preferences and decision-making decision making and risk, spring 2006: session 7
TRANSCRIPT
Preferences and Decision-Making
Decision Making and Risk, Spring 2006: Session 7
Decision Making Framework
Option A
Option B
Outcome A1
Outcome A2
Outcome B1
Outcome B2
Payoff Portfolio A1
Payoff Portfolio A2
Payoff Portfolio B1
Payoff Portfolio B2
p(A1)
p(A2)
p(B1)
p(B2)
Consequences/Payoff Portfolio
Simple consequences$ metric
Complex consequencesRevenuesCostsLearningTurnoverMoraleComp. ResponseFuture Options
TechMarketFacilities
Integrating payoffs to determine overall utility
OutcomesKnown Outcomes
Unknown OutcomesOutcome probabilities
Distribution
Alternatives
Known OptionsUnknown OptionsDeferred Decision
Decision Problem
Discovering the right decision problem.
DecisionProblem
Central Logic in Decision Making
Two key questions in regard to any decision:
What are the consequences of the options? In other words, what will happen with each
alternative?
What is our preference for those consequences? In other words, do we know what we want among
the various consequences that can occur?
The Health Screening Preferences The goal of this questionnaire is to understand
people’s preferences for generic screening and diagnostic tests.
Tests vary on: Accuracy Frequency Invasiveness Time commitment from you, the patient Pain and discomfort Exposure to radiation
Please fill out the questionnaire provided.
Stated Versus Revealed
30.0
9.9
14.0
16.4
11.1
17.1
38
4.7
28
4.7
19
4.7
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Accuracy Frequency Invasiveness Time Commitment Pain and Discomfort Exposure to Radiation
Attribute
Nor
med
Rel
ativ
e Pr
efer
ence
Stated
Revealed
Conjoint Analysis
Decision Options as Attribute Bundles
Each option has multiple attributes Processor Speed, RAM, Screen Size, Price
Decision is a function of what is more important.
Problem? What is not important?
Assessing Preferences
Stated Preference What is important to you?
Independent importance scores. Relative importance scores.
Revealed Preference Forced tradeoffs. More realistic.
ExampleWhen making the decision to buy a laptop computer, how important
on a scale from 5 (very important) to 1 (not so important) is: Price __Processor Speed __Screen Size __RAM __Drives __
Now, please rate each attribute offering on a scale from 5 (acceptable) to 1(not acceptable)Price: $1800 __
$1200 __$1000 __
Screen Size: 17” __15” __14.1” __
ExampleInstead…Consider the following 3 models. Please rank these from 3
(Most Preferred) to 1 (Least Preferred):
1. $1800, 17”, 3GHz, 256MB, DVD/CD _____
2. $1200, 15”, 2.8GHz, 512MB, DVD-RW _____
3. $1500, 15”, 3 GHz, 512 MB, DVD-RW _____
….
Managerial Questions Focus on 2.8 GHz or 3.0 GHz? What drives customer preferences? What if we increased screen size but reduced
screen resolution? How do customers trade-off attributes? What would be the market-share? What if we offered a DVD-RW for $120 more? What if we removed “free shipping” and
offered to upgrade the RAM?
Conjoint Analysis
Conjoint AnalysisConjoint Analysis is a versatile marketing technique that can provide valuable information, enables us to answer all the questions that were listed earlier.
Conjoint AnalysisConjoint Analysis is popular because it is a less expensive and more flexible method than concept testing.
Superior diagnosticity Parallels real-world decisions
Uses of Conjoint
Concept Optimization. Quantifying impact of change in product
design. Volume forecasting: for categories that can be
described fully by components. Measuring Brand Equity. Quantifying price sensitivity. Estimating interactions in “menu” choices with
a survey. Quantifying lifetime value of a customer.
A brief overview
Input: Rankings/ratings of attribute bundles
Output: relative importance of attributes. “what-if” simulations of hypothetical attribute bundles. estimates of market share, volume, and attribute
sensitivity.
Process part-worths, utilities
Assumptions in Conjoint
Product is a bundle of attributes
Attributes are “describable”
Customers are able to rate/rank
Rating/ranking is an indicator of underlying utility
How Conjoint Works Assume CPU and screen size are two attributes
of consequence in a notebook computer.
Assume three CPUs: 2.8 GHz 3.0 GHz 3.4 GHz
Assume two screen sizes: 14.1” 15”
Rank Ordering Combinations
Screen Size
CPU 14.1” 15”
2.8 GHz 6 4
3 GHz 3 2
3.4 GHz 5 1
Generating Utilities
Screen Size
CPU 14.1” 15” Average
2.8 GHz 0 2 1
3.0 GHz 3 4 3.5
3.4 GHz 1 5 3
Average 1.33 3.66
Determining Relevant Attributes
Physical Attributes
Performance Benefit
Psychological positioning
Stimulus Representation
Full-profile all relevant attributes are presented jointly for
each product more realistic from product presentation point of view less realistic and more complex from consumer decision
point of view
Partial profile subset of attributes subset varies over the exercise until stable
utilities are estimated
Response Type
Paired comparison Choose one profile over the other
3.4 GHz CPU with 14.1” screen vs. 3.0 GHz CPU with 15” screen Complexity increases with number of attributes
Ranking Rank the set of attribute bundles in order of
preference. Can be very complicated if number of attribute bundles
increase.
Response Criterion
Preference useful for market share predictions
Purchase likelihood useful for market size estimation
Analyzing Output Aggregate analysis
Homogeniety of sample Importance of each level of attribute Importance of each attribute based on range of importance
scores for the various levels Caveat, misspecification of attribute level can artificially
inflate attribute importance.
Segmentation analysis
Scenario simulations First or maximum choice rule Share of preference rule
Overview of the Conjoint Process
Develop a list of attributes to describe the product. Identify an experimental design to select product profiles. Develop selected product profiles into stimuli and collect
respondents’ evaluations (ratings, rankings, choices). Decompose these evaluations into part worths or utilities
for each attribute level. Report marginal utility curves or aggregate attribute
importance data. Run simulations (using utilities) to estimate share for
benchmark product or other products of interest. Segmentation analysis based on the utilities.
Data Analysis: Simulations
Simulations attempt to predict choices based on utilities.
Specify a competitive scenario of brands available and describe them in terms of attributes.
For every respondent, calculate the total utility of competing brands.
Select a choice rule to apply these utilities (usually the maximum choice rule).
Count the choices to estimate how many respondents would select each brand.
Data Analysis: Simulation Rules All conjoint simulation rules accept the rating scale you
use as a direct measure of utility. A number of choice rules are available and the maximum
utility choice rule has the best track record. Maximum utility choice rule: consumer chooses with
certainty the option offering the highest total utility. Probabilistic choice rules: respondents have a non-zero
probability of choice for all brands available, related to the magnitude of utility each offers.
Simplest probabilistic choice rule is the attraction type rule:
ProbProfile X = UtilityProfile X
ΣUtilitiesAll Profiles in the Scenario
Conjoint Caveats
Products as attribute bundles
Researcher preselects important attributes
Ratings are meaningful
Attributes are actionable