By Rajan Sambandam

Preference measurement can be approached in two ways: with a compositional approach or a decompositional approach. The former is a "bottom-up" approach where feature importance is first ascertained and then used to create product attractiveness scores. The latter is a “top-down” approach where overall evaluations of a product are decomposed to get at feature importance. Conjoint Analysis (CA) is generally a decompositional approach, whereas Self-Explicated Method (SEM) is an example of a compositional approach.

While CA has received considerable attention in the literature and has been used often by practitioners, SEM is seldom used. This is in spite of academic studies showing that SEM can be as good as conjoint in some cases and may even be preferable in specific situations. The objective of this paper is to conduct a split sample study to compare the results of the two methods, in order to demonstrate the application of SEM and study its relative effectiveness when compared to CA. [For a more detailed explanation of CA, please refer to Deriving Value from Research: The Use of Conjoint Analysis for Product Development]

SEM is much easier than CA to design and analyze. As with CA we start with a definition of features and levels that we are interested in studying. But product profiles are not constructed as would be done in CA. Instead, survey respondents are presented the features individually and asked for their evaluations. Specifically, levels of each feature are first presented and respondents evaluate their desirability. So for example, if in an auto study gas mileage were a feature and 20mpg, 25mpg and 30mpg were three levels, then respondents are asked to evaluate the desirability of those three levels on a scale. There are at least two ways of doing this. One is to provide a straight desirability rating on a scale of, say, 0-10. Another is to ask for the most desirable level and assign it a value of 10, ask for the least desirable level and assign it a value of 0, and then have the remaining levels assigned appropriate values in-between 0 and 10.

Once the desirability scores are assigned to various levels, the respondents are asked to evaluate the importance of the features. This can again be done in at least two different ways. Respondents could rate features on regular importance scales (say 0-10). Alternatively, they could use a constant sum scale to assign 100 points in accordance with the importance of each feature. Since there is a built in trade-off in the constant sum scale the importance scores are likely to be more accurate. Once the level desirability and feature importance scores are obtained, simple multiplication of the two produces utility scores for every level of every feature. Thus levels that are desirable and occur in important features will have higher utility scores, while those that occur in less important features will have appropriately lower scores.

Practically the utility scores obtained using SEM are similar to those obtained through CA even though the latter are derived using a much more complicated process. The SEM utilities are available at the individual respondent level and can hence be used for simulations or follow -up segmentation. If it is so straightforward and simple to use, how is it that SEM is not more popular?

There are at least a few issues that may impact the results of SEM and therefore need to be considered before implementation. Respondents approach the task feature by feature and hence the whole product perspective that is often seen in CA is missing. The whole product is what a consumer sees in the marketplace and hence it could be argued that CA is more realistic. Conversely, the advantage of SEM is that a large number of features can be included in the study.

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