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Benefit-Based Conjoint Analysis

Author

Listed:
  • Dong Soo Kim

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

  • Roger A. Bailey

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

  • Nino Hardt

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

  • Greg M. Allenby

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

Abstract

Firms develop products by manipulating the attributes of offerings, and consumers derive utility from the benefits that the attributes afford. While the field of marketing has long been aware of the distinction between attributes and benefits, it has not developed methods for understanding how attributes and benefits are related. This paper develops a benefit-based model for conjoint analysis that assumes consumers satiate on attributes that are perceived to provide the same benefit. A latent-variable model is proposed that estimates the map between attributes and benefits, and is applied to data from two conjoint studies involving a durable product and a household consumable. The model is shown to fit the data better, provide improved predictions, and lead to different product design implications than the standard conjoint model.

Suggested Citation

  • Dong Soo Kim & Roger A. Bailey & Nino Hardt & Greg M. Allenby, 2017. "Benefit-Based Conjoint Analysis," Marketing Science, INFORMS, vol. 36(1), pages 54-69, January.
    • Handle: RePEc:inm:ormksc:v:36:y:2017:i:1:p:54-69
    DOI: 10.1287/mksc.2016.1003
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    References listed on IDEAS

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    Cited by:

    1. Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
    2. Hyowon Kim & Dong Soo Kim & Greg M. Allenby, 2020. "Benefit Formation and Enhancement," Quantitative Marketing and Economics (QME), Springer, vol. 18(4), pages 419-468, December.
    3. Joffre Swait & Cristiano Franceschinis & Mara Thiene, 2020. "Antecedent Volition and Spatial Effects: Can Multiple Goal Pursuit Mitigate Distance Decay?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(2), pages 243-270, February.
    4. Marley, A.A.J. & Swait, J., 2017. "Goal-based models for discrete choice analysis," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 72-88.
    5. Anocha Aribarg & Thomas Otter & Daniel Zantedeschi & Greg M. Allenby & Taylor Bentley & David J. Curry & Marc Dotson & Ty Henderson & Elisabeth Honka & Rajeev Kohli & Kamel Jedidi & Stephan Seiler & X, 2018. "Advancing Non-compensatory Choice Models in Marketing," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 82-92, March.
    6. Hasmat Malik & Asyraf Afthanorhan & Noor Aina Amirah & Nuzhat Fatema, 2021. "Machine Learning Approach for Targeting and Recommending a Product for Project Management," Mathematics, MDPI, vol. 9(16), pages 1-29, August.

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