conjoint analysis

Copyright © 2010 Pearson Education, Inc., publishing as Prentice-Hall. 8-1

Chapter 8Chapter 8 Conjoint Analysis Conjoint Analysis


LEARNING OBJECTIVESUpon completing this chapter, you should be able to

do the following:

• Explain the managerial uses of conjoint analysis.

• Know the guidelines for selecting the variables to be examined by conjoint analysis.

• Formulate the experimental plan for a conjoint analysis.

• Understand how to create factorial designs.

• Explain the impact of choosing rank choice versus ratings as the measure of preference.

Chapter 8 Conjoint AnalysisChapter 8 Conjoint AnalysisChapter 8 Conjoint AnalysisChapter 8 Conjoint Analysis


LEARNING OBJECTIVES continued . . . Upon completing this chapter, you should be able to do the

following:

• Assess the relative importance of the predictor variables and each of their levels in affecting consumer judgments.

• Apply a choice simulator to conjoint results for the prediction of consumer judgments of new attribute combinations.

• Compare a main effects model and a model with interaction terms and show how to evaluate the validity of one model versus the other.

• Recognize the limitations of traditional conjoint analysis and select the appropriate alternative methodology (e.g., choice-based or adaptive conjoint) when necessary .

Chapter 8 Conjoint AnalysisChapter 8 Conjoint AnalysisChapter 8 Conjoint AnalysisChapter 8 Conjoint Analysis


Conjoint analysis . . .Conjoint analysis . . . is a dependence is a dependence technique used to understand how respondents technique used to understand how respondents develop preferences for products or services. The develop preferences for products or services. The dependent variable is a measure of respondent dependent variable is a measure of respondent preference and can be metric or nonmetric preference and can be metric or nonmetric (choice-based conjoint). The independent (choice-based conjoint). The independent variables are dummy variables representing variables are dummy variables representing

attributes of multiattribute products or services.attributes of multiattribute products or services.

Conjoint Analysis DefinedConjoint Analysis Defined


Is not a new “technique” but an application of techniques we have covered already:• Metric conjoint analysis is a regression analysis.• Choice-based conjoint is a discrete regression (e.g., logit).

The researcher first constructs a set of real or hypothetical products by combining selected levels of each attribute (factor):• In most situations, the researcher will need to create an

experimental design.• Some computer programs will create the design (Sawtooth

Software, SPSS Conjoint).

These combinations or profiles are then presented to respondents, who provide their overall evaluations.

Conjoint Analysis . . .Conjoint Analysis . . .


What are the important attributes that could What are the important attributes that could affect preference?affect preference?

How will respondents know the meaning of each How will respondents know the meaning of each factor?factor?

What do the respondents actually evaluate?What do the respondents actually evaluate? How many profiles are evaluated?How many profiles are evaluated?

In developing the conjoint task the In developing the conjoint task the researcher must answer four questions . . researcher must answer four questions . .

..


Is calculated as shown below:

Is a decompositional technique: Conjoint decomposes stated overall preference to determine

preferences for each attribute. That is, the researcher collects data on the overall preference for a stimulus and decomposes it to ratings for the individual attributes.

In contrast, with compositional techniques the researcher collects ratings on many product characteristics and then compares the ratings to an overall preference rating to develop a predictive model.

Individual-, aggregate-, or segment-level models can be estimated.



1.1. Its decompositional nature.Its decompositional nature.

2.2. Specification of the variate.Specification of the variate.

3.3. The fact that estimates can be made at The fact that estimates can be made at the individual level.the individual level.

4.4. Its flexibility in terms of relationships Its flexibility in terms of relationships between dependent and independent between dependent and independent variables.variables.

Conjoint Analysis differs from Conjoint Analysis differs from other multivariate techniques other multivariate techniques

in four distinct areas . . . in four distinct areas . . .


• Define the object or concept with the optimum combination of features.

• Show the relative contributions of each attribute and each level to the overall evaluation of a product.

• Predict customer judgments among objects with differing sets of features.

• Isolate segments of potential customers who place differing importance weights on features (homogeneous within segments, heterogeneous between segments).

• Identify market opportunities by exploring the market potential for feature combinations not currently available.

Managerial Uses of Conjoint Managerial Uses of Conjoint Analysis . . .Analysis . . .

Statistics and Terms Associated withStatistics and Terms Associated withConjoint AnalysisConjoint Analysis

Part-worth functions. The part-worth functions, or utility functions, describe the utility consumers attach to the levels of each attribute.

