Which product description phrases affect sales forecasting? An explainable AI framework by integrating WaveNet neural network models with multiple regression

Abstract

The rapid rise of many e-commerce platforms for individual consumers has generated a large amount of text-based data, and thus researchers have begun to experiment with text mining techniques to extract information from the large amount of textual data to assist in sales forecasting. The existing literature focuses textual data on product reviews; however, consumer reviews are not something that companies can directly control, here we argue that textual product descriptions are also important determinants of consumer choice. We construct an artificial intelligence (AI) framework that combines text mining, WaveNet neural networks, multiple regression, and SHAP model to explain the impact of product descriptions on sales forecasting. Using data from nearly 200,000 sales records obtained from a cross-border e-commerce firm, an empirical study showed that the product description presented to customers can influence sales forecasting, and about 44% of the key phrases greatly affect sales forecasting results, the sales forecasting models that added key product description phrases had improved forecasting accuracy. This paper provides explainable results of sales forecasting, which can provide guidance for firms to design product descriptions with reference to the market demand reflected by these phrases, and adding these phrases to product descriptions can help win more customers.