Group C- Conjoint Analysis

Conjoint Analysis is a technique used to assess the relative importance individuals place on different features of a given product. A conjoint study usually involves showing respondents a set of features and asking them to indicate how much they like or prefer the different attributes of that feature.

In a conjoint analysis, the respondent may be asked to arrange a list of combinations of product attributes in decreasing order of preference. Once this ranking is obtained, a computer is used to find the utilities of different values of each attribute that would result in the respondent's order of preference. This method is efficient in the sense that the survey does not need to be conducted using every possible combination of attributes. The utilities can be determined using a subset of possible attribute combinations.

From these results one can predict the desirability of the combinations that were not tested.

Steps in Developing a Conjoint Analysis

Developing a conjoint analysis involves the following steps:

1. Choose product attributes, for example, appearance, size, or price.

2. Choose the values or options for each attribute. For example, for the attribute of size, one may choose the levels of 5", 10", or 20". The higher the number of options used for each attribute, the more burden that is placed on the respondents.

3. Define products as a combination of attribute options. The set of combinations of attributes that will be used will be a subset of the possible universe of products.

4. Choose the form in which the combinations of attributes are to be presented to the respondents. Options include verbal presentation, paragraph description, and pictorial presentation.

5. Decide how responses will be aggregated. There are three choices – use individual responses, pool all responses into a single utility function, or define segments of respondents who have similar preferences.

6. Select the technique to be used to analyze the collected data. The part-worth model is one of the simpler models used to express the utilities of the various attributes. There also are vector (linear) models and ideal-point (quadratic) models.

The data is processed by statistical software written specifically for conjoint analysis.

Example of Syntax for Conjoint Analysis:

conjoint plan = 'C:\Users\ABC\Desktop\Conjoint\Plan.sav'

/data = 'C:\Users\ABC\Desktop\Conjoint\Data.sav'

/rank = card_1 to card_8

/subject = Name

/factors = CompanyType Industry Salary Growth Satisfaction WLBalance Security

/plot all.

Sample Output:

Subject 1: ABC

                                                 Utilities

		Utility Estimate	Std. Error
CompanyType	National	-1.000	.
	MNC	1.000	.
Industry	Service	.750	.
	Mfg	-.750	.
Salary	Above Avg	-1.250	.
	Below Avg	1.250	.
Growth	Fast	-.750	.
	Medium	.750	.
Satisfaction	High	.000	.
	Moderate	.000	.
WLBalance	Yes	.000	.
	No	.000	.
Security	Yes	-1.250	.
	No	1.250	.
(Constant)		4.500	.

 

 

 

 

 

 

 

              Importance Values

CompanyType	20.000
Industry	15.000
Salary	25.000
Growth	15.000
Satisfaction	.000
WLBalance	.000
Security	25.000

For Subject1 from conjoint analysis we find that most important attribute are Salary and Security.

Fig: Attributewise importance for the whole set of respondents.

 

From the figure above we find that Salary is the most important attribute.

Posted By:

S M Murshid Azam

Roll No -14104

Group C