This example uses three data tables: Pizza, Pizza, and Pizza
Select Help > Sample Data Library and open Pizza, Pizza, and Pizza
The profile data table, Pizza, lists all the pizza choice combinations that you want to present to the subjects. Each choice combination is given an ID.
The responses data table, Pizza, contains the design and results. For the experiment, each subject is given four choice sets, where each choice set consists of two choice profiles (Choice1 and Choice2). The subject selects a preference (Choice) for each choice set. For information about how to construct a choice design, see Discrete Choice Designs in the Design of Experiments Guide. Notice that each value in the Choice column is an ID value in the Profile data table that contains the attribute information.
The subjects data table, Pizza, includes a Subject ID column and a single characteristic of the subject, Gender. Each value of Subject in the Pizza data table corresponds to values in the Subject column in the Pizza data table.
Select Analyze > Consumer Research > Choice to open the launch window.
From the Data Format menu, select Multiple Tables, Cross-Referenced.
Click Select Data Table under Profile Data.
Select Pizza and click OK.
Select ID and click Profile ID.
Select Crust, Cheese, and Topping and click Add.
Profile Data
Select Pizza and click OK.
Select Choice and click Profile ID Chosen.
Select Choice1 and Choice2 and click Profile ID Choices.
Select Subject and select Subject ID.
Response Data Window
Choice1 and Choice2 are the profiles presented to a subject in each of four choice sets. The Choice column contains the chosen preference between Choice1 and Choice2.
Select Pizza and click OK.
Select Subject and click Subject ID.
Select Gender and click Add.
Subject Data Window
Click Run Model.
Choice Model Results
Six effects are entered into the model. The effects Crust, Cheese, and Topping are product attributes. The interaction effects, Gender*Crust, Gender*Cheese, and Gender*Topping are subject-effect interactions with the attributes. These interaction effects enable you to construct products that meet market-segment preferences.
The Effect Summary and Likelihood Ratio Tests reports show strong interactions between Gender and Crust and between Gender and Topping. Notice that the main effects of Crust and Topping are not significant. If you had not included subject-level effects, you might have overlooked important information relative to market segmentation.
Click the red triangle next to Utility Profiler and select Optimization and Desirability > Desirability Functions.
Utility Profiler with Desirability Function
Click the red triangle next to Utility Profiler and select Optimization and Desirability > Maximize Desirability.
Utility Profiler with Optimal Settings for Females
Utility Profiler with Male Level Factor Setting

Help created on 9/19/2017