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Handbook of Marketing Decision Models pp 23–53Cite as

Developments in Conjoint Analysis

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Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 121))

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Notes

  1. 1.

    I will not delve into simulation methods in this chapter; readers are referred to the article by Green et al. (2003). Likewise, I will not delve into the advances in the conduct of conjoint analysis using the web-based administration and the use of visual and sensory characteristics of stimuli, and configurators; readers are referred to the paper by Hauser and Rao (2003).

  2. 2.

    The differences between conjoint measurement (with its psychometric origins and axioms) and conjoint analysis (a more pragmatic methodology) are important from a theoretical perspective. But, I will not delve into them here. See Rao (1976) for a discussion of conjoint measurement.

  3. 3.

    This point was discussed at the Conference to honor Paul E. Green held at the University of Pennsylvania in May 2002.

  4. 4.

    For an introduction to the subject matter of conjoint analysis, see Orme (2006).

  5. 5.

    For exposition purposes, I am considering a ratings-based conjoint analysis where respondents provide preference ratings for a number of product profiles. Later in the chapter, I will describe choice-based conjoint methods as well. In a choice-based conjoint analysis, a respondent is presented several choice sets, each choice set consisting of a small number, four or five, profiles and is asked to make a choice among the alternatives for each choice set.

  6. 6.

    It will be useful to review some terms used in conjoint analysis. Attributes are (mainly) physical characteristics that describe a product; levels are the number of different values an attribute takes; profile is a combination of attributes, each attribute at a particular level, presented to a respondent for an evaluation (or stated preference); choice set is a pre-specified number of profiles presented to a respondent to make a pseudo-choice (stated choice).

  7. 7.

    Wittink and Cattin (1989) and Wittink et al. (1994) arrived at an estimate of over 1,760 commercial applications of conjoint analysis in US and Europe during the five year period, 1986–1991.

  8. 8.

    As conjoint studies are implemented in practice, various other forms have emerged; these include self-explicated methods, adaptive methods and so on. See Hauser and Rao (2003) for details.

  9. 9.

    “Resolution” describe the degree to which estimated main effects are confounded with estimated higher-order level interactions (2, 3, 4, or more) among the attributes; it is usually one more than the smallest order interaction that some main effect is confounded with. In a Resolution-III design, some main effects are confounded with some 2-level interactions.

  10. 10.

    For a discussion of formal choice models, see Corstjens and Gautchi (1983).

  11. 11.

    In this paper, the authors conducted a comprehensive field experiment in a Chinese restaurant during dinnertime using Chinese dinner specials as the context. The study compared hypothetical choice-conjoint method with incentive-aligned choice conjoint method and incentive-aligned contingent evaluation method. In the hypothetical choice conjoint method, the restaurant served the meal chosen by the subject in the holdout choice task and the cost was deducted from the compensation given to the subjects. In the incentive-aligned method, the Chinese dinner special for any subject was randomly chosen from the choices made in the main task of evaluating 12 choice sets at the posted price. This random lottery procedure is widely used in experimental economics and it minimizes the effect of reference point and wealth.

  12. 12.

    For example, the partworth function for price can sometimes be upward sloping contrary to expectations. This may be due to the information role of price versus its allocative role. One approach to correct this is discussed in Rao and Sattler (2003); this method calls for collecting two sets of preferences for profiles without and with a budget constraint.

  13. 13.

    An alternative way to estimate individual-level partworths is to specify heterogeneity using finite mixture (FM) models and to estimate mixture (or segment) level parameters and recover individual-level parameters using posterior analysis (DeSarbo et al. 1992). In comparison using simulated data in the context of ratings-based conjoint analysis, Andrews et al. (2002a and b) found that both the methods (HB and FM) are equally effective in recovering individual-level parameters and predicting ratings of holdout profiles. Further, HB methods perform well even when the individual partworths come from a mixture of distributions and FM methods yield good individual partworth estimates. Both methods are quite robust to underlying assumptions. Given the recent popularity of HB methods, I focus on them in this review chapter. See Rossi et al. (2005) for an exposition of Bayesian methods in marketing.

  14. 14.

    1 If the analyst wishes to incorporate no prior information, one sets the initial βbar and A-matrix equal to zero. In that case, the HB estimates will be asymptotically the same as the OLS results. In a similar manner, constraints on signs or order of partworths (therefore the β-parameters) are incorporated directly in the posterior distribution of the β-vector.

  15. 15.

    See Toubia et al. (2004) for a discussion of this adaptive approach for choice-based conjoint analysis.

  16. 16.

    A tutorial on support vector machines is found in Burgess (1998).

  17. 17.

    Johnson, R.M. (1987) and Green et al. (1991).

  18. 18.

    While the authors developed their theory using continuous changes in the attributes, discrete changes are used here for the purposes of exposition. See their paper for complete theoretical analysis.

  19. 19.

    The problem of separating the informative and allocative roles of price is not trivial. See Rao and Sattler (2003) for an approach and empirical results.

  20. 20.

    The Bass Diffusion Model (Bass 1969) is not particularly useful for this purpose because it is based on sales data obtained for a first few periods after the launch of the new product.

  21. 21.

    Eric Bradlow (2005) presents a wish list for conjoint analysis such as within task learning/variation, embedded prices, massive number of attributes, non-compensatory decision rules, integration of conjoint data with other sources, experimental design (from education literature), getting the right attributes and levels, mix and match, and product-bundle conjoint. There is a considerable overlap between this list and mine described below.

  22. 22.

    The adaptive conjoint analysis (ACA) approach involves presenting two profiles that are as nearly equal as possible in estimated utility measured on a metric scale and developing new pairs of profiles sequentially as a respondent provides response to previous questions. There has been considerable amount of research on this approach. In a recent paper, Hauser and Toubia (2005) found that the result of the metric utility balance used in ACA leads to partworth estimates to be biased due to endogeneity. The author also found that these biases are of the order of response errors and suggest alternatives to metric utility balance to deal with this issue. See also, Liu et al. (2007) who suggest using the likelihood principle in estimation to deal with the endogeneity bias in general.

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Authors and Affiliations

  1. Cornell University, Ithaca, NY, USA

    Vithala R. Rao

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  1. Erasmus University, Rotterdam, The Netherlands

    Berend Wierenga (Prof) (Prof)

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Rao, V.R. (2008). Developments in Conjoint Analysis. In: Wierenga, B. (eds) Handbook of Marketing Decision Models. International Series in Operations Research & Management Science, vol 121. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-78213-3_2

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