conjoint analysis
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Marketing Research 7th Edition
Aaker, Kumar, Day
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 inHelping to select features on a new product/service
Predicting sales
Understanding decision processes/consumer judgments
Marketing Research 7th Edition
Aaker, Kumar, Day
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 Edition
Aaker, Kumar, Day
2. StepsDesignAssumptionsModel estimation and fitInterpreting resultsValidation
Marketing Research 7th Edition
Aaker, Kumar, Day
2.1. DesignMethod:
Select attributes (number, type)Choose model form (additive? dependent variable?)Individual or aggregate estimation?
Traditional, Choice-based or Adaptive conjoint?
Marketing Research 7th Edition
Aaker, Kumar, Day
2.1. Design
Stimuli: Factor (= Attribute) selectionCriteria:
DifferentiateAble to communicateActionable
Price Could enter as separate attribute, mind correlations or infeasible stimuliLevels:
Strive for BalanceRange: Feasible, Relevant, Stretch
Marketing Research 7th Edition
Aaker, Kumar, Day
2.1. Design
Stimuli: Utility specification
Part worth, Ideal Point or Linear model?Main effects or interactions?
Marketing Research 7th Edition
Aaker, Kumar, Day
Alternative ModelsLinear
4
4,5
5
5,5
6
6,5
7
7,5
5 10 15 20 25 30 35
Ideal Point
0
1
2
3
4
5
6
7
8
No sugar Medium Sweetness
Part Worth
0
1
2
3
4
5
6
7
8
1000cc 2000cc 3000cc
Marketing Research 7th Edition
Aaker, Kumar, Day
2.1. DesignData collection:
Presentation: Trade-offFull profile (Fractional factorial)?
Preference Measure:RankingRatingChoice (no-)
Task per respondent (Regular, Adaptive, Hybrid?)
Marketing Research 7th Edition
Aaker, Kumar, Day
Example: Sneakers
3 attributes, 3 levels each:Sole: Rubber, Polyurethane, PlasticUpper: Leather, Canvas, NylonPrice: 30$, 60$, 90$
Fractional Factorial: 9 out of 27 profiles (3 sole x 3 upper x 3 price) evaluated
1
1
1
2
2
2
3
3
3
Marketing Research 7th Edition
Aaker, Kumar, Day
Example: Profiles for Sneakers
Stimulus Sole= attribute 1
Upper= attribute 2
Price= 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)
(attribute level)
Marketing Research 7th Edition
Aaker, Kumar, Day
2.2. Assumptions
Few statistical assumptionsTheory-driven design, estimation and interpretation
Overfitting?GIGO (Garbage in Garbage out)?
2.3. Model Estimation and Fit
E.g. Additive Model, part-worths:
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
m
1iijij
k
1j
x)X(Ui
Marketing Research 7th Edition
Aaker, Kumar, Day
2.3. Model Estimation and Fit (ctd)
Purpose: Find levels of ij that reflect consumers’ stimuli evaluations as closely as possible
Method:Ranking: MONANOVA, LinmapRating: Dummy-variable regressionChoice: MNL or Probit model
Fit:Correlate actual/predicted ranksHit rateR2
Marketing Research 7th Edition
Aaker, Kumar, Day
Example: Profiles for Sneakers
Stimulus Sole= attribute 1
Upper= attribute 2
Price= 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)
(attribute level)
Marketing Research 7th Edition
Aaker, Kumar, Day
2.3. Model Estimation and Fit (ctd)
Example Sneakers: Preference ratings and Variable Indicator Coding (last level = Base) :
9 1 0 1 0 1 07 1 0 0 1 0 15 1 0 0 0 0 06 0 1 1 0 0 15 0 1 0 1 0 06 0 1 0 0 1 05 0 0 1 0 0 07 0 0 0 1 1 06 0 0 0 0 0 1
Sole Upper Price
PreferenceRating Rubber Poly Leather Canvas 30$ 60$Sneaker
123456789
Marketing Research 7th Edition
Aaker, Kumar, Day
2.3. Model Estimation and Fit (ctd)
Intercept 4,222 0.588 7,181 0.019Sole1 b11=1.000 0.544 1,837 0.208Sole 2 b12=-0.333 0.544 -0.612 0.603Upper 1 b21=1.000 0.544 1,837 0.208Upper 2 b22=0.667 0.544 1,225 0.345Price 1 b31=2.333 0.544 4,287 0.05Price 2 b32=1.333 0.544 2,449 0.134
Regression StatisticsMultiple R 0.967R Square 0.934Adjusted R Square 0.738Standard Error 0.667Observations 9
Marketing Research 7th Edition
Aaker, Kumar, Day
2.4. Interpreting results
