If you want to predict the wants, needs and fears of participants when it comes to retirement planning, look into the potential of predictive analytics.
The use of “big data” (a bite-size term often used instead of the nerdier “predictive analytics”) is helping to steer plan sponsor actions towards enhancing 401(k) participant outcomes and providing insights into consumer behavior.
But what’s the best way to jump on this moving train and, more importantly, what does it all mean?
Predictive Analytics and Your 401(k) Plan
First off, predictive analytics is “using many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about the future,” as defined by the International Research Journal of Engineering and Technology.
Applying that to the 401(k) world means that data is being analyzed to help plan sponsors and advisors understand what employees need at different stages of their retirement planning, even before they know they need it. This jump start can be used to initiate auto techniques, proactive communications and even targeted educational sessions to help participants manage their 401(k)s.
Enhancing the Conversation
A number of financial services companies are upping the ante by investing big money into big data. For example, John Hancock Retirement Plan Services (JHRPS) expanded its data analytics capabilities last year to “help plan sponsor clients and advisors make plan and platform decisions to help participants save more for retirement.” It conducted a predictive analytics pilot with a client 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.”
According to JHRPS, they used predictive analytics to “model participant data to identify participant segments – top, normal, and non-contributors – and then enriched the data with third-party data to provide broader insight into the personas.” From there they used machine-learning algorithms to predict future outcomes, which helped identify the non-contributors, while providing insight into what might help them save more.
They learned that the employees who kept opting out were often single mothers or midcareer people who faced similar financial stress when returning to the workforce. To encourage these employees to participate, the sponsor decided to lower the auto-enroll default deferral rate from 6% to 1%, which it coupled with auto escalation. The savings nudge worked: “Nearly 85% of the non-contributors who were auto enrolled at the lower default rate stayed in the plan, and many increased their contribution rates,” according to Pension & Investments.
Personalization for the Win
Artificial Intelligence (AI) technology is another way we can look at participant behavior and retirement planning. To boil down these sci-fi futuristic terms, AI technology has been described as a technique used to conduct predictive analytics.
As another example, the Economist’s EIU Perspectives Series examined how AI technology can affect participant outcomes, noting that “enhanced data and technology capabilities and improved transparency enable participants to access greater expertise and have more control and personalization of their investments.”
Additionally, they observed that AI technology “can provide more and better information and help take some of the guesswork out of the process (for less-engaged participants) and, as a result, they can make more informed investment choices.”
Their conclusion? “Plan sponsors can also take advantage of data, transparency and technology to understand trends in investing and participant activity, as well as participants’ goals in their retirement plans. With better information, sponsors can offer more personalized options.”
The Future is Now
According to research released in 2018, financial services ranked 4th in adoption of big data for predictive analytics, just behind telecommunications, insurance and advertising.
Make no mistake, the interest in big data and what it can do for client modeling and predicting behavior is becoming an industry-wide trend.
Despite the sci fi-sounding jargon, any data, trend predictions and related information should always be thoroughly reviewed and thought out. This could mean setting up a one-on-one conversation with a financial professional about how to approach complicated retirement plan issues because while technology enhancements are great, there is still no substitute for human experience.
However, one thing is clear. The future of data is already here.
Safane, Jack. “Creating Better Retirement Outcomes Using Data, Technology and Transparency.” Perspectives from The Economist Intelligence Unit (EIU), The Economist Newspaper, 1 Aug. 2018.
Berczuk, Melissa. “ John Hancock Retirement Plan Services Expands Predictive Analytics Capabilities to Close Retirement Savings Gap.“ 15, March 2018.
 Pensions & Investments. “Providers mining big data for look into participants.” August 2018.