Just as plan participants can benefit from a goals-based approach to financial planning, so, too, can plan sponsors benefit from taking a goals-based approach to plan design.
Plan sponsors focused on recruiting new graduates, for example, might want a plan that emphasizes student loan repayment, while one with more baby boomer participants might concentrate on education around their retirement income solutions. Employers with a workforce that is largely living paycheck to paycheck might look at incorporating emergency savings options into their retirement plans.
Employers today enjoy a wide range of plan design options, but selecting the best plan still presents a challenge as more plan sponsors focus on striking a balance between achieving cost savings and helping their workforce retire on time. Plan sponsors want to offer retirement plans that attract and retain top talent, but at the same time, they don’t want to spend money on strategies and offerings that are not helping their participants save more or support their overall financial wellness, both in the short and the long term.
Like many 21st century problems, the solution lies in data. Understanding data on three different levels—theoretical data, company-level data, and big data—can give plan sponsors the insight they need to take a purposeful, deliberate approach to their plan design and to improve participant and organizational outcomes.
This is information that’s not necessarily numbers-based, but that helps plan sponsors understand the importance of selecting appropriate retirement solutions for their workforce. This is a helpful way to think about employee productivity relative to their compensation. While the timeline is different for each worker, employees generally cost more than they produce at the outset of their career with the company; then after a few months or a year their productivity shoots up.
For the bulk of their career, the value of an employee’s productivity should be greater than the value of their compensation, resulting in a net gain to the company. That’s the stage at which employers want to keep their workers for as long as possible. At the other end of their career, when workers are emotionally and mentally ready to retire, their productivity declines. Those who are financially able to retire at that time will likely do so. But those who want to retire yet can’t for financial reasons, can weigh on the company, as the value of their compensation becomes out of sync with their productivity.
From an employer perspective, this trajectory illustrates the importance of not only recruiting and retaining the best talent, so that you’re losing less money as new employees onboard, but also the importance of making sure workers can retire when they want. The goal is to have employees financially ready to retire (and confident in their ability to do so) before they are mentally and emotionally ready to retire.
This is the basic data with which most employers are already familiar, but there’s a difference between having the data and truly understanding the story it’s telling as it relates to your plan design and its ability to reach your organizational goals.
While average deferral rates and average participation rates are important, there can also be insights hiding behind the average, and the way you calculate those figures is important. The average deferral rate of plan participants, for example, will likely differ from the average deferral rate of all eligible employees, since those who aren’t participating have a zero percent deferral rate.
Average account balances also typically ignore non-participants, and a few balances on the high end can skew the data significantly. That’s why it’s important to also consider the median balance. If there’s a large difference between the average and the median, that’s a clear indication that a plan may have a few employees who are doing very well on their retirement goals, but many who are not. From a plan design perspective, most employers want a design that promotes the success of all eligible participants, rather than just a few.
Basic data also comes into play in other elements of plan design, such as determining an appropriate glide path (or if a target date makes sense at all), or figuring out what’s driving a trend in participation rates.
The third type of data is big data, which, of course, is being used across virtually every industry these days to match more relevant products and services with end users. It’s an emerging practice in the retirement field, and it holds a lot of promise.
Later this year, Prudential will introduce Plan Health 360, a data and science-based tool, that plan sponsors can use to assess and quantify the health of their plan. The tool scores the plan, benchmarks it against peers in Prudential’s book of business by industry, and suggests areas of improvement.
By understanding why people do what they do, it’s possible to predict what people are going to do in the future and understand how those trajectories help them do what they need to do in the future. Prudential has had a team of data scientists analyze millions of data points to extract multiple, distinct variables that help us understand why a person is going to retire, what triggers that decision, and how those variables interact with each other. That information can help a company turn a retirement program from a cost center into a profit driver, actually generating a return on investment by designing a plan that maximizes the impact of their dollars and drives the retirement patterns desired.
For example, a company may know that auto-enrollment, auto-escalation, and a few other features could mean Joe Smith in accounting will end up with an extra $100,000 in his retirement account. What can big data add to that insight? The extra $100,000 means Joe is much more likely to retire a few months or even a year earlier.
Incorporating data on these three levels allows employers to make evidence-based, objective decisions about plan design to not only provide solutions their employees need but also to make progress toward their organizational goals. By looking at how the design of their plan supports and aligns with their overall, holistic benefits offerings, employers can improve the health of their retirement plans.