General Conjoint Analysis papers

Conjoint analysis has become the most popular and useful way to measure respondents' preferences for simple to complex offerings and predict market choices. This article is taken from Chapter 1 of Getting Started with Conjoint Analysis, a book written by Sawtooth Software president Bryan Orme. It provides a non-technical managerial overview of the technique. It describes the history of the method, the various flavors, its practical uses, and recent developments that have made conjoint analysis even more powerful.

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This paper (a chapter from the book Getting Started with Conjoint Analysis) illustrates how conjoint can be used to provide managers strategic marketing information that is intuitive and actionable. It explains how Choice-Based Conjoint (CBC) can be used to measure brand equity and determine brand sensitivity. The article focuses on how to get managers to "buy in" to conjoint, and some pitfalls to avoid when presenting conjoint data.

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Sawtooth Software offers different conjoint analysis packages, including choice-based (discrete choice) methologies (CBC, ACBC), as well as the older ratings based approaches (ACA, CVA). This paper discusses the main differences between these approaches and offers suggestions regarding applicability to different research situations.

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Covers the essentials for interpreting conjoint analysis data, including part worths, importances, shares of preference and "counting" analysis. The framework for interpreting results is developed from formal definitions of scaled data: Nominal, Ordinal, Interval, and Ratio. Common errors in interpreting conjoint analysis are highlighted.

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Conjoint analysis has been a great success story for the marketing research industry. This paper outlines its development from the late 1960s through the early 2000s. The earliest conjoint analysis approaches were based on either full-profile card sort, or Johnson's tradeoff matrix. Later, Adaptive Conjoint Analysis and discrete choice (CBC) applications dominated. The use of CBC accelerated in the 1990s due largely to the introduction of CBC software in 1993 and the development of HB methods in the mid to late 1990s. The author states: "Much of the recent research and development in conjoint analysis has focused on doing more with less: stretching the research dollar using IT-based initiatives, reducing the number of questions required of any one respondent with more efficient design plans and HB ("data borrowing") estimation, and reducing the complexity of conjoint questions using partial-profile designs." Since 2000, there has been increased interest in the use of optimization routines, greater realism (including "virtual shopping" environments) and real-time adaptive CBC routines.

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This article conveys the basics of conjoint utility estimation using a common software tool: Microsoft's Excel. It covers dummy-coding and experimental design issues for full-profile conjoint analysis (single concept). When using Excel to perform the steps described in this article, you'll need Excel's Analysis Toolpak add-in with Regression Analysis.

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This article provides an excellent case study and tutorial regarding how to bring sophisticated methods like conjoint analysis to an organization.

The author (Goodwin) discusses how conjoint has been adopted at Lifetime Products, Inc., including success stories and suggestions for obtaining buy-in from management. He outlines his history of progression in conjoint methods, from card-sort conjoint, to CBC, to part-profile CBC, and finally to adaptive CBC (ACBC).

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It is advisable to include holdout choice tasks in conjoint interviews even though they may not appear to be needed for the main purpose of the study. This paper, originally published in Sawtooth Solutions, Spring 1997, and updated in 2010, lists the benefits of holdout choices and provides general guidance on how to construct them.

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The Internet is fast becoming a viable way to conduct market research surveys for many situations. This article reports on a Full-Profile conjoint experiment conducted over the Internet. Single-Concept and Pairwise approaches are compared in terms of conjoint importances, utilities and reliability for predicting holdout choice concepts. Very little difference is found in any of the measures, suggesting that both approaches work well in practice for computerized Full-Profile conjoint.

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Sample size considerations for conjoint analysis are often quite different from those for traditional market research surveys. This paper covers such topics as sampling error versus measurement error, confidence intervals, sampling for small populations, and how the choice of market simulation method affects the precision of results. The differences between ACA, traditional conjoint (CVA), and CBC are discussed with respect to sample size decisions. Finally, the paper reviews sample sizes commonly used by conjoint practitioners, and provides some rules-of-thumb and general recommendations.

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Because conjoint utilities are often difficult for non-researchers to understand, researchers sometimes try to convert those to monetary equivalents. This practice is usually a poor use of conjoint analysis, and often misleading. The typical approaches ignore competitive factors and base the analysis on the average respondent. The author suggests that the worth of adding incremental features to products can be better determined through competitive market simulation scenarios.

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This paper reports on an industry survey of 159 commercial research agencies in Europe regarding their use of conjoint analysis during the period July 1986 through June 1991. ACA is shown to be the most popular conjoint software in Europe. The authors (Wittink, Vriens, and Burhenne) provide an excellent discussion of general conjoint issues, and provide insight regarding the industry's adoption of this popular technique.

