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Conjoint Analysis
Y. İlker TOPCU, Ph.D.
www.ilkertopcu.net www.ilkertopcu.org www.ilkertopcu.info
www.facebook.com/yitopcu
twitter.com/yitopcu
Conjoint Analysis
• A statistical method
that can be used as
an indirect priority determination procedure.
• Requires decision makers
to rank or rate alternatives and
Derives priorities
that provide the best fit of the evaluations
for alternatives.
Conjoint Analysis
• A survey research tool that
predicts consumer preferences
in multi attribute decision making
where alternatives are evaluated
w.r.t. several attributes (factors)
in a wide variety of product and service context.
• It became popular in marketing research as it can
predict what consumers will buy when they faced
with the availability of many brands and a great
number of product characteristics.
Conjoint Analysis
• By systematically varying the characteristics of a
product or a service and
observing how survey participants react to these
product/service profiles,
the researcher can statistically deduce the scores for
each characteristic (factor)
with which participants may have been
subconsciously using to evaluate products/services.
Steps
1.
2.
3.
4.
Determining possible combinations
Evaluating possible combinations
Computing conjoint utilities
Revealing priorities
Determining Possible Combinations
• Levels within each factor must be developed.
• There would be many possible combinations of these
factor levels.
• By using experimental design principles of
independence and balance,
some of the combinations are carefully chosen;
therefore participants do not have to evaluate all
possible combinations.
Evaluating Possible Combinations
• In the 70’s, survey participants were requested to
evaluate each of many combinations that are printed
on separate cards one by one by ranking or rating on
a scale.
• In the 80’s, a computerized version called as
Adaptive Conjoint Analysis was utilized, which
could effectively gather more attributes and levels by
focusing on them that were most relevant to each
participant.
Evaluating Possible Combinations
• In the 90’s, Choice Based Conjoint (CBC) became
popular.
• With CBC, participants were requested to choose
among a certain number of possible combinations
instead of ranking them or rating each of them
individually.
CBC
• Nowadays CBC is widely used as consumers in real
life do not score each alternative, instead they simply
choose among them; which make CBC questions
seem more realistic.
• Generally, a certain number of possible combinations
with an additional “none” choice (that can be chosen
if none of the combinations is preferred) are
presented to the participants.
Computing Conjoint Utilities
• By utilizing regression analysis the scores of the
factors can be inferred.
• Sawtooth software can be used for the necessary
calculations required for statistical analysis based on
logistic regression.
• These scores are useful for determining the relative
priorities of each factor.
Computing Conjoint Utilities
• The scores are scaled to an arbitrary additive
constant within each factor.
• The arbitrary origin of the scaling within each factor
is based on dummy coding.
• When using “effects coding”, a specific kind of
dummy coding, scores are scaled to sum to zero
within each factor.
• In this case, the scores can be regarded as conjoint
utilities.
Revealing Priorities
• After finding the range in the utility values of a
factor (i.e. the difference between the maximum
utility and the minimum utility), the percentages
from relative ranges are calculated.
• These normalized ranges are the priorities of the
factors.
Case Study
CUSTOMER ORDER SELECTION
• Potential profit rate per unit of time
• Compatibility of potential order with available
capacity
• Customer credit of future business opportunity
• Negotiability level of production schedule for order
• Level of potential future order with higher profit
Developing Levels
A three-level scale (High-Medium-Low) is used for:
• Potential profit rate per unit of time
• Compatibility of potential order with available
capacity
• Customer credit of future business opportunity
• Negotiability level of production schedule for order
A two-level scale (Exists-Does not exist) is used for
• Level of potential future order with higher profit
Determining Possible Combinations
• By using experimental design,
90 hypothetical customer orders
are chosen among 162 of them
• 30 conjoint cards having 3 possible orders and
an additional “none” alternative are formed.
Evaluating Possible Combinations
CARD 1
Customer credit of future business
opportunity
The potential profit rate per unit of
time
The negotiability level of
production schedule for the order
The level of potential future order
with higher profit
The compatibility of potential
order with available capacity
Alternative 1
Alternative 2
Alternative 3
HIGH
MEDIUM
LOW
MEDIUM
HIGH
LOW
MEDIUM
LOW
HIGH
DOES NOT
EXIST
EXISTS
EXISTS
MEDIUM
LOW
HIGH
Alternative 4
NONE
Computing Conjoint Utilities
DM1
DM2
DM3
Compatibility of potential order with available capacity
HIGH 22.60
75.54
72.60
MEDIUM 29.31
43.44
44.42
LOW -51.92
-118.98
-117.01
Potential profit rate per unit of time
HIGH 19.41
78.54
40.92
MEDIUM 35.07
17.84
38.01
LOW -54.48
-96.38
-78.93
Customer credit of future business opportunity
HIGH 22.13
43.28
53.66
MEDIUM 62.45
-14.78
31.62
LOW -84.59
-28.50
-85.29
Computing Conjoint Utilities
DM3
DM2
DM1
Negotiability level of production schedule for the order
-3.58
-21.59
HIGH -47.14
16.47
25.89
MEDIUM -1.11
-4.30
LOW 48.25
Level of potential future order with higher profit
-5.65
EXISTS 43.40
DOES NOT EXIST -43.40
5.65
-12.89
-11.12
11.12
Revealing Priorities
DM1
DM2
DM3
Ave.
Compatibility of potential
order w. avail. capacity
16.25%
38.90%
37.92%
31.02%
Potential profit rate per unit
of time
17.91%
34.98%
23.97%
25.62%
Customer credit of future
business opportunity
29.41%
14.36%
27.79%
23.85%
The negotiability level of
prod. schedule for order
19.08%
9.50%
5.87%
11.48%
Level of potential future
order with higher profit
17.36%
2.26%
4.45%
8.02%
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