Market researchers face two main challenges as they provide market intelligence for managers: to meet managers’ objectives with useful, valid results and to communicate those results effectively. Failure on either of these points is fatal. Conjoint analysis provides useful results that, when presented well, are easy for managers to embrace and understand. It is no wonder that conjoint analysis is the most rapidly growing and one of the most widely used market research techniques today.
Conjoint analysis aims for greater realism, grounds attributes in concrete descriptions, and results in better discrimination among attribute importance. Conjoint analysis creates a more appropriate context for research. Consider a pairwise trade-off question featuring laptop computers. Of course, conjoint questions can also be asked one product proﬁle at a time, as in a traditional card sort. The rationale behind pairwise comparisons is this: People can make ﬁner distinctions when they directly compare objects. For example, if someone hands you a four-pound rock, takes it away, and then hands you a ﬁve-pound rock, chances are you will not be able to tell which is heavier. But if you hold one rock in each hand, you will have a much better chance of guessing which weighs more.
Choice-based conjoint questions closely reflect what buyers do in the real world—choose among available offerings. Including none as an option enhances the realism, and allows those respondents who are not likely to purchase to express their disinterest. Choice-based data reﬂect choices, not just preferences. If we agree that the ultimate goal of market simulators is to predict choice, then it is only natural that we would value choice-based data. Some managers do not have the training in statistics to grasp the concept of orthogonal designs, main effects assumptions, or part-worth utility estimation. More technical folks, utilizing specialized software, can manage these details. Whether statisticians or otherwise, almost everyone can grasp the idea that realistic models result from realistic questioning methods, and they can be comforted that conjoint analysis is a reliable, time-proven method.
Name : Siddhartha Singh
Roll : 13165
Group : Marketing 6