1. Conjoint Analysis
Sunny Bose

2. Definitions & Key Terms
Conjoint Analysis- Is a term given to a multi variate analytical tool that CONsiders JOINTly the effect of the individual attributes of a product or a brand. This helps the marketer to analyze the utility that each varied combinations of the attributes of the product is providing to the customer.
Utility- The subjective preference judgment of an individual that represent the total value or worth he is putting on the product having a combination of certain attributes.
Part- Worth- The values of the individual attributes that sum up or produce the total utility for the product.
Additive Model- Assumes that individuals just add up the individual Part- Worths to get to the overall utility.
Interaction Model- Unlike the additive model, here the individual also considers the interactions between two independent Part- Worth while valuing the overall utility of the product.

3. Definitions & Key Terms (Contd.)
Factorial Design- Method of designing stimuli by generating all possible combinations of levels. For example a three factor (attribute) conjoint analysis with three levels each will result in 3x3x3 = 27 combinations which will form the total stimuli in the analysis.
Full Profile Method- Analysis carries on based on the respondent’s evaluation of all the possible combinations in the stimuli.
Fractional Factorial Design- Method of designing a stimuli that is a subset of the full factorial design so as to estimate the results based on the assumed compositional rule.
Orthogonality- Joint occurrence of levels of different attributes will be equal or in proportional number of times.
Validation Stimuli- Set of stimuli that are not used for estimation of the Part- Worths. Estimated Part- Worths are then used to predict preference for the validation stimuli to assess validity and reliability of the original estimates.

4. Definitions & Key Terms (Contd.)
Pair wise Comparison Method- Method of presenting a pair of stimuli to the respondent for evaluation, with the respondent selecting one of the stimuli as preferred.
Self Explicated Model- Compositional technique where the respondent provides the Part- Worth estimates directly without making choices.
Adaptive (Hybrid) Conjoint Analysis (ACA)- ACA asks respondents to evaluate attribute levels directly, and then to assess the importance of each attribute, and finally to make paired comparisons between profile descriptions.
Choice Based Conjoint (CBC)- An alternative form of conjoint analysis where the respondent’s task is of choosing a preferred profile similar to what he would actually buy in the marketplace. CBC analysis lets the researcher include a "None" option for respondents, which might read "I wouldn't choose any of these."

5. Usages of Conjoint Analysis
Breaking down customer’s overall utility from the product into values put in by him on the products individual attributes.
Product planning and design
Accommodating conflicting interests-
Buyers want all of the most desirable features at lowest possible price
Sellers want to maximize profits by:
Minimizing the costs of features provided
Providing products that offer greater overall value than the competitors.
Market segmentation based on the utility structures

6. Conjoint Analysis- Process Flow
Identify the research problem
Stage 1
Focused Group is the most practiced
Decide on the attributes and their levels
Stage 2
Traditional, ACA or CBC
Chose the methodology
Stage 3
Rating or rank order
Collect responses
Stage 4
Individual or aggregative
Run analysis
Stage 5
Stage 6
Interpret results
External or internal validity tests
Validate the results
Stage 7
Product designing, market segmentation etc.
Apply the Conjoint results
Stage 8

7. Types of Conjoint Analysis
Traditional Conjoint
Full Profile
Partial Profile / Fractional Factorial Design
Paired Comparison
Self Explicated
Adaptive Conjoint Analysis (ACA)
Choice Based Conjoint (CBC)

8. How Conjoint Analysis works
Decompose the overall utility into its individual attribute’s part- worths
Additive model- Overall utility = Sum total of all part- worths
Total worth/ Utility = Part- worth of level i for factor 1+ Part- worth of level j for factor 2 + …. Part- worth of level n for factor m
Interaction model- Overall utility > Sum total of all part- worths
Total worth/ Utility = Part- worth of level i for factor 1+ Part- worth of level j for factor 2 + …. Part- worth of level n for factor m + I (Interaction effect between the attributes and their level)
Generally, the Traditional Conjoint analyses use additive models whereas ACA and CBC use interaction models

9. Traditional Conjoint Analysis
Full Profile
Partial Profile
Paired Comparison Test
Self Explicated Method

10. Full Profile
Let us assume that a cricket bat maker is planning to launch a new professional level cricket bat. Based on the inputs from focused group, salesman and experts, he finds the following attributes important for a professional bat.
From the table let us take a profile as an example that a respondent would require to rank.
Like the profile in example, a full profile would provide 2x2x2 = 8 combinations
Attribute
Level 1
Level 2
Type
Heavy
Long handle
Wood
Kashmir willow
English willow
Grip
Single
Multi
Attribute
Level
Wood
English Willow
Grip
Single
Type
Long handle

11. Full Profile (Contd.)
Now, let us assume a respondent ranks all these profiles based on his utility from these profiles (1- Highest and 8- Lowest)
Profile
Type
Wood
Grip
Rank 1
Heavy
English willow
Multi 2
Single 3
Kashmir willow 4 5
Long handle 6 7 8

12. Full Profile (Contd.)
To estimate the Part- Worth of each attribute, average ranks or ratings for each attribute level is measured
Attribute Levels
Ranks Across Stimuli
Average Rank (AR)
Deviation from Overall Rank (DOR)
Type
Heavy
1,2,4,5
3.0
-1.5
Long handle
3,6,7,8
6.0
+1.5
Wood
English willow
1,2,3,6
Kashmir willow
4,5,7,8
Grip
Multi
1,3,4,7
3.75
-0.75
Single
2,5,6,8
5.25
+0.75