Relative importance weights. The relative importance weights are estimated and indicate which attributes are important in influencing consumer choice.

Attribute levels. The attribute levels denote the values assumed by the attributes.

Full profiles. Full profiles, or complete profiles of brands, are constructed in terms of all the attributes by using the attribute levels specified by the design.

Pairwise tables. In pairwise tables, the respondents evaluate two attributes at a time until all the required pairs of attributes have been evaluated.

A Simple ExampleA Simple Example

Scenario: a man buying a basic cartridge camera (faced with eight choices)• Major brand $80• Major brand $50• Major brand $30• Major brand $20• Store brand $80• Store brand $50• Store brand $30• Store brand $20

Respondent’s Ranking of Eight Camera Brands

Price($) Major Brand Store Brand Average Rank

20 8 6 7.0

30 7 4 5.5

50 5 2 3.5

80 3 1 2.0

Average rank 5.75 3.25

Note, 8 is most preferred and 1 is least preferred

Respondent’s Utility Values of Eight Camera Brands

Price($) Major Brand Store Brand Average Rank Utility

20 8 6 7.0 1.00

30 7 4 5.5 .70

50 5 2 3.5 .30

80 3 1 2.0 .00

Average rank 5.75 3.25

Utility .75 .25

Rank Order of Respondent’s Total Utilities.

Price($) Major Brand Store Brand Marginal Utility

20 8 (1.75) * 6 (1.25) 1.00

30 7 (1.45) 4 (.95) .70

50 5 (1.05) 2 (.55) .30

80 3 (.75) 1 (.25) .00

Marginal Utility .75 .25

* 1.75 = .75 (major brand utility) + 1.00 ($20 utility)

Level

Level

Level

Pref

eren

ce

Pref

eren

cePr

efer

ence

Linear Quadratic or idea

Part-worth

Selecting the Part-worth relationship

Trade-off Approach

$1.19 $1.39 $1.49 $1.69

Factor 1: Price

Generic

KX-19

Clean-all

Tidy-UP

Fact

or 2

: Bra

nd N

a me

Pros: Easy, simple, few cognitive decisions

Cons: Sacrifice in only see a few attributes at a time, large number of judgments, easy to get confused and pattern response, can’t use pictoral or non written stimuli, only non metric responses, can’t use fractional factorial designs.

Full Profile Approach

Brand Name : KX – 19

Price : $ 1.19

Form: Powder

Color brightener: Yes

Shows all attributes at once

Pros: Better, more realistic, flexible scaling, fewer judgments.

Cons: As the number of factors increases so does the possibility of information overload--can be overwhelming if have > 6 attributes. The order in which the factors are listed on the stimulus card may have an impact on the evaluation.


o To determine the contributions of predictor To determine the contributions of predictor variables and their levels in the determination of variables and their levels in the determination of consumer preferences.consumer preferences.

o To establish a valid model of consumer To establish a valid model of consumer judgments.judgments.

Stage 1: Objectives of Conjoint Stage 1: Objectives of Conjoint AnalysisAnalysis


The research question must be framed around two The research question must be framed around two

major issues . . .major issues . . .

• Is it possible to describe all the attributes that Is it possible to describe all the attributes that

give utility or value to the product or service give utility or value to the product or service

being studied? That is, the researcher must be being studied? That is, the researcher must be

able to define the total utility of object (all able to define the total utility of object (all

attributes that create or detract from overall attributes that create or detract from overall

utility)utility)

• What are the key attributes involved in the What are the key attributes involved in the

choice process for this type of product or choice process for this type of product or

service? That is, must be able to specify factors service? That is, must be able to specify factors

that best that best differentiatedifferentiate between objects. between objects.

Research QuestionResearch Question


Rules of Thumb 8–1 continued . . . Rules of Thumb 8–1 continued . . .

Objectives of Conjoint AnalysisObjectives of Conjoint Analysis• A “successful” conjoint analysis requires that the

researcher: Accurately define all of the attributes (factors) that have a

positive and negative impact on preference Apply the appropriate model of how consumers combine the

values of individual attributes into overall evaluations of an object

• Conjoint analysis results can be used to: Provide estimates of the “utility” of each level within each

attribute Define the total utility of any stimuli so that it can be compared

to other stimuli to predict consumer choices (e.g., market share)


• Selecting a conjoint analysis methodology: Traditional conjoint analysis. Adaptive conjoint analysis. Choice-based conjoint analysis.

