OBJECTIVES

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BACKGROUND

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APPROACH

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RESULTS

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DISCUSSION

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RESULTS

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CONCLUDING REMARKS

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RESULTS

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FUTURE WORK

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Acknowledgements

Market Share Uncertainty Modeling for Decision-analytic Concept Evaluation

Faculty Advisor: Dr. Shun TakaiDepartment of Mechanical and Aerospace Engineering

Student: Swithin Samuel RazuDepartment of Mechanical and Aerospace Engineering

Objectives1

Decision-Analytic Concept Selection (1)• Step 1: Construct an influence diagram

– Decisions• Product concepts : θ• Warranty policy : α• Price : p

– Uncertainties• Competition : ω• Market share : s• Market size : m• Warranty cost : w• Product cost : c

– Prospect• Profit

• Apply decision analysis (DA) [1,2] to consumer product conceptselection

• Model uncertainty in consumer product decision making– Previously applied to system concept selection for a public project [3]– Modeled uncertainty of the government’s option to cancel a project

Decision Uncertainty Prospect

-Form-Technology-Target requirements

Total cost

Price

Profit

Competition

Revenue

Market share

Product conceptUnit product

cost

Unit sold

Market size

Warranty policy

Unit warranty cost

Decision-Analytic Concept Selection (2)

• Step 2: Construct a decision tree

• Step 3: Model uncertainties directly relevant to concept selection– Market share : s (conditioned on θ, α, p, ω)– Warranty cost : w (conditioned on θ, α)– Product cost : c (conditioned on θ)

• Step 4: Choose a concept with the maximum expected utility ofprofit

Profit = (price - unit warranty cost - unit product cost) x unit sold= (price - unit warranty cost - unit product cost) x market size x market share= (p-w-c)ms

Expected utility = E[ u(profit) ]

Warranty Market Warranty Productpolicy size cost cost

θ1 α1 ω1

θi αj ωk

θI αJ ωK

PriceConcept

Profit

ProspectCompetitionMarketshare

p [0, ) m [0, )s | θ,α,p,ω [0,1] w | θ,α [0, ) c | θ [0, )

2

Scope of this research

Market Share Uncertainty Modeling

• Step 1: Model market share uncertainty (distribution) by integrating conjoint analysis and bootstrap– Conjoint analysis [4-6]

• Conjoint analysis enables designers to estimate eachcustomer’s utility of a product concept from which marketshare is estimated

– Bootstrap [7]• Construct distribution from a single sample data applying

sampling with replacements– Concept definition

• New concept versus competitor concepts C1 and C2

3

Scope of this research

Type Fuel Efficiency Warranty PriceConvertible Non-hybrid

C1 10 3 / 36,000 $20,000

2 passengers (miles per gallon) (years/miles)

Hybrid

C2 40 4 / 50,000 $50,000

(miles per gallon) (years/miles)

Sedan

5 passengers

Versus

Warranty PriceType Fuel EfficiencySUV

10 (Gasoline-powered) 3 / 36,000 $20,000 25 (Gasoline-powered) 4 / 50,000 $35,000 40 (Hybrid) 5 / 60,000 $50,000

8 passengers (mpg) (years/miles)

Concept

SUV

8 passengers

Choice-based Conjoint Analysis (CBC)

Car

Type

Convertible

2 passengers

(-1)

Sedan

5 Passengers

(0)

SUV

8 Passengers

(+1)

Fuel Efficiency

10 (miles/gallon) (-1)

40 (miles/gallon) (0)

20 (miles/gallon) (+1)

Warranty

3 years/36k miles

(-1)

4 years/50k miles

(0)

5 years/60k miles

(+1)

Price

$ 20,000

(-1)

$ 35,000

(0)

$ 50,000

(+1)

• Conjoint analysis procedure illustration using automobiles asan example

Procedure IllustrationProduct

Product attributes

Product attribute levels

Step 2: Design choice sets using orthogonal array• 4 factors, 3 levels L27

orthogonal array• 27 choice sets with 3

choices in each set

Choice setsProduct concepts

Step 1: Identify product attributes• 2 performance attributes

– Type, fuel efficiency

• 2 marketing attributes– Warranty, price

• 3 levels for each attribute

4

TypeSurvey Set Price Warranty Fuel Effi ciency Price Warranty Fuel Effi ciency Price Warranty Fuel Effi ciency

