conjoint analysis

Conjoint Analysis

Session: March 22-26; 2010

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


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?


Aaker, Kumar, Day

2. StepsDesignAssumptionsModel estimation and fitInterpreting resultsValidation


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?


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


Aaker, Kumar, Day

2.1. Design

Stimuli: Utility specification

Part worth, Ideal Point or Linear model?Main effects or interactions?


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


Aaker, Kumar, Day

2.1. DesignData collection:

Presentation: Trade-offFull profile (Fractional factorial)?

Preference Measure:RankingRatingChoice (no-)

Task per respondent (Regular, Adaptive, Hybrid?)


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


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)


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


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


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)


Aaker, Kumar, Day


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


Aaker, Kumar, Day


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


Aaker, Kumar, Day

2.4. Interpreting results

Assess part-worths for attribute levelsEvaluate attribute importanceUse choice simulator


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


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


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


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)


Aaker, Kumar, Day

Attribute importance

Example Sneakers:Sole: .286Upper: .214Price: .5

100 € 60 €


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


Aaker, Kumar, Day

2.5. ValidationOn holdout sample?Clusters of respondentsAlternative Models?Significance (overfitting)?

3. Case:Channel and Price Offers for

Safety Products


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?


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’


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


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%


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


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


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


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.


Aaker, Kumar, Day

Estimation ResultsSee output file Caseconj.spo


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


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


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!


Aaker, Kumar, Day

Outcome (ctd)

Customer heterogeneity?E.g. Male vs femaleIndividual analysis?

Product differences in attribute significance, importance, part worths!

E.g. Best info channel depends on product: Bicycle Lock: store, Alarm system: advisor

conjoint analysis

Documents

additive model

aggregate estimation

linear model

separate attribute

attribute selectioncriteria

levels of ij

sole x

attributes number