Predicting online order satisfaction with lagged data using WGAIN-GP

E-commerce has grown a lot in recent years and so has the research performed in this field. In this paper we estimate order satisfaction by predicting outcomes of relevant variables at the moment an order is made, such that companies can act on this signal. In order to deal with data that is not known at the order date (i.e., lagged missing data), we propose an extension of an existing generative imputation method. The Generative Adversarial Imputation Network (GAIN) is suitable for data imputation on tabular datasets. A more stable method is the Wasserstein GAIN (WGAIN). In this paper, we propose to improve this method by adding the Gradient Penalty to WGAIN resulting in WGAIN-GP. We perform experiments on a large dataset from a Dutch online retailer. Using WGAIN-GP we obtain a better accuracy of 61% at the order date compared to 54% and 53% obtained by GAIN and WGAIN, respectively.