In 2025, Trusted Returns once again analyzed how consumer return behavior is evolving and where opportunities lie for companies.

The key finding of the 2025 Returns Report is that fewer customers are sending back their purchases. In 2025, only 46 percent returned items, compared to 51 percent in 2024 and 67 percent during the same period in 2023.

For 2025, this means that although the University of Bamberg forecasts total return volume in Germany to reach a new record of 550 million parcels, individual propensity to return is declining. Artjom Bruch, CEO of Trusted Returns, explains how e-commerce retailers can address this discrepancy by targeting their audiences more precisely based on insights gained from returns.

When Data Goes Unused

Retailers still often ignore who is returning which products and why. They view returns purely from a revenue perspective and overlook the details related to target groups, products, and channels. While many retailers collect reasons for returns, only a fraction systematically and automatically analyze this data. Yet they could use it to gain a better understanding of their customers’ purchasing and return behavior.

As a result, there is a risk that products with high return rates continue to be advertised to target groups that return them disproportionately often. Retailers must consistently use their tools and exclude customer segments that put their margins “at risk” from targeting. With average processing costs of €5 to €20 per return—and up to €50 for complex assortments—avoidable losses quickly accumulate.

Another example of missed opportunities based on return data comes from the Trusted Returns survey on return preferences. According to the findings, women significantly more often prefer alternative return options (63 percent versus 53 percent of men). This clearly indicates that standard offerings are less appealing to them. Nevertheless, both target groups are generally addressed in the same way.

Return Data as a Marketing Compass

Paradoxically, many companies already have the solution at hand: return data. Through systematic and strategic analysis, it can be used as marketing intelligence. A key component is data-driven analysis that considers purchase history, touchpoints, return patterns, and preferred product categories. Targeted management of customer communication or product positioning has a direct impact on return behavior. This makes it possible to stop promoting products with high return rates to specific target groups. For example, if a shoe model is returned disproportionately often by customers over 45, it should no longer be included in corresponding marketing campaigns.

Just like the product portfolio, services can also be adapted to clearly expressed customer preferences: 58 percent would like alternatives to “return for refund,” such as exchanges, vouchers, or repairs. This insight enables differentiated service offerings by target group and opens up options to prevent a complete loss of revenue.

Data also enables channel evaluation: 33 percent of 18- to 34-year-olds use social commerce as a shopping channel, compared to only around 15 percent of those over 55. Return data can precisely show which channels are problematic for certain products and where budgets need to be reallocated.

Only consistent analysis of return reasons makes it possible to improve product information and reliably assess sales channels. Instead of spending millions on scatter losses, retailers can specifically enhance their customer journey.

Breaking Down Silos: Collaboration Between Marketing, E-Commerce, and Product Teams

The optimal use of insights generated from returns depends on regular exchange between marketing, e-commerce, and product teams. In collaboration, insights from return reasons can seamlessly flow into product descriptions and campaign messaging. If the e-commerce team identifies that certain color variants are frequently returned by specific age groups as “looks different than expected,” marketing and product teams can react quickly: better product photos, adjusted descriptions and differentiated targeting parameters. This cross-departmental integration requires new processes: weekly return data reviews, shared KPIs across teams, and automated alerts for unusual return patterns. Only then can a reactive cost center become a proactive management tool.

Data Drives Sustainable Success

Those who strategically leverage return data not only reduce costs but also increase conversion rates, minimize wasted ad spend, and reduce customer frustration. Ultimately, success is achieved only when customers not only decide to buy but also to keep the product.

E-commerce decision-makers must therefore view returns even more strongly as a precise data source for building better relationships with their customers. Retailers who professionalize their return data analysis now will transform a cost factor into a marketing booster.