When asked to do so outright, many consumers are unable to accurately determine the relative importance that they place on product attributes. For example, when asked which attributes are the more important ones, the response may be that they all are important. Furthermore, individual attributes in isolation are perceived differently than in the combinations found in a product. It is difficult for a survey respondent to take a list of attributes and mentally construct the preferred combinations of them. The task is easier if the respondent is presented with combinations of attributes that can be visualized as different product offerings. However, such a survey becomes impractical when there are several attributes that result in a very large number of possible combinations.

Fortunately, conjoint analysis can facilitate the process. Conjoint analysis is a tool that allows a subset of the possible combinations of product features to be used to determine the relative importance of each feature in the purchasing decision. Conjoint analysis is based on the fact that the relative values of attributes considered jointly can better be measured than when considered in isolation.

In a conjoint analysis, the respondent may be asked to arrange a list of combinations of product attributes in decreasing order of preference. Once this ranking is obtained, a computer is used to find the utilities of different values of each attribute that would result in the respondent's order of preference. This method is efficient in the sense that the survey does not need to be conducted using every possible combination of attributes. The utilities can be determined using a subset of possible attribute combinations. From these results one can predict the desirability of the combinations that were not tested.

Developing a conjoint analysis involves the following steps:

1. Choose product attributes, for example, appearance, size, or price.

2. Choose the values or options for each attribute. For example, for the attribute of size, one may choose the levels of 5", 10", or 20". The higher the number of options used for each attribute, the more burden that is placed on the respondents.

3. Define products as a combination of attribute options. The set of combinations of attributes that will be used will be a subset of the possible universe of products.

4. Choose the form in which the combinations of attributes are to be presented to the respondents. Options include verbal presentation, paragraph description, and pictorial presentation.

5. Decide how responses will be aggregated. There are three choices - use individual responses, pool all responses into a single utility function, or define segments of respondents who have similar preferences.

6. Select the technique to be used to analyze the collected data. The part-worth model is one of the simpler models used to express the utilities of the various attributes. There also are vector (linear) models and ideal-point (quadratic) models.

The data is processed by statistical software written specifically for conjoint analysis.

Conjoint analysis was first used in the early 1970's and has become an important marketing research tool. It is well-suited for defining a new product or improving an existing one.