Marketing Strategy
Conjoint Analysis and Product Design
“An economic forecaster is like a
cross-eyed javelin thrower: they
don't win many accuracy contests,
but they keep the crowd's
attention.”
Anonymous
Conjoint
• What happens when I ask someone what’s important
with respect to retailers?
– “In retailers, I like assortment, low price, convenience.”
• If I want to know what they REALLY care about, I
have to ask them to make trade-offs
– “If I have to give up some assortment to get a bit lower price,
fine, but I don’t want to give up convenience.”
Conjoint Analysis makes these tradeoffs
systematic so we can quantify and better
understand the results.
1
Why Use Conjoint Analysis?
Wendy’s “Big Classic”
Nine buns options:
Forty special sauces:
Lettuce
hard, soft
sesame, poppy
cold, toasted, or warmed
square, round
(24 options)
steak sauce
hot sauce
mustard
salad dressing
mayonnaise
chopped
shredded
leaf
Tomato
Four box styles
thin
medium
thick
Conjoint Analysis
Features Considered Jointly
Conjoint Analysis is a set of methods designed to measure
consumer preferences for a multi-attribute product.
Steps in Conjoint Analysis
Identification of the Determinant Attributes
Choice of data collection procedure
Data collection
Data analysis
Strategy Assessment
2
Language of Conjoint
• Attributes
– any clearly definable feature or characteristic
– e.g., cost per flight
• Levels of Attributes
– range of options available for each attribute that
determines the best and worst the attribute can be
– must have at least two levels for each attribute
– e.g., $4, $6, and $8
Language of Conjoint (cont.)
• Preference model
– Comparison of overall preferences (utilities) for a
set of alternatives for an individual respondent
• Importance Weights
– the degree to which variation in the level of an
attribute affects the overall utility
– calculated as the range of part-worth utilities for
the attribute levels standardized such that the partworth for the worst level is set to zero
3
Multiattribute Model of Attitudes
• Identify relevant attributes
• Determine importance weights (W) for those attributes
• Determine beliefs about brand on those attributes (a1)
• Sum all attributes used in evaluating the brand weighted by
the value of each attribute
Customer Preferences
• Objective: measure customer’s utility function
U =  Price +  CPU +  Monitor +  Brand
• Estimating expected market share
Price
Process.
Monitor
Brand
Total
Importance
.4
.3
.2
.1
1
Dell
8
9
10
8
8.7
Compaq
10
6
5
7
7.5
IBM
7
10
7
10
8.2
Share
24.4
4
Another Example
Price
Brand
Horsepower
Upholstery
Sunroof
$23,000
Toyota
220 HP
Cloth
Yes
$25,000
Volkswagen
250 HP
Leather
No
$27,000
Saturn
280 HP
$29,000
Kia
Computerized Survey
5
Conjoint Output
Attribute
Level
Utility (part-worths)
T-value
Price
$23,000
2.10
14.00
Brand
Horsepower
Upholstery
Sunroof
$25,000
1.15
7.67
$27,000
-1.56
10.40
$29,000
-1.69
11.27
5.00
Toyota
0.75
Volkswagen
0.65
4.33
Saturn
-0.13
0.87
Kia
-1.27
8.47
220 HP
-2.24
14.93
250 HP
1.06
7.07
280 HP
1.18
7.87
Cloth
-1.60
10.67
Leather
1.60
10.67
Yes
0.68
4.53
No
-0.68
4.53
Utility
U =  Toyota +  280 HP +  Leather +  No sunroof +  $23,000
U=
0.75 +
1.18 + 1.60
– 0.68
+ 2.10 = 4.95
U=
0.65 (Volkswagen)
= 4.85
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Attribute
Level
Utility (part-worths)
T-value
Price
Brand
$23,000
2.10
14.00
Toyota
0.75
5.00
Volkswagen
0.65
4.33
Horsepower
280 HP
1.18
7.87
Upholstery
Leather
1.60
10.67
Sunroof
No
-0.68
4.53
6
Conjoint Output
Attribute
Level
Utility (part-worths)
T-value
Price
$23,000
2.10
14.00
Brand
Horsepower
Upholstery
Sunroof
$25,000
1.15
7.67
$27,000
-1.56
10.40
$29,000
-1.69
11.27
5.00
Toyota
0.75
Volkswagen
0.65
4.33
Saturn
-0.13
0.87
Kia
-1.27
8.47
220 HP
-2.24
14.93
250 HP
1.06
7.07
280 HP
1.18
7.87
Cloth
-1.60
10.67
Leather
1.60
10.67
Yes
0.68
4.53
No
-0.68
4.53
If we assume a linear relationship
Utility spread
2.10 – 1.36 = 0.74
Dollar spread
($25k and $27k)
Utility spread between the
two tested price points
($25k and $27k)
Attribute
Level
Utility (part-worths)
T-value
Price
$23,000
2.10
14.00
Sunroof
$25,000
1.15
7.67
$27,000
-1.56
10.40
Yes
0.68
No
-0.68
1.36
4.53
4.53
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Share Forecasting
Multinomial
Logit Model
Utilities and Weights
8
Share Forecasting
Multinomial
Logit Model
SAKE = Design of 4 + Max Freq of 34 + Power of 68 + Price of $363
= 0.45 + 0.44 + 0.49 + 0.54 = 1.92 What would its share be?
