Modeling and Multivariate Methods - SAS

404 Performing Choice Modeling Chapter 16
Data for the Choice Platform
Modeling costs have recently decreased with improved product and process development techniques and
methodologies. Prototyping, including pure digital prototyping, is becoming less expensive, so it is possible
to evaluate the attributes and consequences of more alternatives. Another important advancement is the use
of the Internet to deliver choice experiments to a wide audience. You can now inform your customers that
they can have input into the design of the next product edition by completing a web survey.
Choice modeling can be added to Six Sigma programs to improve consumer products. Six Sigma aims at
making products better by improving the manufacturing process and ensuring greater performance and
durability. But, Six Sigma programs have not addressed one very important aspect of product
improvement—making the products that people actually want. Six Sigma programs often consider the
Voice of the Customer and can use customer satisfaction surveys. However, while these surveys can disclose
what is wrong with the product, they fail to identify consumer preferences with regard to specific product
attributes. Choice experiments provide a tool that enables companies to gain insight for actual customer
preferences. Choice modeling analysis can reveal such preferences.
Market research experiments have a long history of success, but performing these experiments has been
expensive, and research has previously focused on price elasticity and competitive situations. It is by using
these same techniques for product design engineering where choice modeling can have the most impact.
Data for the Choice Platform
The Choice platform is unique because it is designed to use data from one, two or three different data tables.
Profile data describe the attributes associated with each choice. Each choice can comprise many different
attributes, and each attribute is listed as a column in the data table. There is a row for each possible
choice, and each possible choice contains a unique ID.
Response data contain the experimental results and have the choice set IDs for each trial as well as the
actual choice selected by the subject. Each subject usually has several trials, or choice sets, to cover several
choice possibilities. There can be more than one row of data for each subject. For example, an
experiment might have 100 subjects with each subject making 12 choice decisions, resulting in 1200
rows in this data table. The Response data are linked to the Profile data through the choice set columns
and the actual choice response column. Choice set refers to the set of alternatives from which the subject
makes a choice. Grouping variables are sometimes used to align choice indices when more than one
group is contained within the data.
Subject data are optional, depending on whether subject effects are to be modeled. This source contains
one or more attributes or characteristics of each subject and a subject identifier. The Subject data table
contains the same number of rows as subjects and has an identifier column that matches a similar
column in the Response data table. You can also put Subject data in the Response data table, but it is
still specified as a subject table.
If all your data are contained in one table, you can use the Choice platform, but additional effort is
necessary. See the section “One-Table Analysis” on page 420.