Applications non parametric conjoint analysis to pricing, market share estimations, product development.

1. Index
INTRODUCTION OF CONJOINT ANALYSIS
Definitions and goals
Parametric Conjoint Approaches
Nonparametric approaches
Permutation method
Parametric bootstrap
Outcomes and Shortcomings
Average Marginal Component Effect

2. Marketing Strategy
Market Share Estimation
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Sales forecasting
Conjoint Analysis Applications
Partial-worths Estimation
CCDVTP
“Create, Communicate, Deliver
the Value to the Target market at a
Profit”
Philip Kotler
VALUE CLIENTS
?
PRODUCT
PROFITS
?
How do custumers perceive the value we create?

3. Parametric Conjoint Analysis applied to a new patent
Anti-theft patent for bicycles
Rating marketing experiment applied to a company interested in evaluating his patent: an anti-theft
product for bike with an innovative characteristic was developed.
Full integrated
Integration: it is a characteristic that keeps the GPS
device safe from the burglar
3 attributes were taken into account:
External/camouflaged
External/visible
Difficult, technician needed
Maintenance/installation, this is a characteristic about
charging the battery with three levels:
Difficult, no technician needed
Easy
Sound alarm, presence of sound alarm with two levels:
Yes – the alarm is present
No – the alarm is not present
The goal: to figure out if a full integration and the insertion of an alarm could be a competitive
advantage that allowed to get a higher market share.
Types of integrations:

4. Application of Conjoint Analysis to a new patent
Outcomes- Partial worths - Importance
1.22
0.30
-1.52
-0.77
-0.12
0.89
0.48
-0.48
1.18
0.33
-1.50
-0.65
-0.10
0.75
0.49
-0.49
-2
-1.5
-1
-0.5
0
0.5
1
1.5
Integ
Est-inv
Est-vis
Complex-tec
Complex-noTec
Easy
Yes
No
Partial worths utilities
Whole
sample
Residuals:
Min 1Q Median 3Q Max
-5,6375 -0,7617 0,2117 0,7805 4,2156
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6,05156 0,06942 87,170 < 2e-16
factor(Integration/invisibility)1 1,17682 0,08503 13,841 < 2e-16
factor(Integration/invisibility)2 0,32760 0,09350 3,504 0,000495
factor(Easy maintenance/installation)1 -0,64635 0,08063 -8,017 6,19e-15
factor(Easy.maintenance/installation)2 -0,10417 0,10587 -0,984 0,325571
factor(Sound.alarm)1 0,48672 0,07449 6,534 1,42e-10
Residual standard error: 1,392 on 570 degrees of freedom
Multiple R-squared: 0.3872, Adjusted R-squared: 0.3818
F-statistic: 72.02 on 5 and 570 DF, p-value: < 2,2e-16

5. Application of Conjoint Analysis to a new patent
Outcomes – Market Share
Rocket
26%
Nigiloc
10%Spybike S.
26%
Lock8
10%
The Cricket
13%
Coban
15%
LogitModel - Rocket without alarm
Rocket
48%
Nigiloc
7%
Spybike S.
19%
Lock8
4%
The
Cricket
10%
Coban
12%
LogitModel - Rocket with alarm
Total
Utility
Max
Model
BTL
Model
Logit
Model
Rocket without
alarm
7,51 28,125 18,62 25,98
Nigiloc 6,11 3,12 15,19 9,77
Spybike
Seatpost
7,51 28,13 18,62 25,98
Lock8 5,8 15,62 14,56 10,38
The Cricket 6,66 6,25 16,52 12,74
Coban 6,67 18,75 16,5 15,16
Product
Total
Utility
Max
Model
BTL
Model
Logit
Model
Rocket with
alarm
8,50 84,38 21,13 47,71
Nigiloc 6,11 0,00 15,19 7,45
Spybike
Seatpost
7,51 4,69 18,58 19,00
Lock8 4,82 3,12 12,02 4,22
The Cricket 6,66 3,12 16,56 9,83
Coban 6,67 4,69 16,52 11,79

6. Procedure to estimate Product Demand
Italian Potential
market
Survey
Analysis
Target market
Statistic
methods
Optimistic
Scenario
Pessimistic Scenario
Sales
Sales during the
first 3 years
Logistic
index
Market Share
Conjoint
analysis
0
5,000
10,000
15,000
20,000
25,000
Year 0
Year 1
Year 2
1.291
4.519
6.456
4.030
14.106
20.152
Sales Sales during the first 3 years
Sales - Worst
case scenario
Sales - Best case
scenario

7. Limits of previous procedure
Assumptions and diagnostics
“When the assumptions are not met the results may not be trustworthy, resulting in a Type
I or Type II error, or over- or under-estimation of significance or effect size(s)”.
Osborne, Jason & Elaine Waters , North Carolina State University and University of Oklahoma
This is confirmed by the following
diagnostic procedure
Data indicate the assumptions of normality and
homoschedasticity may be violated.

