Conjoint Analysis
Conjoint analysis, also called stated preference analysis, is frequently applied in the field of marketing research, as it seeks to determine which combination of features or attributes of a new service or product is most appealing to customers. When asking customers to rate the importance of several factors, they will most likely rate all factors as important. So then the question is: which one is more important than the other? Conjoint analysis can help finding the answer as it is based on the fact that the relative values of attributes considered can better be measured jointly than in isolation.

So how does it work?
Conjoint analysis requires customers to make a series of trade-offs. Analysis of these trade-offs will reveal the relative importance of component attributes. To improve the predictive ability of this analysis, customers should be grouped into similar segments based on objectives, values and/or other factors. For example, customers are given a list with different attributes. They are then asked to rank the attributes from least to most appealing. This forced ranking exercise will indirectly reveal the customers’ priorities and preferences. Ultimately, multiple regression analysis may be used to determine the strength of preferences across target market segments.

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