Main Page | See live article | Alphabetical index

Conjoint analysis, also called multiattribute compositional models, is a statistical technique that originated in mathematical psychology. 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.

## Process

The basic steps are:
• select features to be tested
• show product feature combinations to potential customers
• respondents rank the combinations
• input the data from a representitive sample of potential customers into a statistical software program and choose the conjoint analysis procedure. The software will produce utility functions for each of the features.
• incorporate the most preferred features into the new product or advertisement

## 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. Rank-order preferences are obtained. The responses are codified and input into a statistical program like SPSS or SAS.

## Analysis

The computer uses monotonic analysis of variance or linear programming techniques to create utility functions for each feature. These utilty functions indicate the perceived value of the feature and how sensitive consumer perceptions and preferences are to changes in product features.

• able to use physical objects
• measures preference at the individual level

• 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