Conjoint Analysis
Session: March 22-26; 2010
1. Objectives/Purpose
An extremely powerful and useful analysis tool
Used to determine the relative importance of
various attributes to respondents, based on
their making trade-off judgments
Useful in
Helping to select features on a new product/service
Predicting sales
Understanding decision processes/consumer
judgments
Marketing Research 7th
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1. Objectives (ctd)
E.g.
UvT: What drives students’ choice of
(and willingness to pay for) a room?
How can Albert Heijn compose its
assortment of cereals to improve
customer appeal?
Nike: What are the optimal features
for a new type of sneakers?
Marketing Research 7th
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2. Steps
Design
Assumptions
Model estimation and fit
Interpreting results
Validation
Marketing Research 7th
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2.1. Design
Method:
Select attributes (number, type)
Choose model form (additive? dependent
variable?)
Individual or aggregate estimation?
Traditional, Choice-based or Adaptive
conjoint?
Marketing Research 7th
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2.1. Design
Stimuli: Factor (= Attribute) selection
Criteria:
Differentiate
Able to communicate
Actionable
Price  Could enter as separate attribute,
mind correlations or infeasible stimuli
Levels:
Strive for Balance
Range: Feasible, Relevant, Stretch
Marketing Research 7th
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Aaker, Kumar, Day
2.1. Design
Stimuli: Utility specification
Part worth, Ideal Point or Linear model?
Main effects or interactions?
Marketing Research 7th
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Aaker, Kumar, Day
Alternative Models
Linear
Ideal Point
7,5
8
7
7
6
6,5
5
6
4
5,5
3
5
2
4,5
1
4
0
5
10
15
20
25
30
No sugar
35
Part Worth
8
7
6
5
4
3
2
1
0
1000cc
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3000cc
Medium
Sweetness
2.1. Design
Data collection:
Presentation:
Trade-off
Full profile (Fractional factorial)?
Preference Measure:
Ranking
Rating
Choice (no-)
Task per respondent (Regular, Adaptive,
Hybrid?)
Marketing Research 7th
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Aaker, Kumar, Day
Example: Sneakers
3 attributes, 3 levels each:
3
2
1
Sole: Rubber, Polyurethane, Plastic
1
2
3
Upper: Leather, Canvas, Nylon
1
2
3
Price: 30$, 60$, 90$
Fractional Factorial: 9 out of 27 profiles (3
sole x 3 upper x 3 price) evaluated
Marketing Research 7th
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Example: Profiles for Sneakers
Stimulus
Sole
= attribute 1
Upper
Price
= attribute 2 = attribute 3
1
Rubber
(1)
Leather
(1)
30
(1)
2
Rubber
(1)
Canvas
(2)
60
(2)
3
Rubber
(1)
Nylon
(3)
90
(3)
4
Polyurethane (2)
Leather
(1)
60
(2)
5
Polyurethane (2)
Canvas
(2)
90
(3)
6
Polyurethane (2)
Nylon
(3)
30
(1)
7
Plastic
(3)
Leather
(1)
90
(3)
8
Plastic
(3)
Canvas
(2)
30
(1)
9
Plastic
(3)
Nylon
(3)
60
(2)
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(attribute level)
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2.2. Assumptions
Few statistical assumptions
Theory-driven design, estimation and
interpretation
Overfitting?
GIGO (Garbage in Garbage out)?
