WHICH TYPE OF CONJOINT IS FOR YOU?

CBC is the most common form of conjoint analysis used professionally. Conjoint Analysis has become one of the most powerful quantitative methods in Marketing Research. Conjoint Analysis answers the question of which attributes are important to consumers and how important they really are.
Over the years, many different types of Conjoint have evolved. Each version has its own merits and demerits. In this article, lets understand which one is applicable for you.
CONJOINT ANALYSIS TERMINOLOGY
In the process of a conjoint analysis, various specific terms arise that must be correctly interpreted. These terms will be discussed in the following sections:
Attributes / characteristics
This refers to a characteristic of a service or product. In the example from the following section, 4 attributes have been included: price, airline, transfers and travel duration.
Levels
Levels refer to the various steps or measurements for the previously mentioned attribute or characteristic. The price from the example is subdivided into 3 levels, each with a different amount.
Card
A card is a combination of multiple attributes.
Set
A set refers to a group of cards. Respondents are often asked to choose from the set of cards.
Part-worth utilities
A collection of the most valuable results from the Conjoint Analysis.

TYPES OF CONJOINTS
There are many different types of conjoint analyses, but their use is completely dependent upon what the user wishes to accomplish. From conjoint analyses conducted based on algorithms to simple questionnaires or combined types; a conjoint analysis can be designed completely according to the user’s wishes. The most important two types of conjoint analyses are described below.

FULL-PROFILE
In a Full-Profile Conjoint study, the respondent is shown a single profile of all attributes at the same time and is asked to rate the profile by their preference or likelihood of purchase.
Since Full-Profile conjoint studies ask respondents to consider all attributes at the same time, it is only appropriate for measuring a limited set of attributes
PARTIAL-PROFILE
Partial Profile conjoint differs from Full Profile in that it shows only a few attributes at a time. Partial Profile is used when the number of attributes and the levels within the attributes are large. Due to the large number of possible combinations, every respondent does not see every different combination of attributes and levels.
Using an experimental design, profiles are designed in such a way that the occurrences of attributes are evenly distributed. In other words, each attribute is shown the same number of times as every other attribute. By controlling the attribute pairings, the researcher can estimate the respondent’s utility for each level of each attribute tested using a reduced set of profiles. Using the attribute “color” as an example, you can measure the individual weight of red, blue, green, or any of the colors included in the design.
CHOICE-BASED (CBC)
Choice-based conjoint (CBC) is preferred over other types of conjoint analyses in many cases. This type of conjoint analysis is used in determining the price of a service or product in relation to the attributes or functions. CBC is effective when the acquired data is concrete, such as various product attributes that influence each other or when it concerns price. A CBC conjoint analysis is usually limited to approximately 8 attributes.
In a CBC exercise, an experimental design is often implemented to reduce the number of profile sets shown and help avoid respondent fatigue.
ADAPTIVE CHOICE (ACA)

When the number of attributes is higher than 8, an adaptive conjoint analysis (ACA) can be more useful. An ACA utilises the same overarching principles as a CBC analysis, but differs significantly in terms of subject, implementation and analysis.
Where the CBC gives respondents the choice between multiple products that are described by means of a set of attributes, the ACA gives a description with all attributes as pairs, the aforementioned cards. The advantage is that a large number of characteristics can be used, up to as much as 30 attributes, each with 7 levels. A computer manufacturer can use this analysis to research which set of specifications is most desired by the consumer.
The disadvantage of an ACA analysis is that the large number of attributes means the survey will take at least 45 minutes or longer. Another disadvantage of the ACA version of the conjoint analysis is that the survey is difficult to do by phone, and questionnaires on paper are impossible. In this case, software with algorithms are often used.

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