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Optimal Product Design by Sequential Experiments in High Dimensions

Author

Listed:
  • Mingyu Joo

    (School of Business, University of California, Riverside, Riverside, California 92507)

  • Michael L. Thompson

    (The Procter & Gamble Company, Cincinnati, Ohio 45202)

  • Greg M. Allenby6

    (Department of Marketing and Logistics, Fisher College of Business, Ohio State University, Columbus, Ohio 43210)

Abstract

The identification of optimal product and package designs is challenged when attributes and their levels interact. Firms recognize this by testing trial products and designs prior to launch, during which the effects of interactions are revealed. A difficulty in conducting analysis for product design is dealing with the high dimensionality of the design space and the selection of promising product configurations for testing. We propose an experimental criterion for efficiently testing product profiles with high demand potential in sequential experiments. The criterion is based on the expected improvement in market share of a design beyond the current best alternative. We also incorporate a stochastic search variable selection method to selectively estimate relevant interactions among the attributes. A validation experiment confirms that our proposed method leads to improved design concepts in a high-dimensional space compared with alternative methods.

Suggested Citation

  • Mingyu Joo & Michael L. Thompson & Greg M. Allenby6, 2019. "Optimal Product Design by Sequential Experiments in High Dimensions," Management Science, INFORMS, vol. 65(7), pages 3235-3254, July.
    • Handle: RePEc:inm:ormnsc:v:65:y:2019:i:7:p:3235-3254
    DOI: 10.1287/mnsc.2018.3088
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    References listed on IDEAS

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    Cited by:

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    2. Gupta, Shaphali & Leszkiewicz, Agata & Kumar, V. & Bijmolt, Tammo & Potapov, Dmitriy, 2020. "Digital Analytics: Modeling for Insights and New Methods," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 26-43.

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