EXPLANATION ABOUT CONJOINT AND CLUSTER ANALYSIS WITH UNDERSTANDABLE EXAMPLES

1. CONJOINT ANALYSIS
By
KIRUBAHARAN B.E., MBA.,
RESEARCH SCHOLAR
ANNA UNIVERSITY
CHENNAI

2. introduction
• CONJOINT – combining things that involved
• CONJOINT ANALYSIS : It is a multivariate
technique developed specifically to
understand how respondents develop
preference for any type of products or
services.

3. purpose
• It is used to find how consumers provide their
estimates of preference by judging products
or services formed by combination of
attributes.
( it means combining the separate amount of
value provided to each attribute of product or
service)

4. Questions faced by researcher:
1) What are the important attribute that could
affect preference?
2) How will respondents know the meaning of
each attributes?
3) What do the respondents actually evaluate?
4) How many profiles are evaluated?

5. Example
HBAT IS TRYING TO DEVELOP A NEW
INDUSTRIAL CLEANSER
ATTRIBUTES 1 2
INGREDIETNS PHOSPHATE FREE PHOSPHATE BASED
FORM LIQUID POWDER
BRAND NAME HBAT GENERIC BRAND
VALUES
Here two three attributes are present with two values
so there is a chance for creating 8 possible
combinations that is called as profile.

6. PROFILE FORM INGREDIENTS BRAND REPONDENT1 RESPONDENT2
1 Liquid Phosphate free HBAT 1 1
2 Liquid Phosphate free Generic 2 2
3 Liquid Phosphate based HBAT 5 3
4 Liquid Phosphate based Generic 6 4
5 Powder Phosphate free HBAT 3 7
6 Powder Phosphate free Generic 4 5
7 Powder Phosphate based HBAT 7 8
8 Powder Phosphate based Generic 8 6
The eight profiles represent all combinations of the
three attributes, each with two levels(2x2x2)
The simplest model would represent the preference structure for the industrial
cleanser as determined by adding the three factors
UTILITY = BRAND EFFECT + INGREDIENT EFFECT + FORM

7. CLUSTER ANALYSIS

8. Introduction
• Cluster analysis – it is the process of grouping
observations in to similar kinds in to smaller
group within the larger population
• Purpose: it is used to segment the market for
targeting the customers of brand

9. Questions faced by the researcher:
1) How do we measure similarity?
2) How do we form cluster?
3) How many groups do we form?

10. example
VARIABLE A B C D E F G
V1 3 4 4 2 6 7 6
V2 2 5 7 7 6 7 4
RESPONDENTS
TO MEASAURE THE LOYALTY– V1( store loyalty)
and V2( brand loyalty) were measured for each
respondent on a 0-10 scale, the values of the
seven respondents are shown.

11. Scatter plot
A
B
CD
E
F
G
0
1
2
3
4
5
6
7
8
0 1 2 3 4 5 6 7 8
V1
V2

12. EUCLIDEAN DISTANCES
OBSERVATION A B C D E F G
A _
B 3.162 _
C 5.099 2 _
D 5.099 2.828 2 _
E 5 2.236 2.236 4.123 _
F 6.403 3.606 3 5 1.414 _
G 3.606 2.236 3.606 5 2 3.162 _

13. AGGLOMERATIVE METHOD
STEP MINIMUM
DISTANCE
OBSERVATION
PAIR
CLUSTER MEMBER NO. OF.
CLUSTERS
OVERALL
SIMILARITY
MEASURE
1 1.414 E-F (A) (B) (C) (D) (E-F) (G) 6 1.414
2 2 E-G (A) (B) (C) (D) (E-F-G) 5 2.192
3 2 C-D (A) (B) (C-D) (E-F-G) 4 2.144
4 2 B-C (A) (B-C-D) (E-F-G) 3 2.234
5 2.326 B-E (A) (B-C-D-E-F-G) 2 2.896
6 3.162 A-B (A-B-C-D-E-F-G) 1 3.420

14. NESTED GROUPINGS
6
5
1
2
4
3

15. EXAMPLE FOR 3 DIMENSION

16. THANK YOU