conjoint by idrees iugc
DESCRIPTION
TRANSCRIPT
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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