling can yield higher business value and a better employee experience, Black said during a breakout session Tuesday at EBAs Workplace Benefits Summit.
The use of advanced data analytics takes us from a descriptive approach to a prescriptive approach, she added.
To do that, advisers and employers should move from demographic or single dimension identifiers such as gender or age group to a more individual level, which would include analyzing neighborhood level data such as zip code credit behavior and household level data such as income, net worth, number of children, etc.
On an individual level, advisers and employers can evaluate employees based on all of this information, not just one dimension of data in a vacuum, Black says. By doing so, she adds, advisers and employers can begin to profile employees through the use of enhanced segmentation.
The main goal is to segment the employee population based on buying preferences, Jeff Caldwell, marketing director of Transamerica Employee Benefits said during the same breakout session Tuesday.
We tend to create our marketing messages around certain demographics and very singular data such as age group, he said, adding that the fault in doing so is that two people the exact same age may have very different buying preferences.
By combining all the data and variables together advisers and employers can segment out the employee population and create custom messaging that better engages employees, and also offer products that better fit the needs of the employees.
For instance, Caldwell said, certain segments of an employee population can be identified as taking more risks, while others may be risk adverse. Likewise, some segments of the population may prefer expert advice, while others recoil from it. The messaging used to engage these employee groups will need to be very different if they hope to be successful, Caldwell said.
Being able to highlight products based on employee need, targeting messages to employees based on what is important to them, and using the right media and enrollment process to engage and enroll each employee, makes the use of data analytics a game-changer in voluntary benefit sales, Black added.
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