New Ways to Measure
Consumers’ Judgments
GREEN, PAUL E. AND YORAM WIND (1975), “NEW WAYS
TO MEASURE CONSUMERS’ JUDGMENTS, “HARVARD
BUSINESS REVIEW, JULY-AUGUST, 73-184.
PLEASE CONSIDER READING MY HANDOUT “ADVANCE
RESEARCH METHODS” WHILE STUDYING THIS
ARTICLE. POWER POINTS FOR THE HANDOUT ARE IN
BLACKBOARD AND ON THE WEB
Authors
 Wharton School Professor of Marketing
 Expert in mathematics and statistics
 12 years of Industry experience
 Author of 160 books and 1400 articles
Paul Green
 Wharton School Professor of Marketing
 Expert in buyer behavior and market research
 Editorial Board Member: Journal of Marketing,
Journal of Business Research
 Author of 22 books and 250 research papers
Yorum Wind
Outline of the Article
 The article discusses conjoint analysis and other
multivariate techniques used in business/marketing.
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Conjoint analysis
Multiple regression
Factor analysis
Perceptual mapping
Cluster analysis
Discriminant analysis
 This power point presentation also includes
materials from my handout “Advance Research
Methods.”
Conjoint Analysis
 Conjoint analysis is “a new measurement technique
from the fields of mathematical psychology and
psychometrics that can aid the marketing manager
in sorting out the relative importance of a product’s
multidimensional attributes.”
 It starts with the consumers’ overall judgments
about a set of complex alternatives, and, then
decomposes consumers’ original evaluations into
separate and compatible utility scales from which the
original global judgments can be reconstituted.
How Consumer
Measurement Works
Consumers rate three designs on the
basis of 5 factors. Package design,
brand name, and price have 3 levels.
Good housekeeping seal and money
back guarantee has 2 levels. A total of
18 consumer cards are used giving a
combination of 108 (3x3x3x2x2)
alternatives. Consumers are asked to
rank the cards. Each card is a
combination, an orthogonal array, a
point in a multidimensional space; it
is a choice based on utilities.
Importance of attributes
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Utility combination 18 is 3.6
Package: C (0.6)
Brand: Bissell (0.5)
Piece: $ 1.19 (1.0)
Good Housekeeping: Yes (0.3)
Money-back: yes (0.7)
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All utility scale have same unit
Relative importance of one factor depends
on the levels included in the design
Packed design B highest utility
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Air Carrier Study Example
 Factors to evaluate
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Aircraft/Carrier
Departure time relative to ideal
Punctuality of arrival
Passenger load
Number of stops en route
Attitudes of flight attendants
Entertainment
 Findings of the study
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Two factors comprised the biggest utility differences: punctuality of arrival
and attitudes of flight attendants
Utility difference between the two jet planes is very small
Other factors to consider: departure time and number of stops
Replacement Tire Study Example
 Design to protest TV commercials for sponsor’s
brand tires
 Brand name did not play important role
 Tread mileage & price are important
Potential Use and Limitations
 Can be useful in evaluating consumer judgments for….
- New product formulations
- Package design, brand and promotion
- Pricing and brand alternatives
- Verbalized descriptions of new products
- Alternative service designs
 Limitations include…
- Number of attributes is too large
- Too new method
- Not fit all products and services
- Not all product can be decomposed
Multiple Regression
 Compile explanatory variables to identify a response
variable.
 Sales forecasting for cable subscribers (Y).
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Y=number of subscribers
X1=Advertising rate for one minute of prime time
 X2=Kilowatt power
 X3=Number of families in living area of coverage
 X4=Number of competing stations
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Factor Analysis
 Data reduction technique
 Used to determine the
underlying
dimensions/factors
defining the
attributes/variables
 Attributes provided by
researcher
 Objective is to find
commonalities in factors
Perceptual Mapping
 Use consumer
judgments
 Find objects that are
similar
 Very helpful in brand
positioning
Cluster Analysis
 Used to determine
homogeneous
groups
 Used in
segmentation and
profiling
 Clusters are based
on similarity
Discriminant Analysis
 Develop criterion variables which are categorical
 Based on criterion develop predictor variables
 Criterion variables must fall into two groups
 Identify lung cancer: Yes or no outcome
 Run tests (predictor variables)
 Based on predictor variables we get results to validate one
option
 Can we predict how many people will go bankrupt in
Texas next year?
Discussion Questions
 What is conjoint analysis? What are the common
applications and limitations of conjoint analysis?
How can we overcome its limitations?
 Why should marketers study multivariate analysis
techniques? Define/elaborate various techniques we
discussed in the class and explain how they help
marketers make “informed decisions.”