Marketing Research 7th
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2.3. Model Estimation and Fit
E.g. Additive Model, part-worths:
m
ki
U ( X )    ij xij
i 1 j  1
where U(X)=utility of alternative X, m=# attributes,
ki=#attribute levels of attribute i, xij=1 for level j of i,
0 elsewhere, ij=part worth for level j of i
Bv (Usneakers2)= 11 + 22 + 32
2.3. Model Estimation and Fit (ctd)
Purpose: Find levels of ij that reflect consumers’
stimuli evaluations as closely as possible
Method:
Ranking: MONANOVA, Linmap
Rating: Dummy-variable regression
Choice: MNL or Probit model
Fit:
Correlate actual/predicted ranks
Hit rate
R2
Marketing Research 7th
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Aaker, Kumar, Day
Example: Profiles for Sneakers
Stimulus
Sole
Upper
Price
= attribute 1 = attribute 2 = attribute 3
1
Rubber
(1) Leather
(1)
30
(1)
2
Rubber
(1) Canvas
(2)
60
(2)
3
Rubber
(1) Nylon
(3)
90
(3)
4
Polyrethane (2) Leather
(1)
60
(2)
5
Polyrethane (2) Canvas
(2)
90
(3)
6
Polyrethane (2) Nylon
(3)
30
(1)
7
Plastic
(3) Leather
(1)
90
(3)
8
Plastic
(3) Canvas
(2)
30
(1)
9
Plastic
(3) Nylon
(3)
60
(2)
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(attribute level)
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2.3. Model Estimation and Fit (ctd)
Example Sneakers: Preference ratings
and Variable Indicator Coding (last level
= Base) :
Sneaker
1
2
3
4
5
6
7
8
9
Preference
Rating
Rubber
9
7
5
6
5
6
5
7
6
Poly
1
1
1
0
0
0
0
0
0
Sole
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Leather Canvas
0
0
0
1
1
1
0
0
0
1
0
0
1
0
0
1
0
0
Upper
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60$
30$
0
1
0
0
1
0
0
1
0
1
0
0
0
0
1
0
1
0
Price
0
1
0
1
0
0
0
0
1
2.3. Model Estimation and Fit (ctd)
Regression Statistics
Multiple R
0.967
R Square
0.934
Adjusted R Square
0.738
Standard Error
0.667
Observations
9
Intercept
Sole1
Sole 2
Upper 1
Upper 2
Price 1
Price 2
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4,222
b11=1.000
b12=-0.333
b21=1.000
b22=0.667
b31=2.333
b32=1.333
0.588
0.544
0.544
0.544
0.544
0.544
0.544
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7,181
1,837
-0.612
1,837
1,225
4,287
2,449
0.019
0.208
0.603
0.208
0.345
0.05
0.134
2.4. Interpreting results
Assess part-worths for attribute levels
Evaluate attribute importance
Use choice simulator
Marketing Research 7th
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Assess part-worths for
attribute levels
Example: Indicator Coding, Attribute=Sole
b11= coëfficiënt Sole1=1
b12= coëfficiënt Sole2=-.333
b13=0
Average: (1-.333+0)/3=.222
Calculate part worths such that sum = 0?
-> 11= b11-Average=1-.222=. 778
12= b12-Average=-.333-.222-.556
13= b13-Average=-.222
Marketing Research 7th
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Aaker, Kumar, Day
Example Sneakers:
Outcome Part worth calculations
Sole: 11=.778, 12= -.556, 13= -.222
Upper: 21=.445, 22= .111, 23= -.556
Price: 31=1.111, 32= .111, 33=-1.222
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Part Worths Sneakers
Upper
Sole
1
0,6
0,8
0,4
0,6
0,2
0,4
0,2
0
0
-0,2
Leather
-0,2
rubber
Poly
Canvas
Plas
-0,4
-0,4
-0,6
-0,6
-0,8
-0,8
Price
1,5
1
0,5
0
30
60
-0,5
-1
-1,5
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90
Nylon
Wi 
Evaluate attribute
importance
Ii
m
I
i
i
I i  max j (  ij )  min j (  ij )
where i=attribute, j= attribute level, m= number of attributes,
Ii = range of part worths for attribute,
Wi = attribute importance (share)
Marketing Research 7th
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Aaker, Kumar, Day
Attribute importance
Example Sneakers:
Sole: .286
Upper: .214
Price: .5
100 €
Marketing Research 7th
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60 €
Calculating Attribute importance
Sole
Upper
Price
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Ii
.778+.556 =1.334
.445+.556 =1.001
1.111+1.222 =2.333
 = 4.668
Aaker, Kumar, Day
Wi
1.334/4.668=.286
1.001/4.668=.214
2.333/4.668=.5
2.5. Validation
On holdout sample?
