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Cited by 460

The DOI matches its Crossref record.

Automated Marketing Research Using Online Customer Reviews

Open access
Lee, Thomas Y., Bradlow, Eric T.·2011·Journal of Marketing Research
DOI 10.1509/jmkr.48.5.881
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Abstract

Market structure analysis is a basic pillar of marketing research. Classic challenges in marketing such as pricing, campaign management, brand positioning, and new product development are rooted in an analysis of product substitutes and complements inferred from market structure. In this article, the authors present a method to support the analysis and visualization of market structure by automatically eliciting product attributes and brand's relative positions from online customer reviews. First, the method uncovers attributes and attribute dimensions using the “voice of the consumer,” as reflected in customer reviews, rather than that of manufacturers. Second, the approach runs automatically. Third, the process supports rather than supplants managerial judgment by reinforcing or augmenting attributes and dimensions found through traditional surveys and focus groups. The authors test the approach on six years of customer reviews for digital cameras during a period of rapid market evolution. They analyze and visualize results in several ways, including comparisons with expert buying guides, a laboratory survey, and correspondence analysis of automatically discovered product attributes. The authors evaluate managerial insights drawn from the analysis with respect to proprietary market research reports from the same period analyzing digital imaging products.

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Metadata

Authors
Lee, T. Y., & Bradlow, E. T.
Venue
Journal of Marketing Research
Publisher
SAGE Publications
Published
2011-10
Volume
48
Issue
5
Pages
881-894
ISSN
  • 0022-2437 (ISSN-L)
  • 1547-7193
DOI
10.1509/jmkr.48.5.881
OpenAlex
W3125781831
Cited by
460
References
0
FWCI
42.86
Type
journal article
Language
en
Open access
green

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.