1 $20,000 3 years/30,000 miles 10mpg $20,000 3 years/30,000 miles 10mpg $20,000 3 years/30,000 miles 10mpg2 $20,000 3 years/30,000 miles 10mpg $20,000 4 years/50,000 miles 20mpg $35,000 4 years/50,000 miles 20mpg3 $20,000 3 years/30,000 miles 10mpg $20,000 5 years/60,000 miles 40mpg $50,000 5 years/60,000 miles 40mpg4 $20,000 4 years/50,000 miles 20mpg $35,000 3 years/30,000 miles 10mpg $20,000 4 years/50,000 miles 20mpg5 $20,000 4 years/50,000 miles 20mpg $35,000 4 years/50,000 miles 20mpg $35,000 5 years/60,000 miles 40mpg6 $20,000 4 years/50,000 miles 20mpg $35,000 5 years/60,000 miles 40mpg $50,000 3 years/30,000 miles 10mpg7 $20,000 5 years/60,000 miles 40mpg $50,000 3 years/30,000 miles 10mpg $20,000 5 years/60,000 miles 40mpg8 $20,000 5 years/60,000 miles 40mpg $50,000 4 years/50,000 miles 20mpg $35,000 3 years/30,000 miles 10mpg9 $20,000 5 years/60,000 miles 40mpg $50,000 5 years/60,000 miles 40mpg $50,000 4 years/50,000 miles 20mpg

10 $35,000 3 years/30,000 miles 20mpg $50,000 3 years/30,000 miles 20mpg $50,000 3 years/30,000 miles 20mpg11 $35,000 3 years/30,000 miles 20mpg $50,000 4 years/50,000 miles 40mpg $20,000 4 years/50,000 miles 40mpg12 $35,000 3 years/30,000 miles 20mpg $50,000 5 years/60,000 miles 10mpg $35,000 5 years/60,000 miles 10mpg13 $35,000 4 years/50,000 miles 40mpg $20,000 3 years/30,000 miles 20mpg $50,000 4 years/50,000 miles 40mpg14 $35,000 4 years/50,000 miles 40mpg $20,000 4 years/50,000 miles 40mpg $20,000 5 years/60,000 miles 10mpg15 $35,000 4 years/50,000 miles 40mpg $20,000 5 years/60,000 miles 10mpg $35,000 3 years/30,000 miles 20mpg16 $35,000 5 years/60,000 miles 10mpg $35,000 3 years/30,000 miles 20mpg $50,000 5 years/60,000 miles 10mpg17 $35,000 5 years/60,000 miles 10mpg $35,000 4 years/50,000 miles 40mpg $20,000 3 years/30,000 miles 20mpg18 $35,000 5 years/60,000 miles 10mpg $35,000 5 years/60,000 miles 10mpg $35,000 4 years/50,000 miles 40mpg19 $50,000 3 years/30,000 miles 40mpg $35,000 3 years/30,000 miles 40mpg $35,000 3 years/30,000 miles 40mpg20 $50,000 3 years/30,000 miles 40mpg $35,000 4 years/50,000 miles 10mpg $50,000 4 years/50,000 miles 10mpg21 $50,000 3 years/30,000 miles 40mpg $35,000 5 years/60,000 miles 20mpg $20,000 5 years/60,000 miles 20mpg22 $50,000 4 years/50,000 miles 10mpg $50,000 3 years/30,000 miles 40mpg $35,000 4 years/50,000 miles 10mpg23 $50,000 4 years/50,000 miles 10mpg $50,000 4 years/50,000 miles 10mpg $50,000 5 years/60,000 miles 20mpg24 $50,000 4 years/50,000 miles 10mpg $50,000 5 years/60,000 miles 20mpg $20,000 3 years/30,000 miles 40mpg25 $50,000 5 years/60,000 miles 20mpg $20,000 3 years/30,000 miles 40mpg $35,000 5 years/60,000 miles 20mpg26 $50,000 5 years/60,000 miles 20mpg $20,000 4 years/50,000 miles 10mpg $50,000 3 years/30,000 miles 40mpg27 $50,000 5 years/60,000 miles 20mpg $20,000 5 years/60,000 miles 20mpg $20,000 4 years/50,000 miles 10mpg

