It is on Conjoint Analysis presented by Radhika Gupta, Shivi Agarwal, Neha Arya, Neha Kasturia, Mudita Maheshwari, Dhruval Dholakia, Chinmay Jaggan Anmol Sahani and Madhusudan Partani of FMG-18A, FORE School of Management
Conjoint Analysis is concerned with understanding how people make choices between products or services or a combination of product and service, so that businesses can design new products or services that better meet customers’ underlying needs.
In this instance we can see that for this customer, the optimum weight is 80g. 40g is too light and more than 80g is too heavy. In designing a mobile phone for this customer therefore, we can see that there is no benefit in spending development money to bring the weight of the phone below 80g.
Also, we see that getting the weight right is more than twice as important as looking at the battery life.
In this example we are 5 utility points behind the competition. If we reduced the weight of the phone to 160g we would gain 10 utility points which would mean we would expect to be chosen over the competition. Alternatively, we could to reduce the price a little to have the same impact.
Although one of the virtues of conjoint is its separate treatment of each individual, the most common first interpretation step is to compute average part worth of each attribute level across the entire sample of respondents to give the analyst an overall feeling for which attributes are generally important and what is the most desired level of each.
The averages are useful, convenient, and easy to understand summary measure. In most marketing situations, however, strategies are based on customer segments. There are numbers of alternative approaches to derive segments. Since the value system can be derived for each respondent, a cluster analysis can be used to produce “benefit segments”. Idea behind clusters is to group respondent such that segments have similar within a segment and different across segments. Instead of cluster analysis one may use latent class approach to derive statistically meaningful segments. Another alternative is to look at predefined group of customers based on some prior knowledge about them. For example, current versus prospective customers or heavy versus light volume buyers.
Conjoint Analysis also helps researchers to simulate various competitive scenarios and then estimate how the respondents would react to each scenario. A conjoint simulation is an attempt to understand how the set of respondents would choose among a specified set of profiles. The process provides the researcher with the ability to utilize the estimated parts worth in evaluating a number of scenarios consisting of a number of possible combinations of the profiles.
What is ConjointAnalysis?
• Definition : Conjoint Analysis is a multivariate technique
developed specifically to understand how respondents
develop preferences for any type of object.
• Based on the premise that consumers evaluate the value of
an object by combining the separate amounts of value
provided by each attribute.
• Also known as :
o Multi-attribute Compositional Modelling
o Discrete Choice Modelling
o Stated Preference Research
• Factor: Independent variable the researcher manipulates that
represents a specific attribute
• Level: Specific non-metric value describing a factor. Each factor
must be represented by two or more levels.
• Profile : Combination of all possible levels of factors. For ex: 3
factors with 2 levels each will create (2x2x2) i.e. 8 profiles.
• Utility: An individual’s subjective preference judgment
representing the holistic value or worth of a specific object.
• Part-worth: Estimate from conjoint analysis of the overall
preference or utility associated with each level of each factor
used to define the product or service
Why use Conjoint Analysis ?
Different Perspectives, Different Goals
• Buyers want all of the most desirable features at lowest
• Sellers want to maximize profits by:
1) minimizing costs of providing features
2) providing products that offer greater overall value than the
Conjoint Analysis is concerned with understanding
how people make choices between products or services or a combination of
product and service, so that businesses can design new products or
services that better meet customers’ underlying needs.
• Attribute: An attribute is a general feature of a
product or service – say size, colour, speed, delivery
• Level: Each attribute is then made up of specific
levels. So for the attribute colour, levels might be red,
green, blue and so on.
Understanding Conjoint Analysis
• Conjoint analysis takes these attribute and level
descriptions of product/services by asking people to
make a number of choices between different products.
Would you choose phone A or phone B?
Phone A Phone B
Weight 200g 120g
Battery Life 21 hours 10 hours
Price Rs 5000 Rs 8000
• Researcher can work out numerically (from the responses)
how valuable each of the levels is relative to the others
around it – this value is known as the utility of the level.
• We can also compare across attributes to see which attributes
make have the greatest impact in making a choice.
Utility value for each level of
40g 80g 120g 160g 200g
Relative importance of attributes
Weight Price Battery
How our product/service compares to our
competitors and how we can best optimise the
value we give to the customer?
By Conjoint analysis :
We can total up the utility value our product is
giving the customer and compare it to the
value for the competition
Example (cont.) :
Note : Utility values are in brackets. A lower price has a
Weight 200g (15) 120g (35)
Battery Life 21 hours (15) 10 hours (10)
Price Rs 5000 (25) Rs 8000 (15)
Total Utility 55 60
• Part-worths are calculated by using:
– Multiple Regression with dummy variables
– Multinomial logit Models
• Calculations are done for each respondent
• Importance of a factor is represented by the
range of its levels (difference between highest
and lowest values) divided by the sum of the
ranges across all the factors.
