objectives background approach results discussion results concluding remarks results future work...
TRANSCRIPT
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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
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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
![Page 3: OBJECTIVES BACKGROUND APPROACH RESULTS DISCUSSION RESULTS CONCLUDING REMARKS RESULTS FUTURE WORK Acknowledgements Market Share Uncertainty Modeling for](https://reader030.vdocuments.us/reader030/viewer/2022032607/56649ec65503460f94bd1822/html5/thumbnails/3.jpg)
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
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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
![Page 5: OBJECTIVES BACKGROUND APPROACH RESULTS DISCUSSION RESULTS CONCLUDING REMARKS RESULTS FUTURE WORK Acknowledgements Market Share Uncertainty Modeling for](https://reader030.vdocuments.us/reader030/viewer/2022032607/56649ec65503460f94bd1822/html5/thumbnails/5.jpg)
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 $20,000 4 years/50,000 miles 10mpg
Convertible Sedan SUV
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Procedure Illustration
Step 3: Obtain a customer’s choice • Each customer is shown
27 sets of three automobiles at a time and asked to choose one from each set
5
Step 5: Repeat Steps 3 and 4 for 50 customers• Each customer
chooses a concept with the largest total sum of his/her attribute utilities
Step 4: Estimate a product Attribute utility using conjoint analysis• Obtain product attribute
utilities by applying MLE
A customer’s attribute utility obtained from the choice data
Concept 1
WarrantyPrice
-1 0 +1 -1 0 +1 -1 0 +1
-1 -1 -1 0 0 0 +1 +1 +1
Concept 2
-1 0 +1 -1 0 +1 -1 0 +1
-1 -1 -1 0 0 0 +1 +1 +1
Concept 3
-1 0 +1 -1 0 +1 -1 0 +1
-1 -1 -1 0 0 0 +1 +1 +1
Concept 4
-1 0 +1 -1 0 +1 -1 0 +1
-1 -1 -1 0 0 0 +1 +1 +1
Concept 5
-1 0 +1 -1 0 +1 -1 0 +1
-1 -1 -1 0 0 0 +1 +1 +1
Concept 6
-1 0 +1 -1 0 +1 -1 0 +1
-1 -1 -1 0 0 0 +1 +1 +1
Concept 7
-1 0 +1 -1 0 +1 -1 0 +1
-1 -1 -1 0 0 0 +1 +1 +1
Concept 8
-1 0 +1 -1 0 +1 -1 0 +1
-1 -1 -1 0 0 0 +1 +1 +1
Concept 9
-1 0 +1 -1 0 +1 -1 0 +1
-1 -1 -1 0 0 0 +1 +1 +1
Pre
dic
ted
m
arke
t sh
are
(%)
Predicted market share of each concept at various warranty and price
Choice-based conjoint survey
-25.0
15.49.6
-26
0
26
Convertible SUV Sedan
Part W
orth
Type
-7.6
-0.4
8.0
-9.0
0.0
9.0
10 25 40Pa
rt Wort
h
Fuel Efficiency (Miles per Gallon)
-2.8
-0.7
3.6
-4.0
0.0
4.0
3/36,000 4/50,000 5/60,000
Part W
orth
Warranty (Years/Miles)
13.2
0.8
-14.0
-15.0
0.0
15.0
20,000 35,000 50,000
Part W
orth
Price ($)
![Page 7: OBJECTIVES BACKGROUND APPROACH RESULTS DISCUSSION RESULTS CONCLUDING REMARKS RESULTS FUTURE WORK Acknowledgements Market Share Uncertainty Modeling for](https://reader030.vdocuments.us/reader030/viewer/2022032607/56649ec65503460f94bd1822/html5/thumbnails/7.jpg)
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
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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
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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 (%)
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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.
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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
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• 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