Within each attribute, we also learn the utility of each level, described on a numeric scale. Utility values are represented on a relative scale where higher values mean higher preference. For example, consider these hypothetical results:
Utility Values for Brands
| Card A |
80 |
| Card B |
40 |
| Card C |
35 |
In this case, Card A is the most preferred brand, and Card C is the least preferred. This information can be used to infer consumers' preference profiles for various product features. The conjoint data reveals consumers' brand loyalty, price sensitivity and product feature preferences. One of the most valuable uses of conjoint analysis is predicting the products a buyer would choose. If we know the buyers' preference profiles, we are able to predict a product's overall attractiveness to that group of consumers. Conjoint analysis is a valuable tool for designing products with the maximum likelihood of success in the market. |
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