Convertible Sedan SUV

Procedure Illustration

Step 3: Obtain a customer’s choice • Each customer is shown

27 sets of three automobiles at a time and asked to choose one from each set

5

Step 5: Repeat Steps 3 and 4 for 50 customers• Each customer

chooses a concept with the largest total sum of his/her attribute utilities

Step 4: Estimate a product Attribute utility using conjoint analysis• Obtain product attribute

utilities by applying MLE

A customer’s attribute utility obtained from the choice data

Concept 1

WarrantyPrice

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 2

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 3

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 4

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 5

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 6

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 7

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 8

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 9

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Pre

dic

ted

ma

rke

t s

ha

re

(%)

Predicted market share of each concept at various warranty and price

Choice-based conjoint survey

-25.0

15.49.6

-26

0

26

Convertible SUV Sedan

Part

Wor

th

Type

-7.6

-0.4

8.0

-9.0

0.0

9.0

10 25 40

Part

Wor

th

Fuel Efficiency (Miles per Gallon)

-2.8

-0.7

3.6

-4.0

0.0

4.0

3/36,000 4/50,000 5/60,000

Part

Wor

th

Warranty (Years/Miles)

13.2

0.8

-14.0

-15.0

0.0

15.0

20,000 35,000 50,000

Part

Wor

th

Price ($)

Bootstrap (BS)

• Bootstrap procedure illustration

Procedure Illustration

Step 2: Apply random sampling with replacements to the original dataset • For example, 200

replications (B=200)

Step 1: Randomly sample from the population

6

Step 3: Construct distribution of bootstrap sample mean and make an inference

Sample statisticsData (average)

Initial sample : { 1 , 2 , 3 , 4 , 5 , 6 , 7 } → 4Inference

Bootstrap samples (95% confidence interval)

1 st : { 5 , 1 , 7 , 1 , 2 , 4 , 4 } → 3.4

2 nd : { 6 , 4 , 2 , 5 , 7 , 3 , 6 } → 4.7

3 rd : { 2 , 3 , 5 , 2 , 1 , 1 , 1 } → 2.1

200 th : { 4 , 6 , 4 , 7 , 3 , 4 , 2 } → 4.3

Sampling with replacements

Distribution

1 4 7

Research Tasks

• Apply Bootstrap (BS) to choice-based conjoint analysis (CA)

Procedure Illustration

Step 2: Apply random sampling with replacements to the original dataset • 200 replications

(B=200)• Apply CA to 50

customers in each BS sample

7

Step 3: Construct histogram of predicted market share

Construct a histogram of predicted market share for chosen concept N

Step 1: Randomly sample50 customers

Warranty = 4 years/50,000 milesPrice = $ 50,000

SUV, 5 passengersFuel efficiency = 40 (miles/gallon)

Concept N

Market share{ B-th sample} Market share, sB 50% 100%

{1, 2, 3, …, n}

Market shareSTrue 50% 100%

SampleCustomer

Bootstrap Conjoint analysis Probability

{ 1st sample} Market share, s1 1{ 2nd sample} Market share, s2 0.5

Population Probability

1"True" market share, STrueCustomer {1, 2, 3, …, N}

0.5Random sampling

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Re

lative

Fre

qu

en

cy

Market Share (%)

Concluding Remarks

• Previously CA has been used to obtain point estimate formarket share

• Our approach integrates bootstrap and binomialinference with CA to obtain market share distributions

• This research demonstrates objective data utilizationmethods for customer preference uncertainty modeling

• And an integration of these uncertainty modelingmethods with a decision-analytic product conceptselection.

8

9

References1. Howard, R. A., 1988, “Decision Analysis: Practice and Promise,” Management Science, 34(6), pp. 679-695.2. Keeney, R. L., and Raiffa, H., 1976, Decision with Multiple Objectives: Preferences and Value Tradeoffs, John Wiley & Sons, New York, NY.3. Takai, S., and Ishii, K., 2008, “A Decision-Analytic System Concept Selection for a Public Project,” ASME Journal of Mechanical Design, 130(11), 111101

(10 pages).4. Green, P. E., and Srinivasan, V., 1978, “Conjoint Analysis in Consumer Research: Issues and Outlook,” Journal of Consumer Research, 5, pp. 103-123.5. Green, P. E., and Srinivasan, V., 1990, “Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice,” Journal of

Marketing, 54, pp. 3-19.6. Green, P. E., and Wind, Y., 1975, “New Way to Measure Consumers’ Judgments,” Harvard Business Review, 53, pp. 107-117.7. Efron, B., Tibashirani, R., 1993, An Introduction to the Bootstrap, Chapman & Hall, London

