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

Author

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  • Roelen-Blasberg, Tobias
  • Habel, Johannes
  • Klarmann, Martin

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.

Suggested Citation

  • Roelen-Blasberg, Tobias & Habel, Johannes & Klarmann, Martin, 2023. "Automated inference of product attributes and their importance from user-generated content: Can we replace traditional market research?," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 164-188.
    • Handle: RePEc:eee:ijrema:v:40:y:2023:i:1:p:164-188
    DOI: 10.1016/j.ijresmar.2022.04.004
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    References listed on IDEAS

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