The Effect of Word of Mouth on Sales: New Answers from the Consumer Journey Data with Deep Learning

Online Word-of-Mouth has great impact on product sales. Although aggregate data suggests that customers read review text rather than relying only on summary statistics, little is known about consumers’ review reading behavior and its impact on conversion at the granular level. To fill this research gap, we analyze a comprehensive dataset that tracks individual-level search, review reading, as well as purchase behaviors and achieve two objectives. First, we describe consumers’ review reading behaviors. In contrast to what has been found with aggregate data, individual level consumer journey data shows that around 70% of the time, consumers do not read reviews in their online journeys; they are less likely to read reviews for products that are inexpensive and have many reviews. Second, we quantify the causal impact of quantity and content information of reviews read on sales. The identification relies on the variation in the reviews seen by consumers due to newly added reviews. To extract content information, we apply Deep Learning natural language processing models and identify six dimensions of content in the reviews. We find that aesthetics and price content in the reviews significantly affect conversion. Counterfactual simulation suggests that re-ordering review content can have the same effect as a 1.6% price cut for boosting conversion.