By Maria Douneva
Financial struggles are an uncomfortable, yet pretty universal topic. Already now, 77% of American households have some kind of debt, and with the current inflation and overall economic situation, more and more people will struggle with debt. Being in debt has far-reaching consequences, even impairing physical and mental health.
“Debtors don’t pay back their debt because they don’t have enough money” might seem like the obvious answer to the question of why people are in debt, but that is only part of the story. Many people simply forget to pay. Others do not agree with the claim because they do not understand where the costs come from exactly. Some people do not want to think about debt and try to ignore it. Or they do not have the necessary financial literacy to make good decisions. In sum: Sometimes people have the money and/or intention to pay their bills, but they still do not do it.
Increasing Repayments When There Are Means and Intentions to Pay
There is often a gap between intention and behavior: People know what is good for them (exercising, eating healthy, paying their bills), but they do not act accordingly. One way to bridge this gap is to make concrete plans about how, when, and where to display the desired behavior. What works for exercise and healthy eating also seems to work for paying one’s bills: Planning prompts can help to get out of debt delinquency. Simply asking people to choose between different time windows (24/36/48/72 hours) during which they plan to pay makes them more committed to paying in the first place.
Another method has been tested and implemented by the Commonwealth Bank of Australia: repayment by purchase as a credit card option. It can often feel fruitless to pay small portions of large bills. Compared to the usual way of entering a certain amount, choosing to repay a specific purchase (e.g., a coffee at Starbucks or a utility bill) leads to an increased perception of progress towards reducing debt and ultimately to higher repayments – in a field experiment, 12% more than a control group.
Beware of Backfiring Effects
While these findings appear promising and are easy to incorporate, some well-intended interventions can have undesired effects. For example, U.S. federal regulation requires credit card companies to convey information regarding payoff scenarios that are meant to assist consumers in making good financial decisions, but they actually lead to suboptimal decisions: When presented with a dual payoff scenario with the total amount paid (including interest) if a) minimum payments are made versus b) a 3-year payoff scenario, people infer that b) is the most appropriate option and, on average, pay off less than when presented with only one scenario.
Or, let us take the example of displaying the minimum required payments. Consumers anchor on the amount and pay less of their debt than they otherwise would, leading to higher balances and interest costs (yet, increasing the minimum required level positively impacts repayment for most consumers).
It is therefore important to keep in mind which features consumers pay attention to and how they make decisions, and to A/B test nudging interventions before scaling them. Recent debates about the usefulness of nudges have furthermore highlighted two key aspects when it comes to designing them effectively: making them personalized and dynamic. What works for one person might not work for another, and what initially works might change over time.
Tailoring Communication
There are many factors that can be varied when sending out payment reminders: channel (letter, e-mail, text message, messenger, call, etc.), frequency, timing (e.g., in the morning or in the evening), payment option (e.g., paying in installments, discounts), and of course the message content itself.
Yet, companies and debt collection agencies usually send out the same message to everyone, often filled with legal terms and using a harsh tone. As a company or debt collection agency, you need to understand your customers to effectively tailor your communication. Most of the time, a lot of useful data are gathered anyway, but not used to their full extent. When they are, it seems to pay out: Based on the data of almost 400,000 debtors and roughly 1.7 million communication points, it shows that sending out reciprocity and social comparison information at specific times leads to higher repayments when targeted at certain types of debtors.
If you also use machine learning, you do not even need to have strong assumptions about how your customers will behave. If, for example, you send out a reciprocity message in the evening to a 25-year-old woman who has bought a sweater and she then pays it off, sending it to a 28-year-old woman who bought a jacket will probably also work. For the 60-year-old man who has bought insurance, however, it might not work and then the model could decide to send out a social comparison message in the morning. But it is not necessary to make it that complex from the beginning. The first and most crucial step is to change the way most of us are thinking about debt collection and debtors.
Debtors differ in their motivations, preferences, and their reasons not to pay. It is time for companies and debt collection agencies to make use of the rich knowledge behavioral scientists have about human behavior and apply it to the large amount of data they have at their disposal. It would benefit both their business and their consumers.
This article was edited by Lachezar Ivanov.