Future Work

Acknowledgements

This research is supported by the Intelligent Systems Center at the Missouri University of Science and Technology

9

• Future work– More complex cases involving increased number of competitors

and categories need to be researched– Integration of dynamic competition in decision-analytic

concept selection– Compare experiment-based and simulation-based approaches

Objectives1

Decision-Analytic Concept Selection (1)• Step 1: Construct an influence diagram

– Decisions• Product concepts : θ• Warranty policy : α• Price : p

– Uncertainties• Competition : ω• Market share : s • Market size : m• Warranty cost : w• Product cost : c

– Prospect• Profit

• Apply decision analysis (DA) [1,2] to consumer product concept selection

• Model uncertainty in consumer product decision making– Previously applied to system concept selection for a public project [3]– Modeled uncertainty of the government’s option to cancel a project

Decision Uncertainty Prospect

-Form-Technology-Target requirements

Total cost

Price

Profit

Competition

Revenue

Market share

Product conceptUnit product

cost

Unit sold

Market size

Warranty policy

Unit warranty cost

Decision-Analytic Concept Selection (2)

• Step 2: Construct a decision tree

• Step 3: Model uncertainties directly relevant to concept selection– Market share : s (conditioned on θ, α, p, ω)– Warranty cost : w (conditioned on θ, α)– Product cost : c (conditioned on θ)

• Step 4: Choose a concept with the maximum expected utility of profit

Profit = (price - unit warranty cost - unit product cost) x unit sold = (price - unit warranty cost - unit product cost) x market size x market share = (p-w-c)ms

Expected utility = E[ u(profit) ]

Warranty Market Warranty Productpolicy size cost cost

θ1 α1 ω1

θi αj ωk

θI αJ ωK

PriceConcept

Profit

ProspectCompetitionMarketshare

p [0, ) m [0, )s | θ,α,p,ω [0,1] w | θ,α [0, ) c | θ [0, )

2

Scope of this research

Market Share Uncertainty Modeling

• Step 1: Model market share uncertainty (distribution) by integrating conjoint analysis and bootstrap– Conjoint analysis [4-6]

• Conjoint analysis enables designers to estimate each customer’s utility of a product concept from which market share is estimated

– Bootstrap [7]• Construct distribution from a single sample data applying

sampling with replacements – Concept definition

• New concept versus competitor concepts C1 and C2

3

Scope of this research

Type Fuel Efficiency Warranty PriceConvertible Non-hybrid

C1 10 3 / 36,000 $20,000

2 passengers (miles per gallon) (years/miles)

Hybrid

C2 40 4 / 50,000 $50,000

(miles per gallon) (years/miles)

Sedan

5 passengers

Versus

Warranty PriceType Fuel EfficiencySUV

10 (Gasoline-powered) 3 / 36,000 $20,000 25 (Gasoline-powered) 4 / 50,000 $35,000 40 (Hybrid) 5 / 60,000 $50,000

8 passengers (mpg) (years/miles)

Concept

SUV

8 passengers

Choice-based Conjoint Analysis (CBC)

• Conjoint analysis procedure illustration using automobiles as an example

Procedure IllustrationProduct

Product attributes

Product attribute levels

Step 2: Design choice sets using orthogonal array• 4 factors, 3 levels L27

orthogonal array• 27 choice sets with 3

choices in each set

Choice setsProduct concepts

Step 1: Identify product attributes• 2 performance attributes

– Type, fuel efficiency

• 2 marketing attributes– Warranty, price

• 3 levels for each attribute

4

TypeSurvey Set Price Warranty Fuel Effi ciency Price Warranty Fuel Effi ciency Price Warranty Fuel Effi ciency

