Reducing returns with behavioral psychology tips

Consumers want to live more sustainably and avoid returns. But at the moment of decision when making a digital purchase, there is a lack of concrete action. There are solutions for this "intention-action gap". With the right impulses, a large number of returns can be avoided.

There are two fundamentally different ways for companies to do this: Restriction or support. Restrictive measures include subtle obstacles in the returns process or the exclusion of frequent returners. They make shopping less attractive for customers. Most retailers do not use this approach. There are good reasons for this: Elaboratum's Shopping Frustration Barometer 2021 showed that restrictive measures would lead to abandoned purchases in 53 percent of cases.

Supporting measures to avoid returns

Supporting measures include monetary incentives, improved product information, and behavioral economic interventions such as transparency notices, education on the consequences of returns, and social signals. Behavioral design addresses the behavioral economic interventions that help to avoid returns.

Decision support offered is important

The field of behavioral economics focuses on effectively influencing customer behavior without manipulating customers. The focus is rather on supporting them in a behavior that is advantageous for them and on dispensing with monetary incentives or restrictive measures.

Research has now identified more than 120 intuitive behavior patterns. In the course of behavioral design, effective interventions are used to trigger certain of these behavior patterns and to positively influence behavior in a way that is desired by society as a whole - for example, more conscious consumption, the purchase of more durable products or, precisely, the avoidance of returns. The field experiment showed that even minor changes in communication can lead to significant changes in customer behavior.

Particularly effective interventions

According to the study, there are interventions or behavior patterns that have proven to be particularly effective in preventing returns.

• Social normThis intervention communicates that many other customers are already behaving sustainably by avoiding returns. This motivates visitors to do the same.
• ReciprocityThe tendency to want to return a favor to the other party. The intervention therefore communicates the favor or the advance performance of the online store in the form of product information and size advice in order to motivate cooperative behavior in return.
• Loss AversionLoss aversion describes the fact that people give greater weight to losses than to gains. The presentation of a real time loss of 32 minutes on average due to imprudent ordering - this is how much time customers lose on average due to returns - serves to evoke loss aversion.
• Illusion of ControlThe feeling of self-efficacy and control leads to a positive attitude and can thus promote the willingness of visitors to cooperate.

In the study, the shopping cart, the shopping cart confirmation, and the order confirmation were selected as contact points for the interventions. A concrete example: In order to play out the Behavior Pattern social norm, a hint is shown at the shopping cart confirmation, which gives the shoppers concrete hints and sets an impulse in decision-making and active avoidance of returns.

Interventions have three effects:

1. The rate of returns can be significantly reduced.
2. More deliberate orders often result in lower conversion rates.
3. Nevertheless, there is no drop in sales, because customers keep more items as a result of more conscious ordering.

So reducing returns and growing sales are not mutually exclusive - on the contrary.

Dynamic and differentiated

Online retailing is subject to a high degree of dynamism. Therefore, interventions should also be dynamic and differentiated. This is not possible manually. This is where the possibilities of artificial intelligence can help. Algorithms can learn from the most diverse constellations of influencing variables - for example, the click behavior of users - and predict and play out the optimal message in each case.

Deep learning approaches are used to select the appropriate intervention from the pool of behavior patterns and play it out at suitable contact points in the online store. This enables retailers to respond to changes in customer behavior in real time. The tools for this improved analysis and technology-supported automated delivery of the appropriate interventions already exist.

E-commerce should act now

Interventions based on behavioral psychology can lead to significant changes in behavior. This is also a clear recommendation to all e-commerce retailers: The hurdles are not glaringly high, the need on the other hand is. The time to act is now, the means are available. All that is needed now is the necessary courage to implement them.

* Philipp Spreer is Senior Director at Elaboratum Germany.

The e-commerce consultancy Elaboratum conducted a study together with Behamics and the University of St. Gallen. The field experiment with more than 100,000 online shoppers showed that at least 4 percent of returns could be saved each year in Germany - the equivalent of around 16 million parcels.