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—On Managerially Efficient Experimental Designs

Author

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  • Olivier Toubia

    (Columbia Business School, Columbia University, 522 Uris Hall, 3022 Broadway, New York, New York 10027)

  • John R. Hauser

    (Sloan School of Management, Massachusetts Institute of Technology, E40-179, 1 Amherst Street, Cambridge, Massachusetts 02142)

Abstract

In most marketing experiments, managerial decisions are not based directly on the estimates of the parameters but rather on functions of these estimates. For example, many managerial decisions are driven by whether or not a feature is valued more than the price the consumer will be asked to pay. In other cases, some managerial decisions are weighed more heavily than others. The standard measures used to evaluate experimental designs (e.g., -efficiency or -efficiency) do not accommodate these phenomena. We propose alternative “managerial efficiency” criteria (-errors) that are relatively easy to implement. We explore their properties, suggest practical algorithms to decrease errors, and provide illustrative examples. Realistic examples suggest improvements of as much as 30% in managerial efficiency. We close by considering approximations for nonlinear criteria and extensions to choice-based experiments.

Suggested Citation

  • Olivier Toubia & John R. Hauser, 2007. "—On Managerially Efficient Experimental Designs," Marketing Science, INFORMS, vol. 26(6), pages 851-858, 11-12.
    • Handle: RePEc:inm:ormksc:v:26:y:2007:i:6:p:851-858
    DOI: 10.1287/mksc.1060.0244
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    References listed on IDEAS

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    1. John R. Hauser & Olivier Toubia, 2005. "The Impact of Utility Balance and Endogeneity in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(3), pages 498-507, August.
    2. Olivier Toubia & Duncan I. Simester & John R. Hauser & Ely Dahan, 2003. "Fast Polyhedral Adaptive Conjoint Estimation," Marketing Science, INFORMS, vol. 22(3), pages 273-303.
    3. Hauser, John R & Urban, Glen L, 1986. "The Value Priority Hypotheses for Consumer Budget Plans," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 12(4), pages 446-462, March.
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    5. John C. Liechty & Duncan K. H. Fong & Wayne S. DeSarbo, 2005. "Dynamic Models Incorporating Individual Heterogeneity: Utility Evolution in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(2), pages 285-293, November.
    6. Arora, Neeraj & Huber, Joel, 2001. "Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(2), pages 273-283, September.
    7. Theodoros Evgeniou & Constantinos Boussios & Giorgos Zacharia, 2005. "Generalized Robust Conjoint Estimation," Marketing Science, INFORMS, vol. 24(3), pages 415-429, May.
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    Cited by:

    1. Gensler, Sonja & Hinz, Oliver & Skiera, Bernd & Theysohn, Sven, 2012. "Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs," European Journal of Operational Research, Elsevier, vol. 219(2), pages 368-378.
    2. Daria Dzyabura & John R. Hauser, 2011. "Active Machine Learning for Consideration Heuristics," Marketing Science, INFORMS, vol. 30(5), pages 801-819, September.
    3. Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.
    4. Riccardo Scarpa & John M. Rose, 2008. "Design efficiency for non-market valuation with choice modelling: how to measure it, what to report and why ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(3), pages 253-282, September.
    5. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    6. Rossella Berni & Nedka Dechkova Nikiforova & Patrizia Pinelli, 2024. "An Optimal Design through a Compound Criterion for Integrating Extra Preference Information in a Choice Experiment: A Case Study on Moka Ground Coffee," Stats, MDPI, vol. 7(2), pages 1-16, June.
    7. Qing Liu & Neeraj Arora, 2011. "Efficient Choice Designs for a Consider-Then-Choose Model," Marketing Science, INFORMS, vol. 30(2), pages 321-338, 03-04.
    8. Dominique Olié Lauga & Elie Ofek, 2009. "Market Research and Innovation Strategy in a Duopoly," Marketing Science, INFORMS, vol. 28(2), pages 373-396, 03-04.
    9. Qing Liu & Yihui (Elina) Tang, 2015. "Construction of Heterogeneous Conjoint Choice Designs: A New Approach," Marketing Science, INFORMS, vol. 34(3), pages 346-366, May.

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