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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.
    2. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    3. Sandeep R. Chandukala & Yancy D. Edwards & Greg M. Allenby, 2011. "Identifying Unmet Demand," Marketing Science, INFORMS, vol. 30(1), pages 61-73, 01-02.
    4. Bruno J.D. Jacobs & Bas Donkers & Dennis Fok, 2016. "Model-Based Purchase Predictions for Large Assortments," Marketing Science, INFORMS, vol. 35(3), pages 389-404, May.
    5. Peter E. Rossi, 2014. "Bayesian Non- and Semi-parametric Methods and Applications," Economics Books, Princeton University Press, edition 1, number 10259.
    6. Zvi Gilula & Robert McCulloch, 2013. "Multi level categorical data fusion using partially fused data," Quantitative Marketing and Economics (QME), Springer, vol. 11(3), pages 353-377, September.
    7. Dinesh Puranam & Vishal Narayan & Vrinda Kadiyali, 2017. "The Effect of Calorie Posting Regulation on Consumer Opinion: A Flexible Latent Dirichlet Allocation Model with Informative Priors," Marketing Science, INFORMS, vol. 36(5), pages 726-746, September.
    8. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    9. Jia Liu & Olivier Toubia, 2018. "A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries," Marketing Science, INFORMS, vol. 37(6), pages 930-952, November.
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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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