The Effect of Word of Mouth on Sales: New Answers from the Comprehensive Consumer Journey Data

Xiao Liu (), Dokyun Lee () and Kannan Srinivasan ()
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Xiao Liu: New York University, 40 W 4th Street, New York, NY 10012
Dokyun Lee: Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213
Kannan Srinivasan: Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213

No 16-09, Working Papers from NET Institute

Abstract: 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.

Keywords: Consumer Purchase Journey; Product Reviews; Review Content; Deep Learning (search for similar items in EconPapers)
JEL-codes: C45 D12 M31 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2016-09
New Economics Papers: this item is included in nep-mkt
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