1.
2.
Design of 5 + Max Freq of 50 + Power of 52 + Price of $284
Design of 3 + Max Freq of 26 + Power of 100 + Price of $205
1. 0.45 + 0.48 + 0.13 + 1 = 2.06
2. 0.43 + 0.38 + 0.54 + 0.24 = 1.59
Example – Rank these Beers
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Example: Fitness Facility
Two attribute
Lockers & Sauna
 Lockers come in
small, medium
and large
 Sauna exists or it
doesn’t
 There are six
different
combinations
Sauna
Locker
Yes
No
Small (always)
Large (daily)
Medium
(always)
Rank 2
Rank 4
Rank 1
Rank 3
Large (daily)
Rank 5
Rank 6
We can assign “utility” points
Sauna
Locker
Small (always)
Large (daily)
We can assign
points for each
ranking.
In this example,
the top ranked
choice gets the
most point!
Yes
No
Rank 2 4 Rank 4 2
Average = 3
Medium
(always)
Rank 1 5
Rank 3 3
Average = 4
Large (daily)
Rank 5 1
Rank 6 0
Average = 0.5
Average = 3.33
Average = 1.67
Sauna: Y = 3.33, N = 1.67
Locker: S = 3, M = 4, L = 0.5
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Conjoint Analysis
“Tease” out a “value system” from individual
preferences (numerical values from rank orders in
this example)
The individual is averse to not
having a storage locker “always”
(medium = 3, large = 0.5)
Lockers
Sauna
Unwilling to trade permanent
storage for Sauna
decrease = 3 to 0.5 = 2.5 loss
increase = 1.67 to 3.33 = 1.66 gain
NET LOSS 0.84
s m l
Size
n y
Includes
gap
Markstrat
11
The individual’s Value System
Product
Locker
medium
small
medium
small
large
large
Value
Sauna
Rank
yes 4 + 3.33 = 7.33 1
yes 3 + 3.33 = 6.33 2
no
4 + 1.67 = 5.67 3
no
3 + 1.67 = 4.67 4
yes .5 + 3.33 = 3.83 5
no
.5 + 1.67 = 2.17 6
Original
Rank
1
2
3
4
5
6
Importance Weights
Product
Locker
medium
small
medium
small
large
large
Sauna
yes 4 + 3.33 = 7.33
yes 3 + 3.33 = 6.33
no
4 + 1.67 = 5.67
no
3 + 1.67 = 4.67
yes .5 + 3.33 = 3.83
no
.5 + 1.67 = 2.17
Sauna Range
1.67 – 3.33 = 1.66
Locker Range
0.5 – 4 = 3.5
3.5 + 1.66 = 5.16
Importance of Lockers
1.66/5.16 = 0.32
Importance of Sauna
3.5/5.16 = 0.68
12
Importance of Horsepower
Attribute
Level
Utility (part-worths)
T-value
Price
$23,000
2.10
14.00
$29,000
-1.69
11.27
Brand
Toyota
0.75
5.00
Kia
-1.27
8.47
Horsepower
220 HP
-2.24
14.93
280 HP
1.18
7.87
Upholstery
Sunroof
Cloth
-1.60
10.67
Leather
1.60
10.67
Yes
0.68
4.53
No
-0.68
4.53
Utilities and Weights
High
Design
Max Freq
Power
Price
Low
0.45
0.51
0.72
1
Diff
0.35
0.29
0.05
0
SUM
0.10
0.22
0.67
1.00
1.99
Weight
0.05
0.11
0.34
0.50
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Importance Weights by Segment
Widely Used
•
•
•
•
•
•
Fujitsu - cellular phones
Levi Strauss - blue jeans
Marriott Corporation - hotels
Sunbeam - food processors
condominium design and pricing
snowmobiles, credit cards, aircraft, rural
health care, technical information services
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