8. NonParametric methods
Permutation method
Run regression by respondents
and store the obtained estimates
This approach requires a more relaxed assumption that is exchangeability.
𝛽11 𝛽12 … 𝛽1𝑚(𝑙−1)
𝛽21 𝛽22 … 𝛽2𝑚(𝑙−1)
…
𝛽 𝑛1 𝛽 𝑛1 … 𝛽 𝑛𝑚(𝑙−1)
Perform a sign or signed-rank test,
where for each alternatives the
hypothesis are:
𝐻0 : β=0
𝐻1 : β≠0
(Intercept) Full-integ
External-
Camouflaged
Complex-
technician
Complex-no-
technician
Sound-
alarm-yes
Sign Test 0.00e-16 0.00e-16 2,26E-10 7,05E-12 1,562E-03 4,74E-09
Wilcoxon 3,61E-06 3,78E-06 6,98E-06 4,16E-06 1,18E-02 6,66E-06
P values
"Permutation tests for between-unit fixed effects in multivariate generalized linear mixed models,
Finos”(2014)

9. NonParametric methods
Market share – Parametric bootstrap
In order to add the uncertainty into the model we have run a simulation in which, for each loop, the beta vector is
computed by taking into account the estimates and the standard errors of the betas.
𝑌𝑖,𝑗,𝑠 = β𝑖𝑛𝑡,𝑓𝑢𝑙𝑙,𝑠 𝑤𝑖𝑛𝑡,𝑓𝑢𝑙𝑙 + β𝑖𝑛𝑡,𝑓𝑢𝑙𝑙,𝑠 𝑤𝑖𝑛𝑡,𝑒𝑥𝑡−𝑖𝑛𝑣 + ⋯ + 𝜀𝑖𝑗𝑠
Rating for product
j and respondent i
in simulation s
Dummy variable:
0 or 1
Normal distribution which the estimate
for each loop will be extracted from
μ=βattr,level
σ =𝑆. 𝐸.βattr,level
Normal distribution from which the rumor
for each respondent and product, for each
loop will be extracted
μ= 𝑥 𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙
σ = 𝑠. 𝑑. 𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙
Calculate for each
simulation the MKS
of the products

10. Nonparametric methods - AMCE
CONJOINT ANALYSIS APPLIED TO FOOD AND BEVERAGE SECTOR
Attribute Level Estimate Std. Err z value Pr(>|z|) Significance Holm adjust.
consistency Plain 0.0392 0.005 69.273 4,29E-08 *** 8,58E-06
consistency Crunchy 0.0899 0.006 141.066 3,46E-41 *** 1,38E-38
organic No -0.1567 0.005 -277.191 4,11E-165 *** 3,29E-162
price $5.99 -0.0896 0.006 -147.767 2,07E-45 *** 1,04E-42
price $8.99 -0.1605 0.006 -257.044 1,04E-141 *** 6,27E-139
Taste chocolate 0.1678 0.006 268.345 1,28E-154 *** 8,96E-152
taste Coconut 0.0769 0.006 121.243 7,85E-30 *** 2,36E-27
taste strawberries 0.0563 0.008 65.856 4,53E-07 *** 4,53E-05
Choice-based marketing experiment of an American industry of granola
Price $3.99, $5.99, $8.99
Organic yes,no
Consistency chewy, plain, crunchy
Taste cereal, chocolate, coconut, strawberries
Attribute Levels

11. Nonparametric methods - AMCE
Market Segmentation
Steps:
Collect priori segmentation information for each respondent
Choose a statistical approach to perform to CA data(in our case AMCE)
Run the method for each priori cluster and deal with multiplicity adjustment(Holm)
Interpret the results
Level Holm adj.-Healthy Holm adj.-Unhealthy
plain 9,89E-04 1,98E-09
crunchy 1,16E-10 1,76E-34
no 0,00E+00 1,55E-51
$5.99 2,42E-10 6,94E-41
$8.99 2,92E-31 1,96E-117
chocolate 3,74E-29 3,85E-134
coconut 2,26E-08 1,33E-19
strawberries 5,54E-173 4,75E-07
Bibliography: “Market Segmentation with Choice-Based Conjoint Analysis “, Wayne S.

12. Other applications - Pricing
7.50
8.00
8.50
9.00
9.50
10.00
10.50
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
3.99
4.29
4.59
4.89
5.19
5.49
5.79
6.09
6.39
6.69
6.99
7.29
7.59
7.89
8.19
8.49
8.79
Revenue(Millions$)
MarketShare
Price of our product ($)
Market Share VS Price MKS comp1
MKS comp2
MKS comp3
MKS Our-
prod
Our
Revenue
31.97%
27.47%
24.29%
16.27%17.10%
54.53%
10.18%
18.18%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
comp1 comp2 comp3 Our-prod
MKS old version /MKS new version
MKS
MKS - new feature
Price as continous
variable
Our Revenue: MKS x Price x Target market
10 000 000
Our old product version
Price Organic Consistency Taste
5,99no chewy strawberries
Our new product version
Price Organic Consistency Taste
5,99yes crunchy strawberries

13. Other applications - Pricing
15%
16%
16%
17%
17%
18%
18%
19%
5.99 6.04 6.09 6.14 6.19 6.24 6.29 6.34 6.39 6.44 6.49 6.54 6.59 6.64 6.69 6.74 6.79 6.84 6.89
Price ($)
MKS - Our new product
MKS - Our new product
MKS of the product with new features
MKS of the product with old features

14. Suggested approach
Finally we try to provide a best practice guideline for a Conjoint Analysis experiment
Holm adjstment for
Multiplicity
Collect data
from respondents using profiles with a
rondomized design
Choice-based CA
with AMCE or
Mnlogit model
Market Share
Tools or service Procedures
Cost for each response: 99c
Opensource Software
Opensource Software
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B2B
B2C
Who use R?
www.revolutionanalytics.com/companies-using-r