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Melroy Lopes
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:
Choose product attributes, for example, appearance, size, or price.
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.
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.
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.
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.
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.
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:
Choose product attributes, for example, appearance, size, or price.
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.
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.
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.
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.
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.