A certainty-based approach for dynamic hierarchical classification of product order satisfaction

Abstract

E-commerce companies collaborate with retailers to sell products via their platforms, making it increasingly important to preserve platform quality. In this paper, we contribute by introducing a novel method to predict the quality of product orders shortly after they are placed. By doing so, platforms can act fast to resolve bad quality orders and potentially prevent them from happening. This introduces a trade-off between accuracy and timeliness, as the sooner we predict, the less we know about the status of a product order and, hence, the lower the reliability. To deal with this, we introduce the Hierarchical Classification Over Time (HCOT) algorithm, which dynamically classifies product orders using top-down, non-mandatory leaf-node prediction. We enforce a blocking approach by proposing the Certainty-based Automated Thresholds (CAT) algorithm, which automatically computes optimal thresholds at each node. The resulting CAT-HCOT algorithm has the ability to provide both accurate and timely predictions by classifying a product order's quality on a daily basis if the classification reaches a predefined certainty. CAT-HCOT obtains a predictive accuracy of 94%. Furthermore, CAT-HCOT classifies 40% of product orders on the order date itself, 80% within five days after the order date, and 100% of product orders after 10 days.