Assess part-worths for attribute levelsEvaluate attribute importanceUse choice simulator
Marketing Research 7th Edition
Aaker, Kumar, Day
Assess part-worths for attribute levels
Example: Indicator Coding, Attribute=Sole b11= coëfficiënt Sole1=1
b12= coëfficiënt Sole2=-.333 b13=0Average: (1-.333+0)/3=.222
Calculate part worths such that sum = 0? -> 11= b11-Average=1-.222=. 778
12= b12-Average=-.333-.222-.55613= b13-Average=-.222
Marketing Research 7th Edition
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
Marketing Research 7th Edition
Aaker, Kumar, Day
Part Worths SneakersSole
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
0,8
1
rubber Poly Plas
Upper
-0,8
-0,6
-0,4
-0,2
0
0,2
0,4
0,6
Leather Canvas Nylon
Price
-1,5
-1
-0,5
0
0,5
1
1,5
30 60 90
Marketing Research 7th Edition
Aaker, Kumar, Day
Evaluate attribute importance
)(min)(maxI ijjijji
m
ii
ii
I
IW
where i=attribute, j= attribute level, m= number of attributes, Ii = range of part worths for attribute,Wi = attribute importance (share)
Marketing Research 7th Edition
Aaker, Kumar, Day
Attribute importance
Example Sneakers:Sole: .286Upper: .214Price: .5
100 € 60 €
Marketing Research 7th Edition
Aaker, Kumar, Day
Calculating Attribute importance
Ii Wi Sole .778+.556 =1.334 1.334/4.668=.286 Upper .445+.556 =1.001 1.001/4.668=.214 Price 1.111+1.222 =2.333 2.333/4.668=.5 = 4.668
Marketing Research 7th Edition
Aaker, Kumar, Day
2.5. ValidationOn holdout sample?Clusters of respondentsAlternative Models?Significance (overfitting)?
Marketing Research 7th Edition
Aaker, Kumar, Day
Problem StatementA 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 Edition
Aaker, Kumar, Day
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 Edition
Aaker, Kumar, Day
Research Setup
Use conjoint analysis to assess consumer preference for alternative channel/price combinationsConduct analysis for three types of products:
Bicycle Lock
Fire Blanket
Alarm system
Marketing Research 7th Edition
Aaker, Kumar, Day
Design: StimuliAttributes
Utility: Part worths, additive
Attribute Levels Information Channel
Internet Advisor Brick&Mortar Store
Acquisition Channel
Internet Advisor Brick&Mortar Store
Price Recommended –10%
Recommended Recommended +10%
Marketing Research 7th Edition
Aaker, Kumar, Day
Design: Data CollectionTraditional Method:
Full Profile approach27 possible combinations: fractional, orthogonal design -> 9 profiles/product/respondent
Preference measure: ratingRespondent task: regular, 2 products
Marketing Research 7th Edition
Aaker, Kumar, Day
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
Marketing Research 7th Edition
Aaker, Kumar, Day
Scenario (Stimulus) 1Imagine
You use the internet to gather information on the fire blanketYou purchase the fire blanket in the storeThe recommended price is 46.05EurosIn the store, you pay this recommended price –10%
How do you rate this scenario? …./100
Marketing Research 7th Edition
Aaker, Kumar, Day
Model and Variable Coding
Dataset: see File Caseconj.savCases= respondents*profilesDummy variable regression per product and across respondents,
dependent = ratingIndependent = 6 dummy variables (TI, TA, II, IA, PR, PL): reference scenario =transaction and info in B&M, higher price.
Marketing Research 7th Edition
Aaker, Kumar, Day
InterpretationPart Worths and Attribute importanceE.g. Fire Blanket:
Information channel no significant impactTransaction channel (.365):
Internet -7.78, Advisor -.1, Store 7.88
Price (.635)Low 15.22, Medium –2.83, High –12.38
Marketing Research 7th Edition
Aaker, Kumar, Day
ValidationEstimation 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 Edition
Aaker, Kumar, Day
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 LockStore, medium price: 7.88-2.83=5.05Internet, low price: -7.78+15.22=7.44
Prefer latter option!
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