Automated inference of product attributes and their importance from user-generated content: Can we replace traditional market research?

Tobias Roelen-Blasberg, Johannes Habel and Martin Klarmann

International Journal of Research in Marketing, 2023, vol. 40, issue 1, 164-188

Abstract: User-generated content, particularly online product reviews by customers, provide marketers with rich data of customer evaluations of product attributes. This study proposes, benchmarks, and validates a new approach for inferring attribute-level evaluations from user-generated content. Moreover, little is known about whether and when insights from product reviews gained in such a way are consistent with traditional research methods, such as conjoint analysis and satisfaction driver analysis. To provide first insights into this question, the authors apply their approach to a dataset with almost one million product reviews from 52 product categories and run conjoint and satisfaction driver analyses for these categories. Results indicate that the consistency between methods largely varies across product categories. Initial exploratory analyses suggest that consistency might be higher for categories characterized by low experience qualities, high hedonic value, and high customer willingness to post online reviews—but further work will be necessary to validate these findings.

Keywords: User-generated content; Marketing research automation; Natural language processing; Conjoint analysis; Satisfaction driver analysis; Involvement (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167811622000246
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ijrema:v:40:y:2023:i:1:p:164-188

DOI: 10.1016/j.ijresmar.2022.04.004

Access Statistics for this article

International Journal of Research in Marketing is currently edited by Roland Rust

More articles in International Journal of Research in Marketing from Elsevier
Bibliographic data for series maintained by Catherine Liu ().