13. Full Profile (Contd.)
These deviations of ranks from the overall average rank is used to compute the individual Part- Worths
StD= SDxSV, where SV= No. of levels/ SD= 6/10.125= 0.592
Attribute Levels
Reversed Deviations (RD)
Squared Deviation (SD)
Standardized Deviation
(StD)
Estimated Part- Worth
Type
Heavy
+1.5
2.25
+1.332
+1.154
Long handle
-1.5
-1.332
-1.154
Wood
English willow
Kashmir willow
Grip
Multi
+0.75
0.5625
+0.333
+0.577
Single
-0.75
-0.333
-0.577

14. Full Profile (Contd.)
Let us check whether the Part- worths are reliable
Profile
Type
Wood
Grip
P-W Type
P- W Wood
P-W Grip
Total
P-W
Estimate Rank
Rank 1
Heavy EW
Multi
1.154
1.332
0.333
2.819 2
Single
-0.333
2.153 3 KW
-1.332
0.155 4
-0.511 6 5 LH
-1.154
0.511
-0.155 7
-2.153 8
-2.819

15. Partial Profile
Partial profile is a necessity when the number of attributes and the levels within the attributes are large.
In such a case, it becomes almost impossible for the respondent to evaluate the full profile
4 attributes having 4 levels each will result in 4x4x4x4 = 256 profiles
Partial profile considers a subset of the entire which would be representative of the full profile
This is done through an orthogonal process so that the profiles contain the levels equally or in proportion.
Partial profile eases the pressure of evaluation for the respondent
Out of 256 profiles, a partial profile might contain only 16 representative profiles

16. Paired Comparison Test
Also known as Trade off Approach as the respondent is forced to make a trade- offs between the attribute levels.
Instead of full profiles or partial profiles, trade off matrices are created considering all the levels of two attributes taken at a time.
Incase of more than two attributes sequential trade off matrices are given to be ranked or rated in an order such that there is at least one attribute from the previous matrix is present.
In Paired Comparison Tests, the value of the individual attributes come out from the different ratings its levels receive in a paired combination with the other attributes.

17. Paired Comparison Test (Contd.)
Let us consider that a realtor is considering to build a multi storied residential apartment. From his prior knowledge he knows that other than price, the important considerations for purchasing a flat are:
proximity of schools, markets, hospitals and other utilities,
availability of transportation to various locations of the city
Provision of elevator and garage
On these attributes he can give the following options:
Attributes
Level 1
Level 2
Proximity
Yes No
Transportation
Provision

18. Paired Comparison Test (Contd.)
Unlike Full Profile which would generate 2x2x2 = 8 combinations, the Paired Comparison Test in this case would generate
Attributes
Proximity (Yes)
Proximity (No)
Transportation (Yes) 9 6
Transportation (No) 5 3
Attributes
Proximity (Yes)
Proximity (No)
Provision (Yes) 9 6
Provision (No) 4 2
Attributes
Provision (Yes)
Provision (No)
Transportation (Yes) 10 4
Transportation (No) 2

19. Paired Comparison Test (Contd.)
From the matrices it is evident that when considering the combinations between transportation - provision and transportation – proximity, the respondent has rated the provision (Yes) higher than proximity (Yes) and again provision (No)lower than proximity (No)(transportation is constant).
Value of Provision > Value of Proximity
Similarly, between provision- transportation and provision- proximity, the combinations of transportation (Yes) got higher rating than proximity (Yes) whereas, transportation (No) got lower ratings than proximity (No).
Value of Transportation > Value of Proximity
Finally, taking proximity constant in proximity- provision and proximity- transportation, the combinations with provisions (Yes) have either got equal or higher rating than combinations with transportation (Yes) and provision (No) have equal or lower ratings than transportation (No).
Value of Provision > value of Transportation
Thus, Provision > Transportation > Proximity

20. Self Explicated Method
Purists do not consider it to be a conjoint as there is no trade off to be made.
Compositional techniques as the respondents rate or rank the attributes and their levels.
Preferable option over traditional conjoint when the attributes and their levels are large
Used as a fundamental part of ACA or hybrid conjoint.

21. Self Explicated Method (Contd.)
Please rate the levels in a scale of 1-10 (1- Lowest, 10- Highest) based on the value you think they would provides you and divide 100 points among the attributes based on the importance you give to each of them for contributing to the functionability of a laptop (Total points should not be more or less than 100).
Attribute
Level 1
Level 2
Level 3
Level 4
Hard Disk
150 GB
200 GB
250 GB
300 GB
RAM
1 GB
2 GB
3 GB
4 GB
Processor
1.5 GHz
1.8 GHz
2.0 GHz
2.2 GHz OS
Win XP
Win Vista (Home)
Win Vista (Pro)
Linux

22. Self Explicated Method (Contd.)
Below is the table showing the self explicated ratings. Note, Total possible value for the entire profile = (40)x100= 4000
Hard Disk = 980/4000 = 0.245
RAM = 560/4000 = 0.14
Processor = 750/ 4000 =
Operating System = 440/4000 = 0.11
Attribute
Level 1
Level 2
Level 3
Level 4
Total
Hard Disk (35) 5 6 8 9
(28)x35= 980
RAM (20) 7
(28)x20= 560
Processor (25)
(30)x25= 750
OS (20) 4 3
(22)x20= 440

23. Self Explicated Method (Contd.)
The inherent problem with this method is that respondents inadvertently tend to give higher ratings to the levels that have higher value. As a result, at the initial stage itself this estimation technique is flawed.
Due to the absence of trade off while rating the stimuli, the respondents have the inclination to rate the attributes and their levels based on what he thinks to be most ideal and not what gives him the greatest utility.
When the attributes are large it is taxing on the respondent to rate them or put value to them objectively.

24. Thank You.