Conjoint analysis is typically used to identify the most
desirable combination of features to be offered in a new product or services
(e.g. what features should be offered in a new public transportation system?).
In such studies, respondents are told about the various combinations of
features under consideration and are asked to indicate the combination they
most prefer, to indicate the combination that is their third preference, and so
on. Conjoint analysis uses such preference data to identify the most desirable
combination of features to be included in the new product or service.
A conjoint analysis applies a complex form of analysis of
variance to the preference data obtained from each respondent. This analysis
calculates a value (or utility) for each feature. Features with the highest
values are judged the most important to respondents. Conjoint analysis is
applied to categorical variables, which reflect different features or
characteristics of the product or service under consideration.
Conjoint Analysis Identifies Interdependencies among
variables: Conjoint analysis differs from cross tabulation, regression, LDA,
and AID in that it is not concerned primarily with a single dependent variable.
Rather, conjoint analysis is like cluster and factor analysis in the sense that
these methods try to identify the interdependencies which exist between number
of variables. In the example involving a new public transportation system, the
variables are the features and characteristics that can be designed into the
new system and conjoint analysis tries to measure the relative importance of
various combinations of those features and characteristics.
Strengths of Traditional Conjoint:
•
Good for both product design and pricing issues
•
Can be administered on paper, computer/internet
•
Shows products in full-profile, which many argue mimics real-world
•
Can be used even with very small sample sizes
Weaknesses of Traditional Full-Profile Conjoint:
•
Limited ability to study many attributes (more than about six)
•
Limited ability to measure interactions and other higher-order effects
(cross-effects)
Rajendra Kumar Das
Operations – Group B
11020841156
No comments:
Post a Comment