A basic requirement for most conjoint methods, the linearity and independence of the product attributes, allows various conjoints to be interlinked. Such interlinking is most effective when it is already done at the individual level, as this leaves open all of the different types of analysis options for conjoint data, such as segmentation and simulation.
This allows the following types of tasks to be addressed:
Valid estimation of price importance
In most conjoint studies, price occupies a special place. Often, all of the other attributes are compared to it (for example through the calculation of "monetary values"). Thus, the price is given a key position.
Price has a negative utility. This means that the higher its level, the lower will be the overall utility of the object under study. Price does not serve to describe any particular attribute of the object; rather it reflects the value that the consumer has to provide in order to pay for the overall utility delivered by the object’s various attributes. Because of this it stands in opposition to all the other attributes. This unique position occupied by price can lead to an underestimation of its importance, particularly as the number of attributes under study increases.
With dual conjoints, two conjoints are carried out within a single survey. The first determines the relationship of the product attributes to one another (up to approximately 15 attributes can be studied). In a second conjoint, a subgroup created from the first conjoint is rated together with the price.
The price is then embedded mathematically into the first (attributes) conjoint using the attributes which serve as anchor points and which are used in both conjoints.
Studies of complex structure models
With the help of so-called multistage conjoints, multi-layered causal structures can be studied, which can be broken down into clearly discernible topics. Each topic is queried in a single conjoint. From the extreme points calculated from these topic-specific conjoints, anchor points are derived which are then used in a higher-level conjoint. In addition to these anchor points, this contains fundamental aspects of the object under study, such as price or brand. The results of the topic-specific conjoints are embedded mathematically into the main conjoint, so that all of the attributes can be compared with all others.
Drawbacks and limitations
- A prerequisite for a multistage conjoint is that realistic assumptions are made regarding the model structure of the decision making processes.
- The hierarchy levels must be clearly defined. Allocating an attribute to the wrong level can result in its importance being over or underestimated.
- The topics must be clearly delimited and recognizable for the respondents. Artificial breakdowns threaten the validity of the findings.
Advantages
- Even highly comprehensive structure models with up to approx. 40 attributes can be studied. The risk of underestimating the price can be eliminated with the appropriate model design.