Clusters of respondents
Alternative Models?
Significance (overfitting)?
Marketing Research 7th
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3. Case:
Channel and Price Offers for
Safety Products
Problem Statement
A company specialized in safety-related
products, intends to improve its
channel- and pricing approach for
different types of products.
 Preferred combination, by consumers,
of information channel, selling channel,
and price level?
Marketing Research 7th
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Problem Statement (ctd)
Consumers can obtain information,
and/or purchase products,
through the internet (company’s website)
from a safety consultant /advisor (in home)
in B&M stores
Prices can deviate from a
‘recommended price’
Marketing Research 7th
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Research Setup
Use conjoint analysis to assess consumer
preference for alternative channel/price
combinations
Conduct analysis for three types of products:
Bicycle Lock
Fire Blanket
Alarm system
Marketing Research 7th
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Design: Stimuli
Attributes
Attribute
Information
Channel
Acquisition
Channel
Price
Levels
Internet
Advisor
Internet
Advisor
Recommended
–10%
Recommended
Utility: Part worths, additive
Marketing Research 7th
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Brick&Mortar
Store
Brick&Mortar
Store
Recommended
+10%
Design: Data Collection
Traditional Method:
Full Profile approach
27 possible combinations: fractional,
orthogonal design -> 9
profiles/product/respondent
Preference measure: rating
Respondent task: regular, 2 products
Marketing Research 7th
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Data Collection (ctd)
Info products/recommended prices
(e.g. fire blanket 46.05Euros, Alarm system
315.70Euros, )
Info channels:
B&M store (where, what, chain)
Internet (site, what)
Advisors: where, education/expertise
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Scenario (Stimulus) 1
Imagine
You use the internet to gather information
on the fire blanket
You purchase the fire blanket in the store
The recommended price is 46.05Euros
In the store, you pay this recommended
price –10%
How do you rate this scenario? …./100
Marketing Research 7th
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Model and Variable Coding
Dataset: see File Caseconj.sav
Cases= respondents*profiles
Dummy variable regression per product
and across respondents,
dependent = rating
Independent = 6 dummy variables (TI, TA,
II, IA, PR, PL): reference scenario
=transaction and info in B&M, higher price.
Marketing Research 7th
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Estimation Results
See output file Caseconj.spo
Marketing Research 7th
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Interpretation
Part Worths and Attribute importance
E.g. Fire Blanket:
Information channel no significant impact
Transaction channel (.365):
Internet -7.78, Advisor -.1, Store 7.88
Price (.635)
Low 15.22, Medium –2.83, High –12.38
Marketing Research 7th
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Validation
Estimation Sample: Correlation between
true and predicted scores? (Fire
Blankets: .435)
Holdout sample:
Re-estimate and compare coefficients?
Correlate true and predicted scores in
holdout
Marketing Research 7th
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Outcome
Attribute importance?
E.g. Bicycle Lock: First price (27.6%), then
transaction channel (15.7%), info channel not
important (1.5%)
Most appealing offer customer:
E.g. Bicycle Lock: Store, Low price. Utility: 7.88
+15.23 =23.11
Trade off: e.g. Bicycle Lock
Store, medium price: 7.88-2.83=5.05
Internet, low price: -7.78+15.22=7.44
Prefer latter option!
Marketing Research 7th
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Outcome (ctd)
Customer heterogeneity?
E.g. Male vs female
Individual analysis?
Product differences in attribute
significance, importance, part worths!
E.g. Best info channel depends on product:
Bicycle Lock: store, Alarm system: advisor
Marketing Research 7th
Edition
Aaker, Kumar, Day