Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning
By Xiao Liu, Dokyun Lee, Kannan Srinivasan. 2019
154 citations recorded by the source corpus.
How consumers use review content has remained opaque due to the unstructured nature of text and the lack of review-reading behavior data. The authors overcome this challenge by applying deep learning–based natural language processing on data that tracks individual-level review reading, searching, and purchasing behaviors on an e-commerce site to investigate how consumers use review content. They extract quality and price content from 500,000 reviews of 600 product categories and achieve two objectives. First, the authors describe consumers’ review-content-reading behaviors. Although consumers do not read review content all the time, they do rely on it for products that are expensive or of uncertain quality. Second, the authors quantify the causal impact of read-review content on sales by using supervised deep learning to tag six theory-driven content dimensions and applying a regression discontinuity in time design. They find that aesthetics and price content significantly increase conversion across almost all product categories. Review content has a higher impact on sales when the average rating is higher, ratings variance is lower, the market is more competitive or immature, or brand information is not accessible. A counterfactual simulation suggests that reordering reviews based on content can have the same effect as a 1.6% price cut for boosting conversion.
Subfields: Artificial Intelligence, Marketing, Sociology and Political Science
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Citations in the corpus
- Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands (2018, background): “For a recent example, see Liu et al. (2017). In such cases, inference on λ can proceed directly, as long as care is taken to interpret the results.”
- Machine learning and AI in marketing – Connecting computing power to human insights (2020, unknown): “…Ma, & Jamal, 2016), ensemble trees (Guo, Sriram, & Manchanda, 2018; Yoganarasimhan, 2018), deep neural networks (Liu, Dzyabura, & Mizik, 2018; Liu, Lee, & Srinivasan, 2018), network embedding (Ma, Sun, & Zhang, 2019), among others, have been used in academic marketing research for prediction…”