期刊
MARKETING SCIENCE
卷 31, 期 3, 页码 521-543出版社
INFORMS
DOI: 10.1287/mksc.1120.0713
关键词
text mining; user-generated content; market structure; marketing research
类别
Web 2.0 provides gathering places for Internet users in blogs, forums, and chat rooms. These gathering places leave footprints in the form of colossal amounts of data regarding consumers' thoughts, beliefs, experiences, and even interactions. In this paper, we propose an approach for firms to explore online user-generated content and listen to what customers write about their and their competitors' products. Our objective is to convert the user-generated content to market structures and competitive landscape insights. The difficulty in obtaining such market-structure insights from online user-generated content is that consumers' postings are often not easy to syndicate. To address these issues, we employ a text-mining approach and combine it with semantic network analysis tools. We demonstrate this approach using two cases-sedan cars and diabetes drugs-generating market-structure perceptual maps and meaningful insights without interviewing a single consumer. We compare a market structure based on user-generated content data with a market structure derived from more traditional sales and survey-based data to establish validity and highlight meaningful differences.
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