• Designing stimuli – selecting and defining factors and levels: General characteristics of factors and

levels.o Communicable measures.o Actionable (not fuzzy) measures.

Stage 2: Design of a Conjoint AnalysisStage 2: Design of a Conjoint Analysis


Specification issues regarding factors:o Number of factors – as factors and levels are

added, more stimuli are needed, or else reliability of parameters is reduced.

o Fractional factorial designs may be used when the number of factors is large.

o Factor multicollinearity – some factors are necessarily correlated, such as horsepower and gas mileage, but they may be orthogonal in the experimental design.

o Unique role of price as a factor – correlated with many other factors, price-quality inferences.

Stage 2 continued . . .Stage 2 continued . . .


Specification issues regarding levels:o Balance or equalize the number of

levels across factors.o Range of the factor levels.

Specifying the basic model form: Composition rule – how does the

respondent combine the part-worths to obtain overall worth?

Should the researcher use an additive or an interactive model?

Stage 2 continued . . .Stage 2 continued . . .


• Additive model

• Interactions – some attribute levels are more valuable when paired with certain levels of other attributes. Also, testing interactions requires more stimuli to be evaluated, but may be a more realistic picture of judgments.

• Selecting the part-worth relationship: Linear Quadratic (ideal-point) Part-worth

The Composition Rule: Additive vs. The Composition Rule: Additive vs. InteractiveInteractive


• Choosing a presentation method:Trade-off presentation – compares attributes two at a time.Full-profile presentation – most popular and most realistic..Pairwise presentation – a combination of other two methods.

• Creating the stimuli:o Trade-off presentation: number of trade-off matrices is N(N-1)/2,

where N is the number of factors.o Full-Profile presentation:

Factorial designFractional factorial designBridging design

Conjoint Analysis . . . Data CollectionConjoint Analysis . . . Data Collection


• Creation of an optimal design, with orthogonality Creation of an optimal design, with orthogonality and balance, does not mean all stimuli will be and balance, does not mean all stimuli will be acceptable for evaluation, for several reasons:acceptable for evaluation, for several reasons: Obvious stimuli.Obvious stimuli. Unbelievable stimuli.Unbelievable stimuli. Combinations of attributes may be Combinations of attributes may be

precluded.precluded.

Unacceptable Stimuli . . .Unacceptable Stimuli . . .


• Courses of Action:Courses of Action: Generate another fractional factorial Generate another fractional factorial

design.design. Use a Nearly orthogonal design.Use a Nearly orthogonal design. Exclude prohibited pairs.Exclude prohibited pairs.

Eliminating Unacceptable Stimuli . . .Eliminating Unacceptable Stimuli . . .


• Selecting a measure of consumer Selecting a measure of consumer preference:preference:o Rankings (requires transformation or Rankings (requires transformation or

specialized computer software)specialized computer software)o RatingsRatingso ChoicesChoices

• Survey administrationSurvey administrationo Personal interviews.Personal interviews.o Respondent burden retesting.Respondent burden retesting.



• Few statistical assumptions needed.Few statistical assumptions needed.

• Conceptual assumptions are more Conceptual assumptions are more

important than with other multivariate important than with other multivariate

techniques (e.g., main effects vs. techniques (e.g., main effects vs.

interactive).interactive).

Stage 3: Assumptions of Stage 3: Assumptions of Conjoint AnalysisConjoint Analysis


Stage 4: Estimating the Conjoint Stage 4: Estimating the Conjoint Model and Assessing Overall FitModel and Assessing Overall Fit

• Selecting an estimation technique: Rank-order evaluations require specialized

programs (e.g., MONANOVA, LINMAP). Ratings: Multiple regression. Choices: Logit, probit.

• Evaluating goodness of fit: Potential for overfitting. Validation or holdout stimuli for individual-level

analysis. Validation or holdout respondents for aggregate-

level analysis.


• Ensuring Practical Relevance.Ensuring Practical Relevance.

• Assessing Theoretical Consistency – Assessing Theoretical Consistency – reversals.reversals.

Examining the Estimated Part-WorthsExamining the Estimated Part-Worths


• Factors contributing to reversals:Factors contributing to reversals: Respondent effort.Respondent effort. Data collection method.Data collection method. Research context.Research context.

• Identifying reversals – graphical Identifying reversals – graphical analysis.analysis.

• Remedies for reversals:Remedies for reversals: Do nothing if only a few.Do nothing if only a few. Apply constraints.Apply constraints. Delete respondents.Delete respondents.

Reversals . . .Reversals . . .


