Day 10 - Team D - 16th Sep

The highlight of day 10 was Conjoint Analysis. Conjoint analysis attempts to determine the relative importance consumers attach to salient attributes and the utilities they attach to the levels of attributes. The respondents are presented with stimuli that consist of combinations of attribute levels and asked to evaluate these stimuli in terms of their desirability. Conjoint procedures attempt to assign values to the levels of each attribute, so that the resulting values or utilities attached to the stimuli match, as closely as possible, the input evaluations provided by the respondents.

We entered various values in an excel file related to a food outlet. We first brain stormed various factors and various responses to those factors. For example - Type of food outlet and options can be - Yes or No. 

Also we worked out various options in excel file today -

we found out the most influencing factor through this exercise. In this case it was price of laptop.

Advantages:

1. ·      Estimates psychological trade offs that consumers make when evaluating several attributes together
2. ·      Measures preferences at the individual level
3. ·      Uncovers real or hidden drivers which may not be apparent to the respondent themselves
4. ·      Realistic choice or shopping task
5. ·      Able to use physical objects
6. ·      If appropriately designed, the ability to model interactions between attributes can be used to develop needs based segmentation

We also studied Discriminant Analysis. Discriminant Function Analysis (DA) undertakes the same task as multiple linear regression by predicting an outcome. It is used when:

• the dependent is categorical with the predictor IV’s at interval level such as age, income, attitudes, perceptions, and years of education, although dummy variables can be used as predictors as in multiple regression. Logistic regression IV’s can be of any level of measurement.
• there are more than two DV categories, unlike logistic regression, which is limited to a dichotomous dependent variable.

A discriminant score. This is a weighted linear combination (sum) of the discriminating variables