• This provides relative importance of each
attribute based on the range of its part-worth
To examine the ability of the model to predict the
actual choices of respondents:
• We predict the preference order by summing the
part-worths for the profiles and then rank-
ordering the resulting scores
• Comparing the predictive preference order to the
respondent’s actual preference order assesses
Selecting and defining factors and levels
Its important as it determines
-> effectiveness of the profiles in the task
-> accuracy of results
-> managerial relevance
-> sensory and
-> should be capable of
being put into
Issues in defining factors
• Number of factors
• Inter attribute correlation
• Unique role of price
Specification issues regarding
1) Number and balance of levels
2) Range of levels
• Two major key decisions involved in
specifying the model are as follows:-
1) Specifying the composition rule used:-
a) Additive rule
b) Interactive rule
2) Selecting the type of part worth relation
a) Linear model
b) Quadratic model
c) Separate part worth model
0 2 4
Separate part worth model
Stage 1:Objectivesof the conjoint
• To determine the contributions of predictor
variables and their levels in determination
of consumer preference
• To establish a valid model of consumer
Requirementsfor a successful conjointanalysis
• Defining the total utility of the object
• all attributes that potentially create or detract from
the overall utility of the product or service should
• Specifying the determinant factors
• include the factors that best differentiate between
Stage 3:Assumptions Of ConjointAnalysis
The product is a bundle of attributes.
Utility of a product is a simple function of the utilities
of the attributes.
Utility predicts behaviour (i.e., purchases).
Least restrictive set of statistical assumptions but highly
critical conceptual assumptions.
Stage 4: Estimating the ConjointModel
• Traditional Estimation Approaches:
– For non metric preference measures:
» MONANOVA, LINMAP
– For metric preference measures:
» Multiple regression with dummy variables
– For more complex consumer preference
» Logit model and its extensions
• Extension of Basic Estimation Process
– Bayesian Estimation
– Incorporation of additional variables reflecting
characteristics of the individuals
Stage 5: Interpretingthe results
• Generally done at disaggregate level
• For aggregate behavior, such as market share,
aggregate analysis more accurate.
• Two main things considered in interpretation
– Examining the estimated part-worth
• Magnitude and pattern of part-worth for each factor
• Higher the part-worth, higher the impact on overall utility
– Relative importance of attributes
• Factor with greatest range of part-woths has greatest
contribution to overall utility.
Define the object with the optimal
combination of features
Predict market shares of different
objects with different sets of features
Isolate groups of customers who place
differing importance’s on different
Identify marketing opportunities by
exploring the market potential for
feature combos not currently available
Show the relative contributions of each
attribute and each level to the overall
evaluation of the object
• Used to generally determine
– average part worth of each attribute level across
the entire sample of respondents
• This usually helps in determining the
– The importance of each attribute
– The desired level of each attribute
• In most marketing situations, strategies are
based on customer segments
• Cluster analysis used to produce “benefit
• Clusters are formed to group respondent such
that segments have similar within a segment
and different across segments
• Conjoint Analysis also helps researchers to
simulate various competitive scenarios
• Scenarios are used to estimate how
respondents react to various scenarios
• It is used to understand how the set of
respondents would choose among a specified
set of profiles
Comparingconjoint analysis with other
• Its decompositional nature
• Specification of the variate
• The fact that estimates can be made at
• Its flexibility in terms of relationships
between dependent and independent
• In compositional models the researcher collects
ratings from the respondent on many product
characteristics and then relates theses ratings to
the to some overall preference rating to develop
a predictive model
• Conjoint analysis a type of decompositional
model, differs in that the researcher needs to
know only respondents overall preference for a
• Conjoint analysis employs a variate quite similar in
form to what is used in other multivariate techniques.
• The conjoint variate is a linear combination of effects
of the independent variables (levels of each factor) on
a dependent variable.
• The important difference is that in the conjoint
variate the researcher specifies both the independent
variables (factors) and their values (levels).
Separatemodels for eachindividual
• Conjoint analysis can be carried out at the
• other multivariate methods use each
respondent’s measures as a single
observation and then perform the analysis
using all respondents simultaneously.
Flexibilityin types of relationships
• Conjoint analysis not limited in types of
relationships required between the dependent
and independent variables
• Most dependence methods assume that a
linear relationship exists
• Conjoint analysis can easily handle non linear
relationships as well