There are countless online employee benefits enrollment systems out there today. While each is designed to make everyone’s lives easier — employees, employers, insurance carriers, payroll providers and benefits advisors alike, some don't quite live up to the hype.
While the initial transition from paper enrollment to any one of these online enrollment systems typically yields tremendous upside from an efficiency, speed and data integrity perspective, it's highly unusual for an enrollment system to be fully optimized for peak performance at first launch.
Tweaking and perfecting the system in the quest to maximize performance and outcomes should be an ongoing activity within your organization. Most agree that the goal of optimizing these systems is to make them as easy and intuitive as possible for your employees to use, while also guiding educated, informed and appropriate employee benefit decisions for your workforce.
Much of what’s considered “best practice” in online benefits enrollment has been adopted from best practices in eCommerce. After all, enrolling in benefits these days isn't that far off from purchasing something off Amazon, comparing cars at AutoTrader, or configuring a laptop at Dell.
While this list is by no means complete, here are some best practices you should consider adopting to optimize the configuration of your online benefits enrollment system for peak performance.
Capitalize on Nudge Theory
While "nudge theory" won Richard Thaler a Nobel prize in economics, the concept is quite simple. It’s a subtle policy shift that encourages people to make decisions that are in their broad self-interest.
Put into practice, it simply means using "opt-out" as the default option for certain benefit selections. This requires someone to actively deselect an option. Failure to do so results in auto-enrollment in that benefit. A great example of nudging is pre-selecting a 3% contribution into an employee's 401(k) vs. leaving the field blank. This simple change will have a massive impact on 401(k) participation.