Even though conjoint analysis involves more sophisticated survey design and analysis, and more effort by respondents, simpler approaches can be unrealistic, even useless. Suppose we were conducting a study about laptop computers, and using a survey like the one in the table below.
Even though it is much easier on respondents to ask them to complete a grid such as shown above, these importance questions are not very meaningful. Buyers cannot always get the best of everything in the real world. They must make difficult trade-offs and concessions. When survey respondents (just like buyers) are forced to make difficult trade-offs, we learn the true value of product alternatives. Conjoint analysis aims for greater realism, grounds attributes in concrete descriptions, and results in better discrimination among attribute importances. Conjoint analysis creates a more appropriate context for research.
Choice-based conjoint questions closely mimic 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 reﬂects 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.
Name : Varun Aggarwal
Group : Marketing 6