Conjoint analysis (in marketing)

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See also: Conjoint analysis, Conjoint analysis (in healthcare)

Conjoint analysis, also called multiattribute compositional models, is a statistical technique that originated in mathematical psychology and was developed by marketing professor Paul Green at the Wharton School of the University of Pennsylvania. Today it is used in many of the social sciences and applied sciences including marketing, product management, and operations research. The objective of conjoint analysis is to determine what combination of a limited number of attributes is most preferred by respondents. It is used frequently in testing customer acceptance of new product designs and assessing the appeal of advertisements. It has been used in product positioning, but there are some problems with this application of the technique(see disadvantages). Recently, new mathematical approaches (e.g. evolutionary algorithms) have been developed to study complex consumer behavior (for example, Affinnova's Interactive Discovery & Design by Evolutionary Algorithms (IDDEA) technology). Based on those factors strategic company decisions can be made ensuring the full benefit of conjoint analysis.

[edit] Information collection

Respondents are shown a set of products, prototypes, mock-ups or pictures. Each example is similar enough that consumers will see them as close substitutes, but dissimilar enough that respondents can clearly determine a preference. Each example is composed of a unique combination of product features. The data may consist of individual ratings, rank-orders, or preferences among alternative combinations. The latter is referred to as "choice based conjoint" or "discrete choice analysis."

[edit] Analysis

Any number of algorithms may be used to estimate utility functions. The original methods were monotonic analysis of variance or linear programming techniques, but these are largely obsolete in contemporary marketing research practice. Far more popular are Hierarchical Bayesian procedures that operate on choice data. These utility functions indicate the perceived value of the feature and how sensitive consumer perceptions and preferences are to changes in product features.

[edit] Advantages

  • able to use physical objects
  • measures preferences at the individual level
  • estimates psychological tradeoffs that consumers make when evaluating several attributes together

[edit] Disadvantages

  • only a limited set of features can be used because the number of combinations increases very quickly as more features are added.
  • information gathering stage is complex
  • difficult to use for product positioning research because there is no procedure for converting perceptions about actual features to perceptions about a reduced set of underlying features
  • respondents are unable to articulate attitudes toward new categories


[edit] See also

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[edit] External sources

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