Conjoint Analysis 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.

Steps in Developing a Conjoint Analysis 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.

Conjoint analysis From Wikipedia

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.

The basic steps are:

select features to be tested
show product feature combinations to potential customers
respondents rank, rate, or choose between the combinations
input the data from a representative 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 a 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. 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."

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.

Advantages
able to use physical objects
measures preferences at the individual level

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
estimates psychological tradeoffs that consumers make when evaluating several attributes together

External sources

Green, P. and Srinivasan, V. (1978) Conjoint analysis in consumer research: Issues and outlook, Journal of Consumer Research, vol 5, September 1978, pp 103-123.

Green, P. Carroll, J. and Goldberg, S. (1981) A general approach to product design optimization via conjoint analysis, Journal of Marketing, vol 43, summer 1981, pp 17-35.

Orme, B. (2005) Getting Started with Conjoint Analysis Madison, WI: Research Publishers LLC. ISBN 0-9727297-4-7

"What is Conjoint Analysis?" From Sawtooth Software (includes interactive training module)
Retrieved from "http://en.wikipedia.org/wiki/Conjoint_analysis_%28in_marketing%29"

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