How Data Analytics Can Help You Make Retirement Plan Decisions
If you’re not using data analytics to help you make progress toward improving participant outcomes, then you could be missing out on a key component of plan governance. Data analytics are becoming a meaningful part of defined contribution plan governance for retirement plan fiduciaries.
Data analytics can provide detailed information on different participant segments and help sponsors recognize pain points in their plans.
Defining Pain Points
Think about using detailed analytics to break down plan data into specific employee segments based on key factors like age, job category, and tenure. Analytics highlight the employees and groups most at risk of retirement savings shortfalls, giving you useful insight on the tools and strategies that could best help them. Once you assess the analytics, it’s time to apply them to your plan.
Tactics for Retirement Readiness
PLAN DESIGN | Employers can use information from analytics to make changes or establish plan design features that can nudge participation at more impactful rates.
Plan Design options to consider:
- Low participant rate à Reenroll employees not participating
- Low deferral rate à Implement a higher automatic enrollment default rate
- Low deferral rate à Encourage auto-escalation of those enrolled but not saving enough
- Low deferral rate à Encourage participants to defer more by stretching the company match
- Improper asset allocation à Reenroll all participants into the plan’s QDIA
PLAN TOOLS | Positive employee behavior could be driven by using detailed analytics to help select plan tools and technology whether you want to increase participation, savings, tax efficiency, investing, budgeting or provide other education.
With the help of your financial advisor, plan sponsors can develop tangible goals, scorecards, wellness programs, and more to track progress going forward to improve plan governance and help participants achieving retirement readiness.
Plan Sponsor Take Away
In a recent white paper, Willis Watson Tower stated, “We believe plan-wide statistics on mean or median participation rates, balances or contribution rates measure aggregate data on all participants but offer little in the way of insight into retirement adequacy and meaningful benchmarks for individuals or segments of the population.” (Emphasis added). Therefore, today, when most retirement plan committees look at roll-up 30,000 foot level data, data analytics will help you peek into the effectiveness of your plan.
With the proper analytics, plan sponsors can understand their employees’ needs, then adjust and develop customized plans, enhanced plan features, and communication strategies and provide tools and technology to engage employees in positive behaviors. Analytics highlight the employees and groups most at risk of retirement savings shortfalls, giving you useful insight on the tools and strategies that would best help them reach retirement.