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- See also: Conjoint analysis (in marketing), Conjoint analysis (in healthcare), IDDEA, Rule Developing Experimentation, Value based pricing.
Conjoint analysis, also called multi-attribute compositional models or stated preference analysis, is a statistical technique that originated in mathematical psychology. It is used in surveys developed in applied sciences, often on behalf of marketing, product management, and operations research. It is not to be confused with the theory of conjoint measurement.
Conjoint analysis is a particular application of regression analysis. There is no precise statistical definition of it. Usually two or three of the following properties are applicable:
- data are collected among multiple individuals (respondents) whereas there are multiple data points for each individual, which makes it a layered model
- the dependent variable reflects a choice or trade-off situation
- the independent variables are categorical, thus coded as binary numbers (0,1)
Conjoint analysis requires research participants to make a series of trade-offs. Analysis of these trade-offs will reveal the relative importance of component attributes. To improve the predictive ability of this analysis, research participants should be grouped into similar segments based on objectives, values and/or other factors.
The exercise can be administered to survey respondents in a number of different ways. Traditionally it is administered as a ranking exercise and sometimes as a rating exercise (where the respondent awards each trade-off scenario a score indicating appeal).
In more recent years it has become common practice to present the trade-offs as a choice exercise (where the respondent simply chooses the most preferred alternative from a selection of competing alternatives - particularly common when simulating consumer choices) or as a constant sum allocation exercise (particularly common in pharmaceutical market research, where physicians indicate likely shares of prescribing, and each alternative in the trade-off is the description of a real or hypothetical therapy).
Analysis is traditionally carried out with some form of multiple regression, but more recently the use of hierarchical Bayesian analysis has become widespread, enabling fairly robust statistical models of individual respondent decision behaviour to be developed.
When there are many attributes, experiments with Conjoint Analysis include problems of information overload that affect the validity of such experiments. The impact of these problems can be avoided or reduced by using Hierarchical Information Integration.
Practical applications of conjoint analysis
One practical application of conjoint analysis in business analysis is given by the following example: A real estate developer is interested in building a high rise apartment complex near an urban Ivy League university. To ensure the success of the project, a market research firm is hired to conduct focus groups with current students. Students are segmented by academic year (freshman, upper classmen, graduate studies) and amount of financial aid received. Study participants are given a series of index cards. Each card has 6 attributes to describe the potential building project (proximity to campus, cost, telecommunication packages, laundry options, floor plans, and security features offered). The estimated cost to construct the building described on each card is equivalent. Participants are asked to order the cards from least to most appealing. This forced ranking exercise will indirectly reveal the participants' priorities and preferences. Multi-variate regression analysis may be used to determine the strength of preferences across target market segments.
Federal courts in the United States have allowed expert witnesses to use conjoint analysis to support their opinions on the damages that an infringer of a patent should pay to compensate the patent holder for violating its rights. Nonetheless, legal scholars have noted that the Federal Circuit's jurisprudence on the use of conjoint analysis in patent-damages calculations remains in a formative stage.
Available software tools for conjoint analysis
|Product||Developer||Initial public release||Headquarters||Delivery method||Cost||License|
|1000Minds||1000Minds||2002||New Zealand||SaaS||Free - POA||Proprietary|||
|Survey Analytics||QuestionPro||2002||San Francisco, United States||SaaS||US$1,000 per month||Proprietary|
|Sawtooth Studio||Sawtooth||2005||Orem, United States||Desktop software||From US$$11,500||Proprietary|
|support.CEs package (R)||Hideo Aizaki||2015||N/A||Package for the R programming language||Free||Open-source software|
- Ramirez, Jose Manuel (2009). "Measuring: from Conjoint Analysis to Integrated Conjoint Experiments". Journal of Quantitative Methods for Economics and Business Administration. 9: 28–43. ISSN 1886-516X.
- Cornell University v. Hewlett-Packard Co., 609 F. Supp. 2d 279 (N.D.N.Y. 2009); Sentius Int'l, LLC v. Microsoft Corp., No. 5:13-cv-00825, 2015 WL 331939 (N.D. Cal. Jan. 23, 2015).
- J. Gregory Sidak & Jeremy O. Skog, Using Conjoint Analysis to Apportion Patent Damages, (Criterion Economics Working Paper, Jan. 29, 2016), https://www.criterioneconomics.com/using-conjoint-analysis-to-apportion-patent-damages.html.
- Smith, K. F.; Fennessy, P. F. (2011). "The use of conjoint analysis to determine the relative importance of specific traits as selection criteria for the improvement of perennial pasture species in Australia". Crop and Pasture Science. 62 (4): 355–65. doi:10.1071/CP10320.
- Hideo Aizaki (2015). "support.CEs: Basic Functions for Supporting an Implementation of Choice Experiments".