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
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New Ways to Measure Consumers’ Judgments GREEN, PAUL E. AND YORAM WIND (1975), “NEW WAYS TO MEASURE CONSUMERS’ JUDGMENTS, “HARVARD BUSINESS REVIEW, JULY-AUGUST,
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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
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
Paul Green
Outline of the Article
The article discusses conjoint analysis and other multivariate techniques used in business/marketing. 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
•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)
•All utility scale have same unit•Relative importance of one factor depends on the levels included in the design•Packed design B highest utility
Air Carrier Study Example
Factors to evaluate 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 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 roleTread 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). 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
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.”