Serving Your Users Through Defaults and Anchors
Perry Wright, Senior Behavioral Researcher, Common Cents Lab at Duke University
In a previous issue, I mentioned that here at Common Cents Lab we’re privileged to work with research and industry partners to use behavioral science to improve the personal financial outcomes of their low- to moderate-income users, members, or clients. In that installment, I talked about how the first step in employing behavioral science on behalf of your users is to identify and understand where in your system users are not arriving at the outcomes they intend. Understanding these user-intended outcomes gives you a solid foundation for making changes that are more than simple nudges.
So, what kinds of changes to a system have the largest impact? Well, there are a few, but almost none rival the power of a default.
What is a default? Simply put, a default is a prescribed outcome that happens if a user takes no specific action and proceeds.
We can see defaults all around us. I think about that fast food cheeseburger I had for lunch the other day that came with a large order of fries. I mean, sure, I technically could have specified a side salad, but who wants to put someone through the effort? By just keeping my big mouth shut those fries ended up littering the bottom of my greasy paper sack.
Familiar to readers in the retirement space, we often see the power of defaults in three domains related to retirement saving. The first, and arguably most profound use of defaults, is in opt-out plans, or automatic enrollment. Plan participants are automatically enrolled unless they specifically elect otherwise. The second often follows from the first, default investments. Default investments are the specific investments into which user funds will be contributed if no specific plan is selected. The third place we commonly see defaults is in pre-selected contribution elections, usually a provided percentage of wages that plan participants can adjust to their preferred amounts.
Why do defaults work? Defaults are so powerful because they operate on three behavioral principles, helpfully referred to by behavioral scientist Eric J. Johnson as the 3 E’s:
Easy: defaults make choices easy for users by preselecting an option so that they can move through the decision environment with reduced friction.
Endorsement: defaults provide an implicit endorsement of what selection the system projects it expects users to select, or what the system thinks they should select.
Endowment: defaults endow users with a status quo that they have to actively choose to exchange for another option, something we are reluctant to do.
How can you use defaults to serve users? In most of our professional contexts we don’t enjoy total control over a system to implement strong structural defaults. So, instead we can look at our systems to see what our current defaults are influencing users to do in areas where we can affect change. Above, I mentioned default contribution elections as a common place where defaults influence decisions.
Focusing on default contribution elections for a moment, they’re an interesting example of a place where you may well enjoy more discretion. They’re also helpful in that the influence of the default has a second order effect, called anchoring.
What is anchoring? Anchoring is a behavioral bias by which we are unwittingly influenced by benchmarks, even arbitrary ones. You can think of it as your brain’s desire to treat any first contextual number as the first offer in an internal cognitive negotiation that you don’t know you’re having. We take in an initial number and (often subconsciously) decide if our preference is to go higher or lower, relative to that number. This process is called anchoring-and-adjusting.
In the case of default contribution percentages, the default amount will influence many plan participants to save at the provided amount (the pure default bias), but it will also provide an anchor for those who choose to save at a different amount, which will influence the amount plan participants ultimately elect (the anchoring effect).
For example, for plans that default participants into saving 3% of wages, we expect to see a distribution that clusters around 3%, with most people saving within a handful of percentage points of the anchor. Alternatively, for plans where the default contribution is 7%, we expect to see the cluster a little higher.
This is where knowing your users’ intentions can help guide your interventions. If users consistently say they’d like to be saving more, you can take a first step by bringing new participants into the plan using a slightly higher default contribution, from which participants will adjust to their preference.
Final tips:
In contexts like default retirement contributions, consider that users who don’t take the provided default may more often adjust down instead of up. So, for example, users defaulted to save 3% may likely stick with that default, but are not likely to adjust up. Users defaulted to save 10% may likely adjust down from the default but are subject to the anchoring effect of the 10%, and are therefore likely to adjust down to a percentage that is still higher than 3%.
Providers can look across existing plans to identify where participants are contributing at the appropriate levels to satisfy both their stated and revealed preferences, and can then make informed decisions about adjusting those defaults for incoming participants. For existing users, consider setting a default to auto-escalate so that gradual increases ease plan participants into contribution levels in line with their intended goals.
In a meta-analysis of experiments featuring defaults, researchers found that defaults are more effective when they operate through endorsement or endowment (two of the three E’s above), over simple ease. So consider what your defaults are conveying to users about your recommended choices for them to make.
Additionally, if you observe disparate outcomes across your users, know that research has shown that the smart implementation of defaults in the choice architecture of environments like retirement where many users have lower familiarity with the decisions they are asked to make help reduce those disparities.
Finally, here is a Common Cents Lab case study of an intervention that leveraged many of the ideas discussed above so that you can see them in practice. In this example, members of a credit union expressed an intention to save more for emergencies, so we helped the credit union implement a revised default for redeeming credit card rewards directly into emergency savings, doubling the percentage of rewards converted into savings.
Stay tuned for more! / Perry
Perry Wright is a Senior Behavioral Researcher at Duke University's Common Cents Lab, using behavioral science to create solutions that aim to increase the financial well-being for low- to moderate-income people living in the United States and abroad. The Common Cents Lab is funded by the MetLife Foundation and supported by BlackRock as part of BlackRock's Emergency Savings Initiative. For more information and to connect directly, you can reach Perry by email here.
This piece was featured in the August 11, 2022, edition of Retirement Security Matters. For more fresh thinking on retirement savings innovation, check out the newsletter here.
