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WHAT IS CONJOINT ANALYSIS? Conjoint Analysis is an advanced multivariate

technique that helps to identify what value most in making decisions.

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DEPENDENCE MODEL

Y = X 1 +X2+X3+……….+Xn

Dependent variable=(nonmetric or metric) Independent variable(nonmetric)

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The flexibility and uniqueness of conjoint analysis arise primarily from the following:

An ability to accommodate either a metric or a nonmetric dependent variable

The use of only categorical predictor variables Quite general assumptions about the

relationships of independent variables with the depend ent variable

As we will see in the following sections, conjoint analysis provides the researcher with substantial insight into the composition of consumer preferences while maintaining a high degree of realism.

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HYPOTHETICAL EXAMPLE OF CONJOINT ANALYSIS

Analysis for hypothetical product with three attribute.

Factor Level

Ingredients Phosphate-free Phosphate Based

Form Liquids Powder

Brand Name HBT Generic Brand

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STIMULI DESCRIPTION AND RESPONDENT RANKING FOR CONJOINT ANALYSIS OF INDUSTRIAL CLEANSER EXAMPLE

Stimuli Descriptions

Levels Of: Respondent Rankings Stimulus# Form Ingredient Brand Respondent 1 Respondent 2

1 liquid Phosphate -free

HBAT 1 1

2 liquid Phosphate -free

generic 2 2

3 liquid Phosphate -based

HBAT 5 3

4 liquid Phosphate -based

generic 6 4

5 powder Phosphate -free

HBAT 3 7

6 powder Phosphate -free

generic 4 5

7 powder Phosphate- based

HBAT 7 8

8 powder Phosphate- based

generic 8 6

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CALCULATION OF PART WORTH

Step#1:square the deviation Step#2:calculate the standardizing value Step#3:standerdize each square Step#4:estimate the part worth

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AVERAGE RANKS AND DEVIATIONS FOR RESPONDENT 1 AND 2

Factor level per attribute Ranks HBAT across stimuli

Average rank of level

Deviation from overall average

rank

RESPONDENT 1 Form Liquid 1,2,5,6 3.5 -1

Powder 3,4,7,8 5.5 1Ingredient

Phosphate Free 1,2,3,4 2.5 -2Phosphate-based 5,6,7,8 6.5 2

Brand HBAT 1,3,5,7 4 -5

Generic 2,4,6,8 5 5

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AVERAGE RANKS AND DEVIATIONS FOR RESPONDENT 1 AND 2

Factor level per attribute

Ranks HBAT across stimuli

Average rank of level Deviation from overall average rank

RESPONDENT 2

Form

Liquid 1,2,3,4 2.5 -2

Powder 5,6,7,8 6.5 2

Ingredient

Phosphate Free 1,2,5,7 3.75 -0.75Phosphate-based 3,4,6,8 5.25 0.75

Brand

HBAT 1,3,7,8 4.75 -0.25

Generic 2,4.5,6 4.25 -0.25

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THE MANAGERIAL USE OF CONJOINT ANALYSIS

Define the object Show the relative contribution use estimate of consumer Isolate group of potential customer Identify marketing opportunities

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OBJECTIVES

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TODAY IT IS USED IN…. Social sciences and applied sciences

including marketing, product management, and

operations research. It is used frequently in testing customer acceptance of new product designs, in assessing the appeal of advertisements and in service design. It has been used in product positioning,

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Research question: To what extent does each component (factor) contribute to the total utility of a product?

Total utility = Sum of all partial utilities

Data base of the Conjoint Analysis are preferences of the interviewed subject Important application: Design of a new product according to the requirements of the market

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ADVANTAGES OF CONJOINT ANALYSIS estimates psychological tradeoffs that consumers

make when evaluating several attributes together measures preferences at the individual level uncovers real or hidden drivers which may not be

apparent to the respondent themselves realistic choice or shopping task able to use physical objects if appropriately designed, the ability to model

interactions between attributes can be used to develop needs based segmentation

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DISADVANTAGES OF CONJOINT ANALYSIS

designing conjoint studies can be complex with too many options, respondents resort to

simplification strategies difficult to use for product positioning research because

there is no procedure for converting perceptions about actual features to perceptions about a reduced set of underlying features

respondents are unable to articulate attitudes toward new categories, or may feel forced to think about issues they would otherwise not give much thought to

poorly designed studies may over-value emotional/preference variables and undervalue concrete variables

does not take into account the number items per purchase so it can give a poor reading of market share

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STAGE 1: THE OBJECTIVES OF CONJOINT ANALYSIS

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

To establish a valid model of consumer judgments.

Defining the total Utility of the Object Specifying the Determinant Factors

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STAGE 2: THE DESIGN OF A CONJOINT ANALYSIS

Selecting a Conjoint Analysis Methodology

Traditional conjoint analysis Adaptive conjoint method Choice-based conjoint approach

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  Conjoint Methodology

Characteristic Traditional Conjoint Adaptive/Hybrid Conjoint Choice-Based Conjoint

Upper 9 30 6

Limit on Number of Attributes

Level of Analysis Individual IndividualAggregate or Individual

Model Form Additive Additive

Additive + InteractionChoice Task

Evaluating Full-Profiles stimuli One at a Time

Rating Profile Containing Subsets of

AttributesChoice Between Sets of

stimuliData

Any Format Generally Any FormatCollection

Computer-Based Format

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STAGE 3: ASSUMPTIONS OF CONJOINT ANALYSIS

Normality, Homoscedasticity, Independence

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STAGE 4: ESTIMATING THE CONJOINT MODEL AND ASSESSING OVERALL FIT

Selecting an estimation technique Traditional estimation approaches Extensions to the basic estimation process

ESTIMATED PART-WORTHS

Attribute 1 Attribute 2 Attribute 3

Level Part-Worth Level Part-Worth Level Part-Worth

1 0 1 0.23 1 2.15

2 18.29 2 0 2 0

3 12.76 3 45.59 3 36.59

4 34.53 4 48.38 4 68.28

5 29.54

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STAGE 5: INTERPRETING THE RESULTS

Examining the Estimated Part-Worths ENSURING PRACTICAL RELEVANCE Factors Contributing to Reversals. Identifying Reversals Assessing the Relative Importance of

Attributes

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STAGE 6: VALIDATION OF THE CONJOINT RESULT