1 $20,000 3 years/30,000 miles 10mpg $20,000 3 years/30,000 miles 10mpg $20,000 3 years/30,000 miles 10mpg2 $20,000 3 years/30,000 miles 10mpg $20,000 4 years/50,000 miles 20mpg $35,000 4 years/50,000 miles 20mpg3 $20,000 3 years/30,000 miles 10mpg $20,000 5 years/60,000 miles 40mpg $50,000 5 years/60,000 miles 40mpg4 $20,000 4 years/50,000 miles 20mpg $35,000 3 years/30,000 miles 10mpg $20,000 4 years/50,000 miles 20mpg5 $20,000 4 years/50,000 miles 20mpg $35,000 4 years/50,000 miles 20mpg $35,000 5 years/60,000 miles 40mpg6 $20,000 4 years/50,000 miles 20mpg $35,000 5 years/60,000 miles 40mpg $50,000 3 years/30,000 miles 10mpg7 $20,000 5 years/60,000 miles 40mpg $50,000 3 years/30,000 miles 10mpg $20,000 5 years/60,000 miles 40mpg8 $20,000 5 years/60,000 miles 40mpg $50,000 4 years/50,000 miles 20mpg $35,000 3 years/30,000 miles 10mpg9 $20,000 5 years/60,000 miles 40mpg $50,000 5 years/60,000 miles 40mpg $50,000 4 years/50,000 miles 20mpg

10 $35,000 3 years/30,000 miles 20mpg $50,000 3 years/30,000 miles 20mpg $50,000 3 years/30,000 miles 20mpg11 $35,000 3 years/30,000 miles 20mpg $50,000 4 years/50,000 miles 40mpg $20,000 4 years/50,000 miles 40mpg12 $35,000 3 years/30,000 miles 20mpg $50,000 5 years/60,000 miles 10mpg $35,000 5 years/60,000 miles 10mpg13 $35,000 4 years/50,000 miles 40mpg $20,000 3 years/30,000 miles 20mpg $50,000 4 years/50,000 miles 40mpg14 $35,000 4 years/50,000 miles 40mpg $20,000 4 years/50,000 miles 40mpg $20,000 5 years/60,000 miles 10mpg15 $35,000 4 years/50,000 miles 40mpg $20,000 5 years/60,000 miles 10mpg $35,000 3 years/30,000 miles 20mpg16 $35,000 5 years/60,000 miles 10mpg $35,000 3 years/30,000 miles 20mpg $50,000 5 years/60,000 miles 10mpg17 $35,000 5 years/60,000 miles 10mpg $35,000 4 years/50,000 miles 40mpg $20,000 3 years/30,000 miles 20mpg18 $35,000 5 years/60,000 miles 10mpg $35,000 5 years/60,000 miles 10mpg $35,000 4 years/50,000 miles 40mpg19 $50,000 3 years/30,000 miles 40mpg $35,000 3 years/30,000 miles 40mpg $35,000 3 years/30,000 miles 40mpg20 $50,000 3 years/30,000 miles 40mpg $35,000 4 years/50,000 miles 10mpg $50,000 4 years/50,000 miles 10mpg21 $50,000 3 years/30,000 miles 40mpg $35,000 5 years/60,000 miles 20mpg $20,000 5 years/60,000 miles 20mpg22 $50,000 4 years/50,000 miles 10mpg $50,000 3 years/30,000 miles 40mpg $35,000 4 years/50,000 miles 10mpg23 $50,000 4 years/50,000 miles 10mpg $50,000 4 years/50,000 miles 10mpg $50,000 5 years/60,000 miles 20mpg24 $50,000 4 years/50,000 miles 10mpg $50,000 5 years/60,000 miles 20mpg $20,000 3 years/30,000 miles 40mpg25 $50,000 5 years/60,000 miles 20mpg $20,000 3 years/30,000 miles 40mpg $35,000 5 years/60,000 miles 20mpg26 $50,000 5 years/60,000 miles 20mpg $20,000 4 years/50,000 miles 10mpg $50,000 3 years/30,000 miles 40mpg27 $50,000 5 years/60,000 miles 20mpg $20,000 5 years/60,000 miles 20mpg $20,000 4 years/50,000 miles 10mpg

Convertible Sedan SUV

Procedure Illustration

Step 3: Obtain a customer’s choice • Each customer is shown

27 sets of three automobiles at a time and asked to choose one from each set

5

Step 5: Repeat Steps 3 and 4 for 50 customers• Each customer

chooses a concept with the largest total sum of his/her attribute utilities

Step 4: Estimate a product Attribute utility using conjoint analysis• Obtain product attribute

utilities by applying MLE

A customer’s attribute utility obtained from the choice data

Concept 1

WarrantyPrice

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 2

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 3

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 4

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 5

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 6

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 7

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 8

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Concept 9

-1 0 +1 -1 0 +1 -1 0 +1

-1 -1 -1 0 0 0 +1 +1 +1

Pre

dic

ted

m

arke

t sh

are

(%)