• Aggregate vs. Disaggregate analysis:Aggregate vs. Disaggregate analysis: Individual-level part-worths can be clustered Individual-level part-worths can be clustered

to form segments.to form segments. Finite mixture conjoint models form segments Finite mixture conjoint models form segments

automatically.automatically. Aggregate analysis may predict market shares Aggregate analysis may predict market shares

well but not individual preferences.well but not individual preferences. The most important factor is the one with the The most important factor is the one with the

greatest range of part-worths.greatest range of part-worths.

Stage 5: Interpreting the ResultsStage 5: Interpreting the Results


Stage 6: Validation of the Conjoint Stage 6: Validation of the Conjoint ResultsResults

•InternallInternall

yy

•ExternalExternal

lyly


• Segmentation – groups respondents with similar part-worths to identify segments.

• Profitability analysis – if the cost of each feature is known, the cost of each product can be combined with the expected market share and sales volume to predict its profitability.

• Conjoint simulators – uses “what-if” analysis to predict the share of preferences a stimulus is likely to capture in various competitive scenarios of interest to management.

Managerial Applications of Managerial Applications of Conjoint AnalysisConjoint Analysis


Step 1: Specify the Scenario(s)Step 1: Specify the Scenario(s)

Step 2: Simulate the ChoicesStep 2: Simulate the Choices

Step 3: Calculate Share of PreferenceStep 3: Calculate Share of Preference

Conjoint SimulationsConjoint Simulations


o Self-explicated conjoint models• Respondent provides a rating of the desirability of each level

of an attribute and then rates the relative importance of the attribute overall.

• Part-worths are calculated by combining these ratings.• This is a compositional approach.• If number of factors cannot be reduced to a reasonable level

for a traditional conjoint analysis, this may be an option.o Hybrid conjoint analysis

• Combines self-explicated and traditional conjoint models• Self-explicated values are used to create small subsets of

stimuli for respondents to evaluate.• Collectively, all stimuli are evaluated by a portion of the

respondents.• Suitable alternative when the number of attributes is large• ACA, Sawtooth Software

Alternative Conjoint MethodsAlternative Conjoint Methods

Example: Packaged Soup

Factors Levels

Flavor Onion

Chicken

Veg

Calories 80

100

140

Salt Free Yes

No

Price 1.89

2.49

Dependent Variable is preference (0-10)

3x3x2x2 = 36 possibilities in a full factorial design

Model can be estimated using dummy variable regression where the estimated beta weights are utility preferences

Establish the Dummy Variables

D1 = 1 if onion, 0 = otherwise

D2 = 1 if chicken, 0 = otherwise

D3 = 1 if 80 calories, 0 = otherwise

D4 = 1 if 100 calories, 0 = otherwise

D5 = 1 if salt-free, 0 = otherwise

D6 = 1 if price $1.89, 0 = otherwise

Example: Onion, 80 calorie, Saltfree soup for $1.19 would be coded as

( 1 0 1 0 1 1)

Run Regressions for Each Individual

Y = B1 D1 + B2 D2 + B3 D3 + B4 D4 + B5 D5 + B6 D6 +

Card # Pref Dummy Coding

1 8 1 0 0 1 1 0

2 6 0 1 1 0 1 0

3 3 1 1 1 0 0 0

. . . . . . . .

. . . . . . . .

36 5 0 1 1 1 1 1

Check the fit for each regression for each individual

Calculate Ŷ for each individual

Corr ( Ŷ , Pref) for each individual

This is a measure of internal consistency to see if there is a strong relationship between the revealed preference and the stated preference. Include individuals with high correlations.

Standardized Beta weights are the part worths

Attributes Part worth

Flavor onion 3.50

Chicken 0

Vegetable 3.58

Calories 80 2.17

100 .67

140 0

Salt Free Yes 1.89

No 0

Price 1.19 .67

1.49 0

Note the partworths can be rescaled relative to each other. For example if onion = -.08, chicken= -3.58 and Veg = 0 adding 3.58 to each changes the coding to make chicken 0.

Utility for an Alternative = sum of the utilities

Utility

UtilityUtility

Utility

0

Chicken Onion Vegetable80 100 140

No Yes

$1.89 $2.49

Flavor

Salt-FreePrice

Calories

Graphing Individual Part worths

Importance Weights

Attributes Range Percent

Flavor 0 – 3.58 43%

Calories 0 – 2.17 26%

Salt 0 – 1.89 23%

Price 0 - .67 8%

Total 8.30 100%