The Power of Session-Level Data: Predicting Return Intent along the Browsing Journey

Despite the growing challenge of product returns in e-commerce, limited research explores real-time prediction of return intent. Existing studies typically rely on customer relationship management (CRM) data and require historical context, such as a user's return behavior the last n orders. However, many online shoppers lack this context, because they either browse as guests and can thus not be identified in real-time or are first-time visitors. This reduces the accuracy and coverage of existing return intent assessment methods, which would otherwise lay the groundwork for timely interventions. This paper explores the potential for return prediction based only on browsing behavior using Long Short-Term Memory (LSTM) models. We simulate real-world settings by training and testing on a rolling-window approach on logged browsing data. We find for our model to attain F1 = 0.68 and ROC AUC = 0.70 on average. Furthermore, our model demonstrates superior accuracy compared to methods relying on CRM data without historical user context. A subsequent analysis of the important features, based on the trained LSTM model, draws insights into which browsing behaviors may be early warning signals indicating return. For instance, having a high number of items in the shopping cart from a previous browsing session is strongly associated with high-value returns. Meanwhile, for desktop users, the use of filters appears to be a driving indicator for returns. In a hypothetical calculation, we illustrate the positive economic impact our approach enables to e-commerce companies. Our findings highlight the potential of utilizing browsing behavior for prediction of return intent, providing a foundation for developing real-time return intent assessment strategies.