The document provides definitions and explanations of key terms and concepts related to conjoint analysis. It can be summarized as follows: 1. Conjoint analysis is a multivariate tool used to understand how individuals derive utility from the different attributes of a product or brand. It breaks down the overall utility or preference into partial utilities for each attribute level. 2. Traditional conjoint analysis methods include full profile, partial profile, paired comparison, and self-explicated. Adaptive conjoint analysis and choice-based conjoint are also mentioned. 3. The process involves identifying attributes and levels, collecting responses, analyzing data to estimate part-worths for each level, and validating the results. Part-worths represent

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

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

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 1
2 Heavy English willow Single 2
3 Heavy Kashmir Multi 4
willow
4 Heavy Kashmir Single 5
willow
5 Long handle English willow Multi 3
6 Long handle English willow Single 6
7 Long handle Kashmir Multi 7
willow

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 3.0 -1.5
Kashmir willow 4,5,7,8 6.0 +1.5
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 Squared Deviation Standardized Deviation Estimated Part-
(RD) (SD) (StD) Worth
Type
Heavy +1.5 2.25 +1.332 +1.154
Long handle -1.5 2.25 -1.332 -1.154
Wood
English willow +1.5 2.25 +1.332 +1.154
Kashmir -1.5 2.25 -1.332 -1.154
willow
Grip
Multi +0.75 0.5625 +0.333 +0.577
Single -0.75 0.5625 -0.333 -0.577

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

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 Yes No
Provision Yes No

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) 4 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 Win Vista (Pro) Linux
(Home)

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 = 0.1875
 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) 6 6 7 9 (28)x20= 560
Processor (25) 6 7 8 9 (30)x25= 750
OS (20) 4 6 9 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.