Team A-Day 10

Lets try to understand what is discriminant and conjoint analysis conceptually ?

Discriminant analysis is most often used to help a researcher predict the group or category to which a subject belongs. For example, when applicants are interviewed for admission to international universities, admission team will not know for sure how applicants will perform in the program if taken. Suppose, however, if their I.T. dept. has a list of current students who have been classified into two groups: "high performers" and "low performers." These individuals have been in the university for past few years, and have been evaluated by the professors, and are known to fall into one of these two mutually exclusive categories. The academic dept. also has information on the students’ backgrounds: pre educational attainment, prior work experience, participation in training programs, team attitude measures, personality characteristics, and so forth. This information was known at the time these students were enrolled. The university wants to be able to predict, with some confidence, which future applicants are high performers and which are not. A researcher or consultant can use discriminant analysis, along with existing data, to help in this task.

Conjoint Analysis is an experimental quantitative technique that simulates decision situations customers regularly face.  For eg: While choosing an apparel or a car which feature or attribute should be given higher weightage, this varies from person to person but as a marketer if we want to know then we have to perform this analysis in order to get a correct guess of which attribute would be weighted higher. Types of this analysis are 1.)Choice-Based Conjoint (CBC)  

2.)Adaptive Conjoint Analysis (ACA)

3.) MaxDiff. These are the most commonly used tools. 

These types of analysis provide insights that help companies decide on the optimal set of features that will deliver higher levels of satisfaction to the customer. Consumer researchers have also used conjoint analysis to evaluate the importance of an attribute in forming preferences. Although past researchers have tested the validity and reliability of the overall conjoint analysis results, some of the properties of the individual importance weights have remained unknown.

by 

Abhik Chakraborty