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Integrating Textual Information into Models of Choice and Scaled Response Data

Author

Listed:
  • Hyowon Kim

    (Weatherhead School of Management, Case Western Reserve University, Cleveland, Ohio 44106)

  • Greg M. Allenby

    (Fisher College of Business, The Ohio State University, Columbus, Ohio 43210)

Abstract

This paper proposes a new approach to modeling heterogeneity in choice data that can accommodate fixed-point ratings data and text. Respondent choices, survey responses, and narratives are combined to form latent archetypes that provide an integrated description of respondents in terms of the objects and drivers of their wants. We propose a measure of coherence to assess the value of integrating these data elements and demonstrate the value of integrating text data into an analysis of choice and scaled response data. A conjoint data set is used to illustrate the model where we find that the text data helps clarify the origin of demand.

Suggested Citation

  • Hyowon Kim & Greg M. Allenby, 2022. "Integrating Textual Information into Models of Choice and Scaled Response Data," Marketing Science, INFORMS, vol. 41(4), pages 815-830, July.
    • Handle: RePEc:inm:ormksc:v:41:y:2022:i:4:p:815-830
    DOI: 10.1287/mksc.2021.1337
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    References listed on IDEAS

    as
    1. Marc R. Dotson & Joachim Büschken & Greg M. Allenby, 2020. "Explaining Preference Heterogeneity with Mixed Membership Modeling," Marketing Science, INFORMS, vol. 39(2), pages 407-426, March.
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    Cited by:

    1. Tian, Yu-Xin & Zhang, Chuan, 2023. "An end-to-end deep learning model for solving data-driven newsvendor problem with accessibility to textual review data," International Journal of Production Economics, Elsevier, vol. 265(C).

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