How do you say it matters? A multimodal analytics framework for product return prediction in live streaming e-commerce

Abstract

As a new shopping paradigm, live streaming e-commerce has undergone rapid development in recent years. However, a higher return rate than that of traditional e-commerce not only affects the consumption experience of users but also is detrimental to the development of the entire industry. Previous studies on product returns have mainly focused on traditional e-commerce, with few studies considering signals in unstructured data formats. The main contribution of our research is the design of a multimodal analytics framework to predict product returns in live streaming e-commerce settings. Multimodal features (e.g., visual, textual, and acoustic) and their correlations extracted from different data sources (product descriptions and live streaming records) are considered in our prediction model. In addition, to better study the impact of anchor speech during live streaming, we propose an acoustic feature extraction model called “OpenTransformer,” which extracts acoustic features of anchor audio and outperforms baselines. We have conducted experiments based on a real-world dataset collected from Taobao live streaming. The experimental results demonstrate the effectiveness of using multimodal signals from products and anchors for product return prediction. The managerial implications of our research are that live streaming platforms and potential consumers can prejudge which sold products will have high return rates in a timely fashion, and based on these predictions, merchants and anchors can better design their product descriptions and speech behaviors. This study thus supports more effective development of live streaming e-commerce markets.