Twitter is outraged over this retirement advice

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A Twitter firestorm is swirling around a topic more likely to receive yawns than outrage: A swarm of indignant tweets and comments hit not long after a MarketWatch article offering basic guidance on how much 30-somethings should have saved for retirement.

Cynical and exasperated Twitter users centered on the feeling that the recommendation to save twice one’s salary by age 35 was out of touch with their reality and that a one-size-fits-all approach failed to consider individual life circumstances or macro-economic impediments to saving. Many felt scolded for failing to save what they could not and shamed for past choices or not working hard enough.

It’s a shame because a client’s first-order need is to feel understood. Thirty-somethings are often saddled with the baggage of having invested in their own human capital (student debt, entry level jobs to break in to their field) but have not yet reaped the rewards of those investments. At the same time, their lives are often getting more complicated, with relocation for jobs, marriages, children, and sometimes aging parents. As these forces intersect, thirty-somethings need help at key points figuring out how to balance it all: What to prioritize, when, and how.

Much of the advice offered to this demographic (the article was based on a Fidelity Investments report) has been of limited help. Encouraging saving or investing on the margins (a la micro-investing) and setting savings targets for far off goals (a la retirement calculators) are a good start, but they fail to meet the customer’s potentially most pressing need, which is help with big decisions.

And while life-event based educational content, via listicles and microsites are pervasive, these sources unfortunately stop at the level of general education. No wonder people are frustrated. Even when the industry speaks to their challenges, advice that is relevant but also specific enough in the context of individual circumstances is hard to come by.

The big challenge for the industry lies in being able to scalably provide meaningful advice to this demographic. Doing so requires a deep understanding of the context of the customer’s major life decisions, as well as the ability to tailor advice to an individual household’s situation.

Typically, this level of nuance would best be handled by an experienced professional, who could listen, process the information, and give advice based on an understanding of a client’s individual situation against a backdrop of best practices and a history of providing advice to clients in similar situations. However, providing advice to this demographic presents a real problem for the financial services industry: With a relatively low average level of investable assets, it simply isn’t profitable for most advisors to spend time with these customers to develop or maintain a financial plan.

From a technology standpoint, this is not an easy problem to solve either. It requires tools that can process the intersection of a multitude of variables to provide situation-specific advice that is grounded in sound data-driven logic and behavioral theory. Today, we are reaching an inflection point where the necessary technologies are mature enough to aggregate and leverage diverse data, and where lessons can be learned from the deployment of behavioral economics in nudging responsible behaviors.

Imagine a 30-something couple with two kids considering moving — a typical major life decision with myriad direct and indirect financial consequences. They're overwhelmed by the challenge of how to balance a set of tradeoffs: Saving versus paying off the last of student loans, house size versus commuting time, putting money into property taxes or private school. What if there was a solution that:

  • Aggregates data beyond the client’s financial accounts that is relevant to life decisions with major financial implications. It brings in data on income potential based on career choices; tax implications of local geographies; childcare and housing costs by type and location; school quality ratings and commute times.
  • Knows from their unique behaviors what advice and communications are most likely to motivate them: this couple’s loan repayment history suggests aggressive repayment relative to their income and that they always prioritize the highest interest rate loan, making them a good candidate for messaging and advice that continues with this avalanche method.
  • Acknowledges their personal priorities along with the value of objective advice. Instead of allowing one to overrule the other based on simple rules, advice guidelines and personal import rankings should each sit on their own axis, and be weighted appropriately. While a farther town may come out ahead from an economic perspective, a shorter commute is a top priority for this couple that prizes family time.
  • Allows for the creation of scenarios that capture the interdependencies between individual financial decisions and paint a picture for a client of what’s behind each door: How does staying put compare to buying a three-bedroom house in town X, which would require the purchase of a car and a monthly train pass but enable free public schools with after-school programs?

In short, imagine a digital planning experience in which customers could ask questions and receive satisfying answers to their big tradeoff questions. Those answers would be driven by diverse data sources combined with logic that considers the interplay of independent life choices, and delivery informed by what we know about human behavior.

For the industry, investing in digital direct-to-consumer planning capabilities is imperative to acquire the customers of the future and to serve them profitably. Sure, it’s complex and daunting, offering long-term payoff for shorter-term sacrifice. But you know what? Like 30-somethings and retirement saving, the bottom line is the industry can’t afford not to.

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