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This 1987 article by Norein Klein is often cited in the literature. Abstract:

Some adaptive conjoint analysis methods reduce the attribute space by allowing the respondent to state which attribute levels are completely unacceptable. Utilities are not estimated for these levels, and it is assumed in later choice simulations that respondents would never choose alternatives that possess these levels. This procedure allows a more efficient estimation of conjoint utilities, but its value depends on whether the judgments of acceptability are consistent with respondents' behavior in later choices. In the study reported here, 15 percent of all choices contained an attribute level previously designated unacceptable, indicating some inconsistency between the judgments and choices. However, the overall accuracy of choice predictions was unaffected by the initial elimination of alternatives with unacceptable levels. The practical implications of these findings, and the relationship of judgments of acceptability to decision strategies are discussed.

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Despite over 20 years of conjoint research and hundreds of methodological papers, very little has been published in the way of formal tests of whether conjoint really works in predicting real-world purchase decisions. The authors (Orme, Alpert and Christensen) argue that the holdouts typically used in conjoint validation studies are not very realistic, especially for high-involvement categories. The authors present results from a small pilot study. Respondents completed ACA, full-profile card sort, regular holdout choices and a "Super Holdout Task." The Super Holdout Task took 10 minutes and was an attempt to create a more realistic holdout. The authors compare results from the two types of holdout tasks and detect no significant differences. The authors find no significant difference in holdout hit rates for the Super Holdout Task for ACA and full-profile, though full-profile maintains a small edge. Full profile and CBC importances are shown to be steeper than ACA importances. The authors call for more realistic holdout tasks and urge those who have the resources to publish real-world validations of conjoint analysis. Originally presented at the 1997 Sawtooth Software Conference.

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Joel Huber, Duke University, points out that respondents adopt different strategies for answering different types of conjoint questions. Researchers should understand these simplification strategies and match the right method to the context of actual marketplace decisions. Huber summarizes the strengths of the methods as follows:

* Self-explicated models are best in the case of many attributes, where expectations about levels and associations among attributes are stable. They work better in predicting decisions about independent alternatives than for competitive contexts.
* Paired comparisons are most appropriate for modeling markets in which alternatives are explicitly compared with one another, approximating a deeper search of a broad range of attributes, and where within-attribute value steps are smooth and approximately linear.
* Full-Profile works best when it is desirable to abstract from short run beliefs, when market choices reflect simplification toward the most important variables, and the decision focus is more within alternative rather than explicitly made using side-by-side comparisons between options.
* Choice is most appropriate for simulating immediate response to competitive offerings, when decisions are made based on relatively few attributes with substantial aversion to the worst levels of each attribute, and when consumers make decisions based on comparative differences among attributes.

In contrast to what is becoming popular agreement regarding the superiority of choices, Huber cautions that choices may not always work better than more traditional approaches. (Originally published in our 1997 Sawtooth Software Proceedings.)

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Three ways to treat overall price in conjoint analysis experiments are discussed: traditional approach, conditional price, and continuous/summed price.

The traditional pricing method treats price as a separate attribute with a fixed set of price points that apply to all products. Prices are varied independently of the features. The problem with treating price in this traditional manner is that products with the best features are sometimes shown at the lowest prices (and products with the worst features are sometimes shown at the highest prices). This can lead to dominated choices and lack of realism.

With conditional pricing, incremental amounts are added to the price for premium brands or features, so enhanced products are generally shown at higher prices. One uses a look-up table to determine actual prices shown in the questionnaire. Currently, Sawtooth Software's CBC software allows price to be conditional on up to 3 other attributes.

Continuous/Summed pricing generalizes the idea of conditional prices (beyond the software limitations of just three attributes). Also, it estimates the effect of overall price as a linear coefficient, rather than as a part-worth utility function. After summing the prices across the feature components, price is varied by an additional random component specified by the researcher. One of the challenges of continuous pricing is that the price variable is moderately to strongly correlated with other attributes, depending on the design. A simulation study investigating the stability of the price coefficient within summed pricing is shown. The continuous pricing option is currently available as a standard option in Sawtooth Software's tools, though a power user could implement it with additional work. Continuous pricing will be a part of the forthcoming Adaptive CBC software.

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Joel Huber of Duke University provides an insightful discussion on the history and theoretical underpinnings of conjoint analysis. He traces its development from its roots in psychometrics to its enthusiastic adoption by the market research community. The original paper is quite dated (originally published in our 1987 Sawtooth Software Conference Proceedings), but Joel Huber and Bryan Orme have added additional footnoted commentary from a 2004 perspective. This paper continues to be an excellent resource for today's conjoint practitioners.

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