I think, we can all agree that returns of goods bought online have a massive impact on the carbon footprint of the entire retail industry, costing companies millions and customers time. Hence, there is no question that a reduction in returns is in the interest of everyone involved. But how can online retailers get their customers to the point to shop in such a way that there are fewer returns?
In the study "The Psychology of Returns", the e-commerce consultancy Elaboratum, together with the software provider Behamics and the University of St. Gallen, carried out a field experiment with more than 100,000 online shoppers, empirically investigated how behavioral psychological interventions affect the return behavior of customers.
According to their report, in the Corona year 2020 alone, 15.9% of all online orders were sent back to retailers, which contradicts the rational interests of all those involved, right? An explanation for this phenomenon is the so-called "intention-action gap", describing that we all want to live more sustainably and contribute to reducing the volume of parcels (intention), but at the moment of decision-making, there is a lack of effective impulses to translate one's own good intentions into concrete “actions.”
Finding out whether behavioral psychological interventions can influence returns behavior was the aim of the above mentioned ‘behavioral economic returns experiment', with the goal to clarify whether behavioral psychological interventions can close the gap between "intention" and "action" and significantly influence the customer's return behavior.
For this purpose, behavior of users in various online shops was analyzed and evaluated, based on randomized experimental designs. This means, visitors to the online shops were randomly assigned to a certain contact point (e.g., order confirmation when completing a purchase) or triggered by a certain action (e.g., when several sizes of an article are placed in the shopping cart) a number of stored interventions played out.
Examples of such interventions are references to the behavior of other customers (social norms) or to the personal loss of time caused by a return (loss aversion).
The reactions to the interventions were then compared with the behavior of customers who had not seen a corresponding message. This procedure ensures that the measured differences can be traced back to the effect of the respective intervention without any doubt. In this way it was possible to determine which intervention has a return-reducing effect and how strong it is.
The results of the study show that interventions based on behavioral psychology can lead to significant changes in behavior - without any monetary or restrictive measures. Across four experiments it became evident that the return rate can be reduced by around 4% with the means used in this study. Further conceptual refinement of the interventions, additional behavior patterns and training of the intervention algorithm, could increase this value to at least 5% the authors believe.
Considering that around 15% of all goods ordered online are returned according to the estimates of the online shop operators, dealing with this problem becomes a major challenge. Digital technologies such as AI, Big Data or Virtual Reality are certainly useful to enable customers making their purchasing decisions more reliably, because shop owners often overlook the fact that sometimes there is a lack of information when customers send back an ordered product.
The technology company trbo, for instance, provides a bunch of solutions how retailers could reduce returns through on-site personalization and optimization, since there are tons of innovative technologies around to reduce the number of unpopular returns and some good tips:
- Automate size information country-specific: if the shop notices that the user is currently unsure (e.g., if several sizes of the same product are placed in the shopping cart), a direct reaction is possible, such as a duplicate note with the offer of personal advice or the display of a size table. The fashion industry in particular struggles with return rates (up to 60%) and clearly demonstrate that returns are part of the online retail business. But what is annoying, and where retailers can actively reduce returns, is when the ordered product does not fit. This is where technology could provide size tables in the form of links or overlays that clearly show that the sneaker in US size 7.5 corresponds to shoe size 41 in Europe. Measurements in centimeters (cm) can also help to find the perfect fit. Certainly, this provision is a challenge for some shops, especially with frequently changing assortments, the better it is if it can be done automatically.
- Make precise product information available: unfortunately, products bought online do often not meet the customer's expectations: e.g., the color of the blouse isn’t as expected or the jeans ordered too tight, and all that is only noticed when the goods are unpacked and tried on. Therefore, online retailers should offer their users as much information as possible on the product detail pages so that imagination and reality merge. Information on the material, the fit and other important descriptions are therefore a must for the website and should always be included prominently.
- Set up hybrid service offerings: even if size guides, tables and many customer reviews are easy to find on the site, some users still prefer personal advice because it is too difficult for them to read all the information. Shop operators score points with the option of personal advice from customer service. This can be solved directly on the website using chat functions or chatbots. However, anyone who offers complex products should also offer direct person-to-person advice over the phone. Accordingly, it makes sense to highlight the various contact options for customer service on product detail pages. In other words, personalized product recommendations, tailored to the individual interests of the user and function in a similar way to the recommendations of the customer advisor in brick-and-mortar retail.
- Include customer reviews: experiences from other users not only help to avoid bad purchases but can be used by retailers to obtain information about their range and can then take this into account in the next round of orders. Customer reviews that are prominently integrated on the detail page or even information such as "The pants turn out small therefore, it is better to order one size bigger" help customers to send fewer messages back.
- Have reward mechanisms in place: by creating incentives for customers to shop more consciously and not thoughtlessly buy the goods and then send them back is another option. Because of course there are also those users who never or only seldom send something back - and that should at least be rewarded with a voucher from time to time. For example, an overlay can be displayed in the shop as soon as the user is logged in. A big thank you with a directly redeemable voucher code is definitely making the user happy and encourages him to continue keeping the returns low in the future.
Even if returns cannot be completely avoided, shop operators can still do a lot to noticeably reduce the quota and consequently make a valuable contribution to more sustainability in e-commerce. The best way to find out which measures work particularly well, depending on the range and target group, is through A / B and multivariate tests. And even if retailers primarily target the return rate with a plus in service, they still achieve a positive side effect, namely improving the advisory services on their side, standing out from the competition and ensuring improved customer loyalty.
By Daniela La Marca