Big data’s bringing change—and retirement savings are no exception.
Claiming it’s taking behavioral economics to the next level, John Hancock Retirement Plan Services announced Thursday that it expanded its data analytics capabilities to help plan sponsors and advisors make better decisions to help participants save more for retirement.
It recently conducted a “predictive analytics pilot” with long-term client Farm Credit Foundations, which had very high participation and retirement readiness, but wanted to know why the few non-contributors had opted out after they were auto-enrolled.
“Using predictive analytics, JHRPS modeled participant data to identify participant segments—top, normal, and non-contributors; the team enriched the data with third-party data to provide broader insight into the personas, and then used machine-learning algorithms to predict future outcomes,” according to the company. “The analysis identified who the non-contributors were and provided insight into what might help them save more.”
Interestingly, the non-contributors (as well as the reasons they opted-out) were far different than what FCF initially thought, Lynda Abend, chief data officer with JHRPS, explained.
Farm Credit used the data to make targeted plan design changes, and surprisingly, decreased the auto-sweep default to encourage more employees to participate.
“There was a subset [of employees] that never participated, and FCF wanted to know why,” Abend said. “It wasn’t that they didn’t want to participate, it’s that their life circumstances wouldn’t necessarily allow it. By lowering the automatic default in the sweep, we were able to capture more in keeping with their individual situations.”
By lowering the default, 90 percent of the non-contributors stayed in the plan after the last auto sweep.
In addition, 70 percent of the new contributors remained at the lower default rate, 16 percent elected a higher contribution rate and 4 percent elected to contribute after-tax.
Since then, JHRPS has rolled out its predictive analytics capabilities to several large clients, working to uncover patterns in under-participation.
Now it is using the technology to dissect its full book of business, so insights gleaned from participant data from across the platform can be used to help guide plan sponsors, advisors, consultants, and TPAs working in all markets—small, mid-size, and large plans.
“It reinforces the power of analytics in retirement savings behavior,” Abend concluded. “It’s especially true in customizing the targeted messaging and options to the individual and the level at which they can participate.”