While consumers value a free and easy return process, the costs to e-tailers associated with returns are substantial and increasing. Consequently, merchants are now tempted to implement stricter policies, but must balance this against the risk of losing valuable customers. With this in mind, data-driven and algorithmic approaches have been introduced to predict if a certain order is likely to result in a return. In this application paper, a novel approach, combining information about the customer and the order, is suggested and evaluated on a real-world data set from a Swedish e-tailer in men's fashion. The results show that while the predictive accuracy is rather low, a system utilizing the suggested approach could still be useful. Specifically, it is reasonable to assume that an e-tailer would only act on predicted returns where the confidence is very high, e.g., the top 1-5%. For such predictions, the obtained precision is 0.918-0.969, with an acceptable detection rate.