Predicting Sales from the Language of Product Descriptions

What can a business say to attract customers? E-commerce vendors frequently sell the same items but use different marketing strategies to present their goods. Understanding consumer responses to this heterogeneous landscape of information is important both as business intelligence and, more broadly, a window into consumer attitudes. When studying consumer behavior, the existing literature is primarily concerned with product reviews. In this paper we posit that textual product descriptions are also important determinants of consumer choice. We mine 90,000+ product descriptions on the Japanese e-commerce marketplace Rakuten and identify actionable writing styles and word usages that are highly predictive of consumer purchasing behavior. In the process, we observe the inadequacies of traditional feature extraction algorithms, namely their inability to control for the implicit effects of confounds like brand loyalty and pricing strategies. To circumvent this problem, we propose a novel neural network architecture that leverages an adversarial objective to control for confounding factors, and attentional scores over its input to automatically elicit textual features as a domain-specific lexicon. We show that these textual features can predict the sales of each product, and investigate the narratives highlighted by these words. Our results suggest that appeals to authority, polite language, and mentions of informative and seasonal language win over the most customers.