Topic:Large-Scale Cross-Category Analysis of Consumer Review Content on Sales Conversion Leveraging Deep Learning
Speaker:Xiao Liu, Assistant Professor of Marketing, New York University Stern School of Business
Time:Wednesday, 6 June, 13:30-15:00
Location:Room 217, Guanghua Building 2
Consumers often rely on product reviews to make purchase decisions, but how consumers use review content in their decision making has remained a black box. In the past, extracting information from product reviews has been a labor intensive process that has restricted studies on this topic to single product categories or those limited to summary statistics such as volume, valence, and ratings. This paper uses deep learning natural language processing techniques to overcome the limitations of manual information extraction and shed light into the black box of how consumers use review content. With the help of a comprehensive dataset that tracks individual-level review reading, search, as well as purchase behaviors on an e-commerce portal, we extract six quality and price content dimensions from over 500,000 reviews, covering nearly 600 product categories. The scale, scope, and precision of such a study would have been impractical using human coders or classical machine learning models. We achieve two objectives. First, we describe consumers’ review content reading behaviors. We find that although consumers do not read review content all the time, they do rely on review content for products that are expensive or of uncertain quality. Second, we quantify the causal impact of content information of read reviews on sales. We use a regression discontinuity in time design and leverage the variation in the review content seen by consumers due to newly added reviews. To extract content information, we develop two deep learning models: a full deep learning model that predicts conversion directly and a partial deep learning model that identifies content dimensions. Across both models, we find that aesthetics and price content in the reviews significantly affect conversion across almost all product categories. Review content information has a higher impact on sales when the average rating is higher and the variance of ratings is lower. Consumers depend more on review content when the market is more competitive, immature, or when brand information is not easily accessible. A counterfactual simulation suggests that re-ordering reviews based on content can have the same effect as a 1.6% price cut for boosting conversion.
Xiao Liu joined New York University Stern School of Business as an Assistant Professor of Marketing in July 2015. Professor Liu’s research focuses on quantitative marketing and empirical industrial organization, with a particular interest in consumer financial service innovations and high-tech marketing. Her current methodological approach applies parallel computing techniques to data on a large scale and multimedia tools to unstructured data. Professor Liu is the recipient of the 2014 Marketing Science Institute (MSI) Alden G. Clayton Doctoral Dissertation Proposal Competition Award and the 2014 INFORMS Society for Marketing Science (ISMS) Doctoral Dissertation Proposal Competition Award. She received her B.S. in Finance from Tsinghua University and her Ph.D. in Marketing from Carnegie Mellon University Tepper School of Business.
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