Americans like cars and homes. As of 2017, there are about 150 million auto and mortgage loans outstanding in the United States carrying a sizable average liability. On average, Americans who have a new car loan are $30,032 in the red and homeowners who have a mortgage owe $106,132. This amount of debt is not only psychologically costly; it is also costing us real money. As evidence, the average interest cost for a mortgage totals $5,646 per year.
To help reduce the cost of debt, we partnered with EarnUp in 2016. EarnUp is a company that helps people pay off debt faster by timing their income and loan payments. In an experiment with EarnUp customers, we found that people are more likely to opt into overpaying their monthly loan when the option was framed as a “round up.”
EarnUp moved this from a pilot to a full rollout and launched a “round up option” within their sign-up flow.
In 2017, we partnered with them once again to nudge customers to accelerate their debt pay-down.
Behavioral Diagnosis and Key Insights
While accelerating debt even a little every month can help consumers save thousands of dollars, understanding the impact of these large savings on our future self can be difﬁcult. In our literature review, we found that other domains have successfully increased numeracy by providing concrete comparisons.
For example, researchers in the United Kingdom showed that presenting a label containing how much you would have to walk to burn off a snack pushed people to choose healthier snacks, as compared to just showing the number of calories contained in the snack. Daniel Goldstein and others have shown how newspapers have helped people digest and retain numerical facts by providing perspective-taking comparisons.
The second step of our behavioral audit was to narrow down on a particular concrete comparison that we could use for savings. To do this, we designed a 1,000-person study on Amazon Mechanical Turk. We hypothesized that people may be more motivated by large quantities of small items (a lot of pizzas and coffee) vs. one large item (a car or vacation).
To determine which was most motivating, we gave people the chance to win lottery tickets. We divided people into conditions and each condition was playing for different prizes. Some received the chance to play for a lot of one small-dollar prize (e.g., 1,000 coffees or sodas), some got the chance to play for a little bit of the medium-sized prizes (e.g., ten months of Amazon Prime) and some were playing for one big prize (e.g., a computer). Last, a few people got the chance to play for cash. Note that all the prizes added up to the same monetary amount, and we varied the type of prize people were offered. Then we asked them to answer a series of math puzzles.
Each puzzle answered correctly was a ticket in the lottery. The more they answered, the more effort they put in and the more we assumed they liked their prize option.
In our pre-study, cash rewards motivated the most effort. This wasn’t terribly surprising as the game was hosted on Amazon Mechanical Turk, a platform for earning money. The most interesting results were the least motivating prizes – 1,000 coffees or 1,000 sodas. People were less attracted to the “large number/small prize” combination.
We used the directional insights from our pre-study to design an intervention with EarnUp that compares the amount of savings someone would have from paying down debt faster to larger items they could obtain with those savings (cars or vacations).
We made slight modiﬁcations to EarnUp’s sign-flow to facilitate our experiment, asking people to round up their debt payments to save on interest.
Users are randomly assigned to one of two conditions, our control and our experimental condition. In our control condition, users see how much money they would save by rounding up their debt payment. In our experimental condition, users see how much money they would save by rounding up their debt payment and concrete items they could buy with the money they save (a vacation, a new car, etc.).
We hypothesize that giving the savings amount a reference point will help people internalize the value of EarnUp’s product and increase round up rates.
Our experiment launched in December 2017 and will continue through February 2018, until we achieve a sample size of approximately 1,200. We expect to share results in Q2 2018.