When examining the results of a conjoint analysis, because each attribute’s utilities are on their own scale, a utility level from one attribute cannot be directly compared to another from a different attribute. Not to mention, testing extreme levels within an attribute can result in a high “importance” score for that attribute, when in reality, that attribute has little impact on the overall purchase decision. This presentation will demonstrate how to create more appropriate scores for attributes, or what we will call “impact” scores, based on simulation methods. We will also explore a new way of understanding the impact of the individual features on overall product preference. An approach particularly helpful when comparing across segments or clusters in the data and trying to optimize a product line within a competitive set.
