Using Conjoint Analysis to Model Carpet-Cleaner Preference
In a popular example of conjoint analysis 1, a company interested in marketing a new carpet cleaner wants to examine the influence of five factors on consumer preference—package design, brand name, price, a Good Housekeeping seal, and a money-back guarantee. There are three factor levels for package design, each one differing in the location of the applicator brush; three brand names (K2R, Glory, and Bissell); three price levels; and two levels (either no or yes) for each of the last two factors. The following table displays the variables used in the carpet-cleaner study, with their variable labels and values.
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| Variable name | Variable label | Value label |
|---|---|---|
| package | package design | A*, B*, C* |
| brand | brand name | K2R, Glory, Bissell |
| price | price | $1.19, $1.39, $1.59 |
| seal | Good Housekeeping seal | no, yes |
| money | money-back guarantee | no, yes |
There could be other factors and factor levels that characterize carpet cleaners, but these are the only ones of interest to management. This is an important point in conjoint analysis. You want to choose only those factors (independent variables) that you think most influence the subject's preference (the dependent variable). Using conjoint analysis, you will develop a model for customer preference based on these five factors.
This example makes use of the information in the following data files: carpet_prefs.sav contains the data collected from the subjects, carpet_plan.sav contains the product profiles being surveyed, and conjoint.sps contains the command syntax necessary to run the analysis. See the topic Sample Files for more information.