Predicted market share of each concept at various warranty and price

Choice-based conjoint survey

-25.0

15.49.6

-26

0

26

Convertible SUV Sedan

Part W

orth

Type

-7.6

-0.4

8.0

-9.0

0.0

9.0

10 25 40Pa

rt Wort

h

Fuel Efficiency (Miles per Gallon)

-2.8

-0.7

3.6

-4.0

0.0

4.0

3/36,000 4/50,000 5/60,000

Part W

orth

Warranty (Years/Miles)

13.2

0.8

-14.0

-15.0

0.0

15.0

20,000 35,000 50,000

Part W

orth

Price ($)

Bootstrap (BS)

• Bootstrap procedure illustration

Procedure Illustration

Step 2: Apply random sampling with replacements to the original dataset • For example, 200

replications (B=200)

Step 1: Randomly sample from the population

6

Step 3: Construct distribution of bootstrap sample mean and make an inference

Sample statisticsData (average)

Initial sample : { 1 , 2 , 3 , 4 , 5 , 6 , 7 } → 4Inference

Bootstrap samples (95% confidence interval)

1 st : { 5 , 1 , 7 , 1 , 2 , 4 , 4 } → 3.4

2 nd : { 6 , 4 , 2 , 5 , 7 , 3 , 6 } → 4.7

3 rd : { 2 , 3 , 5 , 2 , 1 , 1 , 1 } → 2.1

200 th : { 4 , 6 , 4 , 7 , 3 , 4 , 2 } → 4.3

Sampling with replacements

Distribution

1 4 7

Research Tasks

• Apply Bootstrap (BS) to choice-based conjoint analysis (CA)

Procedure Illustration

Step 2: Apply random sampling with replacements to the original dataset • 200 replications

(B=200)• Apply CA to 50

customers in each BS sample

7

Step 3: Construct histogram of predicted market share

Construct a histogram of predicted market share for chosen concept N

Step 1: Randomly sample50 customers

Warranty = 4 years/50,000 milesPrice = $ 50,000

SUV, 5 passengersFuel efficiency = 40 (miles/gallon)

Concept N

Market share{ B-th sample} Market share, sB 50% 100%

{1, 2, 3, …, n}

Market shareSTrue 50% 100%

SampleCustomer

Bootstrap Conjoint analysis Probability

{ 1st sample} Market share, s1 1{ 2nd sample} Market share, s2 0.5

Population Probability

1"True" market share, STrueCustomer {1, 2, 3, …, N}

0.5Random sampling

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40 50 60 70 80 90 100

Re

lative

Fre

qu

en

cy

Market Share (%)

Concluding Remarks

• Previously CA has been used to obtain point estimate for market share

• Our approach integrates bootstrap and binomial inference with CA to obtain market share distributions

• This research demonstrates objective data utilization methods for customer preference uncertainty modeling

• And an integration of these uncertainty modeling

methods with a decision-analytic product concept selection.

8

9

References1. Howard, R. A., 1988, “Decision Analysis: Practice and Promise,” Management Science, 34(6), pp. 679-695.2. Keeney, R. L., and Raiffa, H., 1976, Decision with Multiple Objectives: Preferences and Value Tradeoffs, John Wiley & Sons, New York, NY.3. Takai, S., and Ishii, K., 2008, “A Decision-Analytic System Concept Selection for a Public Project,” ASME Journal of Mechanical Design, 130(11),

111101 (10 pages).4. Green, P. E., and Srinivasan, V., 1978, “Conjoint Analysis in Consumer Research: Issues and Outlook,” Journal of Consumer Research, 5, pp. 103-123.5. Green, P. E., and Srinivasan, V., 1990, “Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice,” Journal of

Marketing, 54, pp. 3-19.6. Green, P. E., and Wind, Y., 1975, “New Way to Measure Consumers’ Judgments,” Harvard Business Review, 53, pp. 107-117.7. Efron, B., Tibashirani, R., 1993, An Introduction to the Bootstrap, Chapman & Hall, London

Future Work

Acknowledgements

This research is supported by the Intelligent Systems Center at the Missouri University of Science and Technology

9

• Future work– More complex cases involving increased number of competitors

and categories need to be researched– Integration of dynamic competition in decision-analytic

concept selection – Compare experiment-based and simulation-based approaches